JOINT MODELING OF ATIS, HABIT AND LEARNING IMPACTS ON ROUTE CHOICE BY LABORATORY SIMULATOR EXPERIMENTS

Transcription

1 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 1 JOINT MODELING OF ATIS, HABIT AND LEARNING IMPACTS ON ROUTE CHOICE BY LABORATORY SIMULATOR EXPERIMENTS ENIDE A.I. BOGERS Researcher Delft University of Technology Section Transportation and Planning P.O. Box GA Delft The Netherlands Tel Fax e.a..i.bogers@citg.tudelft.nl FRANCESCO VITI Researcher Delft University of Technology Section Transportation and Planning P.O. Box GA Delft The Netherlands Tel Fax f.viti@citg.tudelft.nl SERGE P. HOOGENDOORN Associate Professor Delft University of Technology Section Transportation and Planning P.O. Box GA Delft The Netherlands Tel Fax s.p.hoogendoorn@ct.tudelft.nl Submitted for presentation and publication at the 84th Annual Meeting of the Transportation Research Board, January 2005, Washington D.C. and for publication in Transportation Research Record 6494 words 2 tables and 4 figures Submission date: November 11 th, 2004

2 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 2 ABSTRACT In this paper, we propose a conceptual modeling framework, as well as derive mathematical submodels for route choice on motorways and urban networks. The models convey the most relevant aspects that play a role in route choice, among other things learning, risk attitude under uncertainty, habit and the impacts of ATIS on route choice and learning. To get insight into the relative importance of the different aspects and processes constituting route choice behavior and as such support the proposed conceptual framework, the models have been estimated using data from two experiments that have been carried out using a so-called interactive travel simulator. The latter is a new research laboratory that combines the advantages of both SP and RP research. Many relevant contributions on the aforementioned aspects that play a role in route choice can be found in literature, but a simultaneous consideration of all is lacking. Based on these contributions from literature, a conceptual framework is developed that integrates these aspects. The results from the laboratory experiments show that people perform best under the most elaborate information scenario and that habit and inertia together with en-route information play a major role in the route choice. Learning about the route attributes is especially important during the first days, but hereafter plays a smaller role than the provided information and the developed habit. Finally, the way the information is presented in the experiments proves to have a large impact on the route choice. INTRODUCTION In route choice situations various aspects and processes are assumed to simultaneously play an important role. These relate to the presence of various sorts of travel information, the way the traveler learns from both this information and past experiences, his or her attitude towards uncertainty and his or her habitual behavior. Many relevant contributions on these aspects can be found in literature, but a simultaneous consideration is still lacking. The paper can be characterized as a first step in a planned series of work. It will make clear what aspects actually are important in route choice and will thus provide future research directions. The main contributions of the work presented in this paper are new insights gained from: development of a conceptual framework that integrates the roles of travel information, experience, habit, risk attitude and learning in route choice situations, based on the current state-of-the-art, an experimental design that underpins relevant parts of this conceptual model, such as travel choice behavior under uncertainty and learning process under information. the analysis of the individuals responses collected using an interactive simulator, by estimating various discrete choice models set-up in line with the conceptual framework. We start with the description of the conceptual framework. The insights from literature that this model is based upon will be described thereafter. By describing the conceptual framework first, the relation between and the relevance of the different parts of the literature review will be clearer to the reader. The considered research questions will be discussed next, followed by a description of the data collection set-up. The next section deals with the various model analyses and their results. The final section summarizes the most important results. CONCEPTUAL MODELING FRAMEWORK Based on literature findings, aspects like risk attitude, bounded rationality, habit and learning from travel information and experience have been found relevant for explaining route choice behavior. To the best of our knowledge however, these aspects have not been described in a unifying and coherent way yet. This is why a framework is developed here that presents these aspects in an interrelated way. In figure 1 the traveler s perception is presented together with the attributes of the routes which eventually influence his or her route choice. Various processes occur in determining this perception:

3 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 3 Based on former route choices, a traveler has personal experiences. From these experiences, he or she can learn about the characteristics of the routes when having chosen them before (in terms of for example mean travel time and reliability), about how to interpret travel information (for example how to interpret 1 kilometer of queue on a route A in terms of delay) and about the reliability of this information (for example, the information can often underestimate the real queue length). It is said here that the traveler can learn, instead of will learn, since due to the existence of the traveler s bounded rationality, he or she will probably not process all this information completely and correctly. In case the traveler receives information about foregone outcomes, he or she can also learn about the characteristics of the routes when the traveler has not chosen them. The perception is altered by the person s risk attitude. Chances on and size of good and bad outcomes can be perceived incorrectly due to a person s risk attitude and reference point. A habit is assumed to have an effect which lies between one of the following two cases. The first case concerns a very strong habit, which leads to chosing the habitual choice rather automatically, without any conscious choice process. In the second case, a traveler perceives his habitual choice to be the best without good consideration of other alternatives. The extent to which the learning process as described above actually occurs is decreased in case of a habit. Individual characteristics of the traveler can influence for example the ease with which he or she learns, the ease with which he or she forms and changes habits and how much risk he or she is willing to take. The purpose of the trip is very important for this last point as well, since one will for example not be likely to accept a risky route when one has to be on time for an important meeting. The type of travel information that is provided influences the perception building process. It is under control of the travel information manager and as such provides a useful mechanism for managing the traffic. This framework provides some general notions about the relations between risk attitude, bounded rationality, habit and learning from travel information and experience and the resulting route choice. It is important to have a relatively complete and qualitative understanding of the route choice process. This way, the route choice process can be described and modeled more accurately. However, the notions from the framework should still be qualitatively and quantitatively validated and elaborated by both empirical and theoretical research. Since the framework is rather extensive, this will take a lot of research. In this paper, a first step is made. Experimental research has been carried out using the travel simulator laboratory (TSL). After the literature review on which the conceptual model has been built, the TSL and the outcomes of the experiments carried out with the TSL will be described and analyzed. STATE-OF-THE-ART REVIEW Travel behavior research has come up with many theories and models aiming to predict the decision outcomes of travelers under a variety of circumstances. In many of these cases, random utility models are used to describe the traveler s trade offs in general and route choice in particular. The question then is, which processes need to be modeled, how should they be modeled, and which variables should be included in the model specification to describe route choice behavior realistically. The effects of learning from information and experience, habitual behavior and risk attititude under uncertainty have all been found in literature to be relevant in a route choice situation. Although it is likely that their influence is mutually related, these topics are described separately in the ensuing. It is not the intention to be exhaustive about each of these subjects, but rather to give an overview of the main contributions and implications for route choice. The insights gained from this literature overview form the basis for the conceptual model that is presented after this section.

4 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 4 Risk Attitude Under Uncertainty Kahneman and Tversky (1) found that people do not follow the rules of objective expected utility maximization in economic choices, but have a certain perception of the probability of a certain outcome and the value of that outcome. They noticed a non-linearity in the estimation of users perceptions, as described in their prospect theory. Furthermore, they found that people exhibit risk behavior which is dependent on the way the decision is framed (2). The question is to what extent this theory is also applicable to route choice decisions. First, consequences of a bad route choice (e.g. being ten minutes late) cannot be directly compared to consequences of a bad investment decision (e.g. losing a thousand euros / dollars). Secondly, people often have less time to think about a route choice than to think about an investment decision, especially in case of en-route information. Finally, the role of habits may be larger in route choice, since especially commuters have to make the same trip every day. Nevertheless, Katsikopoulos et al. (3) found in a route choice experiment the risk attitude of people to be in line with the theory of Tversky and Kahneman. Bogers and Van Zuylen (4) also found proof for this, showing that people were risk averse when they could choose between a short and uncertain alternative and a longer but more certain alternative. So, people attach value not only to the mean travel time, but also to its reliability. A lot of studies on the value of reliability come to the same conclusion. A comprehensive overview is given in Bates et al.. (5). Concluding, risk attitude under uncertainty should be part of the conceptual model. As to the modeling of uncertainty, Van Berkum and Van der Mede (6) include uncertainty of travel times by including the standard deviation of a route travel time to the utility function. Mahmassani and Jou (7) do something similar. Alternatively, Lam and Small (8) show that the 90 th percentile is a better measure for reliability than the standard deviation. Bounded Rationality and Habit Simon (9) was among the first to criticize the assumption used in economy that people are rational and fully informed. He found this economic man unrealistic and proposed a behavioral model of rational choice. In this model, people exhibit bounded rationality and a tendency to search for satisfying choices rather than best, also referred to as satisficing behavior. This means that, even if people are fully informed, they do not have the cognitive ability to process all this information simultaneously and are happy with a good solution instead of trying to find the best solution. It is likely that bounded rationality will also play a role in route choice, since the cognitive limitations of people are always present and a route choice often has to be made under time pressure. It is therefore important for our topic to see to what extent people are able to remember and process past travel experiences and ex-ante and ex-post information. Mahmassani and Jou (7) formulate indifference bounds to account for this bounded rationality and satisficing. The authors argue that as long as the outcome of the route choice falls within the indifference bound, the traveler will not change his route choice. In calculating the indifference bands for route choice, they use the standard deviation of the travel time up to the current day. Habit is another phenomenon that causes people to choose without rationally weighing all the available alternatives and their respective strengths and weaknesses each time a decision needs to be made. Empirical evidence suggests that habit strongly determines the behavior of people in relatively stable situations, e.g. in (10). The advantage of habit is that they provide significant savings on cognitive effort. However, whereas the habit may originate from a process in finding out the optimal behavior given the prevailing circumstances, the circumstances may since then have changed such that alternative behavior would yield better outcomes (11). Furthermore, as habit involves new information not being taken into consideration, it is often very hard to change them by providing information. It can be concluded that habit is an aspect that has to be accounted for in the conceptual model.

