ROUTE CHOICE BEHAVIOR MODEL BASED ON BOUNDED RATIONALITY Toshihiro HIRAOKA, Ikko IRITANI, Kohei OKABE and Hiromitsu KUMAMOTO Dept. of Systems Science, Graduate School of Informatics, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto, 606-8501, JAPAN PHONE:+81-75-753-3370, FAX: +81-75-753-3371, E-mail:
[email protected]
ABSTRACT This paper proposes a sophisticated framework of a route choice behavior model. The model has an explicit relationship between the environmental properties drivers refer to and their route choice criteria. Two experiments about route choice behavior were performed: 1) experimental subjects choose the routes on a map when the destination is unfamiliar, 2) they choose and drive the routes when the destination is familiar. A protocol analysis is used for classification of the environmental properties and the route choice criteria. INTRODUCTION Traffic congestion causes huge economic losses equivalent about to 12 thousand billion yen per year in Japan. Traffic Management Systems (TMS) such as traffic signal controls and VICS (Vehicle Information and Communication Systems) have been already introduced in order to reduce the congestion. VICS provides the information about traffic congestion, route guidance, predicted travel time and parking area for drivers with navigation systems. Traffic flow simulation is frequently used in evaluation of TMS, and a route choice behavior model embedded in the simulator needs to reproduce the behavior of real drivers who choose their routes in response to the change of traffic condition and the traffic information provided. However, there is hardly such a route choice behavior model embedded in simulators currently available. We aim to construct the model that reproduces the actual route choice behavior, and finally, embed it in the traffic flow simulator. As a first step to the goal, in this paper, we propose a sophisticated framework of the route choice behavior model based on the concept of bounded rationality, which considers human finiteness of cognitive resource and variety of motive. The model has an explicit relationship between environmental properties which drivers refer to and their route choice criteria. Moreover, two experiments about route choice behavior are carried out: 1) experimental subjects choose their routes on a map when the destination is unfamiliar, 2) they choose and drive their routes when the destination is familiar. Their utterances are analyzed by a protocol analysis to identify the environmental properties that drivers refer to and the relationship between the properties and their route choice criteria.
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Driving Environment ex) familiar /unfamiliar destination
Alternatives Formation Process :
Vehicles ahead
To extract alternatives from all possible routes
Navi(VICS)
Map
Sign
Information about routes
Judgment Process :
ex) travel time, mileage, a level of congestion, etc.
To estimate route choice criteria
Decision-Making Process : To decide a driving route
Driver’s attributes
ex) driving experience, driving skill, age, driving frequency, etc.
Figure 1: Process of route choice behavior ROUTE CHOICE BEHAVIOR BOUNDED RATIONALITY Travel time is the one of the most popular route choice criterion and travel time minimization is used as the route choice strategy in an ordinary traffic flow simulator. This strategy is based on the idea that human acts rationally. In other words, it is assumed that driver can know all information about all alternative routes accurately and choose the routes that minimize travel time. This idea is called complete rationality. But this assumption is clearly wrong, because there is a limit of information what drivers can acquire and because drivers do not necessarily choose the routes that maximize the value of their behavior. Therefore, we assume that drivers choose their routes in order to maximize the value of behavior based on the bounded ability of collecting and processing data - bounded rationality. THREE PROCESSES OF ROUTE CHOICE BEHAVIOR In a cognitive psychology, it is assumed that a problem solving is performed through three processes: 1) alternatives formation process (structuralization process of the problem), 2) judgment process, and 3) decision-making process. The outline of a route choice behavior model based on the bounded rationality have been already proposed by the authors(1) . Figure 1 shows the processes of route choice behavior.
