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Naturalistic data on time headway behind motorcycles and other vehicles
Safety Science 119 (2019) 162–173
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Naturalistic data on time headway behind motorcycles and other vehicles a,⁎
a
a
Martin Winkelbauer , Martin Donabauer , Alexander Pommer , Reinier Jansen a b
b
T
Kuratorium für Verkehrssicherheit (KFV), Schleiergasse 18, 1100 Vienna, Austria Stichting Wetenschappelijk Onderzoek Verkeersveiligheid (SWOV), Bezuidenhoutseweg 62, 2594 AW Den Haag, the Netherlands
ARTICLE INFO
ABSTRACT
Keywords: Naturalistic research Time headway Motorcycle Car Truck
Roughly 10% of motorcycle accidents in Austria are rear-end collisions. One explanatory factor for this could be short time headway, hence this research project. Naturalistic research provides a suitable basis for studying the distance to various kinds of lead vehicles. Our study utilizes the dataset created in the first large-scale naturalistic driving study in Europe (UDRIVE) and combines this with site-based speed data from Austria. Data on cars was filtered from the UDRIVE dataset and analyzed for the presence of a lead vehicle, with the pertinent information provided by ‘Mobileye’, a real-time video processing unit. The analysis included 1242 h of driving recorded at a frequency of 10 Hz. The difference observed between distances behind cars (1.1 s) and motorcycles (1.2 s) proved to be small. Almost twenty million records from roadside radar speed recorders in Austria collected in 2015 and 2016 were then compared with this data. In general, the two methodologies delivered similar results: time headway decreases with increasing driving speed in both datasets. The analysis of the UDRIVE data also showed minor differences in time headway among drivers in the six UDRIVE countries (France, Germany, Poland, Spain, The Netherlands, United Kingdom). Both datasets suggest that time headway is shortest behind trucks. However, the hypothesis that car drivers maintain less time headway behind motorcycles is not supported by either the UDRIVE or the site-based data. In this paper, we will discuss both these results as well as the strengths and weaknesses of both data collection methods.
1. Introduction
motorcycle than they do behind other vehicles, which is not in line with accident figures. While previous on-road studies have investigated following behavior (Young et al., 2007; Shinar and Schechtman, 2002), none of this research has differentiated between lead vehicle types, where differences in time headway could well explain different accident figures. UDRIVE’s naturalistic data proved ideal for investigating the distances in front of the subject vehicles since one of the measuring instruments used was ‘Mobileye’, an advanced driver assistance system that provides time headway and forward collision warnings. A further data source also became available during our research, when data from several site-based speed measurement systems in Austria was processed and made available for standard analysis. To incorporate this data into our research, we decided to analyze it using the speed measurement time stamps to provide us with comparable time headway distributions. We then used the results of this analysis to cross-validate both data sources.
Motorcyclists are a unique group of road users. They are also probably the most vulnerable group of road users given that they travel at far higher speeds than other vulnerable groups like pedestrians or cyclists (Winkelbauer et al., 2017). In a rear-end collision, motorcyclists have a higher risk of sustaining injuries than other motorists. Even a soft collision can seriously impact the dynamics of a motorcycle and cause the rider to fall off and be injured, while a car would probably only receive light damage to its bodywork, and neither the driver nor any passengers would suffer injury. Time headway is an important criterion when it comes to the causes of rear-end collisions (Knipling et al., 1993). Around 10% of powered two-wheeler (PTW) collisions, 23% of car collisions and almost 28% of truck collisions in Austria are rear-end. Yet in the authors’ own experience, car drivers tend to keep even a shorter distance behind a
Corresponding author. E-mail addresses: [email protected] (M. Winkelbauer), [email protected] (M. Donabauer), [email protected] (A. Pommer), [email protected] (R. Jansen). ⁎
https://doi.org/10.1016/j.ssci.2019.01.020 Received 29 December 2017; Received in revised form 16 January 2019; Accepted 22 January 2019 Available online 30 January 2019 0925-7535/ © 2019 Elsevier Ltd. All rights reserved.
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1.1. Vehicle definitions
impossible to determine, whether the reaction of a driver had been too late or too slow. Hence, little is known about whether delayed reaction, slow reaction or lack of time headway typically cause rear-endings. To shed more light on the problem, it might be good to know, if drivers typically keep appropriate time headway. Naturalistic driving (ND) data provides a suitable basis for investigating time headway because it captures real-life driving behavior without the experimenter bias typically found in driving simulator studies (Weare et al., 2011). Two types of ND studies are often used, depending on where and how the data is collected: vehicle-based (e.g. Buche, 2016; Pommer et al., 2014) or site-based (Pommer and Donabauer, 2017). UDRIVE data provides information on the relative position of the subject vehicle to other road users in front. In the research presented in this paper, this information was used to analyze the distance to the vehicle in front. Lack of attention (and in particular distraction) – as a potential explanation for rear end collisions – was analyzed in another part of the UDRIVE project. The study presented in this paper, hence, only focused on time headway by car drivers relative to other cars, trucks and motorcycles.
Powered two-wheelers (PTWs) are a very diverse group of vehicles. New types of vehicles are continually being developed with new technical principles (e.g. tilting three- and four-wheel vehicles, which still feel and look like PTW), innovative designs (e.g. currently a revival of café-racer-like motorcycles) or new propulsion systems (mostly electric). The EU’s Regulation 168/2013 on type approval introduces a new categorization of two-, three- and four-wheel vehicles which technically or legally belong to some extent to the powered two-wheelers group. In legal terms, a ‘motorcycle’ is a powered vehicle with two wheels. In some countries, this also includes mopeds (i.e. motorcycles with a maximum engine displacement of 50 cm3 and a maximum design speed of 45 km/h). PTWs can also be distinguished by their appearance. ‘Scooters’ and ‘motorbikes’ differ primarily in the way the rider mounts the vehicle. A scooter, for instance, typically has an open space between the handlebar and the seat to allow the rider to get on without having to lift a leg over the seat. However, neither the law nor any of the data acquisition systems used for this research differentiate by such style of vehicle. Our study used a practical approach to differentiate between vehicles. Anything that the data acquisition system (DAS) considered to be a ‘bike’, we classed as a motorcycle (although a PTW might have been a more suitable term, since the DAS also treated mopeds as ‘bikes’). Other vehicle categories detected by the DAS are pedestrians, bicycles, cars and trucks. The site-based observations also did not make a strict distinction between vehicle categories, with the devices only delivering data on vehicle length and driving speed in each record. Vehicles are then categorized on this basis during data processing. These radar devices cannot differentiate between bicycles, mopeds and motorcycles. Any vehicle with a length of between two and three meters is classed as a two-wheeled vehicle. Further categorization could have been done through selection by speed, but the options were quite limited and there were no perceived benefits of such a selection.
1.3. Issues about adequate time headway Although allowing ‘adequate time headway’ is generally considered to be safe behavior, there are also other issues that merit further consideration. First of all, there is a quantitative and a qualitative aspect to collision risk, namely the frequency of traffic collisions and their severity (e.g. measured by the severity of the injuries sustained). If the time headway is short, the potential impact speed in a rear-end collision is also low. Even if the lead vehicle decelerates strongly and the driver of the following vehicle does not react prior to the collision, the impact speed will be low, since there is little space to build up a speed differential. The negative aspect of this scenario is that short time headway will result in numerous collisions, since drivers will not normally be able to react to and compensate for an unexpected deceleration of the lead vehicle. A better solution might be to maintain a distance that allows a driver to react in time and avoid a collision with the vehicle ahead. Logically, to avoid a collision, time headway must be longer than reaction time to avoid a collision, provided the lead vehicle does not decelerate at a greater rate than the following vehicle. The distance between one moving vehicle and another (either ahead or behind) can be measured in meters. German law, for example, requires a minimum headway distance in meters corresponding to half the speed indicated on the speedometer in km/h (e.g. 25 m headway at 50 km/h). Using the time gap between two vehicles is probably the more practical approach, since people often find it difficult to estimate distance (Kaba and Klemenjak, 1993). Measuring the distance as a time gap aptly reflects the necessity for such a gap: people need a certain period of time to process information and react. Although the length of a gap measured in time (time headway) automatically increases with speed (e.g. one second of time headway corresponds to a distance of about 14 m at 50 km/h and about 28 m at 100 km/h), these time gaps should be lengthened at higher speeds to accommodate both the increase in the difference in stopping distances for vehicles with different brake performance and the larger impact of reaction delay. If a vehicle with poor deceleration capabilities (e.g. a car whose brakes are in a poor condition, a motorcycle driven by a rider with poor skills or a heavily-loaded truck) travels at the same speed behind another vehicle with better deceleration capabilities and has to perform an emergency braking maneuver, the impact speed ceteris paribus increases in proportion to the initial driving speed of the two vehicles. The logical consequence: Time headway should be longer at higher speeds. The Austrian courts normally accept a one-second delay for reaction in criminal cases. This delay is frequently referred to as the ‘reaction time’ but, in legal terms, it constitutes the delay in a driver’s reaction to
1.2. Why did we use naturalistic data to analyze time headway? A large portion of this research was carried out as part of the UDRIVE research project, which was funded by the European Commission (Horizon 2020 Programme). UDRIVE was a large-scale naturalistic study in which cars, motorcycles and trucks were fitted with the same data acquisition system and driving data was collected for about 21 months in the period from 2015 to 2017. One of the tasks in the UDRIVE study was to investigate the traffic behavior of vulnerable road users, among which powered two-wheelers form an important group (at least in terms of the numbers of fatalities and injuries). For the analysis presented in this paper, PTW-related issues were investigated using the data collected by cars, not the data collected by motorcycles or trucks. Austrian accident statistics (i.e. the Austrian Road Safety Board’s (KFV) analysis based on data recorded by the police) show that about 10% of motorcycle collisions are rear-endings where the other party is at fault (i.e. the motorcycle is struck from behind). The MAIDS (originally “Motorcycle Accident In-Depth Study”) report (ACEM, 2009) found that roughly 5% of motorcycle collisions involve the motorcycle being hit from behind. Kramlich (2002) found that in about 75% of collisions involving just one car and one motorcycle, the car drivers were at fault. There are two potential reasons for rear-end collisions, at least for most of them: Time headway is too short, even for an appropriately quick reaction, or the reaction of a driver is inappropriately slow. Retrospective accident analysis normally cannot prove one or the other having caused an accident. Only in rare cases, competent eye-witness reports, dash cam videos or the data of an accident data recorder yield reliable reconstruction. There is even more uncertainty: It is almost 163
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something which requires action. In civil cases, the accepted delay can be shorter (0.8 s). In situations that require a particularly high level of attention (e.g. when driving in the vicinity of a school), the acceptable delay may also be reduced. The generally accepted recommendations by all road-safety organizations in Austria are a 1-second delay for speeds below 50 km/h, 2 s for speeds between 50 and 100 km/h and 3 to 4 s for higher speeds. However, there is no legislation regarding delays as a function of the type of lead vehicle, despite the fact that motorcycles are more vulnerable. In all cases, a time headways of less than 0.2 s results in the driving license being withdrawn, 0.2–0.4 s triggers a written warning, a fine and one additional point in the penalty points system (a driver with two points is required to attend a refresher course; three points leads to license withdrawal).
