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Quantifying perceived quality of traffic service and its aggregation structure
Transportation Research Part C 19 (2011) 296–306
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Transportation Research Part C journal homepage: www.elsevier.com/locate/trc
Quantifying perceived quality of traffic service and its aggregation structure Hideyuki Kita *, Akira Kouchi Department of Civil Engineering, Graduate School of Engineering, University of Kobe, 1-1, Rokkodai-cho, Nada-ku, Kobe, 658-8501, Japan Infrastructural Planning Department, Chodai Co. Ltd., 2-20-6, Shin-machi, Nishi-ku, Osaka, 550-0013, Japan
a r t i c l e
i n f o
Keywords: Quality of traffic service Driver’s perception Utility model Order effect Point-basis evaluation Section-basis evaluation
a b s t r a c t This paper proposes a methodology for measuring the perceived quality of service (QOS) of a driver. The proposed method characterizes a driver’s perception of the quality of traffic service as based on not the macroscopic or average traffic conditions but on the microscopic traffic conditions that the driver faces. To ascertain this, two methods are developed in this study. The first one is to estimate the driver’s perceived QOS of traffic service on a point-basis that is formulated based on revealed preference data and a discrete choice model. Existence of order effect is, then, examined and the relationship between pointbasis utility and point-basis perceived utility is clarified. The second method is to relate the point-basis perceived QOS with the section-basis perceived QOS. Through a data analysis, the existence of some formation structures for the QOS perception has been confirmed. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Selecting efficient measures adequately for increasing user satisfaction is fundamental in road planning and traffic management. For this purpose, it is necessary to clarify the driver’s perception and evaluation structure of the quality of traffic service. There exist several studies, each of which proposes a macroscopic traffic condition variable such as travel speed as the substitution index of QOS based on the driver’s perception. However, the following questions may be pointed out in this approach: (1) Drivers are more likely to evaluate QOS by jointly considering several influencing factors. Describing perceived QOS using only one traffic condition variable is questionable. (2) In a case of describing it with a traffic condition variable, no method to select the most adequate index is established. The situation is the same in a case of selecting the best combination of indices. (3) A driver can recognize only the traffic conditions within the field of vision surrounding the driver. QOS evaluation, therefore, should be based not on macroscopic or average traffic conditions, but on the microscopic traffic conditions that the driver faces. (4) The driver’s perception of the QOS of traffic service in the section level may be an aggregated perception of the QOS for the local traffic conditions at every point. However, there is no adequate method of relating the degree of QOS at the point level with the degree of QOS at the section level. To answer these questions, this paper proposes a methodology for measuring the perceived QOS of a driver. In Section 2, the outline and the basic ideas of a series of methods that comprise the proposed methodology are presented. In Section 3, a method to estimate the driver’s perceived QOS of traffic service on a point-basis is formulated based on revealed preference * Corresponding author. E-mail address: [email protected] (H. Kita). 0968-090X/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.trc.2010.05.015
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data and a discrete choice model. In Section 4, the existence of order effect is examined and the relationship between pointbasis utility and point-basis perceived utility is clarified. In Section 5, a method to relate the point-basis perceived QOS with the section-basis perceived QOS is developed. Section 6 is for conclusions. 2. Methodology 2.1. Study review There exist some attempts to evaluate the quality of traffic service based on the driver’s perception. Morrall and Werner (1990) found that the overtaking ratio considerably decreased in heavy traffic conditions, even though the time-of-delay ratio slightly increased, and pointed out that the perceived QOS must have decreased, even though the QOS measured by timeof-delay ratio was mostly at the same level. Based on this finding, they emphasized that the QOS of traffic should be evaluated based on the driver’s perception of the immediate microscopic driving environment. We believe this to be reasonable. However, there exist many indices for describing microscopic driving environments. De Arzoza and McLeod (1993) proposed adopting ‘‘average travel speed” as the QOS index, for example. Flannery and Jovanis (2001) suggested that time-delay was an adequate index. Ishibashi et al. (2006) developed a satisfaction ratings model relating drivers’ perceptions measured by relating dissatisfaction degrees to density, and also showed that the satisfaction rating for the levels of service related to one indicator of traffic conditions is not necessarily the same among all drivers. Those studies tried to find a traffic condition variable that shows a rather high correlation to the stated subjective rating of the perceived QOS, and proposed the variable as the substitution index of QOS based on the driver’s perception. However, drivers may not necessarily evaluate the QOS based on only one traffic condition variable, but may possibly evaluate QOS by jointly considering several influencing factors. Hall et al. (2001), while believing that it is travel time that influences perceived QOS most, suggested that safety, information, and behavioral freedom also influence perceived QOS. Washburn et al. (2004) suggested that elements such as pavement condition and driving manners which are not related to traffic performance are also important, though the main element that influences drivers’ perception of QOS is traffic density. What is common to these two is that they both point out diversity in the driving environment index perceived by drivers, but they differ in content. On the other hand, Hostovsky et al. (2004) discussed how the driving environment index perceived by drivers could vary depending on the difference in road-use characteristics. Using the results of focus group studies, they showed that commuting drivers in urban areas are interested in travel time; commuting drivers in non-urban areas are interested in behavioral freedom; and truck drivers are interested in stable traffic volume and pavement condition. Furthermore, they showed that service standard thresholds are not always the same – they could vary depending on the road types and drivers. Choocharukul et al. (2004) suggested the possibility of the same indices having an effect in two opposite directions depending on the road-use characteristics, as heavy-vehicle mix rate is a negative index for drivers other than truck drivers, while the opposite is the case for truck drivers. From this, one can argue that these suggest the necessity of selecting a proper service standard index that depends on the driver’s attributes and their makeup. Kita (2000) has pointed out that this confusion came from the lack of a methodology to evaluate QOS measurement models, and Kita (2001) proposed a methodological framework for measuring QOS that is based on the driver’s perception. The basic idea of this framework is to describe the driver’s perception of the total QOS of a particular road section by the aggregated perception of the QOS of the microscopic driving conditions at each instance in the road section measured in terms of driver’s utility, and using this to examine modeling adequacy by comparing estimated and revealed driving behaviors. Using this concept, some earlier works of this study were conducted. Kita et al. (2000) correlated driving stress and the traffic conditions surrounding the driver, and proposed a model that would estimate the driver’s utility to the microscopic driving environment that the driver is facing. Kitajima and Kita (2003) conducted experimental driving on an expressway road section, and examined the performance of the proposed utility-based model to describe a driver’s subjective evaluation of the quality of traffic service by using the data obtained in the experimental driving. Kita et al. (2005) refined the model by introducing the Possibility Index for Collision with Urgent Deceleration (PICUD), an index of rear-end collision risk (Uno et al., 2003), instead of TTC, time to collision, and showed a better fit between the driver’s subjective evaluation and the aggregated utility of a driver to the traffic conditions. However, some residual also remains between them. One cause of this lack of fit may come from the influence of ‘‘order effect”. Order effect is the perception, after having experienced prior stronger or weaker stimuli, that two identical stimuli differ from each other in strength. As far as the authors know, no study can be found which deals with such an effect on the driver’s perception of the QOS due to the influence of proximate road sections. In terms of this awareness, we shall investigate the formation structure of the driver’s subjective evaluation of the quality of traffic service, and then propose a method for selecting the most adequate index of QOS based on the driver’s perception. 2.2. Structure of proposed methodology In response to the preceding studies done by the above-mentioned authors, in this study, the author view the ‘‘essence of QOS” as ‘‘desirability felt by drivers regarding traffic service”. This can be interpreted as utility which is brought about by the
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driving environment and experienced by the driver. From this perspective, the approaches so far that attempted to find a relationship between QOS evaluation and the traffic conditions which prescribe it can be regarded as attempts to identify a sort of utility function. This utility may be on the driving environment which a driver directly faces at each moment while driving, or may be on something perceived regarding the whole section driven after driving. If so, then how do they interact with each other? Kahneman (2000) said ‘‘The concept of utility has carried two different meanings in its long history,” and explained each of the two by naming them the memory-based approach and the moment-based approach. – The memory-based approach accepts the subject’s retrospective evaluations of past episodes, their remembered utility, as valid data. – The moment-based approach derives the experienced utility of an episode from real-time measures of the pleasure and pain (moment utility) that the subject experienced during that episode. Also, in Kahneman et al. (1997), some concepts of utility, such as instant utility, decision utility, and remembered utility were put forward and their conceptual relationship was considered. – Instant utility: a measure of hedonic and affective experience, which can be derived from immediate reports of current subjective experience or from psychological indices. – Decision utility: a measure of temporally extended outcomes (TEOs) which is inferred from choices. – Remembered utility: a measure of past TEOs, which is inferred from a subject’s retrospective reports of the total pleasure or displeasure associated with past outcomes. The analyses by Hostovsky et al. (2004) and Choocharukul et al. (2004) can be regarded as a finding regarding the driver’s perception which is equivalent to remembered utility. It, however, does not necessarily clarify its relationship with the microscopic driving environment. In this study, three concepts of utility, which are ‘‘point-basis utility”, ‘‘point-basis perceived utility”, and ‘‘section-basis utility”, as detailed below, are introduced and their interrelationships will be identified. Point-basis utility is the driver’s perception of the microscopic driving environment that the driver faces directly and at each instance during his/her driving. It is assumed that the driver chooses his/her driving action, such as decelerating and lane-changing, to maximize this utility. This is nothing but decision utility as put forward by Kahneman. This study features systematic estimation of this point-basis utility, a sort of instant utility, based on the revealed preference without any interviewing. Point-basis perceived utility is memory-based utility of the point-basis utility that is moment-based utility. Point-basis perceived utility, as memory-based utility, is known to be influenced by the experiences of preceding and/or following driving. In this study, the relationship between moment-based utility and memory-based utility is analyzed by eliminating order effect. The experiment using a video clip as detailed below is used for the analysis. Section-basis utility is memory-based utility perceived by the driver of the QOS of the driven section in retrospect. This is equivalent to Kahneman’s remembered utility. What kinds of pieces of information are subconsciously cut out from the transition of point-basis perceived utility at each point and how the driver edits them are analyzed and their relationship is formulated in this study. An attempt to interrelate moment-based utility per point and memory-based utility per section in this way is another feature of this study (see Table 1). Fig. 1 shows a conceptual framework of the proposed methodology to estimate quality of traffic service based on the driver’s perception. Its basic idea is that a driver perceives the quality of a microscopic driving environment at each point in the course of driving. We estimate this perception without taking into account the influence of order effect, point-basis utility, from a microscopic driving environment surrounding the driver, and then estimate the perception while taking into account the influence of order effect, the point-basis perceived utility. The section-basis utility is evaluated by aggregating this pointbasis-perceived utility over points. If we can relates a macroscopic traffic data with a set of microscopic driving environment that may occur under a given traffic condition, section-basis evaluation to the quality of traffic service based on driver’s perception can be estimated by obtaining macroscopic traffic data such as flow rate. Data obtainable for QOS evaluation are basically macroscopic traffic condition variables. Therefore, for QOS evaluation in a microscopic driving environment, it is necessary to capture what kind of microscopic driving environment takes place and to what extent, under given macroscopic traffic conditions. There are hardly any studies available regarding the above, as far
Table 1 Classification of the utility concepts in this study. Approach/unit of space
Moment-based
Memory-based
Point Section
Point-basis utility (experienced utility, decision utility)
Point-basis perceived utility (instant utility) Section-basis utility (remembered utility)
Utility concepts in parentheses correspond to utility concept in Kahneman (2000).