5 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 5 Learning From Experience And Travel Information Travelers can learn both from experience and from information. Horowitz (12) made one of the first contributions to this field by concentrating on estimating mean perceived travel cost by a weighed average of the realized costs in previous periods. This was formulated in an MNL model. The variability of travel time and the impact of travel information were not dealt with. Avineri and Prashker (13) did consider these topics as well by studying the influence of providing dynamic ex-post information about the realized travel time on route 1 or 2, neither routes, or on both routes. When there is no travel information on the better, but less reliable route, it tends to be chosen less often than when there is information on this route. They explain this by introducing the term sequential sampling process. This means that if the sample of outcomes is small (which is the case for the route of which there is no information) then the route, which is usually fairly good, but is occasionally very poor is likely to be interpreted as worse than it is. In another experiment, they looked at the effect of providing static information about the mean travel times on both routes compared to no information on both routes. With the information, people turned out to choose the route that is on average worse but more certain more frequently compared to the situation where they had no information. The authors explain this by looking at the disaggregated results and come to the conclusion that a lot of indecisive travelers (i.e. travelers who chose route 1 approximately as much as route 2) had moved to the risk averse choice, i.e. the reliable route. Nevertheless, two remarks about the experimental set-up have to be made. The first experiment concerns a simulation experiment, in which mathematical models are used instead of real people. If people behave in the exact manner as in the simulation remains uncertain. In the second experiment real respondents were used, but they each had to make 100 choices. This might lead to fatigue and bad responding, as we have observed in our own experiments as shown in the remainder of the paper. Nevertheless, this study does show that understanding the impact of providing information on route choice is not a straightforward task. Mahmassani and Srinivasan (14) considered various information attiributes and estimated their effects using the so-called dynamic kernel logit model. The nature of the information (i.e. descriptive vs prescriptive information), its correctness and completeness, the availability of feedback information and some generic information effects like under- and overestimation errors all proved to have significant effects on route switching behavior. As for learning, the authors mainly considered experience and correctness of information of the previous day. They did not consider multiple days. Jha et al. (15) combine a nested logit model with Bayesian updating of travel time estimates to capture the learning process. The updating process takes place on the basis of experience and information. The traveler s confidence in the provided information is hereby taken into account. Only two scenarios are considered: with and without information. Furthermore, this is done by simulation experiments instead of by using real respondents. The Bayesian approach seems however very suitable for describing learning by experience and information simultaneously. Chen and Mahmassani (16) also use Bayesian updating to model how people learn from experience. Apart from the fact that they do not consider the effect of travel information, their approach differs from the approach of Jha et al. in that the updating process only takes place occasionally, i.e. not after every new experience. This updating is triggered for instance every N days, when a new experience is very different from the prior ones, or when the confidence in the perceived travel time is low. In sum, experience and travel information are input to the learning process of the traveler. These partly determine the perception the user has of the route attributes and the quality of the information, which lead to a certain route choice. Therefore, learning from experience and travel information has been included in the conceptual model. RESEARCH QUESTIONS In the remainder of the paper, we will focus on answering the following research questions: - How do travelers learn under various information scenarios? - To what extent does the limited memory of travelers affect their route choice performance? - What is the role of habit in route choice behavior? - Can we identify traveler s attitude towards uncertainty?

