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A novel method of vehicle-pedestrian near-crash identification with roadside LiDAR data
Accident Analysis and Prevention 121 (2018) 238–249
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Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
A novel method of vehicle-pedestrian near-crash identification with roadside LiDAR data
T
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Jianqing Wu, Hao Xu , Yichen Zheng, Zong Tian University of Nevada, Reno 1664 N. Virginia Street, MS258, Reno, Nevada, 89557, United States
A R T I C LE I N FO
A B S T R A C T
Keywords: Near crash Pedestrian safety Roadside LiDAR
Safety evaluation based on historical crashes usually has a lot of limitations. In previous studies, near-crashes are considered as surrogate data for safety evaluation. One challenge for the use of near-crashes data is the difficulty of data collection. The driving simulators and naturalistic driving data may not be suitable for safety evaluation at specific sites. The observational site-based methods such as human observers and video analysis also suffer from some limitations such as long time data processing or reduced performance influenced by weather or light condition. The roadside Light Detection and Ranging (LiDAR)-enhanced infrastructure provides a new solution for real-time data collection without the impact from weather or light. The high-resolution trajectories of all road users can be obtained from roadside LiDAR data. This paper aims to fill these gaps by presenting a method for near-crash identification based on the trajectories of road users extracted from roadside LiDAR data. This paper focused on vehicle-pedestrian near-crash identification particularly considering the increased risk of vehiclepedestrian conflicts. Three parameters: Time Difference to the Point of Intersection (TDPI); Distance between Stop Position and Pedestrian (DSPP); Vehicle-pedestrian speed-distance profile, were developed for vehiclepedestrian near-crash identification. The authors also recommended the thresholds for risk assessment of pedestrian safety. This method was coded into an automatic procedure for near-crash identification. This method is expected to significantly improve the current evaluation of pedestrian safety.
1. Introduction Historical crash records are important data sources for safety evaluation on roads. However, not all crashes that happen on roads are reported in the NHTSA Fatality Analysis Reporting Systems (NHTSA, 2018a) or in the National Automotive Sampling System General Estimates System (NHTSA, 2018b), which limits the accuracy of safety evaluation using historical crash data (Hauer, 1997). Furthermore, delay in safety evaluation is unavoidable since it takes time to collect and process the historical crash records. Therefore, researchers and engineers are looking for surrogate data for safety evaluation. Nearcrashes are then selected as a surrogate dataset to assess road safety management. Near-crashes refer to cases where drivers execute rapid evasive maneuvers (i.e., emergency braking and/or steering operation) when facing a potential driving risk or a potential threat (Wu and Jovanis, 2013). Guo et al. (2010) employed two metrics, namely, precision and bias of risk estimation, to assess near-crashes, and indicated that using near-crashes as a crash surrogate could provide definite benefits when data about a sufficient number of crashes are not available.
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One of the challenges for risk assessment using near-crash data is data collection method. The data collection method can be roughly divided into three major parts: driving simulation, naturalistic driving studies (NDS) and observational site-based collection methods. Driving simulation and naturalistic driving data are widely used for near-crash collection in previous studies. Smith et al. (2002) analyzed drivers’ reactions in emergencies using simulation data. A total of 108 participants were asked to drive the simulator under different road conditions in this research. The deceleration and braking behavior were used to extract near-crash data. Lee et al. (2003) developed an economical driving simulator approach to assess crash risk for older drivers. One hundred and twenty-nine older drivers residing in a metropolitan city volunteered to drive the simulator in a 45-min simulated-driving session. Ten reliable assessment criteria were developed to measure each participant's performance. It was found that driving skill of older drivers was found to decline with age. Although driving simulators can provide details about near-crashes with low-cost, the driving behavior in simulated environments still differ from a real situation (Wu and Xu, 2017). NDS can collect driver observation and driving operation unobtrusively, which provides a good opportunity to extract near-crashes
Corresponding author. E-mail addresses: [email protected] (J. Wu), [email protected] (H. Xu), [email protected] (Y. Zheng), [email protected] (Z. Tian).
https://doi.org/10.1016/j.aap.2018.09.001 Received 22 March 2018; Received in revised form 20 July 2018; Accepted 1 September 2018 0001-4575/ © 2018 Elsevier Ltd. All rights reserved.
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most of which are mainly used for pilot programs (Wu et al., 2018a). It will take time to equip connected-vehicle devices into all vehicles, especially the old ones. It was estimated that the mixed traffic with connected vehicles and unconnected vehicles will last for the next decade (Wu and Xu, 2018b). As a result, not all near-crashes can be detected as only partial vehicle movement information can be obtained. It is necessary to find a solution to fill the data gap during the transition period from unconnected vehicles to connected vehicles. Tarko et al. (2016) have investigated the feasibility of using laser ranging technology (LiDAR) for safety and operation management at intersections. The investigation showed that the proposed technology does not experience some of the problems of the current video-based technology but less expensive low-end sensors have limited density of points where measurements are collected that may bring new challenges. Wu et al. (Sun et al., 2018) developed an innovative approach using roadside Light Detection and Ranging (LiDAR) to obtain the real-time high-resolution traffic data (HRTD) of all road users including connected vehicles and unconnected road users on the road. The newest version of 3D LiDAR sensor can scan 360° three-dimensional surrounding objects and report the precise location and speed of each object in its scanned range. Any vehicles, pedestrians, and bicyclists with connected-vehicle devices will immediately benefit from the roadside LiDAR data rather than being limited by whether and how many other vehicles are connected. The trajectories of road users provided by roadside LiDAR are considered as the good data source for near-crash identification (Gelso and Sjoberg, 2017). Different road users may be involved into near-crashes. The major types of near-crashes include vehicle-vehicle near-crash, vehicle-pedestrian near-crash, vehicle-bicycle near-crash, bicycle-pedestrian nearcrash, vehicle-animal near-crash, and other types. Previous studies mainly focused on vehicle-vehicle near-crash analysis, especially on rear-end near-crash analysis (Anon., 2014). Vehicle-pedestrian nearcrash was not well analyzed in previous research, mostly due to the difficulty of vehicle-pedestrian near-crash data collection (Wu and Xu, 2017). Pedestrians were one of the few groups of road users to experience an increase in fatalities in the United States in 2015, totaling 5376 deaths (Wu et al., 2017). On average, a pedestrian was killed every two hours and injured every seven minutes in traffic crashes. Fourteen percent of all traffic fatalities and an estimated 3 percent of those injured in traffic crashes were pedestrians (Sun et al., 2018). Therefore, it is necessary to pay more attention to vehicle-pedestrian conflicts. Considering the advantages of roadside LiDAR data, this paper developed an innovative approach for vehicle-pedestrian nearcrash identification using trajectories of vehicles and pedestrians extracted from roadside LiDAR sensors. Detailed thresholds were recommended to define the risk of vehicle-pedestrian conflicts. The rest of this paper is organized as follows. Section 2 briefly introduces the procedure of trajectory extraction; Section 3 presents the methods for vehicle-pedestrian near-crash identification; and Section 4 concludes the research findings and discussions of this study.
in the real situation (Wu and Xu, 2018a). Near-crashes in NDS were usually identified by detecting unusual vehicle kinematics using accelerometers and gyroscopic sensors installed in the experimental vehicle (Wu and Jovanis, 2013). Moreno and García (2013) developed a methodology using continuous speed profiles to evaluate the safety effectiveness of traffic calming measures (TCMs) on crosstown roads using NDS data. Wu and Jovanis (2013) proposed a multi-stage modeling framework to search through naturalistic driving data and to extract near-crash events. (Fitch et al. (2008) analyzed drivers’ reaction in rear-end conflicts in a naturalistic driving study. Several search criteria were used to identify near-crashes and video review were used to confirm the presence of a rear-end conflict. Wu (2017) summarized different types of right-turn vehicle-pedestrian conflicts using Strategic Highway Research Program 2 (SHRP 2) NDS data. Four typical types of vehicle-pedestrian conflicts were identified by analyzing the time-series data and reviewing the videos. Wang et al. (2015) proposed a novel method to quantify the driving-risk involved in a near-crash event through data mining of tree-based model in the naturalistic driving situation. Deceleration characteristics and brake behavior were used to trigger the camera to start recording the near-crash events in that study. Cheng et al. (2011) analyzed the braking operation in near-crashes using NDS data. According to 100 rear-end near-crashes, if the braking time of drivers were delayed by 0.2 s, 17% of near-crashes would transmit to crashes; and if the braking forces were decreased by 0.1 g (3.22 ft/s2), 33% of near-crashes would transmit to crashes. Although the NDS data have several advantages, those studies still suffered from a major drawback: the NDS data only provide information about the equipped vehicles and their immediate surroundings. For one specific road segment, if there are no vehicles installed with the required NDS devices or the number of vehicles installed with those devices is limited, the near-crashes will be under-reported. Furthermore, the near-crash identification using NDS data is costly since the device installation and data reduction usually require lots of efforts (Xu and Wu, 2018). The observational site-based methods utilize different tools to collect nearcrash data at site. A lot of methods have been developed for surrogate safety analysis. Human observer is the original observational site-based method (Hayward, 1972). Later, different computer-based systems were developed to improve the efficiency (Johnsson et al., 2018). Video analysis is widely used for near-crash data collection. Gettman et al. (2008) collected near-crash data from video image processing since manual (human observer) studies are not capable of recording detailed vehicle trajectory data. Laureshyn et al. (2010) suggested the automatic video analysis for near-crash data collection. Road users are detected using the KLT (Kanade-Lucas-Tomasi) interest point tracker. Trajectories are estimated using foreground–background segmentation, whereas speed is estimated using the shape analysis of interest points (Laureshyn et al., 2009). The practice showed that the automatic video detection system works well for the conflict detection between cyclists and other road users. van der Horst (2013) also successfully used video to collect the conflict between different road users. Stipancic et al. (2018) examined vehicle maneuvers of braking and accelerating extracted from a large quantity of GPS data collected using the smartphones of regular drivers. The current strategy for safety analysis can be improved through connected-vehicle (CV) technology. In the CV network, the real-time traffic data of all road users can be collected and shared with each other though the wireless communication (Wu et al., 2018a). Talebpour et al. (2014) developed two near-crash detection algorithms for near-crash identification in a connected vehicle environment. The initial results revealed that near-crashes were more likely to occur at the situation with high traffic density. It was also showed that connected-vehicle data were the best data source for nearcrash analysis considering the data coverage and accuracy in current practice. That study provided a good reference for near-crash identification using connected-vehicle data. However, the analysis was limited by the prerequisite that all vehicles were already connected with each other. Currently, there are still limited connected-vehicles on roads,
2. Roadside LiDAR data processing In this research, we used the VLP-16 LiDAR sensor manufactured by Velodyne LiDAR™ for analysis. The VLP-16 LiDAR can create a 360° 3D point cloud by using 16 laser/detector pairs mounted in a compact housing. The housing rapidly spins to scan the surrounding environment with an effective range of 60 m (197 ft). The LiDAR has the rotational speed of 5–20 rotations per second, which can generate 600,000 3D points per second. It can cover 360o horizontal field of view and a 30° vertical field of view with ± 15° up and down. The LiDAR sensor can be temporarily installed on a tripod for pilot study or permanently installed on roadside structures for long-term data collection. The height of the LiDAR sensor should not be too higher or too low considering the vertical field of view. And the location of the installation should allow the LiDAR to detect the objects on the road as far 239
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input parameters based on the different point density and distance to the LiDAR sensor.