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Macroscopic traffic conditions of a section
Microscopic traffic conditions at each point Point-basis driving utility model Estimation of point-basis evaluation: point-basis utility (Not taking order effect into account) Order effect Estimation of point-basis evaluation: point-basis perceived utility (Taking order effect into account) Aggregation model over space Estimation of section-basis evaluation of a driver Aggregation model over drivers and trips Distribution of types of drivers, etc. QOS evaluation of a section based on driver’s perception Fig. 1. Structure of the proposed methodology.
as the authors know. This, however, is under development by the authors, which will be reported at another opportunity. Hence, in this paper, we explain the extent shown by the broken line in Fig. 1 which structures the framework of the aforementioned methodology proposed in this study. The proposed methodology in this study is a local analysis, even a part of the whole methodology. It may play an important role when traffic operators need to estimate the QOS perceived by drivers from macroscopic traffic condition variables. The above is the evaluation of the QOS at the time when a certain driver drives a certain section once. QOS, however, must be evaluated and presented in a form tailored to the purpose of evaluation rather than in the simple disaggregated form regarding space and drivers. For this purpose, it requires both disaggregated evaluation data and their aggregating methods. As for the aggregating method, there are various sorts of aggregation over space, trips (when each driver travels multiple times), and drivers, for example. Even though this study proposes only a case in which aggregation is carried out regarding space in relation to a certain driver, this attempt can mark the beginning of developing a theoretical base for QOS evaluation by aggregating the driver’s disaggregated perception of the microscopic driving environment.
3. Utility-based perceived QOS estimation method Driving can be recognized as a series of decision-makings for faster and safer access to the destination. A driver chooses the most desirable actions under a given driving environment, and tries to improve the driving environment through use of these actions. This means that each driver, j, chooses the alternative driving action, ai, at the instance, t, with the maximum utility, U tj
U tj ¼ maxfU tji g ai
ð1Þ
where U tji is a driver’s utility to each of the four alternatives, ai, at time t. This study assumes that the maximum utility U tj corresponds to the perceived QOS of the driver at that instant. It can be thought of as the ‘‘ceiling” of QOS to the driver. The lower the QOS, the lower the ‘‘ceiling” which suppresses the driver’s desire for driving faster, safer, and more comfortably.
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Point-basis driving utility is determined by the alternative driving actions, ai 2 Aði ¼ 1; . . . ; 4Þ, that consist of maintaining speed, acceleration, deceleration, and changing lanes. Regarding such chosen driving actions, random utility U tji ¼ V tji þ e is hypothesized and the choice probability P tji is obtained by the logit model when e follows an independent and identical Gumbel distribution. Deterministic parts for each action, ai, are expressed in the following formulae (Kita et al., 2005):
V tj1 ¼ k1 Lt1 þ k2 Lt2 þ lv 0j v tj for maintaining speed V tj2 ¼ k1 Lt1 þ lv 0j v tj þ Dv for acceleration V tj3 ¼ k1 Lt1 þ lv 0j v tj Dv for deceleration 0 nt nt V tj4 ¼ k1 Lnt for lane changing 1 þ k2 L2 þ lv j v j þ t
ð2aÞ ð2bÞ ð2cÞ ð2dÞ
where V tji is the deterministic parts of point-basis utility taken by the driver j for the driving action ai (i = 1, . . . , 4) at time t; Lt1 is the PICUD to the closest front side vehicle at time t (m); Lt2 is the PICUD to the closest rear side vehicle at time t (m); Lnt 1 is the PICUD to the closest front side vehicle on the neighbor lane at time t (m); Lnt 2 is the PICUD to the closest rear side vehicle on the neighbor lane at time t (m); v 0j is the desired speed of the driver j (m/s); v tj is the achieved speed of the driver j at time t (m/s); v nt j is the achieved speed of the closest front side vehicle on the neighbor lane at time t (m/s); Dv is the speed change at time t (m/s); and k1 ; k2 ; l; t are the parameters. The model identification is made by using a set of video image observation data taken in a section of Hanshin Expressway in Japan. The driver’s utility model is formulated by the risk of collision with the vehicle running in the front or in the rear, and the gap between desired and achieved driving speeds (Kita et al., 2005). QOS can be understood as a function of both satisfaction and expectation. Though it should be for further research to clarify the structure of expectation formation of a driver to the traffic service, this study builds the desired speed into the model as a representative variable. Lt1 above indicates the PICUD which shows the extent of the risk of the concerned vehicle causing a rear-end collision with the closest front side vehicle when the concerned vehicle is following the closest front side vehicle and the driver of the closest front side vehicle brakes suddenly. Similarly, Lt2 indicates the PICUD when the concerned vehicle is followed by the closest rear side vehicle and the driver of the concerned vehicle brakes. Lt1 and Lt2 are assumed to be determined by the choice made by the driver, such as accelerating, decelerating, maintaining speed, or changing lanes, and are defined in the following formulae:
8 2 ðv tj Þ2 ðv t1 Þ > t t t > L ¼ þ s v D t þ > 0 j 2a 2a > < 1 2 for maintaining speed > 2 v tj > > t ðv t2 Þ t t > : L2 ¼ 2a þ s0 v 2 Dt þ 2a 8 2 9 t 2 > t < = v þ 2:75 > v j t v Lt1 ¼ 1 þ st0 for acceleration j þ 2:75 Dt þ > > 2a 2a : ; 8 2 9 t 2 > > t < = v 4:15 v j t v 4:15 D t þ for deceleration Lt1 ¼ 1 þ st0 j > > 2a 2a : ; 8 2 9 8 > < v tj = > > nt ðv nt1 Þ2 nt t > > L1 ¼ 2a þ s0 v j Dt þ 2a > < ; : for lane changing 2 > > > t 2 > vj > ðv nt2 Þ > nt nt : Lnt 2 ¼ 2a þ s0 v 2 Dt þ 2a
ð3aÞ
ð3bÞ
ð3cÞ
ð3dÞ
where v tj is the achieved speed of the driver j at time t (m/s); v t1 is the achieved speed of the closest front side vehicle at time t (m/s); v t2 is the achieved speed of the closest rear side vehicle at time t (m/s); v nt 1 is the achieved speed of the closest front side vehicle on the neighbor lane at time t (m/s); v nt 2 is the achieved speed of the closest rear side vehicle on the neighbor lane at time t (m/s); st0 is the inter-vehicular distance on the same lane at time t (m); snt 0 is the inter-vehicular distance on the neighbor lane at time t (m); Dt is the response time; and a is the degree of deceleration. Parameters of Eqs. (2a)–(2d) are calibrated by using a calibration technique of a discrete choice model based on the behavior and the surrounding traffic conditions data extracted from a set of video data detailed in Section 4.2. These video data are recorded through the charge coupled device (CCD) cameras equipped in the cars for experimental driving. Each data sample consists of a chosen driving action and the values of explanatory variables in Eqs. (2a)–(2d), (3a)–(3d) at each instance, measured on the video pictures at every 30 s. Dt is assumed to be 0.75 (s), and a is assumed to be 3.3 (m/s2).