autonomous driving functions. In the UDRIVE project, Mobileye was only used as a sensor, no feedback was given to the drivers. More details about the data acquisition system (DAS), data collected, data processing and analysis tools is provided in another article in this issue of Safety Science (Van Nes et al., submitted for publication). The analysis presented in this paper was based on a preliminary dataset from the UDRIVE naturalistic database, including 1242 h of driving recorded at a frequency of 10 Hz (134 million records), which were collected from UDRIVE cars in the Netherlands, France, Germany, Poland and the United Kingdom. There were no cars operated at the Spanish UDRIVE test site. Our analysis was made possible in particular by Mobileye gathering data on up to four other road users in one of five categories (‘pedestrian’, ‘bicycle’, ‘bike’, ‘car’, ‘truck’, where ‘bike’ means motorcycle or moped). Basic information from GPS data – in particular map-matched speed and local speed limit – were also used. The following section describes which data from the UDRIVE dataset was used, explains how this data was extracted and analyzed, and presents the results of our analysis. For details on the data acquisition system, see Augros et al, 2013.
1.4. Objectives The objective of our study was to investigate whether car drivers’ time headway depends on the category of lead vehicle; in other words, whether car drivers maintain more or less time headway behind trucks and cars than they do behind motorcycles. While we were conducting the research for this project, another data source became available: KFV has been continuously collecting data on road traffic parameters in Austria for over three decades (e.g. driving speed, seat belt, child restraint and helmet use rates and red light running). This data serves as a basis for the longitudinal comparison of road safety performance indicators (Pommer and Donabauer, 2017). Since driving speed and time headway are among the parameters measured, we were able to compare them with the same parameters measured in the UDRIVE project. While the data acquisition methods for site-based observation remained fairly consistent for a long period of time, three significant changes were implemented in 2015:
2.1.1. Operationalization of following situations A major challenge in our research was selecting the relevant data from the large UDRIVE database. We first had to determine what constitutes a reasonable definition of ‘time headway’, including what driving behind another vehicle actually means when translated into naturalistic data terms. Mobileye provides detailed information on up to four other road users at the same time (such as their lateral and longitudinal position relative to the subject vehicle). Together with current driving speed obtained from the CAN bus, time headway in seconds was calculated as the longitudinal distance of a lead vehicle in meters divided by the driving speed of the subject vehicle in meters per second. Mobileye’s normal purpose is collision warning. Most likely, this is why the Mobileye term for other road uses detected, is “obstacle”. The obstacles do not have a fixed identity. For example, a motorcycle may first be captured as obstacle number two, if another obstacle was captured before it. If the first obstacle is no longer detected, the motorcycle immediately becomes obstacle number one. This makes it difficult to follow specific obstacles in the data. Our investigation (and the others described below) were carried out as an iterative process using ‘SALSA’, the UDRIVE graphic user interface. We explored the variables in the UDRIVE database, in particular those where Mobileye had captured the information, by comparing the measurements with what we could see in the videos. A new variable was calculated and added to the UDRIVE database based on the six conditions listed below. Time headway was calculated for all timestamps where these six conditions were met.
○ All paper/pencil acquisition was replaced by data collection via tablet computer. KFV now uses ‘SODA’ software to program questionnaires; the data entered by the data collectors is transmitted directly to a central database via the internet and is therefore immediately available for analysis. ○ Manual speed data acquisition using handheld radar guns was changed to an automatic system. The new sensors used will be described later in this chapter. ○ The time and money saved by these changes were used to significantly diversify data acquisition, (extending it, for example, to include curve trajectories and the use of personal protective equipment by motorcyclists, start and stop behavior at intersections) and, in particular, to increase the volume of speed data records by a factor of about 100. The above changes strongly improved information on speed and time headway and yielded a useful comparison to other naturalistic (i.e. UDRIVE) data. Nota bene, the KFV data collection was not done as part of UDRIVE, but it was done at the same time and by the same team, who considered comparison to be a promising source of information for both sides (UDRIVE and Austrian data collection).
1. There is at least one ‘bike’ or ‘car’ or ‘truck’ present. These were the relevant values of the Mobileye variable for type of ‘other road user’. 2. The traffic was almost free-flowing. The analysis focused on quasi free-flow situations. In other words, it avoided long episodes in which a motorcycle, car or truck was moving in front of a car in congestion at low speed. Hence, records involving speeds of less than 30 km/h or less than 50% of the local speed limit were excluded. 3. The vehicles were travelling in the same lane. Accordingly, the lateral distance of lead vehicles was limited to 3.5 m to the left or right of the subject vehicle. This value was tested and validated by video observation. 4. There were no vehicles crossing the path of the subject vehicle. A trigger was implemented to avoid capturing vehicles crossing the path of the subject vehicle perpendicular to its driving direction. 5. 5 A “steady-state” following situation was in place. Only following situations in which the subject vehicle was driving behind another
2. Methods 2.1. UDRIVE data The research presented in this paper was carried out as part of the first large-scale naturalistic study in Europe, the UDRIVE project. Roughly 210 vehicles, comprising around 120 cars, 50 trucks and 40 motorcycles, were fitted with comprehensive data acquisition systems to collect data on GPS location, accelerations and rotations as well as CAN bus messages, up to 8 video channels and data from ‘Mobileye’. Mobileye is a real-time video processing unit normally used to provide feedback on time headway, collision warnings or input data for 164
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road user for at least ten seconds were included. 6. No overtaking maneuvers were underway. Overtaking maneuvers in which the subject vehicle was being overtaken were excluded (i.e. maneuvers in which the ‘lead’ vehicle appears from either the right or the left behind the subject vehicle – depending on location – and leaves the scene at a continuously higher speed than the subject vehicle). This did not include situations where another vehicle simply passed the subject vehicle. Video observation showed that such episodes could be successfully excluded by using a variable delivered by Mobileye that determined the longitudinal distance to another road user. All episodes where the average of this relative speed parameter was greater than 1 were excluded.
Table 2 Proportion of interaction by vehicle type.
Av. speed [km/h]
Days with trips
DE FR NL PL UK Unknown
1106 5874 921 914 5373 737
14 43 15 18 50 12
4841 23,227 3490 3914 23,274 2446
35,410 260,911 53,302 27,572 231,093 36,029
32 44 58 30 43 49
415 474 304 346 494 226
Total
14,925
152
61,192
644,317
0.07% 19.88% 1.14% 78.91%
Total N
537,490,379
100.00%
2.1.4. Analysis We did not apply statistical tests on mean differences because the large amount of data (several hundred thousand per group) would always result in significant differences, even though the information was triggered by just a few subjects. Our analysis dataset was not organized based on events (e.g. minimum distance during interaction with a specific vehicle). While it would, of course, have been useful to analyze episodes of interaction from when the other vehicle appeared in front of the subject vehicle until it left the scene (e.g. changed to another lane, turned into another road or simply disappeared over the horizon or behind a corner). However, this proved to be a complicated or even impossible task because interactions were frequently interrupted (e.g. as a result of missing records). We therefore opted for a records-based analysis based on the frequency of prevalence of the relevant following situations in intervals of one-tenth of a second regardless of the total duration of a following situation (i.e. how many records made up one event). 2.2. Site-based data
Table 1 Records by operation site as of 21.03.2017 (analysis date). Travel distance [km]
369,235 106,869,877 6,116,808 424,134,459
Fig. 1 shows the size of the dataset used in our analysis. The proportions of records by lead vehicle differ from country to country, and we did not speculate, for example, on why there are far more records with a motorcycle as the lead vehicle in the Netherlands than in all other countries. However, since we only analyze shares of records per vehicle category, slight differences in sample size do not compromise the overall results either by country or vehicle category. Our sampling choices are not an issue of representativeness, but rather the issues of access time to the SQL server and CPU time.
○ Distance [s] < 3 s. Validation using the videos showed that longer distances can easily lead to a detection of vehicles in other lanes in slight curves. Such long distances also fall outside the scope of our analysis, since shorter distances are considered more dangerous, and the exclusion of longer distances for all vehicle categories would not affect the comparison. ○ Speed was categorized in 5 km/h increments, for example ‘55 km/h’
Trips
Bike Car Truck No lead vehicle
○ Car to car following situations: every 5th trip (record ID ending with 1 or 6); within a trip, every 30th record. ○ Car to truck following situations: every 10th record.
2.1.3. Reduced sample Due to the restriction in our access time to the database, we had to reduce the amount of data we used. To do so, we applied the following restrictions:
Drivers
Proportion (%)
Given the unequal distribution across lead vehicle types, we selected all motorcycle data, but only a subset of the data where the lead vehicle was a car or truck based on the following criteria:
2.1.2. Total sample Our analyses were executed on the data that was available in the database on 21 March 2016. ‘Records’ refer to individual data samples, which were collected at a sample rate of 10 Hz. The data available on this date is shown in Table 1, which also indicates that the operation sites in France and the United Kingdom provided most of the data. Records for which the operation site is unknown were excluded from subsequent analyses. The number of records available by country was determined by the number of vehicles run by the respective ‘operation site’, how quickly these sites had succeeded in acquiring subjects and installing data acquisition systems and how fast the data had been transferred and processed. The total number of records with other road users detected by Mobileye is indicated in Table 2. Interactions with motorcycles (i.e. records in which at least one of the obstacles is a motorcycle) are quite rare in comparison to the other vehicle categories, but about 370,000 records are still more than enough for our analysis purposes.