as possible. The location of the LiDAR should also consider the possible manual destroy. The suitable position of the LiDAR could be on the top of pedestrian signal or the similar height on the wire pole. The approximate height of the LiDAR location is 6∼9 ft above the ground (Wu et al., 2017). In theory, multiple LiDAR sensors installed at different directions can improve the accuracy since multiple LiDAR sensors can generate more points for one object and reduce the object occlusion issue (Wu et al., 2018b). But the integration of multiple LiDAR sensors require more efforts. In this research, we only used one LiDAR sensor for data collection. A series of studies (Wu et al., 2017, 2018b; Wu et al., 2018c; Zhao et al., 2018; Zheng et al., 2018) developed by the authors have been performed to extract the trajectories of road users from roadside LiDAR data. Those studies include background filtering, lane identification, vehicles and pedestrian classification, object tracking. The detailed vehicle and pedestrian trajectories can be obtained from the roadside LiDAR Data using those developed algorithms (Wu et al., 2017, 2018b; Wu et al., 2018c; Zhao et al., 2018; Zheng et al., 2018).
2.4. Object classification It is critical to distinguish pedestrians/bicycles and vehicles from the clustering results since the clustering process cannot label the clusters. Three features were extracted from the clusters obtained from the previous clustering process: 1) Total number of points: A cluster is a maximal set of density-connected points. Although it was noticed that occlusion could affect the total number of points in clusters, in general, vehicle clusters include more points than bicycles and pedestrian clusters at the same distance to the sensor. 2) 2D Distance: For both pedestrian and vehicle clusters, the distance to the LiDAR sensor influences the number of cluster points. The distance of the position reference point of each cluster to the sensor is calculated with the X, Y values in this study. 3) Direction of cluster points distribution: Analysis of cluster points distribution in the 3D space revealed that distribution of pedestrian cluster points is mainly along the vertical direction (z-axis), while the distribution of vehicle cluster points is primarily along the horizontal direction (parallel to the x–y plane) in general. With the least-square linear regression method, a linear function can be generated to describe the main distribution direction of each cluster.
2.1. Background filtering The background points include buildings, trees, ground points, et al. Without excluding background points, it is difficult to cluster and identify the vehicle points correctly. An automatic 3D-density-statisticsbackground-filtering (3D-DSF) algorithm was developed by the authors (Wu et al., 2017). The idea of the 3D-DSF was briefly summarized as follows. The algorithm firstly collects raw data in a period as initial input. The raw data are then aggregated into one 3D space based on their coordinates. The 3D space is then divided into multiple cubes for density statistics. Each cube can be identified as a background space or not. The point density in each cube can be calculated. Compared to the group of background points and ground points, the number of moving vehicle points is fewer. Some cubes can be identified as background space by giving an appropriate threshold. The threshold should be different with different sites and varies with the number of objects in the aggregated frames. A detailed automatic threshold learning were documented by the authors in (Wu et al., 2018a). The location of the background can be then stored in a profile (3D matrix). For real-time data processing, the points in each frame is firstly transferred into a 3D matrix and then compared with the location of background profile. Any point found in the location of background profile is then excluded from the database.
A classification model based on a backpropagation artificial neural network (BP-ANN) was developed to distinguish pedestrians, bicycles and vehicles in the detection range (Zhao et al., 2018). The input data is fed into the input layer. Then, the activity of each hidden layer is determined by the inputs and the weights that connect the input layer and hidden layer. A similar process is between the hidden layer and output layer. The transmission from one neuron in one layer to another neuron in the next layer is independent. The output layer produces the estimated outcomes. The comparison information (error) between target outputs and estimated outputs is given back to the input layer as a guide to adjust the weights in the next training round. Through this iteration process, the neural network gradually learns the inner relationship between input and output by adjusting the weights for each neuron in each layer to reach the best accuracy. When the minimal error is reached, or the number of iterations is beyond the predefined value, the training process is terminated with fixed weights. The previous research (Zhao et al., 2018) showed that the accuracy of the object classification was more than 93%.
2.2. Lane identification Lane location is helpful to get lane-based traffic information. A lane identification algorithm- multi rectified density-based spatial clustering of applications with noise (MCDBSCAN) developed by the authors (Wu et al., 2018b) uses the vehicle trajectories in a time period to identify where the vehicle points are, and then identifies road boundaries. The idea of the MCDBSCAN is that after background filtering, the density of vehicle points should be much higher than other objects if multi frames (such as 1500 frames) are aggregated together. Similar with 3D-DSF, the whole space (here is 2D space) can be divided into small squares. Then by searching the squares with high vehicle points density, the squares representing road areas can be identified. The road boundary can be further extracted by searching the boundary of those squares representing road areas. The lane locations can be detected from road boundary with the width of the lanes.
2.5. Data association The Global Nearest Neighbor (GNN) was applied to track the same vehicles in different frames (Sun et al., 2018). Two factors are considered for object association: distances between an object in a previous frame to all objects in the current frame and the time difference between two considered frames. An object in the current frame is matched to an object in the previous frame if the distance between these two objects is the shortest among all the candidate objects within a certain time period. The candidate objects are selected by the area within the distance threshold. The pilot study (Sun et al., 2018) showed that the developed algorithm can detect the vehicle with a max distance of 30 m from LiDAR sensor with high accuracy.
2.3. Object clustering Points belonging to one object need to be clustered into one group. In a previous study by the authors (Wu et al., 2018c), a revised densitybased spatial clustering of applications with noise (DBSCAN) method was used for object clustering. The revised DBSCAN can have adaptive
2.6. Data processing results After data processing, the trajectories of each moving object can be obtained from the algorithms. Fig.1 shows an example of the 240
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Fig. 1. An Example of Trajectories of Road Users.
Before using those trajectories, it is necessary to check whether the trajectories of all road users were collected. The examination was performed by comparing the results from the data processing algorithm and the results from manually counting in the recorded video. The data collected at N Sierra St @ 11 St in Reno during peak hour under sunny weather in daytime were used for evaluation. Table 2 shows the results of data examination in Fig.1. Table 2 just showed an example of data examination. The previous research conducted by the authors (Wu et al., 2017, 2018c) indicated that the light condition, weather condition and traffic flow had limited influence on the performance of the system (The influence of foggy weather is left unknown). The results show that there is little difference between the actual number of pedestrians and the number of detected pedestrians. The reason for the differences is that some pedestrian group (more than one pedestrian) was detected as one pedestrian when they are close to each other. The detected number of vehicle trajectories was a little higher than the actual number of vehicles. The reason for this issue was that some vehicles were blocked by other vehicles in some frames. As a result, the algorithm discarded the original ObjectID and assigned a
trajectories of road users in 15 min collected at an intersection with four legs (N Sierra St @ 11 St in Reno, Nevada), which were generated by the previous developed LiDAR data processing algorithms. The frequency of LiDAR sensor is 10 HZ in Fig.1, which means each road user’s trajectory was reported every 0.1 s. The circle represents the vehicle point. The triangle represents the pedestrian point and the crossing mark presents the bicycle point. The trajectories of all road users including vehicles, pedestrians, and bicycles can be extracted from the roadside LiDAR data. It is also shown that the system can detect both go-through vehicles and turning vehicles. Each trajectory points recorded the speed, location (xyz coordinates), and timestamp information. An example of vehicle trajectory was illustrated in Table 1. In Table 1, ObjectID is a unique number representing the specific road user. ClusterID is used to distinguish vehicles, pedestrians and bicycles (1 represents vehicle, 0 represents pedestrian and 2 represents bicycle). LaneID is a number representing the location of traffic lane which the vehicle is using. The tracking results in Table 1 showed that the vehicle did not change the lance in the reported frames.