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H. Kita, A. Kouchi / Transportation Research Part C 19 (2011) 296–306 Table 2 Estimated values of parameters.
Estimated value t-Value
k1
k2
l
m
0.0110037 2.981**
0.0221891 6.659**
0.0881876 2.360*
3.5800911 5.212**
q2 = 0.35. * **
5% significant. 1% significant.
The sample size is 206. The estimated values of parameters by using most likelihood estimation technique are shown in Table 2. The likelihood ratio and t-values show that this parameter calibration is made fairly well.
4. Order effect and the point-basis perceived utility 4.1. Method A driver perceives a series of QOS of the driving environment at each instance during his/her driving. In such a situation, an ‘‘order effect” (Kahneman et al., 1997) may occur, so that the perception of the QOS at an instance is influenced by the earlier perception. The ‘‘order effect” refers to the perception, after experiencing a prior stronger or weaker stimulus, of the strength of two stimuli as being different from each other, even though their actual strength is the same. One cause of the above influence of the driving experience in upstream/downstream sections on the perceived QOS in a respective road section may come from this ‘‘order effect”. In this section, we examine whether order effect exists or not in a point-basis evaluation of the quality of traffic conditions. For this purpose, first, we carried out a series of on-road experiments. In these experiments, we collected on-road subjective evaluation data on the facing traffic conditions by recording the driver’s oral statements while driving and a set of video data on the surrounding traffic conditions. From this video data, we cut video clips out and reordered them randomly, inserting a time interval between each video clip. Then we showed it to the subjects, and collected a set of stated subjective evaluation data on the traffic conditions recorded on video clips. This data is named ‘controlled data’. Second, we calculated the point-basis utility of the scenes corresponding to the video clips by using Eqs. (2a)–(2d), (3a)–(3d). After taking these sets of data, we estimated the correlation coefficient between subjective evaluation data while driving and the utility data, and the coefficient between the subjective evaluation data by using controlled video data and the utility data. If the latter one is higher than the former one, it can be understood that the difference is the result of order effect. In other words, the existence of order effect can be ascertained if the values of these two correlation coefficients are different.
4.2. Data 4.2.1. On-road experiment data Various data were collected by carrying out the on-road experiment on the expressway. Table 3 is a summary of this experiment. The section to be used for the experiment was chosen by setting the following requirements: there exists no extreme variation in the road structure within the chosen section; and fluctuation in traffic condition (congested and non-congested) is observed. Traffic volume on the day of the experiment was: 200–250 vehicles/5 min in each direction after eight o’clock; and the average speed of the day was approximately 80 km/h. For the east-bound traffic, however, there was congestion early in the evening, significantly decreasing the average speed to 20 km/h after 4 p.m.
Table 3 Summary of on-road experiment. Item
Description
Section evaluated Date of experiment Number of subjects Method
Between Uozaki entrance and Wakamiya exit on Hanshin Expressway (4 lanes, 15 km)
Data obtained
07:30–17:00 on Sunday November 26, 2006 Weather: cloudy with occasional rain 6 (5 in their 20 s and 1 in 30 s) The driver rates a point-basis evaluation of the segment driven for 5 s after the cue called out every 30 s After completing the round trip, reasons behind the choice of scores for point-basis evaluation as well as section-basis evaluation are confirmed while watching the video clips of the driving environment at a designated waiting place Point-basis evaluation (705 in total), section-basis evaluation (25 in total) Images showing the driving environments (front, rear, right side and left side)
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Prior to selecting the six subjects from those who drive routinely, a screening test was conducted for eight persons to eliminate the ones who appear to have an extreme evaluation mechanism. Three groups consisting of 2 subjects (1 driver and 1 time keeper) and a record taker took turns in participating in the on-road experiment using two vehicles prepared for the experiment. Point-basis evaluation was recorded in the following manner. The time keeper calls out the timing for evaluation every 30 s; then the driver utters the level of ‘‘dissatisfaction” about the segment driven for 5 s after it by using an 11-level score raging from 0 to 10; and the record taker records the scores. The section-basis evaluation was made by evaluating the whole section driven each way. The level of ‘‘dissatisfaction” is used here because of the nature of the service provided by expressways. On expressways smooth driving is taken for granted. Due to this, it is assumed that the drivers perceive a smooth driving environment as ‘‘natural” instead of ‘‘satisfactory”. The unit of interval for the rating scale is basically ‘‘1”, but smaller intervals were also accepted depending on the situation. Furthermore, in the on-road experiment, caution was exercised to prevent the evaluation made during the first run from becoming the criterion for evaluation of the rest of the experiment. In order to ensure this, before starting the on-road experiment, a preview session was held, where subjects could prepare their evaluation scales, while adjusting the ranges by watching video images of various traffic conditions. No serious problem was noted in or after the experiment and the stated data were rather stable within and over subjects. Data collection on the subjective evaluation through oral statements during driving was smoothly conducted without problems, and for the most part no amendment was made to the records by the drivers during the checking by watching video replays after finishing every run. From these results, the obtained data can be evaluated rather reliable. As for the driving environment data, to ensure accurate collection of driving environment data, CCD cameras were installed, with each camera facing toward the front, the rear, the right side and the left side respectively, to film the surrounding conditions. Furthermore, in order to capture the driving speed accurately, the speedometer was filmed simultaneously with the above. The traffic conditions in the front, in the rear, on the right and on the left are displayed on one screen segmented in four by using a multi-viewer and in this way the images were synchronized (Fig. 2). Using these images, the driving speed of the vehicles used for the experiment, distance to the closest vehicle in the front, distance to the closest vehicle in the rear, driving speed of the closest vehicle in the front, and driving speed of the closest vehicle in the rear were measured afterwards. 4.2.2. Video clip survey data Due to the difficulty of order effect control in a real driving survey, we introduced a video clip survey to collect a set of data for analysis. A set of video data which records the driver’s view in several runs of experimental driving in a road section is used to prepare video clip data. To prevent the subject’s memory of the traffic conditions at the time of on-road experiment from influencing the evaluation, this verification exercise was conducted one month after the on-road experiment. Each original video data set is divided into several clips. The length of each clip is 5 s for the analysis of point-basis evaluation. 5 s is a length of time in which subjects can recognize the immediate traffic conditions, but in which no big change in traffic conditions occurs. For surveying the driver’s perception with no influence of experiences in other sections, each clip is projected alone, with a certain interval between clips. Through a series of examinations to find an interval of a sufficient length of time, 20 min. was selected for point-basis evaluation to avoid the influence of experience in other points or sections. Here, we will call the data as ‘‘controlled data”. The driver’s evaluation of each driving condition displayed in a video clip is stated in 11 levels of degree of dissatisfaction, respectively. The data sets coming from five trips made by four subjects were collected and a total 42 samples of point-basis evaluation data were obtained. Existence or degree of order effect on the driver’s perception to the quality of traffic service is examined by comparing data from these two survey sets.
Fig. 2. Images depicting driving environment (displayed on one screen segmented in four).
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H. Kita, A. Kouchi / Transportation Research Part C 19 (2011) 296–306 Table 4 With and without order effect comparison in correlation coefficients between point-basis perceived evaluation and point-basis utility. Data set
a
b
c
d
e
With order effect Without order effect
0.844 0.899
0.307 0.769
0.763 0.787
0.579 0.785
0.268 0.521
t-value = 2.54.
4.3. Verification of presence of order effect in point-basis evaluation The following are the results of a comparison for verification of presence of the order effect. Table 4 shows correlation coefficients between the point-basis perceived evaluation value stated by the subjects and point-basis utility value. Pointbasis utility calculated by the model independently from the traffic conditions of other points does not contain order effect. It can be judged that order effect exists if the point-basis utility highly correlates with controlled data which contains no order effect, and does not exist if it highly correlates with subjective evaluation data. Hereafter, the maximum value of the deterministic part of point-basis utility, max fV tji g, is used for the value of point-basis utility, and is described as a ai new notation, utj .
n o utj ¼ max V tji
ð4Þ
ai
Point-basis utility has a fairly high correlation with subjective evaluations without order effect rather than those with order effect. This result is significant in the significance level of 5%. This means that there exists an order effect in the point-basis subjective evaluation of the traffic service of a driver. 4.4. Point-basis perceived utility Taking the result shown in the preceding section that order effect occurs also in the QOS perception in driving into consideration, we assume the following utility model, and estimate the parameters.