Duration [h]
Records
includes 55.00–59.99 km/h, resulting in a new, aggregated speed variable. ○ Distance of a car to a motorcycle in front (car to bike following situations): all data.
The time headway analysis was carried out for three speed categories. The upper threshold for the category ‘low speed’ was set at 60 km/h, since this is the speed that is typical for a speed limit of 50 km/h in the Austrian data. 85 km/h was set as the second threshold as a result of pilot data analysis. At speeds above 85 km/h, the pilot analysis results suggested a noticeable change in the number of records per vehicle category, which may be caused by limits in the range of Mobileye’s sensors.
Country
Lead vehicle
The Sierzega SR4 traffic detection device (Fig. 2) is mounted at the roadside to collect five items of information for each vehicle passing by: speed, direction of travel, vehicle length, distance to previous vehicle and the time stamp for each record. Vehicle length is used to determine the type of vehicle, which after a lot of calibrating provides satisfactory accuracy. KFV has six of these roadside traffic detection devices and relocates them all once a week throughout the whole year. In addition to individual driving data, the devices acquire metadata like exact location, local speed limits for all vehicle categories, road category, width of all lanes, etc. During 2015 and 2016, 1.6 million motorcycles, 16.5 million cars and 0.9 million trucks were recorded passing one of these six Sierzega SR4 devices. 165
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Fig. 1. Analysis data set: records per country and vehicle type.
between the three lead vehicle categories. At higher speeds (Fig. 4), time headway distributions show a slightly different picture. Up to 0.8 s, the three following situation categories show similar values, but the result for the 0.9–1.2 s range is lower for cars behind motorcycles (i.e. car to bike). The time headway frequency distribution of cars behind motorcycles at speeds between 60 and 85 km/h shows two peaks at approximately 0.8 s and 1.25 s. The time headway frequency distribution at speeds of 85 km/h and above (Fig. 5) shows considerable differences between the vehicle categories. For example, a relatively large proportion of car to bike following situations are found between 0.7 and 1.4 s, whereas car to car and car to truck following situations are found more often above 1.6 s. Fig. 2. Sierzega SR4 traffic detection device.
3.1.2. Average time headway values by speed Fig. 6 displays the proportion of records by speed category. The graphs for cars and motorcycles look similar despite the difference in their total numbers. The graph for trucks, however, looks different. As can be seen in Fig. 6 there were relatively few records of trucks at low speeds. Fig. 7 shows that up to 75 km/h, the average time headway is comparable between car to bike, car to car, and car to truck following situations. At speeds above 75 km/h, however, distinct patterns start to emerge. Especially above 90 km/h, the average time headway in car to bike following situations is approximately 0.2 s lower than in car to car following situations. Furthermore, the average time headway in car to truck following situations appears to increase with speed for speeds above 90 km/h.
3. Results 3.1. UDRIVE data 3.1.1. Mode of distance by speed category At low speeds (up to 60 km/h; Fig. 3), there is almost no difference in time headway between car to car and car to bike following situations. However, car drivers maintain slightly less distance behind trucks. Value frequencies from 0.3 to 0.9 s are almost equal for all three lead vehicle categories, but values from 1.0 to 1.5 s are more frequent for subject vehicles behind trucks. This means that in the most relevant time headway range (up to 1 s) at this speed range (i.e. up to 60 km/h), there is no notable difference
Fig. 3. Distribution of time headway [s]; driving speed < 60 km/h. 166
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Fig. 4. Distribution of time headway [s]; driving speed 60–85 km/h.
3.1.3. Time headway by vehicle category, speed and country Before focusing on time headway, we first have to consider the comparability of data between the five countries with car operation sites. The number of records collected by speed category could be seen as a reflection of subject vehicle mileage in the each UDRIVE country. As already shown above, driving speed is an important moderating variable for time headway. Hence, similar distributions of driving speeds in the five countries would be advantageous for a good comparison of time headway. Figs. 8–10 show these distributions for motorcycles, cars and trucks. These figures show that the distribution of driving speed in following situations is comparable between France, UK, Germany, and Poland. However, in the Netherlands a relatively large proportion of the following situations are found at driving speeds above 85 km/h, whereas a relatively low proportion of the following situations are found at low driving speeds. Fig. 11 shows the distances of subject cars behind motorcycles. The curves in Figs. 12 and 13 are much smoother due to the larger number of records. Although the values lie within a range of about 0.4 s, there is no indication that car drivers in one country follow motorcycles at a closer distance than their counterparts in other countries. The data displayed in Fig. 12 suggests that French car drivers follow other cars about 0.2 s closer than their counterparts in the other countries. In the case of trucks (Fig. 13), the behavior of German drivers differs slightly from that of their counterparts in all the other countries: at medium speeds, they keep a longer distance of about 0.2 s.
that we used for the UDRIVE data. The results are displayed accordingly in Figs. 14–16. 4. Discussion Time headway of car drivers with cars, trucks and motorcycles as a lead vehicles was investigated using two different sources: a) time headway measured by the Mobileye (a retail driver assistance system) in about 120 highly instrumented passenger cars during the UDRIVE naturalistic driving study, and b) time headway measured by 6 Sierzega RADAR traffic observation devices which had been mounted along roads in Austria and relocated weekly for about three years. 4.1. Research question and hypothesis Our hypothesis that car drivers maintaining less time headway behind motorcycles could be the reason for rear-ending collisions was not supported by the results of our research. The required or useful time headway behind a motorcycle is not necessarily equal to the safety gap required for cars behind other cars or trucks. On the contrary, the smaller silhouette of a motorcycle reduces the size of its projection on the iris, thereby increasing the chance of it being overlooked as an obstacle. Hence, driving behind a motorcycle might even require more time headway than driving behind a passenger car or a truck. 4.2. Time headway analysis
3.2. Results of site-based observation; comparison to UDRIVE data
Our results show that the relatively small number of records of cars behind motorcycles results in less smooth curves than for other vehicle
We analyzed the site-based data by applying the same procedure
Fig. 5. Distribution of time headway [s]; driving speed > 85 km/h. 167
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Fig. 6. Number of records (N) by speed and vehicle type.
categories; in other words, that a low volume of data causes a high variance. Nevertheless, the data seems to be more than good enough to provide a clear picture. The frequency distributions of different time headways recorded by Mobileye for three categories of road users (motorcycles, cars, trucks) are similar, with only some minor exceptions: at low speeds (< 60 km/h), UDRIVE subjects followed trucks slightly closer than motorcycles and cars, while values of 0.8–1.5 s are less frequent for motorcycles at medium speeds (60–86 km/h), thus indicating that UDRIVErs maintained a longer distance behind motorcycles. UDRIVErs in general maintained less time headway at higher speeds than at lower speeds, while from a preventive safety point of view, the reverse should be the case. The time headway frequency distribution for cars behind motorcycles at speeds of between 60 and 85 km/h shows two peaks. There is no obvious explanation for this result. We could argue that this is caused by a small proportion of car drivers who ignore the presence of a motorcycle and focus instead on the vehicle in front of it. However, there is 0.6 s difference between the two peaks, which would mean that the motorcycles in this case maintain an average distance of only 0.6 s behind their own lead vehicles, which is too short to be plausible. Therefore, some other (unknown) issue might be influencing time headway choice. The time headway distribution at speeds of 85 km/h and above (Fig. 5) is possibly biased by the methodology and sensors. We argue that motorcycles are difficult to detect by Mobileye when they are further away. More precisely, our data suggests that hardly any motorcycles are detected at a time headway of 2.2 s or more, which corresponds to a range of 52 m at 85 km/h; other vehicle types are detected at up to approximately 200 m. It is unlikely that this is because there are
no motorcycles on the road. Indeed, it is more likely that technical limitations with Mobileye video resolution mean that motorcycles are not detected at these longer distances. However, we would like to note that this is not a criticism of Mobileye’s quality and performance, since its main intended purpose does not require detection of powered twowheelers at such distances. Under European law, heavy goods vehicles (HGVs; trucks with a gross vehicle weight of more than 3.5 tons) must be equipped with a speed limiter, which prevents them from driving faster than 90 km/h. Our data does contain some records (Fig. 6) where the measured speed is higher than 90 km/h, which is most likely to be due to speeding or to Mobileye wrongly identifying light goods vehicles as HGVs. These results may also indicate that some of the situations considered to be following aren’t following, they might be the approaching phase of “highway business as usual”, where a passenger car runs slightly faster than a truck, approaches the truck form behind and then overtakes – which would indicate that the algorithm considering relative speed needs some improvement. The relatively small number of records of trucks travelling at low speeds (Fig. 7) suggests that there are far more HGVs moving at higher speeds. We could conclude that this is related to the type of roads they frequent. HGVs – in particular the largest ones – are not frequently encountered in urban traffic: they are vehicles that are designed for trunk roads and highways, where speeds are generally higher. Another explanation could be that Mobileye is more likely to detect trucks at greater distances than cars or motorcycles, a plausible explanation already used above for cars and motorcycles. From a preventive safety point of view, the information in Fig. 7 could be considered alarming. Instead of maintaining greater distances
Fig. 7. Average distance [s] by speed and vehicle type. 168
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Fig. 8. Distribution of records, cars following motorcycles, per country and speed from UDRIVE data.