Table 1 An Example of Information in the Trajectory. FrameID (timestamp)
ObjectID
ClusterID
LaneID
X
Y
Z
Speed (mph)
60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78
8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
−8.24224 −8.51415 −8.72231 −8.94619 −9.17672 −9.39569 −9.63093 −9.85299 −10.0852 −10.1838 −10.4004 −10.6431 −10.914 −11.1481 −11.3602 −11.5625 −11.7352 −11.9576 −12.1542
21.3304 19.8953 18.4309 16.8296 15.2833 13.7902 12.3663 10.8807 9.33633 7.82723 6.32561 4.81187 3.35081 1.91493 0.488809 −0.94298 −2.41331 −3.85616 −5.24951
1.55442 1.51944 1.44307 1.3902 1.33178 1.37331 1.19088 1.23769 1.12628 1.08065 1.03641 0.978234 0.943973 0.907171 0.882104 0.870454 0.446864 0.412941 0.463339
33.6877517 33.3386578 33.2724286 33.8102176 34.1008121 34.1263692 34.0523811 34.1553584 33.9090707 33.6604664 33.6968252 34.0996248 34.0206101 33.5412528 33.2503926 32.931783 32.6917614 32.5794066 32.3673508
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method for vehicle-pedestrian near-crash identification, which is the motivation of this paper. A novel method which considers the time difference of reaching the same point between vehicles and pedestrians, distance between stopped vehicles and pedestrians, as well as the speed-distance profile of vehicles, was demonstrated in this paper. The details of the method were introduced in the following parts.
Table 2 Results of Evaluation.
Manual Extraction from the Video (Actual Number) LiDAR Processing Algorithms Accuracy
Number of Pedestrian Trajectories
Number of Bicycle Trajectories
Number of Vehicle Trajectories
36
2
258
32
2
262
3.1. Time difference to the point of intersection
88.9%
100%
98.4%
The point of intersection (PI) between the trajectories of vehicles and pedestrians can be the potential conflicting point (Wu, 2017). By comparing the trajectories of vehicles and pedestrians, the location of PI can be easily extracted. The timestamps when vehicles and pedestrians reach PI should be different under normal maneuvers. If the timestamps are same, this indicates that a crash happens. Therefore, time difference to the point of intersection (TDPI) was developed for near-crash identification. TDPI was defined as “the time difference between one vehicle and one pedestrian reaching the same point in their trajectories” in this paper. The TDPI is similar with the concept of Post-Encroachment Time, which is a well-established indicator known since 1970s (Allen et al., 1978). The TDPI can be calculated through Eq. (1).
new ObjectID to the same vehicle, indicating there may be two discontinuous trajectories representing the same vehicle. Overall, the percentage of those errors was relatively low. We consider the trajectories obtained from the data processing algorithms is good enough for vehicle-pedestrian near-crash analysis. 3. Vehicle-pedestrian near-crash identification Several methods for near-crash identification have been developed in previous studies. Time to collision (TTC) is a widely used method to quantify and characterize near-crashes (Smith et al., 1784). TTC is defined as the travel-time difference between a leading vehicle and a following vehicle, which may lead to collision if these vehicles maintain their current speeds without the performance of evasive maneuvers. Moreover, most current interpretations of continuous safety indicators use only a single value to qualify the whole interaction, for example, the minimum TTC (Mahmud et al., 2017). A previous study (Mohamed and Saunier, 2018) suggested a TTC of 1.5 s as the detection threshold between near-crashes and normal maneuvers. However, The TTC parameter assumes objects have constant speed without considering deceleration/acceleration, which may not reflect the actual situation. Intersection is a major location of vehicle-pedestrian conflict (Xu and Wu, 2018). Vehicles may decelerate/accelerate at intersections, which does not meet the assumption of TTC. In the study by Fitch et al. Fitch et al. (2008), a deceleration greater than 0.25 g (8.04 ft/s2) combing with TTC-1.5 s was used to identify possible near-crash events. Those non-threatening events were filtered out by reviewing the corresponding video. Wang et al. (2015) used a threshold of acceleration (longitudinal: −1.5 m/s2 (-4.92 ft/s2), lateral: −1 m/s2 (3.28 ft/s2)) to identify the occurrence of near-crashes. Other than the widely used indicator-TTC, a bunch of indicators have been developed for nearcrash identification, including temporal proximal indicators, distance based proximal indicators and deceleration based indicators (Mahmud et al., 2017). Mohamed and Saunier (2018) proposed a framework to predict road users’ future positions depending on different extrapolation hypotheses: kinematic methods such as constant velocity and motion pattern matching learnt from the observed trajectories. They developed two safety indicators: aggregated safety indicator distributions and indicator profile classification. The framework is applied to the safety diagnosis of left-turn and opposite-direction interactions at a signalized intersection. It is shown that motion pattern matching is able to compute the safety indicators earlier and to provide a larger number of measurements. Most above-mentioned methods were used for rearend near-crash identification (Talebpour et al., 2014). Those methods had a same assumption: drivers took rapid evasive maneuvers in nearcrashes. However, the assumption may not be established for vehiclepedestrian near-crash identification. Drivers may not make any hard braking behavior during the conflict with pedestrian, such as those leftturn vehicles who do not yield to pedestrians crossing the street. Considering the different features between vehicle-vehicle conflict and vehicle-pedestrian conflict, those vehicle-vehicle near-crash identification methods could not be directly used for vehicle-pedestrian nearcrash extraction. Therefore, it is necessary to develop a systemic
TDPI = ABS (
Tv−Tp F
)
(1)
Where TDPI is time difference of vehicle and pedestrian to the point of intersection (PI), unit: second (s). Tv is the timestamp when the vehicle reaches the point of intersection (PI). Tp is the timestamp when the pedestrian reaches the point of intersection (PI). F is the frequency of data collection, unit: HZ. The TDPI is obtained from the real trajectories without any assumption about speed like TTC. Apparently, a shorter TDPI is more dangerous than a longer one. Fig. 2 illustrates two examples of different TDPIs. The frequency of data collection in Fig. 2 is 10 HZ. In Fig. 2 (a), one pedestrian reached PI at the timestamp-3880 and one vehicle reached PI at the timestamp-3913. The vehicle crossed the PI before the pedestrian finished crossing the intersection. In Fig. 2(b), one pedestrian reached PI at the timestamp-3880 and one vehicle reached PI at the timestamp-4901. The vehicle stopped before PI and waited for the pedestrian to finish crossing the intersection. The TDPI in Fig. 2 can be calculated through Eq. (1). TDPI in Fig. 2 (a) is 2.3 s, and in Fig (b) is 148.8 s. Apparently, the situation in Fig. 2 (a) is more dangerous than that in Fig. 2 (b) since TDPI in Fig. 2 (a) is much shorter and closer to driver reaction time. A controlled study in 2000 found the average driver reaction time to brake was 2.3 s. A few states, including California, have adopted a standard driver reaction time of 2.5 s (Anon., 2018a). Therefore, any event having TDPI less than 2.5 s is recommended to be considered as a near-crash since the time left for driver to react may be not enough to avoid emergency situations. Therefore, the case in Fig. 2 (a) can be considered as a near-crash. When 2.5 s ≤ TDPI≤3.5 s, the crash risk is not as high as those cases with TDPI less than 2.5 s, but pedestrians may feel uncomfortable under the short TDPI. Those cases are considered as crash relevant, which can still be used for safety assessment when the near-crash events are also limited. If TDPI is higher than 3.5 s, the time left for driver reaction is enough, those cases are considered as normal maneuvers. The thresholds provided in this part are only recommended ones based on the authors’ best knowledge. Engineers can define their own thresholds for risk assessment.
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Fig. 2. Examples of TDPI.
Fig. 3. Two Different DSPP with Similar TDPI.
variance in the speed calculation (Wu and Xu, 2018b), we select the timestamp when the speed of vehicle is firstly less than 1.0mph for DSPP calculation. Fig. 3 shows two examples of different DSPPs with similar TDPIs. The DSPP can be then calculated using Eq. (2).
Table 3 Near-Crash Identification with DSPP.
Intersection Signalized midblock crosswalk Uncontrolled midblock crosswalk
Near-Crash
Normal Maneuver
DSPP < 1.2 m (4 ft) DSPP < 12 m (40 ft) DSPP < 6.1 m (20 ft)
DSPP≥1.2 m (4 ft) DSPP≥12 m (40 ft) DSPP≥6.1 m (20 ft)
DSPP =
(Xv−Xp)2 + (Yv−Yp)ˆ2
(2)
Where DSPP is the distance between vehicle and pedestrian when vehicle firstly reduced speed to less than 1.0 mph, unit: m; Xv is the X-axis of vehicle, unit: meter (s); Yv is the Y-axis of vehicle, unit: meter (s); Xp is the X-axis of pedestrian, unit: meter (s); Yp is the Y-axis of pedestrian, unit: meter (s). The DSPP in Fig. 3(a) is 11.86 m (38.91 ft) and in Fig. 3(b) is 4.21 m (13.81 ft). The corresponding videos of Fig. 3 (a) and Fig. 3 (b) can be reviewed through the following links: https://youtu.be/QObqni4UaSI and https://youtu.be/ovmX6ERaoII, respectively. Though the TDPIs (5.7 s and 5.9 s) in this two cases were similar, the crash risks in those
3.2. Distance between stop position and pedestrian The TDPI may not identify all near-crashes in some cases, such as the driver took emergency brakes and already fully stopped before reaching PI. Drivers may wait before PI until the pedestrian passes PI. In that case, the TDPI may be still normal since the conflict occurred before PI. To address this situation, the distance between vehicle stopped position and pedestrian (DSPP) is developed. The DSPP is defined as “the distance between one vehicle and one pedestrian when the vehicle firstly stopped before reaching the pedestrian”. Considering the 243
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Fig. 4. Speed-Distance Profile of Vehicles.