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Cues Information Source I1 1 c1 Judgment Process N
c1 1
1 x1
Decision-Making Process
~x 1
1
xˆ1
1
1
c2
~x ~ Q K x
1
xK x Q 1
xˆ K xˆ Q 1
1
N
∆1
1
1
c2 2 Q
xK
Criterion Values
1
cP N cP P
~x Q K
xˆ K
Estimated Criterion Values
Unified Estimated Criterion Values
Q
Information Source IP
∆Q
Cost of Routes
rq
Selected Route
Figure 2: Judgment process and decesion-making process Alternatives formation process Drivers choose some alternative routes from all possible routes between OD (OriginDestination) pairs. Generally, they choose alternatives from characteristic routes that are easy to search because they can’t know all possible routes. Judgment process This paper proposes a sophisticated framework of judgment process and decisionmaking process. Figure 2 shows the schematic view of the two processes. The judgment process has two steps: 1) extraction of finite environmental properties, 2) estimation of criterion value. At the first step in the judgment process, drivers extract a limited number of environmental properties called cues from properties of information sources. In Figure 2, Ip denotes an information source p (1 ≤ p ≤ P ) and cnp denotes the value of cue n (1 ≤ n ≤ NP ) of source Ip . Examples of information sources and environmental properties are shown in Table 1. Secondly, drivers estimate a criterion value by using extracted cues. The relationship between the estimated criterion value and cues is determined subjectively through some factors such as the driver’s experience and the environment where they drive. Symbol xqk denotes the real criterion value of criterion k for route q and xqk denotes its estimates. Function gkq in the following equation represents the relationship between cues and route choice criterion estimates. NP 1 N2 1 1 x˜qk = gkq (c11 , · · ·, cN 1 , c2 , · · ·, c2 , · · ·, cP , · · ·, cP )
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(1)
Table 1: Examples of information source and environmental property Information source route road (as a route element) traffic signboard signal vehicles ahead
Environmental property the number of right or left turns,... width, distance, location, class(trunk/branch/etc.),... congestion area, predicted travel time,... color, timing,... velocity, the number of vehicles,...
Let us consider a practical example. At first, a driver extracts cues such as the number of right or left turns, road class, signal color from information sources such as map, road, signal. Then, he/she estimates travel time based on the knowledge such as “I can arrive at the destination early along a trunk road, because of a high speed driving,” or understandability based on the experience such as “This route is easy to understand because I’ve already driven along the route.” Route choice criteria include tangible ones such as travel time and mileage, and abstract ones such as runnability and comfortableness(1)−(4). Decision-making process In the decision-making process, drivers evaluate a route cost ∆q of route q based on their route choice strategy and accordingly choose the route to drive. That is, decision-making process has also two steps: 1) evaluation of alternative route cost, 2) choice of route. Route cost ∆q is evaluated as follows on the basis of the multiattribute utility theory: ∆q = f (xq1 · ··, xqK )
(2)
where K is the total number of criteria. The first approximation of function f is a simple weighted summation: ∆q =
K k=1
wk αk xqk =
K k=1
wk xˆqk
(3)
where wk is a weight coefficient of route choice criterion k, αk is an unit conversion coefficient unifying different types of criteria. The weight coefficient vector W = (w1 , w2, · · ·, wK ) represents the importance of each route choice criteria. This vector can be changed according to the driver’s route choice strategy. In the case of little cognitive resource, human tends to refer to a specific criterion and ignore others. A zero weight coefficient corresponds to a criterion ignored in the route choice strategy. In another research(2, 3) , the authors carried out a questionnaire survey and obtained some insight about the weight coefficient vector W .