Fig. 9. Distribution of records, cars following other cars, per country and speed from UDRIVE data.
at higher speeds, drivers do exactly the opposite. At least up to a speed of 90 km/h, drivers maintain less time headway at higher speeds. There is no relevant difference with respect to the type of vehicle ahead. Previous studies show that the reaction time of drivers varies between one and more than two seconds (Lamm et al., 1999; van Petegem et al., 2015). Therefore, our analysis indicates dangerous behavior by drivers but not particularly dangerous behavior vis-à-vis motorcycles. The comparison of UDRIVErs in different countries showed only minor differences in behavior. There were remarkably fewer records of Polish drivers driving behind trucks at typical highway speeds. German drivers maintained more distance behind trucks at medium speeds, while French drivers seemed to follow other cars closer than their counterparts in the other countries. For motorcycles, all values for all UDRIVE countries at all speeds were within a range of 0.4 s, noteworthy differences between UDRIVE countries could not be found. In general, the graphs for all UDRIVE countries look quite similar, with two notable exceptions. First, data from the Netherlands is a general outlier as the UDRIVE subject cars seem to travel predominantly at speeds above 85 km/h (most of the records for all three vehicle categories were collected within this speed range) when the criteria for a following situation were met. Possibly, the Dutch traffic situations featured more congestion than the other countries, and consequently, potential following situations were excluded due to speed below 50% of the local speed limit. Second, for Poland, in comparison with the other countries, record distribution is similar for cars and motorcycles, but dissimilar for trucks. This may indicate that either (a) trucks in Poland travel less frequently at speeds above 70 km/h, or (b) subject vehicles in Poland moved less frequently at speeds above
70 km/h behind trucks, (c) there were no roads within the operation area of the Polish subject vehicles on which cars would typically move behind trucks in this speed range or d) Polish drivers never use the lanes predominantly used by trucks. The most likely explanation here is that Polish car drivers did not use highways as frequently as their counterparts in other countries (for whatever reason). The data did not provide information to support either one of these possibilities. If the numbers of records for time headway are weighted by country of origin (i.e. the counts for each country are weighted by the number of vehicles in said country), hardly any differences in time headway are observed between following situations with cars, trucks and motorcycles (see Fig. 17). Looking beyond Europe, Young et al. (2007) report an average time headway that is approximately 0.1–0.2 s higher than our findings using the UDRIVE database. While this could be attributed to differences in the driving environment (i.e. Australia vs. Europe), we also cannot rule out the possibility that it is caused by the fact that Young et al. report time headway as a function of the local speed limit. Without knowing the actual driving speed for each speed limit, a direct comparison of average time headway is not possible. Nonetheless, our observation that time headway decreases with driving speed seems consistent with Young et al.’s finding that time headway decreases with speed limit. 4.3. Comparison of the results of the site-based observation vs. UDRIVE data While the distribution of the following distance behind cars (Fig. 14) at a speed limit of 50 km/h looks quite similar in both the site169
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Fig. 10. Distribution of records, cars following trucks, per country and speed from UDRIVE data.
Fig. 11. Average time headway [s], cars following motorcycles, by country and speed from UDRIVE data.
based and the UDRIVE data, the mode1 differs. In the UDRIVE data, the mode of time headway (1.3 s) is notably higher than in the Austrian data (0.9 s). For the following distance behind motorcycles, the curves for the UDRIVE and Austrian data are almost congruent, which means that there is a significant difference between cars and motorcycles in the Austrian data. This difference can most likely be attributed to the methodology used. Mobileye’s capability to detect headway distances and subsequently compute time headway is limited to about 2–3 s (an exact value is not known). The roadside traffic detection devices limit their detection of time gaps to 23 s. The same principle applies for the difference between cars and trucks. Mobileye detects a larger and closer object more easily than a smaller object at a longer distance. Nevertheless, the system is more than adequate at the distances that are relevant for warning a driver of an obstacle. Roadside traffic detection devices were never mounted along highways or other multilane roads, since the data they record is not sufficiently reliable when there is more than one lane in each direction. However, UDRIVE data does include highways. This is important, since time headway and speed are typically higher on highways. Furthermore, roadside traffic detection devices measure time gaps directly, while time headway had to be calculated from other measurements in UDRIVE. Although we considered it unlikely, there were good reasons to argue that measurement by site-based detection devices and vehicle-based image-processed data might produce different results. The comparison of average values for distance by speed category 1
also reveals some similarities between the UDRIVE and site-based data. While the curve for time headway behind cars is almost congruent in UDRIVE data and site-based data (Fig. 15), time headway behind trucks differs notably. The site-based data indicates that car drivers in Austria follow trucks at a closer distance than drivers in the UDRIVE countries. However, this difference does not exceed 0.15 s, which corresponds to about 10% of the total values. Time headway behind motorcycles is even slightly higher in the Austrian site-based data over the entire range of speeds, but the difference is less than one tenth of a second for all speed categories (Fig. 16). Nevertheless, the two methods of measuring time headway deliver comparable results, and any differences can be plausibly explained. Accordingly, we can conclude that this comparison supports the assumption that the Mobileye system delivers accurate and useful data for research on the relative positions of other road users. 4.4. Generalizability of results and technical limitations There are some limitations to the UDRIVE setup. The study involves about 120 car drivers at five different operation sites in five European countries, most of which are in urban areas. Further, the UDRIVE fleet only includes small subject cars and is restricted to a limited number of models of one make. This limits the validity of the results in two ways. First, the sample consists of subjects who typically purchase vehicles of this type (and make). Second, the analysis does not consider vehiclerelated parameters such as driver perspective (which differs according to vehicle type, e.g. a people carrier, a sports utility vehicle or a small sports car). The perceived driving speed might also be different in a high-power sports car compared with a luxury sedan (e.g. based on the level of internal noise).
The value of the 50th percentile. 170
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Fig. 12. Average distance [s], cars following other cars, by country and speed from UDRIVE data.
Trucks and cars were detected by Mobileye at greater distances than motorcycles. There is a substantial difference here between cars and trucks, which is logical given their sizes. In any case, the different curves found for cars and trucks are caused by the availability of far more observations at longer distances for these two vehicle categories. The most frequent value would appear to be about one second for all three speed ranges and all three vehicle categories, leading to the conclusion that car drivers generally do not maintain enough distance (time headway) at higher speed. In other words, they do not sufficiently adapt their safety distance – measured as the time gap – to their driving speed. There is some evidence that car drivers who also ride motorcycles have fewer accidents with motorcycles (several relevant sources are cited in de Craen (2011)). If this were applied to the results of the UDRIVE analysis, drivers should conclude – on their own –that more time headway is required behind motorcycles and adapt their behavior accordingly. While this might indeed be the case, UDRIVE data on drivers does not currently include the relevant information required to facilitate such an analysis.
statistically significant. For the UDRIVE data, there is no sample to test. The sample is a full record of the population; in other words, we included any records in the UDRIVE dataset where the selected triggers applied. There is no selected sample that would reflect the actual status of the full population. Since there is no information on either systematic or random differences between UDRIVErs and their vehicles and the rest of the driver population in the world (or at least at the UDRIVE operation sites), significance tests, if they can be applied at all, are of limited value in the current approach. However, the above issue may be alleviated to some extent by further pre-processing the data. A key difference between the two data collection methods is that site-based observation does not provide information on the driver (unless license plates are scanned), whereas several variables on drivers are typically collected in naturalistic driving projects. An example of driver-related variables is the driver identification number. A follow-up study could investigate differences in time headway across countries using the driver as unit of analysis, for example by using general linear modelling. 4.6. Methodological issues
4.5. Statistical limitations
Given all the similarities found in the UDRIVE and site-based data, as well the small differences between different countries, small driver and car samples support the use of naturalistic studies as a reasonable data source even for general questions. In our study, it proved useful to display time headway in increments of one tenth of a second as a share of all records, thus facilitating a comparison of the data from two different sources. Analysis using SQL queries likewise proved to be a quick and
In this study individual following situations have been used as unit of analysis, because this approach facilitated a comparison between the naturalistic driving data collected with instrumented cars in the UDRIVE project and the site-based data collected through traffic detection devices in Austria. A statistical power analysis is of limited value for the kind of data used in this research. The site-based data contains 18 million records, so even the smallest difference would appear
Fig. 13. Average distance [s], cars following trucks, by country and speed from UDRIVE data. 171
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Fig. 14. Time headway from roadside detection device at 50 km/h speed limit by vehicle category from site-based data.
Fig. 15. Time headway from roadside detection device at 100 km/h speed limit by vehicle category from site-based data.
Fig. 16. Time headway from roadside detection device by speed and vehicle category from site-based data.
feasible approach given the size of the database, the limited analysis and the limited resources in terms of CPU time and data transfer rates. As already indicated, it would probably have been useful to analyze a full event, in other words, the development of time headway from the moment one vehicle starts following another until the point in time at which the situation can no longer be considered to be a following event. However, the time at which rear-endings typically occur in such events is not known. A comprehensive study of videos of following events could probably identify the critical moment in such an encounter but
would not improve the information concerning the prevalence of critical distances between the subject and lead vehicles. Hence, the simple approach of counting relevant records seems to provide the most suitable data for answering our research questions. Differences in time headway can be excluded as an explanation for different shares of rearendings among various vehicle types. Since they can be excluded, differences in time headway may be required to achieve an equal level of safety. This topic should form the subject of further research. 172
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Fig. 17. Average distance [s] per speed and vehicle type, all countries equally weighted.
Conflicts of interest
Zweiradsicherheit e. V., Essen. p. 55ff. Lamm, R. et al., 1999. In: SWOV (2012). Volgtijd en verkeersveiligheid. SWOV Factsheet, December 2012. SWOV, Leidschendam. van Petegem, J.H. et al., 2015. European Sight Distances in perspective - EUSight: Literature review report; Deliverable 2.1 of CEDR Call 2013: Safety. CEDR, russels. 20. Pommer, A., Donabauer, M., 2017. Geschwindigkeiten im Straßenverkehr 2015/16. Kuratorium für Verkehrssicherheit, Wien. Pommer, A., Winkelbauer, M., Donabauer, M., 2014. Naturalistic Riding - Ergebnisse einer naturalistischen Feldstudie. Projekt-Endbericht. Kuratorium für Verkehrssicherheit, Wien. Shinar, D., Schechtman, E., 2002. Headway feedback improves intervehicular distance: a field study. Hum. Factors 44 (3), 474–481. Van Nes, N., Bärgman, J., Christoph, M., 2017. Improving safety and sustainability of our road traffic by insights in real driving behavior: the large scale European naturalistic driving study UDRIVE (submitted for publication). Weare, A., Reed, N., et al., 2011. Naturalistic Riding Study: Data Collection & Analysis. 2 BE SAFE Deliverable 6 of work package 2. TRL, London. Winkelbauer, M., Soteropoulos, A., Schneider, F., Tomasch, E., 2017. Unfallursachen bei Motorradunfällen. KFV - Sicher Leben. Band #4. Kuratorium für Verkehrssicherheit, Wien, 2017. Young, K.L., Regan, M.A., Triggs, T.J., Tomasevic, N., Stephan, K., Mitsopoulos, E., 2007. Impact on car driving performance of a following distance warning system: findings from the Australian Transport Accident Commission SafeCar Project. J. Intell. Transport. Syst. 11 (3), 121–131.