Fig. 5. An Example of Near-Crash Identification with Speed-Distance Profile.
only 4.21 m (13.81 ft) away from the pedestrians, which was more dangerous for the pedestrian compared to the case in Fig. 3 (a). Drivers should stop before the yield line or stop line to give a safe distance to pedestrians. The Manual on Uniform Traffic Control Devices (MUTCD) (Federal Highway Administration, 2009) specified that the distance between yield/stop line to crosswalk (LTC) should be placed a minimum of 1.2 m (4 ft) in advance of the nearest crosswalk line at controlled intersection. Stop lines at midblock signalized locations should be placed at least 12 m (40 feet) in advance of the nearest signal indication. And if yield or stop lines are used at a crosswalk that crosses an uncontrolled multi-lane approach, the yield lines or stop lines should be placed 6.1 to 15 m (20 to 50 feet) in advance of the nearest crosswalk line. For normal maneuvers, DSPP should be no shorter than LTC. The recommended thresholds for near-crash identification at different sites are shown in Table 3. Engineers can also define their own thresholds based on the features of different sites.
Table 4 Near-Crash Identification. Risk
Thresholds
Near-crash
TDPI < 2.5 s or 0 < DSPP < lTC or vehicle speed within area A in speed-distance profile 2.5 s ≤ TDPI≤3.5 s or vehicle speed within area B in speeddistance profile TDPI > 3.5 s or DSPP ≥ lTC or vehicle speed within area (a) in speed-distance profile within area C in speed-distance profile
Crash Relevant Low risk
two situations were completely different. Fig. 3 (a) shows an example that one pedestrian crossed the intersection while drivers stopped far away from the pedestrian. The DSPP was 11.86 m (38.91 ft), which was far enough and safe for pedestrian. But in Fig. 3 (b), the left-turn driver did not see the pedestrian crossing the road in advance and stopped in the middle of the intersection when the vehicle was close to the pedestrian. The distance when the drivers stopped at the intersection was 244
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Fig. 6. Map of Data Collection Sites.
indicating they stopped at the same location before the pedestrian. However, they may decrease with different decelerations before stop. Apparently, the vehicle sharply stopped is more dangerous to pedestrians. The speed-distance profile is used to address this situation. Fig. 4 shows an example of the speed distribution of vehicles with different distance of vehicle to pedestrian (within 100 ft). Most points in Fig. 4 were located in the area with a distance longer than 20 ft to pedestrians. The distribution of the points was dispersed at the same distance, indicating different vehicles had different speeds. The 85th percentile speed was calculated. In this specific site, the 85th percentile speed was about 15mph before vehicles dramatically slowed down. The total stopping distance can be estimated by summing the perception-reaction distance and the braking distance. For a vehicle with 15mph, the estimated stopping distance is 44 ft (Anon., 2018b).
Table 5 Road Information. Location
AADT
Speed limit (mph)
Road width (ft)
Pedestrian involved crashes counts from 2014 to 2017
Midblock in 15th St N Virginia St@ 10th St
3500
15
36
NaN
11000
25
64
4
3.3. Speed-distance profile The DSPP did not show the impact of different speeds of vehicles on crash risk. For example, two vehicles may have the same DSPPs, 245
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campus of University of Nevada, Reno (UNR). There is a steep slope at this site. A lot of students and faculties in the university have safety concern about this midblock. The directions studied at this site are EastWest direction and West-East direction (pedestrians crossing 15th St). The other site was selected at a T-intersection installed with Rectangular Rapid Flash Beacon (RRFB) on major street-N Virginia St@ 10th Street. The N Virginia St is the major street and 10th Street is the minor street. The directions studied at this site are also East-West direction and West-East direction (pedestrians crossing N Virginia St). A vehicle-pedestrian crash happened at this intersection on July 19th, 2017, which was reported in the following link: http://mynews4.com/ news/local/reno-police-investigate-vehicle-pedestrian-crash-near-unrcampus. Further examination was required to evaluate pedestrian safety at this sites. Table 5 summarizes the road information of the two sites. The LiDAR was installed on a tripod for temporary data collection, as shown in Fig. 7. The peak hour data were collected on at the same weekday. The collection time was 25 min (15,000 frames) at both sites. The algorithm has been implemented in Matlab and was deployed on the Dell desktop equipped with Intel Core i7-4790 CPU (3.60 GHz) and 16 GB of RAM. The time cost for extracting TDPI, DSPP and speed-distance profile from the trajectories is very limited (about 2 min for the first site and 1 min for the second site). Fig. 8 and Fig. 9 show the results of TDPI, DSPP and speed-distance profile at this two sites. An example of near-crash (the event with a 2.1-seconds of TDPI in Fig. 9 (a)) occurred at the second site was shown in the following video: https://youtu.be/-3-tgqeQsHE. The video showed that the driver failed to yield to the pedestrian. By checking the videos, the near-crash events can be easily confirmed. The results of case studies shows that the vehicle-pedestrian near-crash events can be identified using the proposed method.
Fig. 7. Roadside LiDAR Installation.
The area in Fig. 4 can be further divided into four subareas using the 85th percentile speed line and the stopping distance, as shown in Fig. 5. In area A, vehicles had higher speeds within the stopping distance, which were more dangerous to pedestrians compared to those in other areas. Events located in area A can be considered as near-crashes. An example of event in area A can be found through the link: https:// youtu.be/TeEERfWOgzo. In this event, when the distance between the pedestrian and the vehicle was 8 m (26.2 ft), the speed of the vehicle was 20.6mph. The vehicle did not stop before the pedestrian and passed the pedestrian before the pedestrian finished the crossing. For the cases in area B, the vehicles had lower speeds within stopping distance or had higher speeds out of stopping distance, which were considered as crash relevant. Crash risk in area B was lower than that in area A. An example of event in area B can be found through the link: https://youtu.be/ e9jhlbuk8uw. In this event, the vehicle tried to pass the crossing with high speed when the pedestrian already reached the midblock crosswalk. Though this event may not have the high crash risk like those in area A, the driver’s aggressive behavior may let the pedestrian feel uncomfortable. In area C, vehicles had lower speed out of stopping distance, which were considered as safe events to pedestrians. The trajectory of the same vehicle may be located in different subareas in Fig. 5, the area with highest crash risk should be used for risk assessment. It should be noted that it is better to check the records in area A manually to make sure they are near-crashes since some points may be the outliers from area B or area C.
4. Conclusion and discussion This paper presents a novel method for vehicle-pedestrian nearcrash identification using the trajectories of vehicles and pedestrians extracted from roadside LiDAR data. Three factors: TDPI, DSSP and speed-distance profile are combined for vehicle-pedestrian near-crash identification. The proposed method was coded into an automatic procedure, which can release the onerous labor work for near-crash identification. The case studies showed that the crash risk can be easily estimated without waiting for historical crash records. Though this paper provided the recommended parameters for vehicle-pedestrian near-crash identification, the engineers can select their own thresholds based on the different features of specific sites. The proposed method may be very useful for before-and-after pedestrian safety assessment of one specific site, or can be used to identify the site with highest pedestrian crash risk from the sites pool. There are still some improvement in further studies. For the TDPI, while it is easier to measure or calculate, it is validity is less proven compared to TTC. The dimensions of vehicles play a role as well as who is passing the conflict zone first. For example, a pedestrian waits at a curb and make a step just as the car has passed – TDPI is low, but risk is minimal (Laureshyn et al., 2010). This situation can cause false reports. The road boundary information can be used to improve the accuracy of TDPI. With the road boundary information, we can exclude the situation that pedestrians wait at a curb (out of road boundary). In the next step, we will involve the road boundary information in TDPI to improve the accuracy. The DSPP and speed-distance profile are developed by the authors for safety evaluation. Since they have hardly ever been used before, no validation is available. The reliability and validity of those indicators required more examinations in future studies. Some thresholds about those indicators were provided in the paper based on the authors’ best knowledgement. The selection of the thresholds also requires further analysis (Hauer, 1993). Bicycle-involved near-crash was
3.4. Thresholds of near-crash identification The TDPI, DSPP and Speed-Distance profile can all be used for nearcrash identification. The final thresholds were determined by combining these three factors. All events can be divided into three parts based on their risks: near-crash, crash relevant, and low risk. Table 4 shows the final recommended thresholds for near-crash identification. This algorithm has been coded into an automatic procedure in Matlab for near-crash identification. 3.5. Case study To evaluate the applicability of the new algorithm, two case studies were conducted. The following criteria are recommended to select the strategic sites for installing LiDAR: 1) The sites with high historical pedestrian-involved crash frequency. 2) The sites where public have concern or complaint about pedestrian safety. 3) New opened intersections (need design and operation evaluation). In this research, the roadside LiDAR data were collected at two sites in Reno, Nevada. Fig. 6 shows the map of the data collection sites. One site was selected at one unsignalized midblock in 15th St on the 246
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Fig. 8. Near-Crash identification at One Midblock in 15th Street.
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Fig. 9. Near-Crash identification at N Virginia St@10th St.
Transportation (NDOT) under Grant No. P224-14-803/TO #13. The authors gratefully acknowledge this financial support. This research was also supported by engineers with the Nevada Department of Transportation, the Regional Transportation Commission of Washoe County, Nevada, and the City of Reno.
not considered in this paper since the bicycle data were limited. Videos were not recorded in the two case studies. In the next step, videos will be used to validate the near-crashes. The two case studies only provided primary validation results of the near-crashes using limited data. More data are expected to be collected to provide the systematic validation of the near-crashes in further studies. The implementation of the proposed method relies on the accurate trajectories of vehicles and pedestrians. However, there may be some errors in the output of the current LiDAR data processing algorithms (Johnsson et al., 2018), which may lead to some outliers in the trajectories. The users of this method are encouraged to use the LiDAR video to confirm the near-crash events and exclude those outliers in the results. The previous study (Gettman et al., 2008) mentioned that object occlusion was a major reason of this error. Setting up multiple LiDARs in different directions are expected to solve this issue. The authors are working on data integration for multiple LiDAR sensors.