V tpointj ¼ /1 utj þ /2 utj ujtDt þ /3
ð5Þ
where V tpointj is the point-basis perceived evaluation of subject j at time t; utj is the point-basis utility of subject j at time t; Dt ut is the point-basis utility of subject j at a previous point (30 s ago); and /1, /2, /3 are the parameters. j Point-basis utility increases as the QOS increases. The point-basis perceived evaluation values, on the other hand, increase as the level of dissatisfaction goes up. Parameter /1, therefore, should be negative. Additionally, it is known that the evaluation values improve when order effect is present compared to when it is absent, in a case where a driver drives from the section with bad traffic conditions to the section with good traffic conditions (Kitajima and Kita, 2003). Here, the point-basis
t
Estimeted Remembered Utility, Vpoint j
10 9 8 7 6 5 4 3
0.79
2 1 0
0
1
2
3
4
5
6
7
8
9
Driver's QOS Ratings Fig. 3. Estimated point-basis perceived QOS and driver’s ratings.
10
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Table 5 Representative values and their correlation coefficient. Index
Correlation coefficient
Average Maximum Minimum Mode The last point Average (upstream section) Average (downstream section) Maximum (upstream section) Maximum (downstream section)
0.87 0.89 0.73 0.64 0.74 0.74 0.82 0.81 0.81
(b) Maximum of point-basis evaluation
Average of point-basis evaluation
Maximum of point-basis evaluation
(a) Average of point-basis evaluation
Stated section-basis evaluation
Stated section-basis evaluation
Fig. 4. Correlation between point-basis evaluation and section-basis evaluation. Dt perceived evaluation increases as the effect increases. If utj ut > 0, the second term on the right side acts to improve the j point-basis evaluation value (value becomes smaller). Parameter /2, therefore, also should be negative. Fig. 3 shows a fairly good correspondence between estimated perceived utility and driver’s ratings of the QOS in the point-basis by using a set of experimental data at Hanshin Expressway in Kobe. The sample size is 115.
5. Aggregation structure of point-basis perception in the section-basis perception Next, we formulate an aggregation model to relate point-basis perceptions and section-basis perception. By jointly using this and the above models, section-basis perception of the QOS can be estimated from microscopic traffic conditions at each point in the section. This is similar to clarifying a formation structure of ‘‘remembered utility” from a series of ‘‘instant utility” (Kahneman et al., 1997). To estimate section-basis evaluation from point-basis evaluation values, first an analysis was made of how the point-basis evaluation is affecting the driver’s subjective evaluation of the entire section. Section-basis evaluation values of 25 sections and the point-basis evaluation values of 705 points included in these sections were used to obtain correlation coefficients between the subjective evaluation values of the entire sections and the average, maximum, minimum, mode and last instance values of the subjective point-basis evaluation values at each point. Correlation coefficients between the subjective evaluation values of the entire sections and the average and maximum values of the halved sections (made by dividing the entire section into the first half, or upstream section, and the last half, or downstream section) were also obtained. These are sorted and shown in Table 5. Across the board, a high correlation is observed between the average value and maximum value of point-basis evaluation and the evaluation values of the entire sections. When the scatter plots of the section-basis evaluation values and other evaluation values were checked, however, it was learned that the evaluation values of the entire sections are almost always higher than the average values of point-basis evaluation and, at the same time, they are almost always lower than the maximum
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H. Kita, A. Kouchi / Transportation Research Part C 19 (2011) 296–306 Table 6 Estimated parameter values. Parameter
Estimator (t-value)
u1 u2 u3
0.550 (1.735) 0.603 (2.175) 1.202 (1.301) 0.810
Estimated section-basis evaluation
R2
0.90
Stated section-basis evaluation Fig. 5. Comparison of estimated and stated section-basis index.
values of point-basis evaluation (see Fig. 4). This suggests that the drivers make their section-basis evaluation based on both the average values and the maximum values. In response to the above, the following relationship is selected as an aggregation structure.
V section ¼ u1 V point þ u2 V max point þ u3
ð6Þ
point and V max are the average and the maximum of point-basis perwhere Vsection is a section-basis perception of the QOS; V point ceptions, respectively; and u1, u2, u3 are the parameters. Estimation result of parameters of Eq. (6) is shown in Table 6. Fig. 5 shows a correspondence between section-basis perceived evaluation estimated from Eq. (6) and stated section-basis evaluation. The above aggregation structure appears to give a fairly good representation capability.
6. Conclusions This study developed a series of methods to estimate the point-basis perceived QOS, and to aggregate them into a sectionbasis one, based on the utility maximization principle with revealed preference data. This attempt to interrelate momentbased utility per point and memory-based utility per section evaluation of QOS is quantified based on the evaluation made by the driver when he/she recalls his/her driving experience of the section. For this recognition, the evaluation made by recalling the driving experience of the section and the evaluation of each moment during driving are appropriately interrelated. Even though the data analysis is very limited, the existence of some formation structures for the QOS perception mechanism has been confirmed. The proposed methodology in this study is a local analysis, even a part of the whole methodology. It may serve an important role for estimating the perceived QOS by drivers from macroscopic traffic condition variables, by combining three additional methods to be developed: the first one to estimate the distribution of microscopic traffic conditions at each point from macroscopic traffic condition of a sector, the second one to estimate aggregated subjective QOS evaluation of a road section over trips and drivers from disaggregate level section-basis QOS evaluation of heterogeneous drivers, and the third one to select appropriate macroscopic traffic condition variables for describing estimated QOS index in the section-basis. Development of these methods remains for further studies.
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The microscopic traffic conditions vary in point and time even in a steady traffic stream. Hence the QOS in the section level as an aggregate of them also varies over drivers. This fact suggests to us the need to introduce a new idea of ‘‘distributed QOS”. The proposed methods in this study may contribute to understand its formation structure. References Choocharukul, K., Sinha, K.C., Mannering, F.L., 2004. User perceptions and engineering definitions of highway level of service: an exploratory statistical comparison. Transportation Research Part A: Policy and Practice 38 (9–10), 677–689. De Arzoza, R.E., McLeod, D.S., 1993. Methodology to assess level of service on US-1 in the Florida keys. Transportation Research Record 1398, 1–6. Flannery, A., Jovanis, P., 2001. Driver’s perception to the quality-of-service of road traffic. In: Proc. of the TRB Conference on Advanced Highway Capacity Modeling Technique and Quality of Service, Truckee, CA. Hall, F.L., Wakefield, S., AI-Kaisy, A., 2001. Freeway quality of service: what really matters to drivers and passengers? Transportation Research Record 1776, 17–23. Hostovsky, C., Wakefield, S., Hall, F.L., 2004. Freeway users’ perceptions of quality of service. Transportation Research Record 1883, 150–157. Ishibashi, T., Kouchi, A., Sakai, T., 2006. Analysis of relationships between drivers’ satisfaction with traffic characteristics and service levels of expressways. In: Proc. of 5th Int. Symp. on Highway Capacity and Quality of Service, Yokohama. Kahneman, D., 2000. Experienced utility and objective happiness – a moment-based approach. In: Kahneman, D., Toversky, A. (Eds.), Choices, Values, and Frames. Cambridge University Press, New York, pp. 673–692. Kahneman, D., Wakker, P.P., Sarin, R., 1997. Back to Bentham? Explorations of experienced utility. The Quarterly Journal of Economics CXIII, 375–405. Kita, H., 2000. Level-of-Service Measure of Road Traffic Based on the Driver’s Perception. Transportation Research Circular E-108. National Research Council, Washington, DC. pp. 53–62. Kita, H., 2001. A Method for Selecting Level-of Service Measure Based on the Driver’s Perception, Paper presented at the Mid-Summer Meeting, Highway Capacity and Quality of Service, TRB, Truckee, CA. Kita, H., Tanimoto, K., Shiotani, N., 2000. An analysis of ETC and lane change assistance: level of service evaluation based on driver’s perception. In: Proc. of the 20th Annual Conference of JSTE (in Japanese). Kita, H., Kotsubo, H., Tanimoto, K., 2005. A structure of driver’s perception to the quality of traffic services. In: Proc. of Infrastructure Planning, vol. 32. JSCE (in Japanese). Kitajima, M., Kita, H., 2003. A model analysis on the structure of driver’s perception to the quality of traffic services. In: Proc. of the 55th Annual Meeting, JSCE Chugoku Branch (in Japanese). Morrall, J.F., Werner, A., 1990. Measuring level of service of two-lane highways by overtakings. Transportation Research Record 1287, 62–69. Uno, N., Iida, Y., Yasuhara, S., Suganuma, M., 2003. Objective analysis of traffic conflict and modeling of vehicular speed adjustment at weaving section. Infrastructure Planning Review 20 (4), 989–996 (in Japanese). Washburn, S.S., Ramlackhan, K., McLeod, D.S., 2004. Quality-of-service perceptions by rural freeway travelers. Transportation Research Record 1883, 132– 139.