None. References ACEM – Association des Constructeurs Européens de Motocycles, 2009. MAIDS – in-depth investigations of accidents involving powered two wheelers. Final Report 2.0, p. 55. Augros, X., Heinig, K., Val, C., Kurol, J., Liden, J., Palander, M., 2013. UDRIVE deliverable D21.1 DAS Technical Specification of the EU FP7 Project UDRIVE (www. udrive.eu). Buche, T., 2016. Factors that increase and decrease motorcyclist crash risk. In: 11th IFZ Motorcycle Conference, Cologne, 2016. de Craen, S., Doumen, M., Bos, N., van Norden, Y., 2011. The roles of motorcyclists and car drivers in conspicuity-related motorcycle crashes. SWOV-rapport R-2011-25, p. 28. Kaba, A., Klemenjak, W., 1993. Informationsaufnahme im Straßenverkehr. Kuratorium für Verkehrssicherheit, Wien. p. 169ff. Knipling, R.R., Mironer, M., Hendricks, D.L., Tijerina, L., Everson, J., Allen, J.C., Wilson, C., 1993. Assessment of IVHS countermeasures for collision avoidance: rear-end crashes (NHTSA Tech. Report DOT HS 807 995). U.S. Department of Transportation, Washington, DC. Kramlich, T., 2002. Noch immer gefährliche Begegnungen: Die häufigsten Gefahrensituationen für Motorradfahrer und die resultierenden Verletzungen. In: Proceedings of the 2002 International Motorcycle Conference. Institut für
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Naturalistic data on time headway behind motorcycles and other vehicles a,⁎
a
a
Martin Winkelbauer , Martin Donabauer , Alexander Pommer , Reinier Jansen a b
b
T
Kuratorium für Verkehrssicherheit (KFV), Schleiergasse 18, 1100 Vienna, Austria Stichting Wetenschappelijk Onderzoek Verkeersveiligheid (SWOV), Bezuidenhoutseweg 62, 2594 AW Den Haag, the Netherlands
ARTICLE INFO
ABSTRACT
Keywords: Naturalistic research Time headway Motorcycle Car Truck
Roughly 10% of motorcycle accidents in Austria are rear-end collisions. One explanatory factor for this could be short time headway, hence this research project. Naturalistic research provides a suitable basis for studying the distance to various kinds of lead vehicles. Our study utilizes the dataset created in the first large-scale naturalistic driving study in Europe (UDRIVE) and combines this with site-based speed data from Austria. Data on cars was filtered from the UDRIVE dataset and analyzed for the presence of a lead vehicle, with the pertinent information provided by ‘Mobileye’, a real-time video processing unit. The analysis included 1242 h of driving recorded at a frequency of 10 Hz. The difference observed between distances behind cars (1.1 s) and motorcycles (1.2 s) proved to be small. Almost twenty million records from roadside radar speed recorders in Austria collected in 2015 and 2016 were then compared with this data. In general, the two methodologies delivered similar results: time headway decreases with increasing driving speed in both datasets. The analysis of the UDRIVE data also showed minor differences in time headway among drivers in the six UDRIVE countries (France, Germany, Poland, Spain, The Netherlands, United Kingdom). Both datasets suggest that time headway is shortest behind trucks. However, the hypothesis that car drivers maintain less time headway behind motorcycles is not supported by either the UDRIVE or the site-based data. In this paper, we will discuss both these results as well as the strengths and weaknesses of both data collection methods.
1. Introduction
motorcycle than they do behind other vehicles, which is not in line with accident figures. While previous on-road studies have investigated following behavior (Young et al., 2007; Shinar and Schechtman, 2002), none of this research has differentiated between lead vehicle types, where differences in time headway could well explain different accident figures. UDRIVE’s naturalistic data proved ideal for investigating the distances in front of the subject vehicles since one of the measuring instruments used was ‘Mobileye’, an advanced driver assistance system that provides time headway and forward collision warnings. A further data source also became available during our research, when data from several site-based speed measurement systems in Austria was processed and made available for standard analysis. To incorporate this data into our research, we decided to analyze it using the speed measurement time stamps to provide us with comparable time headway distributions. We then used the results of this analysis to cross-validate both data sources.
Motorcyclists are a unique group of road users. They are also probably the most vulnerable group of road users given that they travel at far higher speeds than other vulnerable groups like pedestrians or cyclists (Winkelbauer et al., 2017). In a rear-end collision, motorcyclists have a higher risk of sustaining injuries than other motorists. Even a soft collision can seriously impact the dynamics of a motorcycle and cause the rider to fall off and be injured, while a car would probably only receive light damage to its bodywork, and neither the driver nor any passengers would suffer injury. Time headway is an important criterion when it comes to the causes of rear-end collisions (Knipling et al., 1993). Around 10% of powered two-wheeler (PTW) collisions, 23% of car collisions and almost 28% of truck collisions in Austria are rear-end. Yet in the authors’ own experience, car drivers tend to keep even a shorter distance behind a
Corresponding author. E-mail addresses: [email protected] (M. Winkelbauer), [email protected] (M. Donabauer), [email protected] (A. Pommer), [email protected] (R. Jansen). ⁎
https://doi.org/10.1016/j.ssci.2019.01.020 Received 29 December 2017; Received in revised form 16 January 2019; Accepted 22 January 2019 Available online 30 January 2019 0925-7535/ © 2019 Elsevier Ltd. All rights reserved.
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1.1. Vehicle definitions
impossible to determine, whether the reaction of a driver had been too late or too slow. Hence, little is known about whether delayed reaction, slow reaction or lack of time headway typically cause rear-endings. To shed more light on the problem, it might be good to know, if drivers typically keep appropriate time headway. Naturalistic driving (ND) data provides a suitable basis for investigating time headway because it captures real-life driving behavior without the experimenter bias typically found in driving simulator studies (Weare et al., 2011). Two types of ND studies are often used, depending on where and how the data is collected: vehicle-based (e.g. Buche, 2016; Pommer et al., 2014) or site-based (Pommer and Donabauer, 2017). UDRIVE data provides information on the relative position of the subject vehicle to other road users in front. In the research presented in this paper, this information was used to analyze the distance to the vehicle in front. Lack of attention (and in particular distraction) – as a potential explanation for rear end collisions – was analyzed in another part of the UDRIVE project. The study presented in this paper, hence, only focused on time headway by car drivers relative to other cars, trucks and motorcycles.
Powered two-wheelers (PTWs) are a very diverse group of vehicles. New types of vehicles are continually being developed with new technical principles (e.g. tilting three- and four-wheel vehicles, which still feel and look like PTW), innovative designs (e.g. currently a revival of café-racer-like motorcycles) or new propulsion systems (mostly electric). The EU’s Regulation 168/2013 on type approval introduces a new categorization of two-, three- and four-wheel vehicles which technically or legally belong to some extent to the powered two-wheelers group. In legal terms, a ‘motorcycle’ is a powered vehicle with two wheels. In some countries, this also includes mopeds (i.e. motorcycles with a maximum engine displacement of 50 cm3 and a maximum design speed of 45 km/h). PTWs can also be distinguished by their appearance. ‘Scooters’ and ‘motorbikes’ differ primarily in the way the rider mounts the vehicle. A scooter, for instance, typically has an open space between the handlebar and the seat to allow the rider to get on without having to lift a leg over the seat. However, neither the law nor any of the data acquisition systems used for this research differentiate by such style of vehicle. Our study used a practical approach to differentiate between vehicles. Anything that the data acquisition system (DAS) considered to be a ‘bike’, we classed as a motorcycle (although a PTW might have been a more suitable term, since the DAS also treated mopeds as ‘bikes’). Other vehicle categories detected by the DAS are pedestrians, bicycles, cars and trucks. The site-based observations also did not make a strict distinction between vehicle categories, with the devices only delivering data on vehicle length and driving speed in each record. Vehicles are then categorized on this basis during data processing. These radar devices cannot differentiate between bicycles, mopeds and motorcycles. Any vehicle with a length of between two and three meters is classed as a two-wheeled vehicle. Further categorization could have been done through selection by speed, but the options were quite limited and there were no perceived benefits of such a selection.