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Contents lists available at ScienceDirect
Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
A novel method of vehicle-pedestrian near-crash identification with roadside LiDAR data
T
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Jianqing Wu, Hao Xu , Yichen Zheng, Zong Tian University of Nevada, Reno 1664 N. Virginia Street, MS258, Reno, Nevada, 89557, United States
A R T I C LE I N FO
A B S T R A C T
Keywords: Near crash Pedestrian safety Roadside LiDAR
Safety evaluation based on historical crashes usually has a lot of limitations. In previous studies, near-crashes are considered as surrogate data for safety evaluation. One challenge for the use of near-crashes data is the difficulty of data collection. The driving simulators and naturalistic driving data may not be suitable for safety evaluation at specific sites. The observational site-based methods such as human observers and video analysis also suffer from some limitations such as long time data processing or reduced performance influenced by weather or light condition. The roadside Light Detection and Ranging (LiDAR)-enhanced infrastructure provides a new solution for real-time data collection without the impact from weather or light. The high-resolution trajectories of all road users can be obtained from roadside LiDAR data. This paper aims to fill these gaps by presenting a method for near-crash identification based on the trajectories of road users extracted from roadside LiDAR data. This paper focused on vehicle-pedestrian near-crash identification particularly considering the increased risk of vehiclepedestrian conflicts. Three parameters: Time Difference to the Point of Intersection (TDPI); Distance between Stop Position and Pedestrian (DSPP); Vehicle-pedestrian speed-distance profile, were developed for vehiclepedestrian near-crash identification. The authors also recommended the thresholds for risk assessment of pedestrian safety. This method was coded into an automatic procedure for near-crash identification. This method is expected to significantly improve the current evaluation of pedestrian safety.
1. Introduction Historical crash records are important data sources for safety evaluation on roads. However, not all crashes that happen on roads are reported in the NHTSA Fatality Analysis Reporting Systems (NHTSA, 2018a) or in the National Automotive Sampling System General Estimates System (NHTSA, 2018b), which limits the accuracy of safety evaluation using historical crash data (Hauer, 1997). Furthermore, delay in safety evaluation is unavoidable since it takes time to collect and process the historical crash records. Therefore, researchers and engineers are looking for surrogate data for safety evaluation. Nearcrashes are then selected as a surrogate dataset to assess road safety management. Near-crashes refer to cases where drivers execute rapid evasive maneuvers (i.e., emergency braking and/or steering operation) when facing a potential driving risk or a potential threat (Wu and Jovanis, 2013). Guo et al. (2010) employed two metrics, namely, precision and bias of risk estimation, to assess near-crashes, and indicated that using near-crashes as a crash surrogate could provide definite benefits when data about a sufficient number of crashes are not available.
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One of the challenges for risk assessment using near-crash data is data collection method. The data collection method can be roughly divided into three major parts: driving simulation, naturalistic driving studies (NDS) and observational site-based collection methods. Driving simulation and naturalistic driving data are widely used for near-crash collection in previous studies. Smith et al. (2002) analyzed drivers’ reactions in emergencies using simulation data. A total of 108 participants were asked to drive the simulator under different road conditions in this research. The deceleration and braking behavior were used to extract near-crash data. Lee et al. (2003) developed an economical driving simulator approach to assess crash risk for older drivers. One hundred and twenty-nine older drivers residing in a metropolitan city volunteered to drive the simulator in a 45-min simulated-driving session. Ten reliable assessment criteria were developed to measure each participant's performance. It was found that driving skill of older drivers was found to decline with age. Although driving simulators can provide details about near-crashes with low-cost, the driving behavior in simulated environments still differ from a real situation (Wu and Xu, 2017). NDS can collect driver observation and driving operation unobtrusively, which provides a good opportunity to extract near-crashes
Corresponding author. E-mail addresses: [email protected] (J. Wu), [email protected] (H. Xu), [email protected] (Y. Zheng), [email protected] (Z. Tian).
https://doi.org/10.1016/j.aap.2018.09.001 Received 22 March 2018; Received in revised form 20 July 2018; Accepted 1 September 2018 0001-4575/ © 2018 Elsevier Ltd. All rights reserved.
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most of which are mainly used for pilot programs (Wu et al., 2018a). It will take time to equip connected-vehicle devices into all vehicles, especially the old ones. It was estimated that the mixed traffic with connected vehicles and unconnected vehicles will last for the next decade (Wu and Xu, 2018b). As a result, not all near-crashes can be detected as only partial vehicle movement information can be obtained. It is necessary to find a solution to fill the data gap during the transition period from unconnected vehicles to connected vehicles. Tarko et al. (2016) have investigated the feasibility of using laser ranging technology (LiDAR) for safety and operation management at intersections. The investigation showed that the proposed technology does not experience some of the problems of the current video-based technology but less expensive low-end sensors have limited density of points where measurements are collected that may bring new challenges. Wu et al. (Sun et al., 2018) developed an innovative approach using roadside Light Detection and Ranging (LiDAR) to obtain the real-time high-resolution traffic data (HRTD) of all road users including connected vehicles and unconnected road users on the road. The newest version of 3D LiDAR sensor can scan 360° three-dimensional surrounding objects and report the precise location and speed of each object in its scanned range. Any vehicles, pedestrians, and bicyclists with connected-vehicle devices will immediately benefit from the roadside LiDAR data rather than being limited by whether and how many other vehicles are connected. The trajectories of road users provided by roadside LiDAR are considered as the good data source for near-crash identification (Gelso and Sjoberg, 2017). Different road users may be involved into near-crashes. The major types of near-crashes include vehicle-vehicle near-crash, vehicle-pedestrian near-crash, vehicle-bicycle near-crash, bicycle-pedestrian nearcrash, vehicle-animal near-crash, and other types. Previous studies mainly focused on vehicle-vehicle near-crash analysis, especially on rear-end near-crash analysis (Anon., 2014). Vehicle-pedestrian nearcrash was not well analyzed in previous research, mostly due to the difficulty of vehicle-pedestrian near-crash data collection (Wu and Xu, 2017). Pedestrians were one of the few groups of road users to experience an increase in fatalities in the United States in 2015, totaling 5376 deaths (Wu et al., 2017). On average, a pedestrian was killed every two hours and injured every seven minutes in traffic crashes. Fourteen percent of all traffic fatalities and an estimated 3 percent of those injured in traffic crashes were pedestrians (Sun et al., 2018). Therefore, it is necessary to pay more attention to vehicle-pedestrian conflicts. Considering the advantages of roadside LiDAR data, this paper developed an innovative approach for vehicle-pedestrian nearcrash identification using trajectories of vehicles and pedestrians extracted from roadside LiDAR sensors. Detailed thresholds were recommended to define the risk of vehicle-pedestrian conflicts. The rest of this paper is organized as follows. Section 2 briefly introduces the procedure of trajectory extraction; Section 3 presents the methods for vehicle-pedestrian near-crash identification; and Section 4 concludes the research findings and discussions of this study.
in the real situation (Wu and Xu, 2018a). Near-crashes in NDS were usually identified by detecting unusual vehicle kinematics using accelerometers and gyroscopic sensors installed in the experimental vehicle (Wu and Jovanis, 2013). Moreno and García (2013) developed a methodology using continuous speed profiles to evaluate the safety effectiveness of traffic calming measures (TCMs) on crosstown roads using NDS data. Wu and Jovanis (2013) proposed a multi-stage modeling framework to search through naturalistic driving data and to extract near-crash events. (Fitch et al. (2008) analyzed drivers’ reaction in rear-end conflicts in a naturalistic driving study. Several search criteria were used to identify near-crashes and video review were used to confirm the presence of a rear-end conflict. Wu (2017) summarized different types of right-turn vehicle-pedestrian conflicts using Strategic Highway Research Program 2 (SHRP 2) NDS data. Four typical types of vehicle-pedestrian conflicts were identified by analyzing the time-series data and reviewing the videos. Wang et al. (2015) proposed a novel method to quantify the driving-risk involved in a near-crash event through data mining of tree-based model in the naturalistic driving situation. Deceleration characteristics and brake behavior were used to trigger the camera to start recording the near-crash events in that study. Cheng et al. (2011) analyzed the braking operation in near-crashes using NDS data. According to 100 rear-end near-crashes, if the braking time of drivers were delayed by 0.2 s, 17% of near-crashes would transmit to crashes; and if the braking forces were decreased by 0.1 g (3.22 ft/s2), 33% of near-crashes would transmit to crashes. Although the NDS data have several advantages, those studies still suffered from a major drawback: the NDS data only provide information about the equipped vehicles and their immediate surroundings. For one specific road segment, if there are no vehicles installed with the required NDS devices or the number of vehicles installed with those devices is limited, the near-crashes will be under-reported. Furthermore, the near-crash identification using NDS data is costly since the device installation and data reduction usually require lots of efforts (Xu and Wu, 2018). The observational site-based methods utilize different tools to collect nearcrash data at site. A lot of methods have been developed for surrogate safety analysis. Human observer is the original observational site-based method (Hayward, 1972). Later, different computer-based systems were developed to improve the efficiency (Johnsson et al., 2018). Video analysis is widely used for near-crash data collection. Gettman et al. (2008) collected near-crash data from video image processing since manual (human observer) studies are not capable of recording detailed vehicle trajectory data. Laureshyn et al. (2010) suggested the automatic video analysis for near-crash data collection. Road users are detected using the KLT (Kanade-Lucas-Tomasi) interest point tracker. Trajectories are estimated using foreground–background segmentation, whereas speed is estimated using the shape analysis of interest points (Laureshyn et al., 2009). The practice showed that the automatic video detection system works well for the conflict detection between cyclists and other road users. van der Horst (2013) also successfully used video to collect the conflict between different road users. Stipancic et al. (2018) examined vehicle maneuvers of braking and accelerating extracted from a large quantity of GPS data collected using the smartphones of regular drivers. The current strategy for safety analysis can be improved through connected-vehicle (CV) technology. In the CV network, the real-time traffic data of all road users can be collected and shared with each other though the wireless communication (Wu et al., 2018a). Talebpour et al. (2014) developed two near-crash detection algorithms for near-crash identification in a connected vehicle environment. The initial results revealed that near-crashes were more likely to occur at the situation with high traffic density. It was also showed that connected-vehicle data were the best data source for nearcrash analysis considering the data coverage and accuracy in current practice. That study provided a good reference for near-crash identification using connected-vehicle data. However, the analysis was limited by the prerequisite that all vehicles were already connected with each other. Currently, there are still limited connected-vehicles on roads,
2. Roadside LiDAR data processing In this research, we used the VLP-16 LiDAR sensor manufactured by Velodyne LiDAR™ for analysis. The VLP-16 LiDAR can create a 360° 3D point cloud by using 16 laser/detector pairs mounted in a compact housing. The housing rapidly spins to scan the surrounding environment with an effective range of 60 m (197 ft). The LiDAR has the rotational speed of 5–20 rotations per second, which can generate 600,000 3D points per second. It can cover 360o horizontal field of view and a 30° vertical field of view with ± 15° up and down. The LiDAR sensor can be temporarily installed on a tripod for pilot study or permanently installed on roadside structures for long-term data collection. The height of the LiDAR sensor should not be too higher or too low considering the vertical field of view. And the location of the installation should allow the LiDAR to detect the objects on the road as far 239
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input parameters based on the different point density and distance to the LiDAR sensor.