6 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 6 - What is the traveler s perception of the different route attributes, such as travel time, arriving too late, and arriving too early? This will be achieved by estimating a discrete choice model capturing route attributes and the different processes indicated in the conceptual framework.the data that are used are collected via a controlled laboratory experiment, as is explained in the following section. EXPERIMENTAL SET UP For the data collection the interactive travel simulator of Delft University of Technology, called the travel simulator laboratory (TSL), was used. The TSL (18) is an interactive tool to investigate travel choice behavior. The TSL combines the benefits of SP and RP approaches, while avoiding their respective limitations. In this case, it is used to investigate route choice. In setting up the experiment, a number of design choices have been made. These are explained below. (The words that appear in the conceptual framework are printed in italics). Learning behavior The number of choices a subject had to make was set to 25, representing 25 consecutive days. This way, learning behavior and the development of a preference for a certain route could be observed. This may lead to the development of habit. Of course, more repetitions would be even better for this purpose and from a statistical point of view. From a previous study, however, it became clear that it was hard to recruit respondents for very long experiments. Furthermore, once they had been recruited, it was observed that a lot of people gave up after approximately 20 repetitions. Therefore, 25 repeated choice situations seemed to be a good compromise. To be able to say to what extent a person has learned, some kind of measure is needed. For this reason, after a short introduction, people were asked how large their preferences were regarding the following travel time attributes: arriving early, in-vehicle travel time, arriving late. If they stated no dislike for a travel time attribute, the weight given to this attribute was 1; for minor dislike this was 2 and for a large dislike this was 3. These weights were normalized in such a way, that their sum equaled 5. During the experiment, a score could be determined by the sum of the normalized weight for a route attribute times the number of minutes spent for that attribute. They were told that their objective was to minimize the sum of the scores over all 25 days. After each choice, the score was displayed. To give the respondents even more feedback about their performance, they had to wait an amount of time after one question before they could go on to the next one. This waiting time was high if their route choice had resulted in a high travel time and low if it had resulted in a low travel time. This made learning by experience more realistic. Personal characteristics Some questions about their personal characteristics were asked. These included their gender, age, education, profession and their driving frequency. Risk attitude The weights derived from the relative dislike of the three route attributes also give us some information on their risk attitude. It is likely that a risk-averse traveler will for instance be very anxious to arrive late and will not mind arriving early. The number of routes that were available was set to 2. One route had a lower mean travel time and a larger variance; the other route had a higher mean travel time and a smaller variance. It is designed like this to gain further information about people s risk attitude from their choices. The traffic simulator A dynamic traffic model was used to determine the queue lengths and the resulting travel time on the routes. The traffic demand on a route followed a cosine function along the time, thus resembling the build up and reduction of congestion. By definition, the provided (instantaneous) queue length information is retarded and hence does not completely correspond to the queues and