1.3. Issues about adequate time headway Although allowing ‘adequate time headway’ is generally considered to be safe behavior, there are also other issues that merit further consideration. First of all, there is a quantitative and a qualitative aspect to collision risk, namely the frequency of traffic collisions and their severity (e.g. measured by the severity of the injuries sustained). If the time headway is short, the potential impact speed in a rear-end collision is also low. Even if the lead vehicle decelerates strongly and the driver of the following vehicle does not react prior to the collision, the impact speed will be low, since there is little space to build up a speed differential. The negative aspect of this scenario is that short time headway will result in numerous collisions, since drivers will not normally be able to react to and compensate for an unexpected deceleration of the lead vehicle. A better solution might be to maintain a distance that allows a driver to react in time and avoid a collision with the vehicle ahead. Logically, to avoid a collision, time headway must be longer than reaction time to avoid a collision, provided the lead vehicle does not decelerate at a greater rate than the following vehicle. The distance between one moving vehicle and another (either ahead or behind) can be measured in meters. German law, for example, requires a minimum headway distance in meters corresponding to half the speed indicated on the speedometer in km/h (e.g. 25 m headway at 50 km/h). Using the time gap between two vehicles is probably the more practical approach, since people often find it difficult to estimate distance (Kaba and Klemenjak, 1993). Measuring the distance as a time gap aptly reflects the necessity for such a gap: people need a certain period of time to process information and react. Although the length of a gap measured in time (time headway) automatically increases with speed (e.g. one second of time headway corresponds to a distance of about 14 m at 50 km/h and about 28 m at 100 km/h), these time gaps should be lengthened at higher speeds to accommodate both the increase in the difference in stopping distances for vehicles with different brake performance and the larger impact of reaction delay. If a vehicle with poor deceleration capabilities (e.g. a car whose brakes are in a poor condition, a motorcycle driven by a rider with poor skills or a heavily-loaded truck) travels at the same speed behind another vehicle with better deceleration capabilities and has to perform an emergency braking maneuver, the impact speed ceteris paribus increases in proportion to the initial driving speed of the two vehicles. The logical consequence: Time headway should be longer at higher speeds. The Austrian courts normally accept a one-second delay for reaction in criminal cases. This delay is frequently referred to as the ‘reaction time’ but, in legal terms, it constitutes the delay in a driver’s reaction to
1.2. Why did we use naturalistic data to analyze time headway? A large portion of this research was carried out as part of the UDRIVE research project, which was funded by the European Commission (Horizon 2020 Programme). UDRIVE was a large-scale naturalistic study in which cars, motorcycles and trucks were fitted with the same data acquisition system and driving data was collected for about 21 months in the period from 2015 to 2017. One of the tasks in the UDRIVE study was to investigate the traffic behavior of vulnerable road users, among which powered two-wheelers form an important group (at least in terms of the numbers of fatalities and injuries). For the analysis presented in this paper, PTW-related issues were investigated using the data collected by cars, not the data collected by motorcycles or trucks. Austrian accident statistics (i.e. the Austrian Road Safety Board’s (KFV) analysis based on data recorded by the police) show that about 10% of motorcycle collisions are rear-endings where the other party is at fault (i.e. the motorcycle is struck from behind). The MAIDS (originally “Motorcycle Accident In-Depth Study”) report (ACEM, 2009) found that roughly 5% of motorcycle collisions involve the motorcycle being hit from behind. Kramlich (2002) found that in about 75% of collisions involving just one car and one motorcycle, the car drivers were at fault. There are two potential reasons for rear-end collisions, at least for most of them: Time headway is too short, even for an appropriately quick reaction, or the reaction of a driver is inappropriately slow. Retrospective accident analysis normally cannot prove one or the other having caused an accident. Only in rare cases, competent eye-witness reports, dash cam videos or the data of an accident data recorder yield reliable reconstruction. There is even more uncertainty: It is almost 163
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something which requires action. In civil cases, the accepted delay can be shorter (0.8 s). In situations that require a particularly high level of attention (e.g. when driving in the vicinity of a school), the acceptable delay may also be reduced. The generally accepted recommendations by all road-safety organizations in Austria are a 1-second delay for speeds below 50 km/h, 2 s for speeds between 50 and 100 km/h and 3 to 4 s for higher speeds. However, there is no legislation regarding delays as a function of the type of lead vehicle, despite the fact that motorcycles are more vulnerable. In all cases, a time headways of less than 0.2 s results in the driving license being withdrawn, 0.2–0.4 s triggers a written warning, a fine and one additional point in the penalty points system (a driver with two points is required to attend a refresher course; three points leads to license withdrawal).
autonomous driving functions. In the UDRIVE project, Mobileye was only used as a sensor, no feedback was given to the drivers. More details about the data acquisition system (DAS), data collected, data processing and analysis tools is provided in another article in this issue of Safety Science (Van Nes et al., submitted for publication). The analysis presented in this paper was based on a preliminary dataset from the UDRIVE naturalistic database, including 1242 h of driving recorded at a frequency of 10 Hz (134 million records), which were collected from UDRIVE cars in the Netherlands, France, Germany, Poland and the United Kingdom. There were no cars operated at the Spanish UDRIVE test site. Our analysis was made possible in particular by Mobileye gathering data on up to four other road users in one of five categories (‘pedestrian’, ‘bicycle’, ‘bike’, ‘car’, ‘truck’, where ‘bike’ means motorcycle or moped). Basic information from GPS data – in particular map-matched speed and local speed limit – were also used. The following section describes which data from the UDRIVE dataset was used, explains how this data was extracted and analyzed, and presents the results of our analysis. For details on the data acquisition system, see Augros et al, 2013.
1.4. Objectives The objective of our study was to investigate whether car drivers’ time headway depends on the category of lead vehicle; in other words, whether car drivers maintain more or less time headway behind trucks and cars than they do behind motorcycles. While we were conducting the research for this project, another data source became available: KFV has been continuously collecting data on road traffic parameters in Austria for over three decades (e.g. driving speed, seat belt, child restraint and helmet use rates and red light running). This data serves as a basis for the longitudinal comparison of road safety performance indicators (Pommer and Donabauer, 2017). Since driving speed and time headway are among the parameters measured, we were able to compare them with the same parameters measured in the UDRIVE project. While the data acquisition methods for site-based observation remained fairly consistent for a long period of time, three significant changes were implemented in 2015:
2.1.1. Operationalization of following situations A major challenge in our research was selecting the relevant data from the large UDRIVE database. We first had to determine what constitutes a reasonable definition of ‘time headway’, including what driving behind another vehicle actually means when translated into naturalistic data terms. Mobileye provides detailed information on up to four other road users at the same time (such as their lateral and longitudinal position relative to the subject vehicle). Together with current driving speed obtained from the CAN bus, time headway in seconds was calculated as the longitudinal distance of a lead vehicle in meters divided by the driving speed of the subject vehicle in meters per second. Mobileye’s normal purpose is collision warning. Most likely, this is why the Mobileye term for other road uses detected, is “obstacle”. The obstacles do not have a fixed identity. For example, a motorcycle may first be captured as obstacle number two, if another obstacle was captured before it. If the first obstacle is no longer detected, the motorcycle immediately becomes obstacle number one. This makes it difficult to follow specific obstacles in the data. Our investigation (and the others described below) were carried out as an iterative process using ‘SALSA’, the UDRIVE graphic user interface. We explored the variables in the UDRIVE database, in particular those where Mobileye had captured the information, by comparing the measurements with what we could see in the videos. A new variable was calculated and added to the UDRIVE database based on the six conditions listed below. Time headway was calculated for all timestamps where these six conditions were met.
○ All paper/pencil acquisition was replaced by data collection via tablet computer. KFV now uses ‘SODA’ software to program questionnaires; the data entered by the data collectors is transmitted directly to a central database via the internet and is therefore immediately available for analysis. ○ Manual speed data acquisition using handheld radar guns was changed to an automatic system. The new sensors used will be described later in this chapter. ○ The time and money saved by these changes were used to significantly diversify data acquisition, (extending it, for example, to include curve trajectories and the use of personal protective equipment by motorcyclists, start and stop behavior at intersections) and, in particular, to increase the volume of speed data records by a factor of about 100. The above changes strongly improved information on speed and time headway and yielded a useful comparison to other naturalistic (i.e. UDRIVE) data. Nota bene, the KFV data collection was not done as part of UDRIVE, but it was done at the same time and by the same team, who considered comparison to be a promising source of information for both sides (UDRIVE and Austrian data collection).
1. There is at least one ‘bike’ or ‘car’ or ‘truck’ present. These were the relevant values of the Mobileye variable for type of ‘other road user’. 2. The traffic was almost free-flowing. The analysis focused on quasi free-flow situations. In other words, it avoided long episodes in which a motorcycle, car or truck was moving in front of a car in congestion at low speed. Hence, records involving speeds of less than 30 km/h or less than 50% of the local speed limit were excluded. 3. The vehicles were travelling in the same lane. Accordingly, the lateral distance of lead vehicles was limited to 3.5 m to the left or right of the subject vehicle. This value was tested and validated by video observation. 4. There were no vehicles crossing the path of the subject vehicle. A trigger was implemented to avoid capturing vehicles crossing the path of the subject vehicle perpendicular to its driving direction. 5. 5 A “steady-state” following situation was in place. Only following situations in which the subject vehicle was driving behind another
2. Methods 2.1. UDRIVE data The research presented in this paper was carried out as part of the first large-scale naturalistic study in Europe, the UDRIVE project. Roughly 210 vehicles, comprising around 120 cars, 50 trucks and 40 motorcycles, were fitted with comprehensive data acquisition systems to collect data on GPS location, accelerations and rotations as well as CAN bus messages, up to 8 video channels and data from ‘Mobileye’. Mobileye is a real-time video processing unit normally used to provide feedback on time headway, collision warnings or input data for 164
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road user for at least ten seconds were included. 6. No overtaking maneuvers were underway. Overtaking maneuvers in which the subject vehicle was being overtaken were excluded (i.e. maneuvers in which the ‘lead’ vehicle appears from either the right or the left behind the subject vehicle – depending on location – and leaves the scene at a continuously higher speed than the subject vehicle). This did not include situations where another vehicle simply passed the subject vehicle. Video observation showed that such episodes could be successfully excluded by using a variable delivered by Mobileye that determined the longitudinal distance to another road user. All episodes where the average of this relative speed parameter was greater than 1 were excluded.
Table 2 Proportion of interaction by vehicle type.
Av. speed [km/h]
Days with trips
DE FR NL PL UK Unknown
1106 5874 921 914 5373 737
14 43 15 18 50 12
4841 23,227 3490 3914 23,274 2446
35,410 260,911 53,302 27,572 231,093 36,029
32 44 58 30 43 49
415 474 304 346 494 226
Total
14,925
152
61,192
644,317
0.07% 19.88% 1.14% 78.91%
Total N
537,490,379
100.00%
2.1.4. Analysis We did not apply statistical tests on mean differences because the large amount of data (several hundred thousand per group) would always result in significant differences, even though the information was triggered by just a few subjects. Our analysis dataset was not organized based on events (e.g. minimum distance during interaction with a specific vehicle). While it would, of course, have been useful to analyze episodes of interaction from when the other vehicle appeared in front of the subject vehicle until it left the scene (e.g. changed to another lane, turned into another road or simply disappeared over the horizon or behind a corner). However, this proved to be a complicated or even impossible task because interactions were frequently interrupted (e.g. as a result of missing records). We therefore opted for a records-based analysis based on the frequency of prevalence of the relevant following situations in intervals of one-tenth of a second regardless of the total duration of a following situation (i.e. how many records made up one event). 2.2. Site-based data
Table 1 Records by operation site as of 21.03.2017 (analysis date). Travel distance [km]
369,235 106,869,877 6,116,808 424,134,459
Fig. 1 shows the size of the dataset used in our analysis. The proportions of records by lead vehicle differ from country to country, and we did not speculate, for example, on why there are far more records with a motorcycle as the lead vehicle in the Netherlands than in all other countries. However, since we only analyze shares of records per vehicle category, slight differences in sample size do not compromise the overall results either by country or vehicle category. Our sampling choices are not an issue of representativeness, but rather the issues of access time to the SQL server and CPU time.