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EXPERIMENTS EXPERIMENTAL OBJECT Experiments are performed to clarify factors such as driver’s route choice strategy, route choice criteria, cues and the relationship between them. However these factors depend on many parameters such as trip characteristics (trip purpose, arrival time, etc.), driver’s attributes (age, occupation, etc.), traveling experience (knowledge about the route, dependence on the traffic information, etc.). A questionnaire analysis(2, 3) shows that a traveling experience of the route significantly influences the driver’s route choice behavior. Drivers unfamiliar to the destination usually use a map to decide the outline of the route before departure and drivers familiar to the destination change the route dynamically according to the traffic condition while driving. Therefore, two experiments are carried out. Experiment 1: Subject unfamilar to the destination chooses a route on a map. Experiment 2: Subject familiar to the destination changes the route dynamically. PROTOCOL ANALYSIS The environmental properties and the route choice criteria have been analyzed by questionnaires and interviews. However, the methods are less accurate than a protocol analysis(5), an effective method that clarifies their cognitive process by analyzing their verbal data which are spoken while performing their tasks. EXPERIMENTAL PROCEDURE Experimental subjects are three Japanese men, two of them being 24 years old and another being 23. They have driven cars more than one year. The following two experiments were done in the daytime on February 2002. Experiment 1: route choice behavior to unfamiliar destination on a map Experimental subjects used a map on a desk to decide their route to an unfamiliar destination, speaking aloud what they thought. Both an origin and a destination are in the urban area of Osaka city, and a straight line distance is 8[km]. The route was unfamiliar to them. The situation of the experiment was recorded on videotape by a video camcorder (Victor GR-DV2000), and the contents of utterance were also recorded through the microphone by a IC recorder (OLYMPUS DM-1). Figure 3 shows the situation of the experiment. Experiment 2: dynamic route choice behavior to familiar destination while driving Subjects drove a car to a familiar destination without car navigation system. Both an origin and a destination are in the urban area of Kyoto city, and a straight line distance is 4.9[km]. The route was familiar enough to drive without referring to a map. The subjects were directed to speak aloud what they thought only when they thought something about the route while driving. The front scenery of the running car was recorded by a video camcorder, and the contents of utterance were also recorded by a IC recorder. After driving, the recorded scenery was projected on the screen. Video replay was posed at the scene where subjects had spoken aloud, 5
Speak all about what you think Video camcorder
I thinkarrival it may be Early early inthisthisroute... way... along
Subject
Microphone
Map
IC recorder
Figure 3: The outline of Experiment 1 Screen 1. Choose a route before departure
2. Speak when you he/she thinks think something about a route.
Very crowded.
Speaker
3. Ask about the utterance speech
I’ll turn at the next
Very crowded. I’ll turn at the next intersection
intersection.
Because ...
Projector
Why did you speak it ? watch ? ? What did you observe
Microphone Video camcorder
Subject
Subject
IC recorder
IC recorder
Experimenter
(b) after driving
(a) while driving
Figure 4: The outline of Experiment 2 and the experimenter interviewed the subjects to clarify the following items: 1) reason of the utterance; the subjects were asked the reason why they spoke aloud about the route, 2) object they were watching; the subjects were asked what they were watching at that moment. Figures 4 (a) and (b) show the situation of the experiments while and after driving.
SPEECH ANALYSIS BY PROTOCOL ANALYSIS RESULTS OF EXPERIMENT 1: UNFAMILIAR DESTINATION CASE The utterances confirming or explaining the route are found in the transcribed data of Experiment 1. These types of utterances are defined as a report of route. Route choice decision-making is considered to be finished when a subject uttered a report of route. Conversely, he/she is considered to be choosing a route when he/she utters something except a report of route. The observed utterances were expressed in Japanese and there were many imperfect sentences. These sentences were corrected and results are shown in Table 2. Assume that drivers choose routes to satisfy three fundamental and rational desires: A) a desire to arrive at the destination early, B) a desire to consume less cognitive resources, C) a desire to drive on a comfortable route. In other words, drivers 6
Table 2: Examples of utterances after corrections Utterances ... go straight through Route 308 under this expressway ... ... and here, I’ll enter Route 13 ... .. .
Type report of route report of route
... because a road under the expressway is wider... ... the road will be wide. So, I’ll arrive early... ... because both routes pass in the urban area, traffic congestion of them may be similar... .. .
other other other
... I can go to the front of the station if I turn at Nihonbashi... report of route
Utterance about relationship between Utterance about cue
cue and route choice criteria I’ll arrive early I’ll arrive early
The road The road
because the road because the road
is wide is wide
will be wide will be wide
width
travel time
number of times to turn
runnability
Information source: road
route choice criteria
Figure 5: Two types of utterances are considered to choose a route based on three route choice criteria; travel time, understandability and runnability. Two types of utterances are found: 1) about cue, 2) about relationship between cue and route choice criterion (Figure 5). The following section examines the cues and the relationship between cues and route choice criteria. Cues from information sources Three types of cues are found. 1) Road width: The utterances such as “This road seems to be very wide” indicate that the subjects extract road width as a cue from information source road. The cue is fuzzy- and subjective-valued such as “wide” and “narrow.” 2) The number of right or left turns: The utterances such as “I won’t lose my way because of smaller number of right or left turns” indicate that the subjects 7
Table 3: Combinations of cue (environmental property) and route choice criterion Utterance Route 25 is easy to understand because it is a big road. I’ll arrive early because the road seems wide. The road seems easy to drive because it will be a big road. It won’t become crowded because the way is wide and one-way traffic. I won’t lose my way because of smaller number of turns. Roads around this station will be jammed.