as possible. The location of the LiDAR should also consider the possible manual destroy. The suitable position of the LiDAR could be on the top of pedestrian signal or the similar height on the wire pole. The approximate height of the LiDAR location is 6∼9 ft above the ground (Wu et al., 2017). In theory, multiple LiDAR sensors installed at different directions can improve the accuracy since multiple LiDAR sensors can generate more points for one object and reduce the object occlusion issue (Wu et al., 2018b). But the integration of multiple LiDAR sensors require more efforts. In this research, we only used one LiDAR sensor for data collection. A series of studies (Wu et al., 2017, 2018b; Wu et al., 2018c; Zhao et al., 2018; Zheng et al., 2018) developed by the authors have been performed to extract the trajectories of road users from roadside LiDAR data. Those studies include background filtering, lane identification, vehicles and pedestrian classification, object tracking. The detailed vehicle and pedestrian trajectories can be obtained from the roadside LiDAR Data using those developed algorithms (Wu et al., 2017, 2018b; Wu et al., 2018c; Zhao et al., 2018; Zheng et al., 2018).
2.4. Object classification It is critical to distinguish pedestrians/bicycles and vehicles from the clustering results since the clustering process cannot label the clusters. Three features were extracted from the clusters obtained from the previous clustering process: 1) Total number of points: A cluster is a maximal set of density-connected points. Although it was noticed that occlusion could affect the total number of points in clusters, in general, vehicle clusters include more points than bicycles and pedestrian clusters at the same distance to the sensor. 2) 2D Distance: For both pedestrian and vehicle clusters, the distance to the LiDAR sensor influences the number of cluster points. The distance of the position reference point of each cluster to the sensor is calculated with the X, Y values in this study. 3) Direction of cluster points distribution: Analysis of cluster points distribution in the 3D space revealed that distribution of pedestrian cluster points is mainly along the vertical direction (z-axis), while the distribution of vehicle cluster points is primarily along the horizontal direction (parallel to the x–y plane) in general. With the least-square linear regression method, a linear function can be generated to describe the main distribution direction of each cluster.
2.1. Background filtering The background points include buildings, trees, ground points, et al. Without excluding background points, it is difficult to cluster and identify the vehicle points correctly. An automatic 3D-density-statisticsbackground-filtering (3D-DSF) algorithm was developed by the authors (Wu et al., 2017). The idea of the 3D-DSF was briefly summarized as follows. The algorithm firstly collects raw data in a period as initial input. The raw data are then aggregated into one 3D space based on their coordinates. The 3D space is then divided into multiple cubes for density statistics. Each cube can be identified as a background space or not. The point density in each cube can be calculated. Compared to the group of background points and ground points, the number of moving vehicle points is fewer. Some cubes can be identified as background space by giving an appropriate threshold. The threshold should be different with different sites and varies with the number of objects in the aggregated frames. A detailed automatic threshold learning were documented by the authors in (Wu et al., 2018a). The location of the background can be then stored in a profile (3D matrix). For real-time data processing, the points in each frame is firstly transferred into a 3D matrix and then compared with the location of background profile. Any point found in the location of background profile is then excluded from the database.
A classification model based on a backpropagation artificial neural network (BP-ANN) was developed to distinguish pedestrians, bicycles and vehicles in the detection range (Zhao et al., 2018). The input data is fed into the input layer. Then, the activity of each hidden layer is determined by the inputs and the weights that connect the input layer and hidden layer. A similar process is between the hidden layer and output layer. The transmission from one neuron in one layer to another neuron in the next layer is independent. The output layer produces the estimated outcomes. The comparison information (error) between target outputs and estimated outputs is given back to the input layer as a guide to adjust the weights in the next training round. Through this iteration process, the neural network gradually learns the inner relationship between input and output by adjusting the weights for each neuron in each layer to reach the best accuracy. When the minimal error is reached, or the number of iterations is beyond the predefined value, the training process is terminated with fixed weights. The previous research (Zhao et al., 2018) showed that the accuracy of the object classification was more than 93%.
2.2. Lane identification Lane location is helpful to get lane-based traffic information. A lane identification algorithm- multi rectified density-based spatial clustering of applications with noise (MCDBSCAN) developed by the authors (Wu et al., 2018b) uses the vehicle trajectories in a time period to identify where the vehicle points are, and then identifies road boundaries. The idea of the MCDBSCAN is that after background filtering, the density of vehicle points should be much higher than other objects if multi frames (such as 1500 frames) are aggregated together. Similar with 3D-DSF, the whole space (here is 2D space) can be divided into small squares. Then by searching the squares with high vehicle points density, the squares representing road areas can be identified. The road boundary can be further extracted by searching the boundary of those squares representing road areas. The lane locations can be detected from road boundary with the width of the lanes.
2.5. Data association The Global Nearest Neighbor (GNN) was applied to track the same vehicles in different frames (Sun et al., 2018). Two factors are considered for object association: distances between an object in a previous frame to all objects in the current frame and the time difference between two considered frames. An object in the current frame is matched to an object in the previous frame if the distance between these two objects is the shortest among all the candidate objects within a certain time period. The candidate objects are selected by the area within the distance threshold. The pilot study (Sun et al., 2018) showed that the developed algorithm can detect the vehicle with a max distance of 30 m from LiDAR sensor with high accuracy.
2.3. Object clustering Points belonging to one object need to be clustered into one group. In a previous study by the authors (Wu et al., 2018c), a revised densitybased spatial clustering of applications with noise (DBSCAN) method was used for object clustering. The revised DBSCAN can have adaptive
2.6. Data processing results After data processing, the trajectories of each moving object can be obtained from the algorithms. Fig.1 shows an example of the 240
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Fig. 1. An Example of Trajectories of Road Users.
Before using those trajectories, it is necessary to check whether the trajectories of all road users were collected. The examination was performed by comparing the results from the data processing algorithm and the results from manually counting in the recorded video. The data collected at N Sierra St @ 11 St in Reno during peak hour under sunny weather in daytime were used for evaluation. Table 2 shows the results of data examination in Fig.1. Table 2 just showed an example of data examination. The previous research conducted by the authors (Wu et al., 2017, 2018c) indicated that the light condition, weather condition and traffic flow had limited influence on the performance of the system (The influence of foggy weather is left unknown). The results show that there is little difference between the actual number of pedestrians and the number of detected pedestrians. The reason for the differences is that some pedestrian group (more than one pedestrian) was detected as one pedestrian when they are close to each other. The detected number of vehicle trajectories was a little higher than the actual number of vehicles. The reason for this issue was that some vehicles were blocked by other vehicles in some frames. As a result, the algorithm discarded the original ObjectID and assigned a
trajectories of road users in 15 min collected at an intersection with four legs (N Sierra St @ 11 St in Reno, Nevada), which were generated by the previous developed LiDAR data processing algorithms. The frequency of LiDAR sensor is 10 HZ in Fig.1, which means each road user’s trajectory was reported every 0.1 s. The circle represents the vehicle point. The triangle represents the pedestrian point and the crossing mark presents the bicycle point. The trajectories of all road users including vehicles, pedestrians, and bicycles can be extracted from the roadside LiDAR data. It is also shown that the system can detect both go-through vehicles and turning vehicles. Each trajectory points recorded the speed, location (xyz coordinates), and timestamp information. An example of vehicle trajectory was illustrated in Table 1. In Table 1, ObjectID is a unique number representing the specific road user. ClusterID is used to distinguish vehicles, pedestrians and bicycles (1 represents vehicle, 0 represents pedestrian and 2 represents bicycle). LaneID is a number representing the location of traffic lane which the vehicle is using. The tracking results in Table 1 showed that the vehicle did not change the lance in the reported frames.