7 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 7 the travel time that is actually experienced. More specifically, at the start of the peak hour the given queue length was too low and at the end too high. White noise had been added to the queue length that was displayed, to make sure that even if people had learned how much delay a kilometer of queue takes on a certain route, still some uncertainty remains like in reality. Although it is possible to make the TSL interactive, i.e. let the choices of the respondents influence the traffic flows, this was not applied in the current experiments. Travel information At the beginning, a map is shown with the two routes. It is said that route 1 is 19 km long and route 2 is 21 km long. To assess the impact of en-route travel information, three different information scenarios were used, see table 1. Each respondent was assigned to one of them. The provided information concerned the number of kilometers of queue on a route, displayed on a variable message sign (VMS). It would be interesting to test even more information scenarios, but it was unclear beforehand whether the number of available respondents would allow for this. Besides en-route information, the impact of post-trip information was studied. To this end, different information scenarios were considered. Scenario 2 and 3 present the traveler with information on forgone pay-offs. Scenario 3 provides insight into the question to what extent the limited memory (one cause for the existence of bounded rationality) of people is relevant in their perception of the routes characteristics. Further design choices As in reality, only a short time was given to people for choosing a route. This makes a fully rational choice improbable and results in an error in the perception. To motivate the respondents to consciously do the experiment until the end, a prize was awarded to the person with the best score. Critical note The choice to use the scoring mechanism and motivate people to minimize their score by awarding a prize to the person with the lowest score had the advantage of being able to assess how well people learned and compare this between information scenarios. However, it obviously influences the behavior of people. When the answers to the valuation of the different travel times (which forms the input for the score computation) reflected their true valuation, their revealed behavior would be quite realistic. If not, their behavior would be different in reality. The authors intend to further work on this issue in the future. ANALYSIS AND RESULTS Two experiments were performed, the main difference being the difference in variance of travel times on the two routes. This difference was intentionally made much larger in the second experiment. This way, insight could be gained into the valuation of reliability in route choice. First Experiment General Results In this experiment valid answers from 14 respondents per information scenario were collected. As for the valuation of the different time components, respondents stated on average the highest weight for arriving late, medium weight for arriving early and the lowest weight to driving time. Based on their individual weights, the score is computed. On average, the score shows a very sharp improvement in the first days, then stays stable and at the end gets a bit worse again, see figure 2. The mean score under information scenario 1, 2, and 3 are respectively 36, 36 and 30. Under the most elaborate information scenario, scenario 3, the score is significantly better. Scenario 1 and 2 give the same score. This is rather remarkable, since under scenario 2 people receive information

8 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 8 about the foregone payoff, while under scenario 1 they do not. We will look further into this in the second experiment. The difference between scenario 2 and 3 concerns the fact that under scenario 3 the information stays on screen for all periods, whereas for scenario 2 the information on screen concerns only the last period. Apparently, the perception a traveler has of the route attributes and / or information quality becomes more accurate when all information stays on screen, i.e. when the traveler receives a memory aid. So somehow, the way the memory works can distort the perception of a traveler. The model formulations in the next section will address this phenomenon. Model Formulation And Estimation For this model, Horowitz simple learning model with the ability to include time lagged variables was used as a starting point from the model used to analyze to experimental data. Learning can then be captured by looking at the weights given to recent experiences or information on recent foregone outcomes (such as the realized travel time, lateness, or earliness in the previous period) and to the overall expected travel time. From the conceptual framework, various other variables were presumed relevant. Habit was accounted for by the variable number of times the person had already chosen that route in the past; information was represented by the queue length as displayed on the VMS. Further variables that were defined and tested in various model structures included the variance and the worst realized travel time so far. Since the data are panel data, correlation exists between the subsequent choices of a person. For this reason an MNL model, which assumes all error terms from the utility specifications to be i.i.d., can not be used. We therefore apply a mixed MNL model in which an individual and alternative specific constant is added. The distribution over all the respondents of this constant is assumed to be normal. It s mean and standard deviation are estimated. The model has the following structure: U ik (t) = βx i (t) + ASC i k + ε i With i route index k person index t current time period β a vector of parameters X a vector of variables ASC alternative specific constant ASC ~ N(µ, σ) over all k ε i ~ Gumble i.i.d. A critical remark concerns the appropriateness of a utility specification, specifically its compensatory nature, for this choice situation. A habit may namely be so strong that it leads to an immediate choice. Other alternatives are in this case not considered and their attributes can subsequently not compensate for anything. When a habit of choosing one route is this strong, the variable number of times the route has already been chosen by the individual will have a very high value. This means that in the random utility specification, this route will have a very large utility because of this variable and the probability that it will be chosen will subsequently be very high. Therefore, although conceptually not right, the random utility model will still give sound results in case of a habit. With the mentioned variables various model specifications have been tested using the software package Biogeme (17). The models were not only estimated on all data, but also on the data concerning a specific information scenario. The two best models are given in table 2. What is striking is that the alternative specific constant has a very large impact and its value does not differ significantly between the persons (the sigma estimate is insignificant). Further important variables include the queue length information as displayed on the VMS and the number of times a person has already chosen the route in the past periods.