Cue
Route choice criterion
width of road
understandability
width of road
travel time
width of road
runnability
width of road
a level of traffic congestion
the number of right or left turns
understandability
location of road
a level of traffic congestion
extract the number of right or left turns as a cue from information source route. The cue is fuzzy and subjective such as “small.” 3) Location of road: The utterances such as “Roads around this station will be jammed” indicate the subjects extract location of the road as a cue from information source road. The cue is fuzzy and subjective such as “around the station.” Relationship between cues and route choice criteria Some utterances indicate that the subjects evaluate a route based on a level of traffic congestion. An example is “Traffic congestion of them may be similar.” A crowded route affects not only travel time but also runnability because of a increase of driver’s mental workload. That is, A level of traffic congestion criterion can be reduced to more basic criterion: 1) travel time or 2) runnability or 3) travel time and runnability. When the reduction is not obvious, a level of congestion is used as it is. As shown in Table 3, six combinations of the relationship between cue and route choice criterion were found in the causal utterances such as “A then B” and “B because of A” where A and B denote cue and route choice criterion, respectively. From the concept of affordance(6), width of road can be considered to have a lot of affordance. For instance, “a big road” affords to drivers early arrival, easy driving, and easy route understanding. Frequency of utterances about cues and route choice criteria Tables 4 and 5 show the frequencies of cue and route choice criteria in the utterances, respectively. From Table 4 experimental subjects refer to width of road frequently. It implies that width of road has a lot of affordance for route choice to an unfamiliar destination and drivers recognize the affordance. And also, Table 5 indicates that there are many utterances about understandability. It suggests that drivers tend to choose the route that requires less cognitive resource 8
Table 4: Frequency of utterances about cues Cue
Information source width road the number of times to turn route location road
Subject A B C 1 7 6 0 2 0 0 2 2
Table 5: Frequency of utterances about route choice criteria Criterion trivel time understandability runnability level of traffic congestion
Subject A B C 0 2 0 4 13 1 0 0 2 0 2 1
when they choose a route to an unfamiliar destination. This result is consistent with the result of a questionnaire analysis(2, 3) . RESULTS OF EXPERIMENT 2: FAMILIAR DESTINATION CASE It is difficult for the experimental subjects to speak aloud spontaneously while driving because they used a lot of cognitive resources for driving actions such as operating a steering wheel, an accelerator and a brake. Hence the utterances while driving are relatively imperfect to their cognitive process. Therefore the interview was done to supplement the utterances. Table 6 shows an example of interview results. The utterances were analyzed on the assumption that drivers have three basic route choice criteria: 1) travel time, 2) runnability and 3) understandability, and the assumption that a level of congestion can be reduced to 1) travel time, 2) runnability and 3) travel time and runnability. The utterances can be classified into two types: 1) utterance that mentions only about route choice criteria including a level of congestion, 2) utterance that mentions the causal relation such as “A then B” and “B because of A.” The causal relation Table 6: Example of interview results (Utterance while driving) It may be jammed in the right-turn lane. I will go straight after checking it. (Utterances in a interview) [E: an experimenter, S: an experimental subject] E: Why did you say like that? S: I watched many cars in the right-turn lane, though you can’t see in this scene. .. . E: Then, what were you watching at carefully? S: The last car in the right-turn lane. 9
can be classified into two sub-types by definition of A, while B denotes an action: 2-1) A denotes a route choice criterion, 2-2) A denotes a cue. After all there are three types in the utterances: 1), 2-1) and 2-2) Cues from information sources Four types of cues are found in the three types of utterances. 1) Congestion information on traffic signboard: The utterances while driving such as “Judging from the signboard, there is no congestion” indicate that the subjects extract existence of congestion as a cue from information source traffic signboard. 2) Signal color: The utterances while driving such as “I turned at the intersection because the signal was changing” indicate that the subjects extract signal color as a cue from information source signal. 3) Velocity of vehicles ahead: The utterances in the interview such as “Because cars ahead don’t move at all” indicate that the subjects extract velocity as a cue from information source vehicles ahead. 4) The number of vehicles: The subject who uttered “That street is also jammed, so...” was asked about what he watched, and the answer was “the volume of cars.” This indicates that he extracts the number of vehicles as a cue from information source vehicles ahead. Some experimental subjects uttered “A queue of vehicles in a right-turn lane” in the interview. In the utterance, it is difficult to identify an exact cue such as velocity and the number of vehicles from information source vehicles ahead. Relationship between cues and route choice criteria Table 7 shows the combinations of cue and route choice criterion observed in the utterances of Experiment 2. The three types of reductions about a level of congestion were already defined in section of Experiment 1. Criterion travel time was identified from the level of congestion utterance “It is jammed,” because the subject answered “Because it would take a lot of time” in the interview. This types of reductions and Table 7 suggest that drivers familiar to destination tend to extract cues only related to travel time. In other words, the subjects searched selectively the cues which afforded to take a lot of time and choose the route dynamically when the cues are found. This result is consistent with the results of the questionnaire analysis(2, 3) .