Table 1 An Example of Information in the Trajectory. FrameID (timestamp)
ObjectID
ClusterID
LaneID
X
Y
Z
Speed (mph)
60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78
8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
−8.24224 −8.51415 −8.72231 −8.94619 −9.17672 −9.39569 −9.63093 −9.85299 −10.0852 −10.1838 −10.4004 −10.6431 −10.914 −11.1481 −11.3602 −11.5625 −11.7352 −11.9576 −12.1542
21.3304 19.8953 18.4309 16.8296 15.2833 13.7902 12.3663 10.8807 9.33633 7.82723 6.32561 4.81187 3.35081 1.91493 0.488809 −0.94298 −2.41331 −3.85616 −5.24951
1.55442 1.51944 1.44307 1.3902 1.33178 1.37331 1.19088 1.23769 1.12628 1.08065 1.03641 0.978234 0.943973 0.907171 0.882104 0.870454 0.446864 0.412941 0.463339
33.6877517 33.3386578 33.2724286 33.8102176 34.1008121 34.1263692 34.0523811 34.1553584 33.9090707 33.6604664 33.6968252 34.0996248 34.0206101 33.5412528 33.2503926 32.931783 32.6917614 32.5794066 32.3673508
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method for vehicle-pedestrian near-crash identification, which is the motivation of this paper. A novel method which considers the time difference of reaching the same point between vehicles and pedestrians, distance between stopped vehicles and pedestrians, as well as the speed-distance profile of vehicles, was demonstrated in this paper. The details of the method were introduced in the following parts.
Table 2 Results of Evaluation.
Manual Extraction from the Video (Actual Number) LiDAR Processing Algorithms Accuracy
Number of Pedestrian Trajectories
Number of Bicycle Trajectories
Number of Vehicle Trajectories
36
2
258
32
2
262
3.1. Time difference to the point of intersection
88.9%
100%
98.4%
The point of intersection (PI) between the trajectories of vehicles and pedestrians can be the potential conflicting point (Wu, 2017). By comparing the trajectories of vehicles and pedestrians, the location of PI can be easily extracted. The timestamps when vehicles and pedestrians reach PI should be different under normal maneuvers. If the timestamps are same, this indicates that a crash happens. Therefore, time difference to the point of intersection (TDPI) was developed for near-crash identification. TDPI was defined as “the time difference between one vehicle and one pedestrian reaching the same point in their trajectories” in this paper. The TDPI is similar with the concept of Post-Encroachment Time, which is a well-established indicator known since 1970s (Allen et al., 1978). The TDPI can be calculated through Eq. (1).
new ObjectID to the same vehicle, indicating there may be two discontinuous trajectories representing the same vehicle. Overall, the percentage of those errors was relatively low. We consider the trajectories obtained from the data processing algorithms is good enough for vehicle-pedestrian near-crash analysis. 3. Vehicle-pedestrian near-crash identification Several methods for near-crash identification have been developed in previous studies. Time to collision (TTC) is a widely used method to quantify and characterize near-crashes (Smith et al., 1784). TTC is defined as the travel-time difference between a leading vehicle and a following vehicle, which may lead to collision if these vehicles maintain their current speeds without the performance of evasive maneuvers. Moreover, most current interpretations of continuous safety indicators use only a single value to qualify the whole interaction, for example, the minimum TTC (Mahmud et al., 2017). A previous study (Mohamed and Saunier, 2018) suggested a TTC of 1.5 s as the detection threshold between near-crashes and normal maneuvers. However, The TTC parameter assumes objects have constant speed without considering deceleration/acceleration, which may not reflect the actual situation. Intersection is a major location of vehicle-pedestrian conflict (Xu and Wu, 2018). Vehicles may decelerate/accelerate at intersections, which does not meet the assumption of TTC. In the study by Fitch et al. Fitch et al. (2008), a deceleration greater than 0.25 g (8.04 ft/s2) combing with TTC-1.5 s was used to identify possible near-crash events. Those non-threatening events were filtered out by reviewing the corresponding video. Wang et al. (2015) used a threshold of acceleration (longitudinal: −1.5 m/s2 (-4.92 ft/s2), lateral: −1 m/s2 (3.28 ft/s2)) to identify the occurrence of near-crashes. Other than the widely used indicator-TTC, a bunch of indicators have been developed for nearcrash identification, including temporal proximal indicators, distance based proximal indicators and deceleration based indicators (Mahmud et al., 2017). Mohamed and Saunier (2018) proposed a framework to predict road users’ future positions depending on different extrapolation hypotheses: kinematic methods such as constant velocity and motion pattern matching learnt from the observed trajectories. They developed two safety indicators: aggregated safety indicator distributions and indicator profile classification. The framework is applied to the safety diagnosis of left-turn and opposite-direction interactions at a signalized intersection. It is shown that motion pattern matching is able to compute the safety indicators earlier and to provide a larger number of measurements. Most above-mentioned methods were used for rearend near-crash identification (Talebpour et al., 2014). Those methods had a same assumption: drivers took rapid evasive maneuvers in nearcrashes. However, the assumption may not be established for vehiclepedestrian near-crash identification. Drivers may not make any hard braking behavior during the conflict with pedestrian, such as those leftturn vehicles who do not yield to pedestrians crossing the street. Considering the different features between vehicle-vehicle conflict and vehicle-pedestrian conflict, those vehicle-vehicle near-crash identification methods could not be directly used for vehicle-pedestrian nearcrash extraction. Therefore, it is necessary to develop a systemic
TDPI = ABS (
Tv−Tp F
)
(1)
Where TDPI is time difference of vehicle and pedestrian to the point of intersection (PI), unit: second (s). Tv is the timestamp when the vehicle reaches the point of intersection (PI). Tp is the timestamp when the pedestrian reaches the point of intersection (PI). F is the frequency of data collection, unit: HZ. The TDPI is obtained from the real trajectories without any assumption about speed like TTC. Apparently, a shorter TDPI is more dangerous than a longer one. Fig. 2 illustrates two examples of different TDPIs. The frequency of data collection in Fig. 2 is 10 HZ. In Fig. 2 (a), one pedestrian reached PI at the timestamp-3880 and one vehicle reached PI at the timestamp-3913. The vehicle crossed the PI before the pedestrian finished crossing the intersection. In Fig. 2(b), one pedestrian reached PI at the timestamp-3880 and one vehicle reached PI at the timestamp-4901. The vehicle stopped before PI and waited for the pedestrian to finish crossing the intersection. The TDPI in Fig. 2 can be calculated through Eq. (1). TDPI in Fig. 2 (a) is 2.3 s, and in Fig (b) is 148.8 s. Apparently, the situation in Fig. 2 (a) is more dangerous than that in Fig. 2 (b) since TDPI in Fig. 2 (a) is much shorter and closer to driver reaction time. A controlled study in 2000 found the average driver reaction time to brake was 2.3 s. A few states, including California, have adopted a standard driver reaction time of 2.5 s (Anon., 2018a). Therefore, any event having TDPI less than 2.5 s is recommended to be considered as a near-crash since the time left for driver to react may be not enough to avoid emergency situations. Therefore, the case in Fig. 2 (a) can be considered as a near-crash. When 2.5 s ≤ TDPI≤3.5 s, the crash risk is not as high as those cases with TDPI less than 2.5 s, but pedestrians may feel uncomfortable under the short TDPI. Those cases are considered as crash relevant, which can still be used for safety assessment when the near-crash events are also limited. If TDPI is higher than 3.5 s, the time left for driver reaction is enough, those cases are considered as normal maneuvers. The thresholds provided in this part are only recommended ones based on the authors’ best knowledge. Engineers can define their own thresholds for risk assessment.
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Fig. 2. Examples of TDPI.
Fig. 3. Two Different DSPP with Similar TDPI.
variance in the speed calculation (Wu and Xu, 2018b), we select the timestamp when the speed of vehicle is firstly less than 1.0mph for DSPP calculation. Fig. 3 shows two examples of different DSPPs with similar TDPIs. The DSPP can be then calculated using Eq. (2).
Table 3 Near-Crash Identification with DSPP.
Intersection Signalized midblock crosswalk Uncontrolled midblock crosswalk
Near-Crash
Normal Maneuver
DSPP < 1.2 m (4 ft) DSPP < 12 m (40 ft) DSPP < 6.1 m (20 ft)
DSPP≥1.2 m (4 ft) DSPP≥12 m (40 ft) DSPP≥6.1 m (20 ft)
DSPP =
(Xv−Xp)2 + (Yv−Yp)ˆ2
(2)
Where DSPP is the distance between vehicle and pedestrian when vehicle firstly reduced speed to less than 1.0 mph, unit: m; Xv is the X-axis of vehicle, unit: meter (s); Yv is the Y-axis of vehicle, unit: meter (s); Xp is the X-axis of pedestrian, unit: meter (s); Yp is the Y-axis of pedestrian, unit: meter (s). The DSPP in Fig. 3(a) is 11.86 m (38.91 ft) and in Fig. 3(b) is 4.21 m (13.81 ft). The corresponding videos of Fig. 3 (a) and Fig. 3 (b) can be reviewed through the following links: https://youtu.be/QObqni4UaSI and https://youtu.be/ovmX6ERaoII, respectively. Though the TDPIs (5.7 s and 5.9 s) in this two cases were similar, the crash risks in those
3.2. Distance between stop position and pedestrian The TDPI may not identify all near-crashes in some cases, such as the driver took emergency brakes and already fully stopped before reaching PI. Drivers may wait before PI until the pedestrian passes PI. In that case, the TDPI may be still normal since the conflict occurred before PI. To address this situation, the distance between vehicle stopped position and pedestrian (DSPP) is developed. The DSPP is defined as “the distance between one vehicle and one pedestrian when the vehicle firstly stopped before reaching the pedestrian”. Considering the 243
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Fig. 4. Speed-Distance Profile of Vehicles.