9 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 9 The recently experienced travel time and lateness and the expected travel time are also learned by travelers, but had not a very large impact on the utility. Therefore, the rational learning process should not be overestimated, although in the first few days learning was clearly present, see figure 2. The variance of the travel times proved not to be very important for the routes utilities. A possible explanation could be that the differences in variance were not large enough to be perceived by the travelers. In the second experiment these differences were much larger. Second Experiment General Results For this experiment valid choices were collected for 21 respondents per information scenario. In this experiment too, people show a very fast improvement in score during the first days, especially under scenario 3. See figure 3. The mean scores under information scenario 1, 2 and 3 are respectively 42, 46 and 39. All differences are significant on the 95% level. Again, something unexpected has occurred under scenario 2. The score under this scenario is in this experiment even significantly worse than under scenario 1. In the next section, models are estimated per scenario so as to gain insight into what occurred under information scenario 2. Model Formulation And Estimation Also in this experiment various specifications for the model were tested. The best model estimates are given in table 2. Refer for the variables that were tested to the section on the first experiment. In this experiment, the alternative specific constant for route 2 was much more negative. However, since in this experiment route 2 had on average a much smaller queue length and the queue length also had a large impact on the utility, its utility was most of the time better than that of route 1. Again, a route gains in utility as it is chosen more often, indicating the existence of reinforcement learning and habit. The models including the variance of the travel time as a variable do not belong to the best models as given here. In the next section, however, it will become clear that variance does matter. Looking at table 2, it can be clearly seen that the results for scenario 2 are very different from the results for the other scenarios. Under scenario 2, more weight is given to the number of times the route was already chosen, and to the resulting travel time and lateness of the previous day. Less weight is given to the queue length information. Apparently, participants under scenario 2 are more myopic (near-sighted) and inert than under scenario s 1 and 3. Compared to scenario 3, this is understandable, since in scenario 3 all past results remain on screen. Compared to scenario 1, however, an explanation is very hard to find. The only plausible explanation relates to the design of the experiment. The respondents could only look at the post trip information during the 10 seconds they had to make another route choice. The route they had actually chosen was always marked yellow. Probably, this drew the respondent s attention to such an extent that he or she would choose this same route again. In scenario 1, this effect was not present, since only the chosen route was displayed. In scenario 3 this was probably less present, because all choices remained on screen making it more probable that each route was marked yellow at least a few times. Following this reasoning, it can be concluded that the way the information is presented makes a big difference. This is a very important conclusion for designing route choice experiments and of course for the presentation of actual traffic information. As mentioned earlier, the results for scenario 1 and 3 are similar. Still, the score is better under scenario 3. The first observation would lead to the conclusion that either providing ex-post information on the not chosen route or the memory aid does not really make a difference. Valid responses for scenario 2 are however needed to give a positive answer to this question.

10 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 10 Synthesis of Experiments 1 and 2 The model estimations from the previous section yielded comparable results. The impact of the alternative specific constant differed, but the best model specifications for experiment 1 were also the best model specifications for experiment 2. Therefore, we can draw a number of conclusions: People are very reactive on the queue length information, and can interpret the given information well enough to make efficient travel choices. People are myopic. The lateness and the travel time of the previous day are very important to the traveler. People inhibit inertia and habit. The number of times a route was already chosen by them adds to the utility of that route. Especially in the second experiment where there is a lot of uncertainty, the number of times a route was already chosen and the queue length information were more important than the expected travel time. Therefore, the influence of rational learning in making route choices should not be overestimated in an information rich and uncertain environment. People may return to their habitual behavior, thus simplifying the choice situation and make the cognitive burden manageable. The presentation of the post-trip information in the experiment plays under information scenario 2 a significant role in the choice behavior. Memory effects and / or information about foregone payoffs are important. People strongly value reliability. This is explained next. The main difference between the first and the second experiment related to the variance of the travel time. In the first experiment, the variance was similar on both routes; in the second experiment the variance was much larger on route 1. Route 1 did, however, on average have a smaller travel time. When looking at the development of the choices over time, a distinct difference can be seen, see figure 4. This difference can only be due to the larger difference in variance in experiment 2. People strongly preferred the reliable route in experiment 2. Therefore, it can be concluded that people clearly prefer the reliable route and have a certain risk averseness. CONCLUSIONS In this paper we have proposed a conceptual framework for route choice behavior. The modeling framework identifies the joint relation between en-route information, post-trip information, learning, habit, and includes generic route attributes, including uncertainty. The uncertainty pertains both to the information and to the expected traffic conditions (travel time, congestion, etc.). Based on the framework, which itself was based on a literature survey, two laboratory experiments have been setup in order to quantify experimentally how these different factors affect route choice behavior. Looking back at the research questions, the following conclusions can be drawn. Travelers react primarily on the en-route queue length information. However, ex-post information can help them learn and improve their performance. This follows from the fact that under the most elaborate ex-post information scenario, which in fact provides a memory aid by displaying all past realized travel times for both routes, travelers have significantly the best scores. The experiments also showed that the presentation of the information also influences travelers performances. Furthermore, habit plays an important role. Since habit is closely related to reinforcement learning, a modeling approach that explicitly deals with this could provide us with more insight into how a habit is formed. From the synthesis of the two experiments we can further conclude that travelers have a certain risk averseness and value certainty / reliability of a route. Their stated preferences also pointed towards this conclusion, since on the average, the traveler has by far the largest dislike of arriving late. The importance of habit, risk attitude and the presentation of information furthermore lead to the conclusion that studies which only focus on a rather rational description of day-to-day learning only cover a limited part of the way route choices are made over time in reality. Therefore, future research directions will focus on how the perception of the route attributes which leads to a choice actually is formed. The role of habit and risk attitude will in particular be subjects of further research.