○ Distance [s] < 3 s. Validation using the videos showed that longer distances can easily lead to a detection of vehicles in other lanes in slight curves. Such long distances also fall outside the scope of our analysis, since shorter distances are considered more dangerous, and the exclusion of longer distances for all vehicle categories would not affect the comparison. ○ Speed was categorized in 5 km/h increments, for example ‘55 km/h’
Trips
Bike Car Truck No lead vehicle
○ Car to car following situations: every 5th trip (record ID ending with 1 or 6); within a trip, every 30th record. ○ Car to truck following situations: every 10th record.
2.1.3. Reduced sample Due to the restriction in our access time to the database, we had to reduce the amount of data we used. To do so, we applied the following restrictions:
Drivers
Proportion (%)
Given the unequal distribution across lead vehicle types, we selected all motorcycle data, but only a subset of the data where the lead vehicle was a car or truck based on the following criteria:
2.1.2. Total sample Our analyses were executed on the data that was available in the database on 21 March 2016. ‘Records’ refer to individual data samples, which were collected at a sample rate of 10 Hz. The data available on this date is shown in Table 1, which also indicates that the operation sites in France and the United Kingdom provided most of the data. Records for which the operation site is unknown were excluded from subsequent analyses. The number of records available by country was determined by the number of vehicles run by the respective ‘operation site’, how quickly these sites had succeeded in acquiring subjects and installing data acquisition systems and how fast the data had been transferred and processed. The total number of records with other road users detected by Mobileye is indicated in Table 2. Interactions with motorcycles (i.e. records in which at least one of the obstacles is a motorcycle) are quite rare in comparison to the other vehicle categories, but about 370,000 records are still more than enough for our analysis purposes.
Duration [h]
Records
includes 55.00–59.99 km/h, resulting in a new, aggregated speed variable. ○ Distance of a car to a motorcycle in front (car to bike following situations): all data.
The time headway analysis was carried out for three speed categories. The upper threshold for the category ‘low speed’ was set at 60 km/h, since this is the speed that is typical for a speed limit of 50 km/h in the Austrian data. 85 km/h was set as the second threshold as a result of pilot data analysis. At speeds above 85 km/h, the pilot analysis results suggested a noticeable change in the number of records per vehicle category, which may be caused by limits in the range of Mobileye’s sensors.
Country
Lead vehicle
The Sierzega SR4 traffic detection device (Fig. 2) is mounted at the roadside to collect five items of information for each vehicle passing by: speed, direction of travel, vehicle length, distance to previous vehicle and the time stamp for each record. Vehicle length is used to determine the type of vehicle, which after a lot of calibrating provides satisfactory accuracy. KFV has six of these roadside traffic detection devices and relocates them all once a week throughout the whole year. In addition to individual driving data, the devices acquire metadata like exact location, local speed limits for all vehicle categories, road category, width of all lanes, etc. During 2015 and 2016, 1.6 million motorcycles, 16.5 million cars and 0.9 million trucks were recorded passing one of these six Sierzega SR4 devices. 165
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Fig. 1. Analysis data set: records per country and vehicle type.
between the three lead vehicle categories. At higher speeds (Fig. 4), time headway distributions show a slightly different picture. Up to 0.8 s, the three following situation categories show similar values, but the result for the 0.9–1.2 s range is lower for cars behind motorcycles (i.e. car to bike). The time headway frequency distribution of cars behind motorcycles at speeds between 60 and 85 km/h shows two peaks at approximately 0.8 s and 1.25 s. The time headway frequency distribution at speeds of 85 km/h and above (Fig. 5) shows considerable differences between the vehicle categories. For example, a relatively large proportion of car to bike following situations are found between 0.7 and 1.4 s, whereas car to car and car to truck following situations are found more often above 1.6 s. Fig. 2. Sierzega SR4 traffic detection device.
3.1.2. Average time headway values by speed Fig. 6 displays the proportion of records by speed category. The graphs for cars and motorcycles look similar despite the difference in their total numbers. The graph for trucks, however, looks different. As can be seen in Fig. 6 there were relatively few records of trucks at low speeds. Fig. 7 shows that up to 75 km/h, the average time headway is comparable between car to bike, car to car, and car to truck following situations. At speeds above 75 km/h, however, distinct patterns start to emerge. Especially above 90 km/h, the average time headway in car to bike following situations is approximately 0.2 s lower than in car to car following situations. Furthermore, the average time headway in car to truck following situations appears to increase with speed for speeds above 90 km/h.
3. Results 3.1. UDRIVE data 3.1.1. Mode of distance by speed category At low speeds (up to 60 km/h; Fig. 3), there is almost no difference in time headway between car to car and car to bike following situations. However, car drivers maintain slightly less distance behind trucks. Value frequencies from 0.3 to 0.9 s are almost equal for all three lead vehicle categories, but values from 1.0 to 1.5 s are more frequent for subject vehicles behind trucks. This means that in the most relevant time headway range (up to 1 s) at this speed range (i.e. up to 60 km/h), there is no notable difference
Fig. 3. Distribution of time headway [s]; driving speed < 60 km/h. 166
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Fig. 4. Distribution of time headway [s]; driving speed 60–85 km/h.
3.1.3. Time headway by vehicle category, speed and country Before focusing on time headway, we first have to consider the comparability of data between the five countries with car operation sites. The number of records collected by speed category could be seen as a reflection of subject vehicle mileage in the each UDRIVE country. As already shown above, driving speed is an important moderating variable for time headway. Hence, similar distributions of driving speeds in the five countries would be advantageous for a good comparison of time headway. Figs. 8–10 show these distributions for motorcycles, cars and trucks. These figures show that the distribution of driving speed in following situations is comparable between France, UK, Germany, and Poland. However, in the Netherlands a relatively large proportion of the following situations are found at driving speeds above 85 km/h, whereas a relatively low proportion of the following situations are found at low driving speeds. Fig. 11 shows the distances of subject cars behind motorcycles. The curves in Figs. 12 and 13 are much smoother due to the larger number of records. Although the values lie within a range of about 0.4 s, there is no indication that car drivers in one country follow motorcycles at a closer distance than their counterparts in other countries. The data displayed in Fig. 12 suggests that French car drivers follow other cars about 0.2 s closer than their counterparts in the other countries. In the case of trucks (Fig. 13), the behavior of German drivers differs slightly from that of their counterparts in all the other countries: at medium speeds, they keep a longer distance of about 0.2 s.
that we used for the UDRIVE data. The results are displayed accordingly in Figs. 14–16. 4. Discussion Time headway of car drivers with cars, trucks and motorcycles as a lead vehicles was investigated using two different sources: a) time headway measured by the Mobileye (a retail driver assistance system) in about 120 highly instrumented passenger cars during the UDRIVE naturalistic driving study, and b) time headway measured by 6 Sierzega RADAR traffic observation devices which had been mounted along roads in Austria and relocated weekly for about three years. 4.1. Research question and hypothesis Our hypothesis that car drivers maintaining less time headway behind motorcycles could be the reason for rear-ending collisions was not supported by the results of our research. The required or useful time headway behind a motorcycle is not necessarily equal to the safety gap required for cars behind other cars or trucks. On the contrary, the smaller silhouette of a motorcycle reduces the size of its projection on the iris, thereby increasing the chance of it being overlooked as an obstacle. Hence, driving behind a motorcycle might even require more time headway than driving behind a passenger car or a truck. 4.2. Time headway analysis
3.2. Results of site-based observation; comparison to UDRIVE data
Our results show that the relatively small number of records of cars behind motorcycles results in less smooth curves than for other vehicle
We analyzed the site-based data by applying the same procedure
Fig. 5. Distribution of time headway [s]; driving speed > 85 km/h. 167
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Fig. 6. Number of records (N) by speed and vehicle type.
categories; in other words, that a low volume of data causes a high variance. Nevertheless, the data seems to be more than good enough to provide a clear picture. The frequency distributions of different time headways recorded by Mobileye for three categories of road users (motorcycles, cars, trucks) are similar, with only some minor exceptions: at low speeds (< 60 km/h), UDRIVE subjects followed trucks slightly closer than motorcycles and cars, while values of 0.8–1.5 s are less frequent for motorcycles at medium speeds (60–86 km/h), thus indicating that UDRIVErs maintained a longer distance behind motorcycles. UDRIVErs in general maintained less time headway at higher speeds than at lower speeds, while from a preventive safety point of view, the reverse should be the case. The time headway frequency distribution for cars behind motorcycles at speeds of between 60 and 85 km/h shows two peaks. There is no obvious explanation for this result. We could argue that this is caused by a small proportion of car drivers who ignore the presence of a motorcycle and focus instead on the vehicle in front of it. However, there is 0.6 s difference between the two peaks, which would mean that the motorcycles in this case maintain an average distance of only 0.6 s behind their own lead vehicles, which is too short to be plausible. Therefore, some other (unknown) issue might be influencing time headway choice. The time headway distribution at speeds of 85 km/h and above (Fig. 5) is possibly biased by the methodology and sensors. We argue that motorcycles are difficult to detect by Mobileye when they are further away. More precisely, our data suggests that hardly any motorcycles are detected at a time headway of 2.2 s or more, which corresponds to a range of 52 m at 85 km/h; other vehicle types are detected at up to approximately 200 m. It is unlikely that this is because there are
no motorcycles on the road. Indeed, it is more likely that technical limitations with Mobileye video resolution mean that motorcycles are not detected at these longer distances. However, we would like to note that this is not a criticism of Mobileye’s quality and performance, since its main intended purpose does not require detection of powered twowheelers at such distances. Under European law, heavy goods vehicles (HGVs; trucks with a gross vehicle weight of more than 3.5 tons) must be equipped with a speed limiter, which prevents them from driving faster than 90 km/h. Our data does contain some records (Fig. 6) where the measured speed is higher than 90 km/h, which is most likely to be due to speeding or to Mobileye wrongly identifying light goods vehicles as HGVs. These results may also indicate that some of the situations considered to be following aren’t following, they might be the approaching phase of “highway business as usual”, where a passenger car runs slightly faster than a truck, approaches the truck form behind and then overtakes – which would indicate that the algorithm considering relative speed needs some improvement. The relatively small number of records of trucks travelling at low speeds (Fig. 7) suggests that there are far more HGVs moving at higher speeds. We could conclude that this is related to the type of roads they frequent. HGVs – in particular the largest ones – are not frequently encountered in urban traffic: they are vehicles that are designed for trunk roads and highways, where speeds are generally higher. Another explanation could be that Mobileye is more likely to detect trucks at greater distances than cars or motorcycles, a plausible explanation already used above for cars and motorcycles. From a preventive safety point of view, the information in Fig. 7 could be considered alarming. Instead of maintaining greater distances
Fig. 7. Average distance [s] by speed and vehicle type. 168
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Fig. 8. Distribution of records, cars following motorcycles, per country and speed from UDRIVE data.