Fig. 5. An Example of Near-Crash Identification with Speed-Distance Profile.
only 4.21 m (13.81 ft) away from the pedestrians, which was more dangerous for the pedestrian compared to the case in Fig. 3 (a). Drivers should stop before the yield line or stop line to give a safe distance to pedestrians. The Manual on Uniform Traffic Control Devices (MUTCD) (Federal Highway Administration, 2009) specified that the distance between yield/stop line to crosswalk (LTC) should be placed a minimum of 1.2 m (4 ft) in advance of the nearest crosswalk line at controlled intersection. Stop lines at midblock signalized locations should be placed at least 12 m (40 feet) in advance of the nearest signal indication. And if yield or stop lines are used at a crosswalk that crosses an uncontrolled multi-lane approach, the yield lines or stop lines should be placed 6.1 to 15 m (20 to 50 feet) in advance of the nearest crosswalk line. For normal maneuvers, DSPP should be no shorter than LTC. The recommended thresholds for near-crash identification at different sites are shown in Table 3. Engineers can also define their own thresholds based on the features of different sites.
Table 4 Near-Crash Identification. Risk
Thresholds
Near-crash
TDPI < 2.5 s or 0 < DSPP < lTC or vehicle speed within area A in speed-distance profile 2.5 s ≤ TDPI≤3.5 s or vehicle speed within area B in speeddistance profile TDPI > 3.5 s or DSPP ≥ lTC or vehicle speed within area (a) in speed-distance profile within area C in speed-distance profile
Crash Relevant Low risk
two situations were completely different. Fig. 3 (a) shows an example that one pedestrian crossed the intersection while drivers stopped far away from the pedestrian. The DSPP was 11.86 m (38.91 ft), which was far enough and safe for pedestrian. But in Fig. 3 (b), the left-turn driver did not see the pedestrian crossing the road in advance and stopped in the middle of the intersection when the vehicle was close to the pedestrian. The distance when the drivers stopped at the intersection was 244
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Fig. 6. Map of Data Collection Sites.
indicating they stopped at the same location before the pedestrian. However, they may decrease with different decelerations before stop. Apparently, the vehicle sharply stopped is more dangerous to pedestrians. The speed-distance profile is used to address this situation. Fig. 4 shows an example of the speed distribution of vehicles with different distance of vehicle to pedestrian (within 100 ft). Most points in Fig. 4 were located in the area with a distance longer than 20 ft to pedestrians. The distribution of the points was dispersed at the same distance, indicating different vehicles had different speeds. The 85th percentile speed was calculated. In this specific site, the 85th percentile speed was about 15mph before vehicles dramatically slowed down. The total stopping distance can be estimated by summing the perception-reaction distance and the braking distance. For a vehicle with 15mph, the estimated stopping distance is 44 ft (Anon., 2018b).
Table 5 Road Information. Location
AADT
Speed limit (mph)
Road width (ft)
Pedestrian involved crashes counts from 2014 to 2017
Midblock in 15th St N Virginia St@ 10th St
3500
15
36
NaN
11000
25
64
4
3.3. Speed-distance profile The DSPP did not show the impact of different speeds of vehicles on crash risk. For example, two vehicles may have the same DSPPs, 245
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campus of University of Nevada, Reno (UNR). There is a steep slope at this site. A lot of students and faculties in the university have safety concern about this midblock. The directions studied at this site are EastWest direction and West-East direction (pedestrians crossing 15th St). The other site was selected at a T-intersection installed with Rectangular Rapid Flash Beacon (RRFB) on major street-N Virginia St@ 10th Street. The N Virginia St is the major street and 10th Street is the minor street. The directions studied at this site are also East-West direction and West-East direction (pedestrians crossing N Virginia St). A vehicle-pedestrian crash happened at this intersection on July 19th, 2017, which was reported in the following link: http://mynews4.com/ news/local/reno-police-investigate-vehicle-pedestrian-crash-near-unrcampus. Further examination was required to evaluate pedestrian safety at this sites. Table 5 summarizes the road information of the two sites. The LiDAR was installed on a tripod for temporary data collection, as shown in Fig. 7. The peak hour data were collected on at the same weekday. The collection time was 25 min (15,000 frames) at both sites. The algorithm has been implemented in Matlab and was deployed on the Dell desktop equipped with Intel Core i7-4790 CPU (3.60 GHz) and 16 GB of RAM. The time cost for extracting TDPI, DSPP and speed-distance profile from the trajectories is very limited (about 2 min for the first site and 1 min for the second site). Fig. 8 and Fig. 9 show the results of TDPI, DSPP and speed-distance profile at this two sites. An example of near-crash (the event with a 2.1-seconds of TDPI in Fig. 9 (a)) occurred at the second site was shown in the following video: https://youtu.be/-3-tgqeQsHE. The video showed that the driver failed to yield to the pedestrian. By checking the videos, the near-crash events can be easily confirmed. The results of case studies shows that the vehicle-pedestrian near-crash events can be identified using the proposed method.
Fig. 7. Roadside LiDAR Installation.
The area in Fig. 4 can be further divided into four subareas using the 85th percentile speed line and the stopping distance, as shown in Fig. 5. In area A, vehicles had higher speeds within the stopping distance, which were more dangerous to pedestrians compared to those in other areas. Events located in area A can be considered as near-crashes. An example of event in area A can be found through the link: https:// youtu.be/TeEERfWOgzo. In this event, when the distance between the pedestrian and the vehicle was 8 m (26.2 ft), the speed of the vehicle was 20.6mph. The vehicle did not stop before the pedestrian and passed the pedestrian before the pedestrian finished the crossing. For the cases in area B, the vehicles had lower speeds within stopping distance or had higher speeds out of stopping distance, which were considered as crash relevant. Crash risk in area B was lower than that in area A. An example of event in area B can be found through the link: https://youtu.be/ e9jhlbuk8uw. In this event, the vehicle tried to pass the crossing with high speed when the pedestrian already reached the midblock crosswalk. Though this event may not have the high crash risk like those in area A, the driver’s aggressive behavior may let the pedestrian feel uncomfortable. In area C, vehicles had lower speed out of stopping distance, which were considered as safe events to pedestrians. The trajectory of the same vehicle may be located in different subareas in Fig. 5, the area with highest crash risk should be used for risk assessment. It should be noted that it is better to check the records in area A manually to make sure they are near-crashes since some points may be the outliers from area B or area C.
4. Conclusion and discussion This paper presents a novel method for vehicle-pedestrian nearcrash identification using the trajectories of vehicles and pedestrians extracted from roadside LiDAR data. Three factors: TDPI, DSSP and speed-distance profile are combined for vehicle-pedestrian near-crash identification. The proposed method was coded into an automatic procedure, which can release the onerous labor work for near-crash identification. The case studies showed that the crash risk can be easily estimated without waiting for historical crash records. Though this paper provided the recommended parameters for vehicle-pedestrian near-crash identification, the engineers can select their own thresholds based on the different features of specific sites. The proposed method may be very useful for before-and-after pedestrian safety assessment of one specific site, or can be used to identify the site with highest pedestrian crash risk from the sites pool. There are still some improvement in further studies. For the TDPI, while it is easier to measure or calculate, it is validity is less proven compared to TTC. The dimensions of vehicles play a role as well as who is passing the conflict zone first. For example, a pedestrian waits at a curb and make a step just as the car has passed – TDPI is low, but risk is minimal (Laureshyn et al., 2010). This situation can cause false reports. The road boundary information can be used to improve the accuracy of TDPI. With the road boundary information, we can exclude the situation that pedestrians wait at a curb (out of road boundary). In the next step, we will involve the road boundary information in TDPI to improve the accuracy. The DSPP and speed-distance profile are developed by the authors for safety evaluation. Since they have hardly ever been used before, no validation is available. The reliability and validity of those indicators required more examinations in future studies. Some thresholds about those indicators were provided in the paper based on the authors’ best knowledgement. The selection of the thresholds also requires further analysis (Hauer, 1993). Bicycle-involved near-crash was
3.4. Thresholds of near-crash identification The TDPI, DSPP and Speed-Distance profile can all be used for nearcrash identification. The final thresholds were determined by combining these three factors. All events can be divided into three parts based on their risks: near-crash, crash relevant, and low risk. Table 4 shows the final recommended thresholds for near-crash identification. This algorithm has been coded into an automatic procedure in Matlab for near-crash identification. 3.5. Case study To evaluate the applicability of the new algorithm, two case studies were conducted. The following criteria are recommended to select the strategic sites for installing LiDAR: 1) The sites with high historical pedestrian-involved crash frequency. 2) The sites where public have concern or complaint about pedestrian safety. 3) New opened intersections (need design and operation evaluation). In this research, the roadside LiDAR data were collected at two sites in Reno, Nevada. Fig. 6 shows the map of the data collection sites. One site was selected at one unsignalized midblock in 15th St on the 246
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Fig. 8. Near-Crash identification at One Midblock in 15th Street.
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Fig. 9. Near-Crash identification at N Virginia St@10th St.
Transportation (NDOT) under Grant No. P224-14-803/TO #13. The authors gratefully acknowledge this financial support. This research was also supported by engineers with the Nevada Department of Transportation, the Regional Transportation Commission of Washoe County, Nevada, and the City of Reno.
not considered in this paper since the bicycle data were limited. Videos were not recorded in the two case studies. In the next step, videos will be used to validate the near-crashes. The two case studies only provided primary validation results of the near-crashes using limited data. More data are expected to be collected to provide the systematic validation of the near-crashes in further studies. The implementation of the proposed method relies on the accurate trajectories of vehicles and pedestrians. However, there may be some errors in the output of the current LiDAR data processing algorithms (Johnsson et al., 2018), which may lead to some outliers in the trajectories. The users of this method are encouraged to use the LiDAR video to confirm the near-crash events and exclude those outliers in the results. The previous study (Gettman et al., 2008) mentioned that object occlusion was a major reason of this error. Setting up multiple LiDARs in different directions are expected to solve this issue. The authors are working on data integration for multiple LiDAR sensors.
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Acknowledgements This research was funded by the SOLARIS Institute, a Tier 1 University Transportation Center (UTC) under Grant No. DTRT13-GUTC55 and matching funds by the Nevada Department of 248
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