11 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 11 ACKNOWLEDGEMENTS This research was carried out in the research programme AMICI, which is sponsored by the Dutch Foundation of Scientific Research NWO. We would like to thank the reviewers for their comments. REFERENCES 1. Kahneman, D., and A Tversky. Prospect theory: An analysis of decisions under risk, Econometrica, 47, 1979, pp Tversky, A., and D. Kahneman. The framing of decisions and the psychology of choice, Science, 211, 1981, pp Katsikopoulos, K.V., D.L. Fisher, Y. Duse-Anthony, and S.A. Duffy., Risk attitude reversals in drivers' route choice when range of travel time is provided, Human Factors, 44(3), 2002, pp Bogers, E.A.I., and H.J. van Zuylen, The importance of reliability in route choices in freight transport for various actors on various levels, Proceedings European Transport Conference 2004, Strasbourg, France 5. Bates, J., Jones, P Polak, and A. Cook. The valuation of reliability for personal travel. Transportation Research Part E: Logistics and Transportation Review, 37(2-3), 2001, pp Berkum, E. v. and P. v. d. Mede. The impact of traffic information: Dynamics in route and departure time choice. Delft University of Technology, Delft, Mahmassani, H. S. and R.-C. Jou. Transferring insights into commuter behavior dynamics from laboratory experiments to field surveys. Transportation Research Part A: Policy and Practice, 34(4), 2000, pp Lam, T. C., and K.A Small. The value of time and reliability: measurement from a value pricing experiment, Transportation Research Part E: Logistics and Transportation Review, 37(2-3), 2001, pp Simon, H. A. A behavioral model of rational choice. Quarterly Journal of Economics. 69, 1955, pp Bamberg, S., and P. Schmidt., Incentives, morality or habit: Predicting students car use for university routes with the models of Ajzen, Schwartz and Triandis, Environment and behavior, 35 (2), 2003, pp Jager, W. Breaking bad habits : a dynamical perspective on habit formation and change, in: Hendrickx, L., Jager, W., Steg, L. (Eds), Human decision making and environmental perception: understanding and assisting human decision making in real-life settings, Horowitz, J. L. The stability of stochastic equilibrium in a two-link transportation network. Transportation Research Part B: Methodological, 18(1): 13-28, Avineri, E., and J. N. Prashker. The impact of travel time information on travelers' learning under uncertainty, Proceedings Tenth international conference on travel behavior research, Lucerne, Switzerland, Srinivasan, K. K. and H. S. Mahmassani. Analyzing heterogeneity and unobserved structural effects in route-switching behavior under ATIS: a dynamic kernel logit formulation. Transportation Research Part B: Methodological, 37(9), 2003, pp Jha, M., S. Madanat, et al.. Perception updating and day-to-day travel choice dynamics in traffic networks with information provision. Transportation Research Part C: Emerging Technologies, 6(3), 1998, Chen, R. B. and H. S. Mahmassani. Travel time perception and learning mechanisms in traffic networks. In Transportation Research Record: Journal of the Transportation Research Board, TRB, National Research Council, Washington, D.C., 2004