Contents lists available at ScienceDirect
Transportation Research Part C journal homepage: www.elsevier.com/locate/trc
Quantifying perceived quality of traffic service and its aggregation structure Hideyuki Kita *, Akira Kouchi Department of Civil Engineering, Graduate School of Engineering, University of Kobe, 1-1, Rokkodai-cho, Nada-ku, Kobe, 658-8501, Japan Infrastructural Planning Department, Chodai Co. Ltd., 2-20-6, Shin-machi, Nishi-ku, Osaka, 550-0013, Japan
a r t i c l e
i n f o
Keywords: Quality of traffic service Driver’s perception Utility model Order effect Point-basis evaluation Section-basis evaluation
a b s t r a c t This paper proposes a methodology for measuring the perceived quality of service (QOS) of a driver. The proposed method characterizes a driver’s perception of the quality of traffic service as based on not the macroscopic or average traffic conditions but on the microscopic traffic conditions that the driver faces. To ascertain this, two methods are developed in this study. The first one is to estimate the driver’s perceived QOS of traffic service on a point-basis that is formulated based on revealed preference data and a discrete choice model. Existence of order effect is, then, examined and the relationship between pointbasis utility and point-basis perceived utility is clarified. The second method is to relate the point-basis perceived QOS with the section-basis perceived QOS. Through a data analysis, the existence of some formation structures for the QOS perception has been confirmed. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Selecting efficient measures adequately for increasing user satisfaction is fundamental in road planning and traffic management. For this purpose, it is necessary to clarify the driver’s perception and evaluation structure of the quality of traffic service. There exist several studies, each of which proposes a macroscopic traffic condition variable such as travel speed as the substitution index of QOS based on the driver’s perception. However, the following questions may be pointed out in this approach: (1) Drivers are more likely to evaluate QOS by jointly considering several influencing factors. Describing perceived QOS using only one traffic condition variable is questionable. (2) In a case of describing it with a traffic condition variable, no method to select the most adequate index is established. The situation is the same in a case of selecting the best combination of indices. (3) A driver can recognize only the traffic conditions within the field of vision surrounding the driver. QOS evaluation, therefore, should be based not on macroscopic or average traffic conditions, but on the microscopic traffic conditions that the driver faces. (4) The driver’s perception of the QOS of traffic service in the section level may be an aggregated perception of the QOS for the local traffic conditions at every point. However, there is no adequate method of relating the degree of QOS at the point level with the degree of QOS at the section level. To answer these questions, this paper proposes a methodology for measuring the perceived QOS of a driver. In Section 2, the outline and the basic ideas of a series of methods that comprise the proposed methodology are presented. In Section 3, a method to estimate the driver’s perceived QOS of traffic service on a point-basis is formulated based on revealed preference * Corresponding author. E-mail address: [email protected] (H. Kita). 0968-090X/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.trc.2010.05.015
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data and a discrete choice model. In Section 4, the existence of order effect is examined and the relationship between pointbasis utility and point-basis perceived utility is clarified. In Section 5, a method to relate the point-basis perceived QOS with the section-basis perceived QOS is developed. Section 6 is for conclusions. 2. Methodology 2.1. Study review There exist some attempts to evaluate the quality of traffic service based on the driver’s perception. Morrall and Werner (1990) found that the overtaking ratio considerably decreased in heavy traffic conditions, even though the time-of-delay ratio slightly increased, and pointed out that the perceived QOS must have decreased, even though the QOS measured by timeof-delay ratio was mostly at the same level. Based on this finding, they emphasized that the QOS of traffic should be evaluated based on the driver’s perception of the immediate microscopic driving environment. We believe this to be reasonable. However, there exist many indices for describing microscopic driving environments. De Arzoza and McLeod (1993) proposed adopting ‘‘average travel speed” as the QOS index, for example. Flannery and Jovanis (2001) suggested that time-delay was an adequate index. Ishibashi et al. (2006) developed a satisfaction ratings model relating drivers’ perceptions measured by relating dissatisfaction degrees to density, and also showed that the satisfaction rating for the levels of service related to one indicator of traffic conditions is not necessarily the same among all drivers. Those studies tried to find a traffic condition variable that shows a rather high correlation to the stated subjective rating of the perceived QOS, and proposed the variable as the substitution index of QOS based on the driver’s perception. However, drivers may not necessarily evaluate the QOS based on only one traffic condition variable, but may possibly evaluate QOS by jointly considering several influencing factors. Hall et al. (2001), while believing that it is travel time that influences perceived QOS most, suggested that safety, information, and behavioral freedom also influence perceived QOS. Washburn et al. (2004) suggested that elements such as pavement condition and driving manners which are not related to traffic performance are also important, though the main element that influences drivers’ perception of QOS is traffic density. What is common to these two is that they both point out diversity in the driving environment index perceived by drivers, but they differ in content. On the other hand, Hostovsky et al. (2004) discussed how the driving environment index perceived by drivers could vary depending on the difference in road-use characteristics. Using the results of focus group studies, they showed that commuting drivers in urban areas are interested in travel time; commuting drivers in non-urban areas are interested in behavioral freedom; and truck drivers are interested in stable traffic volume and pavement condition. Furthermore, they showed that service standard thresholds are not always the same – they could vary depending on the road types and drivers. Choocharukul et al. (2004) suggested the possibility of the same indices having an effect in two opposite directions depending on the road-use characteristics, as heavy-vehicle mix rate is a negative index for drivers other than truck drivers, while the opposite is the case for truck drivers. From this, one can argue that these suggest the necessity of selecting a proper service standard index that depends on the driver’s attributes and their makeup. Kita (2000) has pointed out that this confusion came from the lack of a methodology to evaluate QOS measurement models, and Kita (2001) proposed a methodological framework for measuring QOS that is based on the driver’s perception. The basic idea of this framework is to describe the driver’s perception of the total QOS of a particular road section by the aggregated perception of the QOS of the microscopic driving conditions at each instance in the road section measured in terms of driver’s utility, and using this to examine modeling adequacy by comparing estimated and revealed driving behaviors. Using this concept, some earlier works of this study were conducted. Kita et al. (2000) correlated driving stress and the traffic conditions surrounding the driver, and proposed a model that would estimate the driver’s utility to the microscopic driving environment that the driver is facing. Kitajima and Kita (2003) conducted experimental driving on an expressway road section, and examined the performance of the proposed utility-based model to describe a driver’s subjective evaluation of the quality of traffic service by using the data obtained in the experimental driving. Kita et al. (2005) refined the model by introducing the Possibility Index for Collision with Urgent Deceleration (PICUD), an index of rear-end collision risk (Uno et al., 2003), instead of TTC, time to collision, and showed a better fit between the driver’s subjective evaluation and the aggregated utility of a driver to the traffic conditions. However, some residual also remains between them. One cause of this lack of fit may come from the influence of ‘‘order effect”. Order effect is the perception, after having experienced prior stronger or weaker stimuli, that two identical stimuli differ from each other in strength. As far as the authors know, no study can be found which deals with such an effect on the driver’s perception of the QOS due to the influence of proximate road sections. In terms of this awareness, we shall investigate the formation structure of the driver’s subjective evaluation of the quality of traffic service, and then propose a method for selecting the most adequate index of QOS based on the driver’s perception. 2.2. Structure of proposed methodology In response to the preceding studies done by the above-mentioned authors, in this study, the author view the ‘‘essence of QOS” as ‘‘desirability felt by drivers regarding traffic service”. This can be interpreted as utility which is brought about by the