Fig. 9. Distribution of records, cars following other cars, per country and speed from UDRIVE data.
at higher speeds, drivers do exactly the opposite. At least up to a speed of 90 km/h, drivers maintain less time headway at higher speeds. There is no relevant difference with respect to the type of vehicle ahead. Previous studies show that the reaction time of drivers varies between one and more than two seconds (Lamm et al., 1999; van Petegem et al., 2015). Therefore, our analysis indicates dangerous behavior by drivers but not particularly dangerous behavior vis-à-vis motorcycles. The comparison of UDRIVErs in different countries showed only minor differences in behavior. There were remarkably fewer records of Polish drivers driving behind trucks at typical highway speeds. German drivers maintained more distance behind trucks at medium speeds, while French drivers seemed to follow other cars closer than their counterparts in the other countries. For motorcycles, all values for all UDRIVE countries at all speeds were within a range of 0.4 s, noteworthy differences between UDRIVE countries could not be found. In general, the graphs for all UDRIVE countries look quite similar, with two notable exceptions. First, data from the Netherlands is a general outlier as the UDRIVE subject cars seem to travel predominantly at speeds above 85 km/h (most of the records for all three vehicle categories were collected within this speed range) when the criteria for a following situation were met. Possibly, the Dutch traffic situations featured more congestion than the other countries, and consequently, potential following situations were excluded due to speed below 50% of the local speed limit. Second, for Poland, in comparison with the other countries, record distribution is similar for cars and motorcycles, but dissimilar for trucks. This may indicate that either (a) trucks in Poland travel less frequently at speeds above 70 km/h, or (b) subject vehicles in Poland moved less frequently at speeds above
70 km/h behind trucks, (c) there were no roads within the operation area of the Polish subject vehicles on which cars would typically move behind trucks in this speed range or d) Polish drivers never use the lanes predominantly used by trucks. The most likely explanation here is that Polish car drivers did not use highways as frequently as their counterparts in other countries (for whatever reason). The data did not provide information to support either one of these possibilities. If the numbers of records for time headway are weighted by country of origin (i.e. the counts for each country are weighted by the number of vehicles in said country), hardly any differences in time headway are observed between following situations with cars, trucks and motorcycles (see Fig. 17). Looking beyond Europe, Young et al. (2007) report an average time headway that is approximately 0.1–0.2 s higher than our findings using the UDRIVE database. While this could be attributed to differences in the driving environment (i.e. Australia vs. Europe), we also cannot rule out the possibility that it is caused by the fact that Young et al. report time headway as a function of the local speed limit. Without knowing the actual driving speed for each speed limit, a direct comparison of average time headway is not possible. Nonetheless, our observation that time headway decreases with driving speed seems consistent with Young et al.’s finding that time headway decreases with speed limit. 4.3. Comparison of the results of the site-based observation vs. UDRIVE data While the distribution of the following distance behind cars (Fig. 14) at a speed limit of 50 km/h looks quite similar in both the site169
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Fig. 10. Distribution of records, cars following trucks, per country and speed from UDRIVE data.
Fig. 11. Average time headway [s], cars following motorcycles, by country and speed from UDRIVE data.
based and the UDRIVE data, the mode1 differs. In the UDRIVE data, the mode of time headway (1.3 s) is notably higher than in the Austrian data (0.9 s). For the following distance behind motorcycles, the curves for the UDRIVE and Austrian data are almost congruent, which means that there is a significant difference between cars and motorcycles in the Austrian data. This difference can most likely be attributed to the methodology used. Mobileye’s capability to detect headway distances and subsequently compute time headway is limited to about 2–3 s (an exact value is not known). The roadside traffic detection devices limit their detection of time gaps to 23 s. The same principle applies for the difference between cars and trucks. Mobileye detects a larger and closer object more easily than a smaller object at a longer distance. Nevertheless, the system is more than adequate at the distances that are relevant for warning a driver of an obstacle. Roadside traffic detection devices were never mounted along highways or other multilane roads, since the data they record is not sufficiently reliable when there is more than one lane in each direction. However, UDRIVE data does include highways. This is important, since time headway and speed are typically higher on highways. Furthermore, roadside traffic detection devices measure time gaps directly, while time headway had to be calculated from other measurements in UDRIVE. Although we considered it unlikely, there were good reasons to argue that measurement by site-based detection devices and vehicle-based image-processed data might produce different results. The comparison of average values for distance by speed category 1
also reveals some similarities between the UDRIVE and site-based data. While the curve for time headway behind cars is almost congruent in UDRIVE data and site-based data (Fig. 15), time headway behind trucks differs notably. The site-based data indicates that car drivers in Austria follow trucks at a closer distance than drivers in the UDRIVE countries. However, this difference does not exceed 0.15 s, which corresponds to about 10% of the total values. Time headway behind motorcycles is even slightly higher in the Austrian site-based data over the entire range of speeds, but the difference is less than one tenth of a second for all speed categories (Fig. 16). Nevertheless, the two methods of measuring time headway deliver comparable results, and any differences can be plausibly explained. Accordingly, we can conclude that this comparison supports the assumption that the Mobileye system delivers accurate and useful data for research on the relative positions of other road users. 4.4. Generalizability of results and technical limitations There are some limitations to the UDRIVE setup. The study involves about 120 car drivers at five different operation sites in five European countries, most of which are in urban areas. Further, the UDRIVE fleet only includes small subject cars and is restricted to a limited number of models of one make. This limits the validity of the results in two ways. First, the sample consists of subjects who typically purchase vehicles of this type (and make). Second, the analysis does not consider vehiclerelated parameters such as driver perspective (which differs according to vehicle type, e.g. a people carrier, a sports utility vehicle or a small sports car). The perceived driving speed might also be different in a high-power sports car compared with a luxury sedan (e.g. based on the level of internal noise).
The value of the 50th percentile. 170
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Fig. 12. Average distance [s], cars following other cars, by country and speed from UDRIVE data.
Trucks and cars were detected by Mobileye at greater distances than motorcycles. There is a substantial difference here between cars and trucks, which is logical given their sizes. In any case, the different curves found for cars and trucks are caused by the availability of far more observations at longer distances for these two vehicle categories. The most frequent value would appear to be about one second for all three speed ranges and all three vehicle categories, leading to the conclusion that car drivers generally do not maintain enough distance (time headway) at higher speed. In other words, they do not sufficiently adapt their safety distance – measured as the time gap – to their driving speed. There is some evidence that car drivers who also ride motorcycles have fewer accidents with motorcycles (several relevant sources are cited in de Craen (2011)). If this were applied to the results of the UDRIVE analysis, drivers should conclude – on their own –that more time headway is required behind motorcycles and adapt their behavior accordingly. While this might indeed be the case, UDRIVE data on drivers does not currently include the relevant information required to facilitate such an analysis.
statistically significant. For the UDRIVE data, there is no sample to test. The sample is a full record of the population; in other words, we included any records in the UDRIVE dataset where the selected triggers applied. There is no selected sample that would reflect the actual status of the full population. Since there is no information on either systematic or random differences between UDRIVErs and their vehicles and the rest of the driver population in the world (or at least at the UDRIVE operation sites), significance tests, if they can be applied at all, are of limited value in the current approach. However, the above issue may be alleviated to some extent by further pre-processing the data. A key difference between the two data collection methods is that site-based observation does not provide information on the driver (unless license plates are scanned), whereas several variables on drivers are typically collected in naturalistic driving projects. An example of driver-related variables is the driver identification number. A follow-up study could investigate differences in time headway across countries using the driver as unit of analysis, for example by using general linear modelling. 4.6. Methodological issues
4.5. Statistical limitations
Given all the similarities found in the UDRIVE and site-based data, as well the small differences between different countries, small driver and car samples support the use of naturalistic studies as a reasonable data source even for general questions. In our study, it proved useful to display time headway in increments of one tenth of a second as a share of all records, thus facilitating a comparison of the data from two different sources. Analysis using SQL queries likewise proved to be a quick and
In this study individual following situations have been used as unit of analysis, because this approach facilitated a comparison between the naturalistic driving data collected with instrumented cars in the UDRIVE project and the site-based data collected through traffic detection devices in Austria. A statistical power analysis is of limited value for the kind of data used in this research. The site-based data contains 18 million records, so even the smallest difference would appear
Fig. 13. Average distance [s], cars following trucks, by country and speed from UDRIVE data. 171
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Fig. 14. Time headway from roadside detection device at 50 km/h speed limit by vehicle category from site-based data.
Fig. 15. Time headway from roadside detection device at 100 km/h speed limit by vehicle category from site-based data.
Fig. 16. Time headway from roadside detection device by speed and vehicle category from site-based data.
feasible approach given the size of the database, the limited analysis and the limited resources in terms of CPU time and data transfer rates. As already indicated, it would probably have been useful to analyze a full event, in other words, the development of time headway from the moment one vehicle starts following another until the point in time at which the situation can no longer be considered to be a following event. However, the time at which rear-endings typically occur in such events is not known. A comprehensive study of videos of following events could probably identify the critical moment in such an encounter but
would not improve the information concerning the prevalence of critical distances between the subject and lead vehicles. Hence, the simple approach of counting relevant records seems to provide the most suitable data for answering our research questions. Differences in time headway can be excluded as an explanation for different shares of rearendings among various vehicle types. Since they can be excluded, differences in time headway may be required to achieve an equal level of safety. This topic should form the subject of further research. 172
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Fig. 17. Average distance [s] per speed and vehicle type, all countries equally weighted.
Conflicts of interest
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