12 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P Bierlaire, M. BIOGEME: a free package for the estimation of discrete choice models, Proceedings of the 3rd Swiss Transportation Research Conference, Ascona, Switzerland, TSL homepage. S.P. Hoogendoorn, Delft University of Technology, Accessed July 2004

13 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 13 CAPTIONS FIGURE 1 Relevant aspects in route choice and their relationships TABLE 1 The Information Scenarios FIGURE 2 The development of the score in experiment 1. The lower the score, the better. FIGURE 3 The development of the score in experiment 2. The lower the score, the better. TABLE 2 Absolute Values and Relative Weights of the Explaining Variables in Model 1 and 2 for the Different Information Scenarios FIGURE 4 The development of route choices over time for both experiments

14 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 14 TRAVEL INFORMATION MANAGER travel information static characteristics gender, age, trip purpose... habit perception (incl. learning) risk attitude TRAVELLER route choice experience TRAFFIC forgone outcomes outcome traffic situation FIGURE 1 Relevant aspects in route choice and their relationships

15 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 15 TABLE 1: The Information Scenarios Information En route Ex-post information on realized travel times scenario information 1 Yes Only on chosen route for latest period 2 Yes On both routes for latest period 3 Yes On both routes, remains on screen for all past periods

16 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P SCENARIO Mean SCORE DAY FIGURE 2: The development of the score in experiment 1. The lower the score, the better.

17 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P SCENARIO Mean SCORE DAY FIGURE 3: The development of the score in experiment 2. The lower the score, the better.

18 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 18 TABLE 2: Absolute Values and Relative Weights of the Explaining Variables in Model 1 and 2 for the Different Information Scenarios Experiment 1 ASC 2k Q ik (t) T ik (t-1) N ik (t) L ik (t-1) Rho 2 Model number mean s.d. 1 (all scenarios) ns (scenario 1) ns ns (scenario 2) ns ns (scenario 3) ns ns (all scenarios) ns (scenario 1) ns ns ns (scenario 2) ns ns ns (scenario 3) ns ns ns Experiment 1 ASC 2k Q ik (t) T ik (t-1) N ik (t) L ik (t-1) Rho 2 Model number mean s.d. 1 (all scenarios) 12.25% ns 73.94% 7.64% 6.16% (scenario 1) 22.18% ns 72.88% 4.94% ns (scenario 2) ns ns 79.24% 9.43% 11.33% (scenario 3) ns ns 83.52% 8.64% 7.84% (all scenarios) 11.12% ns 78.39% 2.32% 8.17% (scenario 1) ns ns 93.66% ns 6.34% (scenario 2) ns ns 89.45% ns 10.55% (scenario 3) ns ns 90.56% ns 9.44% Experiment 2 ASC 2k Q ik (t) T ik (t-1) N ik (t) L ik (t-1) Rho 2 Model number mean s.d. 1 (all scenarios) ns (scenario 1) ns (scenario 2) ns (scenario 3) ns ns (all scenarios) ns (scenario 1) ns ns (scenario 2) ns ns (scenario 3) ns ns Experiment 2 ASC 2k Q ik (t) T ik (t-1) N ik (t) L ik (t-1) Rho 2 Model number mean s.d. 1 (all scenarios) 48.71% ns 38.57% 9.26% 3.46% (scenario 1) 47.08% ns 42.51% 7.11% 3.30% (scenario 2) 40.04% ns 37.40% 15.66% 6.90% (scenario 3) 54.83% ns 37.54% 7.63% ns (all scenarios) 47.65% ns 40.85% 1.51% 9.99% (scenario 1) 47.23% ns 45.05% ns 7.71% (scenario 2) ns ns 65.69% 6.96% 27.35% (scenario 3) 54.63% ns 37.70% ns 7.66% Q ik (t) = queue length on route i for person k at day t as displayed on VMS in km T ik (t-1) = travel time in minutes one day ago on route i for person k N ik (t) = number of times route i was already chosen by person k at day t L ik (t-1) = lateness in minutes for person k at the previous day on route i ns = not significant at the 95% level

19 Bogers, E.A.I.,Viti, F., Hoogendoorn, S.P. 19 FIGURE 4: The development of route choices over time for both experiments