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driving environment and experienced by the driver. From this perspective, the approaches so far that attempted to find a relationship between QOS evaluation and the traffic conditions which prescribe it can be regarded as attempts to identify a sort of utility function. This utility may be on the driving environment which a driver directly faces at each moment while driving, or may be on something perceived regarding the whole section driven after driving. If so, then how do they interact with each other? Kahneman (2000) said ‘‘The concept of utility has carried two different meanings in its long history,” and explained each of the two by naming them the memory-based approach and the moment-based approach. – The memory-based approach accepts the subject’s retrospective evaluations of past episodes, their remembered utility, as valid data. – The moment-based approach derives the experienced utility of an episode from real-time measures of the pleasure and pain (moment utility) that the subject experienced during that episode. Also, in Kahneman et al. (1997), some concepts of utility, such as instant utility, decision utility, and remembered utility were put forward and their conceptual relationship was considered. – Instant utility: a measure of hedonic and affective experience, which can be derived from immediate reports of current subjective experience or from psychological indices. – Decision utility: a measure of temporally extended outcomes (TEOs) which is inferred from choices. – Remembered utility: a measure of past TEOs, which is inferred from a subject’s retrospective reports of the total pleasure or displeasure associated with past outcomes. The analyses by Hostovsky et al. (2004) and Choocharukul et al. (2004) can be regarded as a finding regarding the driver’s perception which is equivalent to remembered utility. It, however, does not necessarily clarify its relationship with the microscopic driving environment. In this study, three concepts of utility, which are ‘‘point-basis utility”, ‘‘point-basis perceived utility”, and ‘‘section-basis utility”, as detailed below, are introduced and their interrelationships will be identified. Point-basis utility is the driver’s perception of the microscopic driving environment that the driver faces directly and at each instance during his/her driving. It is assumed that the driver chooses his/her driving action, such as decelerating and lane-changing, to maximize this utility. This is nothing but decision utility as put forward by Kahneman. This study features systematic estimation of this point-basis utility, a sort of instant utility, based on the revealed preference without any interviewing. Point-basis perceived utility is memory-based utility of the point-basis utility that is moment-based utility. Point-basis perceived utility, as memory-based utility, is known to be influenced by the experiences of preceding and/or following driving. In this study, the relationship between moment-based utility and memory-based utility is analyzed by eliminating order effect. The experiment using a video clip as detailed below is used for the analysis. Section-basis utility is memory-based utility perceived by the driver of the QOS of the driven section in retrospect. This is equivalent to Kahneman’s remembered utility. What kinds of pieces of information are subconsciously cut out from the transition of point-basis perceived utility at each point and how the driver edits them are analyzed and their relationship is formulated in this study. An attempt to interrelate moment-based utility per point and memory-based utility per section in this way is another feature of this study (see Table 1). Fig. 1 shows a conceptual framework of the proposed methodology to estimate quality of traffic service based on the driver’s perception. Its basic idea is that a driver perceives the quality of a microscopic driving environment at each point in the course of driving. We estimate this perception without taking into account the influence of order effect, point-basis utility, from a microscopic driving environment surrounding the driver, and then estimate the perception while taking into account the influence of order effect, the point-basis perceived utility. The section-basis utility is evaluated by aggregating this pointbasis-perceived utility over points. If we can relates a macroscopic traffic data with a set of microscopic driving environment that may occur under a given traffic condition, section-basis evaluation to the quality of traffic service based on driver’s perception can be estimated by obtaining macroscopic traffic data such as flow rate. Data obtainable for QOS evaluation are basically macroscopic traffic condition variables. Therefore, for QOS evaluation in a microscopic driving environment, it is necessary to capture what kind of microscopic driving environment takes place and to what extent, under given macroscopic traffic conditions. There are hardly any studies available regarding the above, as far
Table 1 Classification of the utility concepts in this study. Approach/unit of space
Moment-based
Memory-based
Point Section
Point-basis utility (experienced utility, decision utility)
Point-basis perceived utility (instant utility) Section-basis utility (remembered utility)
Utility concepts in parentheses correspond to utility concept in Kahneman (2000).
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Macroscopic traffic conditions of a section
Microscopic traffic conditions at each point Point-basis driving utility model Estimation of point-basis evaluation: point-basis utility (Not taking order effect into account) Order effect Estimation of point-basis evaluation: point-basis perceived utility (Taking order effect into account) Aggregation model over space Estimation of section-basis evaluation of a driver Aggregation model over drivers and trips Distribution of types of drivers, etc. QOS evaluation of a section based on driver’s perception Fig. 1. Structure of the proposed methodology.
as the authors know. This, however, is under development by the authors, which will be reported at another opportunity. Hence, in this paper, we explain the extent shown by the broken line in Fig. 1 which structures the framework of the aforementioned methodology proposed in this study. The proposed methodology in this study is a local analysis, even a part of the whole methodology. It may play an important role when traffic operators need to estimate the QOS perceived by drivers from macroscopic traffic condition variables. The above is the evaluation of the QOS at the time when a certain driver drives a certain section once. QOS, however, must be evaluated and presented in a form tailored to the purpose of evaluation rather than in the simple disaggregated form regarding space and drivers. For this purpose, it requires both disaggregated evaluation data and their aggregating methods. As for the aggregating method, there are various sorts of aggregation over space, trips (when each driver travels multiple times), and drivers, for example. Even though this study proposes only a case in which aggregation is carried out regarding space in relation to a certain driver, this attempt can mark the beginning of developing a theoretical base for QOS evaluation by aggregating the driver’s disaggregated perception of the microscopic driving environment.
3. Utility-based perceived QOS estimation method Driving can be recognized as a series of decision-makings for faster and safer access to the destination. A driver chooses the most desirable actions under a given driving environment, and tries to improve the driving environment through use of these actions. This means that each driver, j, chooses the alternative driving action, ai, at the instance, t, with the maximum utility, U tj
U tj ¼ maxfU tji g ai
ð1Þ
where U tji is a driver’s utility to each of the four alternatives, ai, at time t. This study assumes that the maximum utility U tj corresponds to the perceived QOS of the driver at that instant. It can be thought of as the ‘‘ceiling” of QOS to the driver. The lower the QOS, the lower the ‘‘ceiling” which suppresses the driver’s desire for driving faster, safer, and more comfortably.
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Point-basis driving utility is determined by the alternative driving actions, ai 2 Aði ¼ 1; . . . ; 4Þ, that consist of maintaining speed, acceleration, deceleration, and changing lanes. Regarding such chosen driving actions, random utility U tji ¼ V tji þ e is hypothesized and the choice probability P tji is obtained by the logit model when e follows an independent and identical Gumbel distribution. Deterministic parts for each action, ai, are expressed in the following formulae (Kita et al., 2005):
V tj1 ¼ k1 Lt1 þ k2 Lt2 þ lv 0j v tj for maintaining speed V tj2 ¼ k1 Lt1 þ lv 0j v tj þ Dv for acceleration V tj3 ¼ k1 Lt1 þ lv 0j v tj Dv for deceleration 0 nt nt V tj4 ¼ k1 Lnt for lane changing 1 þ k2 L2 þ lv j v j þ t
ð2aÞ ð2bÞ ð2cÞ ð2dÞ
where V tji is the deterministic parts of point-basis utility taken by the driver j for the driving action ai (i = 1, . . . , 4) at time t; Lt1 is the PICUD to the closest front side vehicle at time t (m); Lt2 is the PICUD to the closest rear side vehicle at time t (m); Lnt 1 is the PICUD to the closest front side vehicle on the neighbor lane at time t (m); Lnt 2 is the PICUD to the closest rear side vehicle on the neighbor lane at time t (m); v 0j is the desired speed of the driver j (m/s); v tj is the achieved speed of the driver j at time t (m/s); v nt j is the achieved speed of the closest front side vehicle on the neighbor lane at time t (m/s); Dv is the speed change at time t (m/s); and k1 ; k2 ; l; t are the parameters. The model identification is made by using a set of video image observation data taken in a section of Hanshin Expressway in Japan. The driver’s utility model is formulated by the risk of collision with the vehicle running in the front or in the rear, and the gap between desired and achieved driving speeds (Kita et al., 2005). QOS can be understood as a function of both satisfaction and expectation. Though it should be for further research to clarify the structure of expectation formation of a driver to the traffic service, this study builds the desired speed into the model as a representative variable. Lt1 above indicates the PICUD which shows the extent of the risk of the concerned vehicle causing a rear-end collision with the closest front side vehicle when the concerned vehicle is following the closest front side vehicle and the driver of the closest front side vehicle brakes suddenly. Similarly, Lt2 indicates the PICUD when the concerned vehicle is followed by the closest rear side vehicle and the driver of the concerned vehicle brakes. Lt1 and Lt2 are assumed to be determined by the choice made by the driver, such as accelerating, decelerating, maintaining speed, or changing lanes, and are defined in the following formulae:
8 2 ðv tj Þ2 ðv t1 Þ > t t t > L ¼ þ s v D t þ > 0 j 2a 2a > < 1 2 for maintaining speed > 2 v tj > > t ðv t2 Þ t t > : L2 ¼ 2a þ s0 v 2 Dt þ 2a 8 2 9 t 2 > t < = v þ 2:75 > v j t v Lt1 ¼ 1 þ st0 for acceleration j þ 2:75 Dt þ > > 2a 2a : ; 8 2 9 t 2 > > t < = v 4:15 v j t v 4:15 D t þ for deceleration Lt1 ¼ 1 þ st0 j > > 2a 2a : ; 8 2 9 8 > < v tj = > > nt ðv nt1 Þ2 nt t > > L1 ¼ 2a þ s0 v j Dt þ 2a > < ; : for lane changing 2 > > > t 2 > vj > ðv nt2 Þ > nt nt : Lnt 2 ¼ 2a þ s0 v 2 Dt þ 2a
ð3aÞ
ð3bÞ
ð3cÞ
ð3dÞ
where v tj is the achieved speed of the driver j at time t (m/s); v t1 is the achieved speed of the closest front side vehicle at time t (m/s); v t2 is the achieved speed of the closest rear side vehicle at time t (m/s); v nt 1 is the achieved speed of the closest front side vehicle on the neighbor lane at time t (m/s); v nt 2 is the achieved speed of the closest rear side vehicle on the neighbor lane at time t (m/s); st0 is the inter-vehicular distance on the same lane at time t (m); snt 0 is the inter-vehicular distance on the neighbor lane at time t (m); Dt is the response time; and a is the degree of deceleration. Parameters of Eqs. (2a)–(2d) are calibrated by using a calibration technique of a discrete choice model based on the behavior and the surrounding traffic conditions data extracted from a set of video data detailed in Section 4.2. These video data are recorded through the charge coupled device (CCD) cameras equipped in the cars for experimental driving. Each data sample consists of a chosen driving action and the values of explanatory variables in Eqs. (2a)–(2d), (3a)–(3d) at each instance, measured on the video pictures at every 30 s. Dt is assumed to be 0.75 (s), and a is assumed to be 3.3 (m/s2).