CONCLUSIONS On the basis of the bounded rationality, this paper proposed the framework of a route choice behavior model that described explicitly the relationship between environmental properties called cues and route choice criteria that drivers used on their actual route choice. 10
Table 7: Combinations of cue and route choice criterion Information source vehicles ahead vehicles ahead vehicles ahead vehicles ahead traffic signboard signal
Cues the number of vehicles velocity the number of vehicles velocity existence of congestion signal color
Route choice criterion travel time travel time a level of congestion a level of congestion a level of congestion travel time
This paper considered that drivers chose the routes based on the route choice criteria, travel time, understandability and runnability, reflecting three fundamental desires: A) a desire to arrive at the destination early, B) a desire to consume less cognitive resources, and C) a desire to drive on a comfortable route. Two experiments were performed to analyze route choice behavior: 1) drivers to familiar destination chose the routes on a map, 2) drivers to unfamiliar destination chose the routes while driving. A protocol analysis was used to investigate driver’s utterances during experimentes. Cues that the experimental subjects extracted and relationship between cues and the route choice criteria were identified from the experimental results. Experiment 1 yielded: 1) understandability dominated over other route choice criteria, 2) width of road was frequently extracted as a cue for evaluation of all criteria. Experiment 2 resulted in: 1) drivers tended to use the route choice strategy that attached much importance to travel time, 2) drivers searched selectively the cues which afforded to take lots of time and chose the route dynamically when the cues were found. More data have to be collected to quantify parameters of the proposed route choice behavior model to reproduce actual behavior for more realistic traffic flow simulator. This study is supported by Grant-in-Aid for Scientific Research from Japanese Society for Promotion of Science (No.13750220). REFERENCES (1) T. Hiraoka, K. Okabe, H. Kumamoto and K. Tenmoku: A study of driver’s dynamic route selection based on simplified 3D traffic flow simulator, Proc.of SICE2001, CD-ROM (2001) [in Japanese] (2) T. Hiraoka, K. Okabe, H. Kumamoto, O. Nishihara and K. Tenmoku: Analysis of route choice behaviour based on questionnaire and 3D traffic flow simulator, Proc.of KES2001, pp.186–190 (2001) (3) T. Hiraoka, K. Okabe, H. Kumamoto and K. Tenmoku: Analysis of route choice behaviour based on questionnaire, Proc.of JSAE2001, pp.186–190 (2001) [in Japanese] 11
(4) T.-Y. Chen, et al.: Using a weight-assesing model to identify route choice criteria and information effects, Transportation Research, Part A, No.35, pp.197–224 (2001) (5) K. A. Ericsson and H. A. Simon: Protocol Analysis - Verbal reports as data, Rev. ed., MIT Press (1993) (6) E. S. Read: Encountering the world -toward an ecological psychology, Oxford University Press (1996) (7) S. Yoshikawa and O. Takagi: A research of the decision making process of driving behavior by the protocol analysis, Journal of social psychology, Vol.14, No.1, pp.31–42 (1998) [in Japanese]
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