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Estimated value t-Value
k1
k2
l
m
0.0110037 2.981**
0.0221891 6.659**
0.0881876 2.360*
3.5800911 5.212**
q2 = 0.35. * **
5% significant. 1% significant.
The sample size is 206. The estimated values of parameters by using most likelihood estimation technique are shown in Table 2. The likelihood ratio and t-values show that this parameter calibration is made fairly well.
4. Order effect and the point-basis perceived utility 4.1. Method A driver perceives a series of QOS of the driving environment at each instance during his/her driving. In such a situation, an ‘‘order effect” (Kahneman et al., 1997) may occur, so that the perception of the QOS at an instance is influenced by the earlier perception. The ‘‘order effect” refers to the perception, after experiencing a prior stronger or weaker stimulus, of the strength of two stimuli as being different from each other, even though their actual strength is the same. One cause of the above influence of the driving experience in upstream/downstream sections on the perceived QOS in a respective road section may come from this ‘‘order effect”. In this section, we examine whether order effect exists or not in a point-basis evaluation of the quality of traffic conditions. For this purpose, first, we carried out a series of on-road experiments. In these experiments, we collected on-road subjective evaluation data on the facing traffic conditions by recording the driver’s oral statements while driving and a set of video data on the surrounding traffic conditions. From this video data, we cut video clips out and reordered them randomly, inserting a time interval between each video clip. Then we showed it to the subjects, and collected a set of stated subjective evaluation data on the traffic conditions recorded on video clips. This data is named ‘controlled data’. Second, we calculated the point-basis utility of the scenes corresponding to the video clips by using Eqs. (2a)–(2d), (3a)–(3d). After taking these sets of data, we estimated the correlation coefficient between subjective evaluation data while driving and the utility data, and the coefficient between the subjective evaluation data by using controlled video data and the utility data. If the latter one is higher than the former one, it can be understood that the difference is the result of order effect. In other words, the existence of order effect can be ascertained if the values of these two correlation coefficients are different.
4.2. Data 4.2.1. On-road experiment data Various data were collected by carrying out the on-road experiment on the expressway. Table 3 is a summary of this experiment. The section to be used for the experiment was chosen by setting the following requirements: there exists no extreme variation in the road structure within the chosen section; and fluctuation in traffic condition (congested and non-congested) is observed. Traffic volume on the day of the experiment was: 200–250 vehicles/5 min in each direction after eight o’clock; and the average speed of the day was approximately 80 km/h. For the east-bound traffic, however, there was congestion early in the evening, significantly decreasing the average speed to 20 km/h after 4 p.m.
Table 3 Summary of on-road experiment. Item
Description
Section evaluated Date of experiment Number of subjects Method
Between Uozaki entrance and Wakamiya exit on Hanshin Expressway (4 lanes, 15 km)
Data obtained
07:30–17:00 on Sunday November 26, 2006 Weather: cloudy with occasional rain 6 (5 in their 20 s and 1 in 30 s) The driver rates a point-basis evaluation of the segment driven for 5 s after the cue called out every 30 s After completing the round trip, reasons behind the choice of scores for point-basis evaluation as well as section-basis evaluation are confirmed while watching the video clips of the driving environment at a designated waiting place Point-basis evaluation (705 in total), section-basis evaluation (25 in total) Images showing the driving environments (front, rear, right side and left side)
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Prior to selecting the six subjects from those who drive routinely, a screening test was conducted for eight persons to eliminate the ones who appear to have an extreme evaluation mechanism. Three groups consisting of 2 subjects (1 driver and 1 time keeper) and a record taker took turns in participating in the on-road experiment using two vehicles prepared for the experiment. Point-basis evaluation was recorded in the following manner. The time keeper calls out the timing for evaluation every 30 s; then the driver utters the level of ‘‘dissatisfaction” about the segment driven for 5 s after it by using an 11-level score raging from 0 to 10; and the record taker records the scores. The section-basis evaluation was made by evaluating the whole section driven each way. The level of ‘‘dissatisfaction” is used here because of the nature of the service provided by expressways. On expressways smooth driving is taken for granted. Due to this, it is assumed that the drivers perceive a smooth driving environment as ‘‘natural” instead of ‘‘satisfactory”. The unit of interval for the rating scale is basically ‘‘1”, but smaller intervals were also accepted depending on the situation. Furthermore, in the on-road experiment, caution was exercised to prevent the evaluation made during the first run from becoming the criterion for evaluation of the rest of the experiment. In order to ensure this, before starting the on-road experiment, a preview session was held, where subjects could prepare their evaluation scales, while adjusting the ranges by watching video images of various traffic conditions. No serious problem was noted in or after the experiment and the stated data were rather stable within and over subjects. Data collection on the subjective evaluation through oral statements during driving was smoothly conducted without problems, and for the most part no amendment was made to the records by the drivers during the checking by watching video replays after finishing every run. From these results, the obtained data can be evaluated rather reliable. As for the driving environment data, to ensure accurate collection of driving environment data, CCD cameras were installed, with each camera facing toward the front, the rear, the right side and the left side respectively, to film the surrounding conditions. Furthermore, in order to capture the driving speed accurately, the speedometer was filmed simultaneously with the above. The traffic conditions in the front, in the rear, on the right and on the left are displayed on one screen segmented in four by using a multi-viewer and in this way the images were synchronized (Fig. 2). Using these images, the driving speed of the vehicles used for the experiment, distance to the closest vehicle in the front, distance to the closest vehicle in the rear, driving speed of the closest vehicle in the front, and driving speed of the closest vehicle in the rear were measured afterwards. 4.2.2. Video clip survey data Due to the difficulty of order effect control in a real driving survey, we introduced a video clip survey to collect a set of data for analysis. A set of video data which records the driver’s view in several runs of experimental driving in a road section is used to prepare video clip data. To prevent the subject’s memory of the traffic conditions at the time of on-road experiment from influencing the evaluation, this verification exercise was conducted one month after the on-road experiment. Each original video data set is divided into several clips. The length of each clip is 5 s for the analysis of point-basis evaluation. 5 s is a length of time in which subjects can recognize the immediate traffic conditions, but in which no big change in traffic conditions occurs. For surveying the driver’s perception with no influence of experiences in other sections, each clip is projected alone, with a certain interval between clips. Through a series of examinations to find an interval of a sufficient length of time, 20 min. was selected for point-basis evaluation to avoid the influence of experience in other points or sections. Here, we will call the data as ‘‘controlled data”. The driver’s evaluation of each driving condition displayed in a video clip is stated in 11 levels of degree of dissatisfaction, respectively. The data sets coming from five trips made by four subjects were collected and a total 42 samples of point-basis evaluation data were obtained. Existence or degree of order effect on the driver’s perception to the quality of traffic service is examined by comparing data from these two survey sets.
Fig. 2. Images depicting driving environment (displayed on one screen segmented in four).
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a
b
c
d
e
With order effect Without order effect
0.844 0.899
0.307 0.769
0.763 0.787
0.579 0.785
0.268 0.521
t-value = 2.54.
4.3. Verification of presence of order effect in point-basis evaluation The following are the results of a comparison for verification of presence of the order effect. Table 4 shows correlation coefficients between the point-basis perceived evaluation value stated by the subjects and point-basis utility value. Pointbasis utility calculated by the model independently from the traffic conditions of other points does not contain order effect. It can be judged that order effect exists if the point-basis utility highly correlates with controlled data which contains no order effect, and does not exist if it highly correlates with subjective evaluation data. Hereafter, the maximum value of the deterministic part of point-basis utility, max fV tji g, is used for the value of point-basis utility, and is described as a ai new notation, utj .
n o utj ¼ max V tji
ð4Þ
ai
Point-basis utility has a fairly high correlation with subjective evaluations without order effect rather than those with order effect. This result is significant in the significance level of 5%. This means that there exists an order effect in the point-basis subjective evaluation of the traffic service of a driver. 4.4. Point-basis perceived utility Taking the result shown in the preceding section that order effect occurs also in the QOS perception in driving into consideration, we assume the following utility model, and estimate the parameters.
V tpointj ¼ /1 utj þ /2 utj ujtDt þ /3
ð5Þ
where V tpointj is the point-basis perceived evaluation of subject j at time t; utj is the point-basis utility of subject j at time t; Dt ut is the point-basis utility of subject j at a previous point (30 s ago); and /1, /2, /3 are the parameters. j Point-basis utility increases as the QOS increases. The point-basis perceived evaluation values, on the other hand, increase as the level of dissatisfaction goes up. Parameter /1, therefore, should be negative. Additionally, it is known that the evaluation values improve when order effect is present compared to when it is absent, in a case where a driver drives from the section with bad traffic conditions to the section with good traffic conditions (Kitajima and Kita, 2003). Here, the point-basis
t
Estimeted Remembered Utility, Vpoint j
10 9 8 7 6 5 4 3
0.79
2 1 0
0
1
2
3
4
5
6
7
8
9
Driver's QOS Ratings Fig. 3. Estimated point-basis perceived QOS and driver’s ratings.
10
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Table 5 Representative values and their correlation coefficient. Index
Correlation coefficient
Average Maximum Minimum Mode The last point Average (upstream section) Average (downstream section) Maximum (upstream section) Maximum (downstream section)
0.87 0.89 0.73 0.64 0.74 0.74 0.82 0.81 0.81
(b) Maximum of point-basis evaluation
Average of point-basis evaluation
Maximum of point-basis evaluation
(a) Average of point-basis evaluation
Stated section-basis evaluation
Stated section-basis evaluation
Fig. 4. Correlation between point-basis evaluation and section-basis evaluation. Dt perceived evaluation increases as the effect increases. If utj ut > 0, the second term on the right side acts to improve the j point-basis evaluation value (value becomes smaller). Parameter /2, therefore, also should be negative. Fig. 3 shows a fairly good correspondence between estimated perceived utility and driver’s ratings of the QOS in the point-basis by using a set of experimental data at Hanshin Expressway in Kobe. The sample size is 115.
5. Aggregation structure of point-basis perception in the section-basis perception Next, we formulate an aggregation model to relate point-basis perceptions and section-basis perception. By jointly using this and the above models, section-basis perception of the QOS can be estimated from microscopic traffic conditions at each point in the section. This is similar to clarifying a formation structure of ‘‘remembered utility” from a series of ‘‘instant utility” (Kahneman et al., 1997). To estimate section-basis evaluation from point-basis evaluation values, first an analysis was made of how the point-basis evaluation is affecting the driver’s subjective evaluation of the entire section. Section-basis evaluation values of 25 sections and the point-basis evaluation values of 705 points included in these sections were used to obtain correlation coefficients between the subjective evaluation values of the entire sections and the average, maximum, minimum, mode and last instance values of the subjective point-basis evaluation values at each point. Correlation coefficients between the subjective evaluation values of the entire sections and the average and maximum values of the halved sections (made by dividing the entire section into the first half, or upstream section, and the last half, or downstream section) were also obtained. These are sorted and shown in Table 5. Across the board, a high correlation is observed between the average value and maximum value of point-basis evaluation and the evaluation values of the entire sections. When the scatter plots of the section-basis evaluation values and other evaluation values were checked, however, it was learned that the evaluation values of the entire sections are almost always higher than the average values of point-basis evaluation and, at the same time, they are almost always lower than the maximum
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Estimator (t-value)
u1 u2 u3
0.550 (1.735) 0.603 (2.175) 1.202 (1.301) 0.810
Estimated section-basis evaluation
R2
0.90
Stated section-basis evaluation Fig. 5. Comparison of estimated and stated section-basis index.
values of point-basis evaluation (see Fig. 4). This suggests that the drivers make their section-basis evaluation based on both the average values and the maximum values. In response to the above, the following relationship is selected as an aggregation structure.
V section ¼ u1 V point þ u2 V max point þ u3
ð6Þ
point and V max are the average and the maximum of point-basis perwhere Vsection is a section-basis perception of the QOS; V point ceptions, respectively; and u1, u2, u3 are the parameters. Estimation result of parameters of Eq. (6) is shown in Table 6. Fig. 5 shows a correspondence between section-basis perceived evaluation estimated from Eq. (6) and stated section-basis evaluation. The above aggregation structure appears to give a fairly good representation capability.
6. Conclusions This study developed a series of methods to estimate the point-basis perceived QOS, and to aggregate them into a sectionbasis one, based on the utility maximization principle with revealed preference data. This attempt to interrelate momentbased utility per point and memory-based utility per section evaluation of QOS is quantified based on the evaluation made by the driver when he/she recalls his/her driving experience of the section. For this recognition, the evaluation made by recalling the driving experience of the section and the evaluation of each moment during driving are appropriately interrelated. Even though the data analysis is very limited, the existence of some formation structures for the QOS perception mechanism has been confirmed. The proposed methodology in this study is a local analysis, even a part of the whole methodology. It may serve an important role for estimating the perceived QOS by drivers from macroscopic traffic condition variables, by combining three additional methods to be developed: the first one to estimate the distribution of microscopic traffic conditions at each point from macroscopic traffic condition of a sector, the second one to estimate aggregated subjective QOS evaluation of a road section over trips and drivers from disaggregate level section-basis QOS evaluation of heterogeneous drivers, and the third one to select appropriate macroscopic traffic condition variables for describing estimated QOS index in the section-basis. Development of these methods remains for further studies.
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The microscopic traffic conditions vary in point and time even in a steady traffic stream. Hence the QOS in the section level as an aggregate of them also varies over drivers. This fact suggests to us the need to introduce a new idea of ‘‘distributed QOS”. The proposed methods in this study may contribute to understand its formation structure. References Choocharukul, K., Sinha, K.C., Mannering, F.L., 2004. User perceptions and engineering definitions of highway level of service: an exploratory statistical comparison. Transportation Research Part A: Policy and Practice 38 (9–10), 677–689. De Arzoza, R.E., McLeod, D.S., 1993. Methodology to assess level of service on US-1 in the Florida keys. Transportation Research Record 1398, 1–6. Flannery, A., Jovanis, P., 2001. Driver’s perception to the quality-of-service of road traffic. In: Proc. of the TRB Conference on Advanced Highway Capacity Modeling Technique and Quality of Service, Truckee, CA. Hall, F.L., Wakefield, S., AI-Kaisy, A., 2001. Freeway quality of service: what really matters to drivers and passengers? Transportation Research Record 1776, 17–23. Hostovsky, C., Wakefield, S., Hall, F.L., 2004. Freeway users’ perceptions of quality of service. Transportation Research Record 1883, 150–157. Ishibashi, T., Kouchi, A., Sakai, T., 2006. Analysis of relationships between drivers’ satisfaction with traffic characteristics and service levels of expressways. In: Proc. of 5th Int. Symp. on Highway Capacity and Quality of Service, Yokohama. Kahneman, D., 2000. Experienced utility and objective happiness – a moment-based approach. In: Kahneman, D., Toversky, A. (Eds.), Choices, Values, and Frames. Cambridge University Press, New York, pp. 673–692. Kahneman, D., Wakker, P.P., Sarin, R., 1997. Back to Bentham? Explorations of experienced utility. The Quarterly Journal of Economics CXIII, 375–405. Kita, H., 2000. Level-of-Service Measure of Road Traffic Based on the Driver’s Perception. Transportation Research Circular E-108. National Research Council, Washington, DC. pp. 53–62. Kita, H., 2001. A Method for Selecting Level-of Service Measure Based on the Driver’s Perception, Paper presented at the Mid-Summer Meeting, Highway Capacity and Quality of Service, TRB, Truckee, CA. Kita, H., Tanimoto, K., Shiotani, N., 2000. An analysis of ETC and lane change assistance: level of service evaluation based on driver’s perception. In: Proc. of the 20th Annual Conference of JSTE (in Japanese). Kita, H., Kotsubo, H., Tanimoto, K., 2005. A structure of driver’s perception to the quality of traffic services. In: Proc. of Infrastructure Planning, vol. 32. JSCE (in Japanese). Kitajima, M., Kita, H., 2003. A model analysis on the structure of driver’s perception to the quality of traffic services. In: Proc. of the 55th Annual Meeting, JSCE Chugoku Branch (in Japanese). Morrall, J.F., Werner, A., 1990. Measuring level of service of two-lane highways by overtakings. Transportation Research Record 1287, 62–69. Uno, N., Iida, Y., Yasuhara, S., Suganuma, M., 2003. Objective analysis of traffic conflict and modeling of vehicular speed adjustment at weaving section. Infrastructure Planning Review 20 (4), 989–996 (in Japanese). Washburn, S.S., Ramlackhan, K., McLeod, D.S., 2004. Quality-of-service perceptions by rural freeway travelers. Transportation Research Record 1883, 132– 139.