1. Risk Attitude Reversals in Drivers ’ Route Choice When Range of Travel Time Information is Provided Jin-Yong Sung Hamid Hussain

2. Research Ideas/Question Depending on risk attitude, how is the travel time influenced? Depending on risk attitude, how is the travel time influenced? Can a relatively simple model for the risk attitude account for the influence of the travel time? Can a relatively simple model for the risk attitude account for the influence of the travel time?

3. Background Support Developing a significant amount of route choice models for the description of the drivers ’ attitudes toward travel time variability. Developing a significant amount of route choice models for the description of the drivers ’ attitudes toward travel time variability. Predicting which route choice scenarios drivers are risk averse or seeking by using models. Predicting which route choice scenarios drivers are risk averse or seeking by using models. Terming a driver as risk averse (or risk seeking) if he or she more often choose the route with the smaller variability (or lager variability). Terming a driver as risk averse (or risk seeking) if he or she more often choose the route with the smaller variability (or lager variability). Providing route choice data of high external validity by the specific driving simulation environment. Providing route choice data of high external validity by the specific driving simulation environment.

4. Theoretical Basis for Analyzing Question/Hypothesis  Automobile drivers become risk averse when choosing the route having an average travel time shorter than the certain travel time of a reference route.  Conversely, drivers become risk seeking when choosing the route having an average travel time longer than the certain travel time of a reference route.  Using a simulator is sufficient for the analysis of data representative of real route choices.

5. Applicability/Practical Contributions  The efficiency of the transportation network can be improved by determining the percentage of drivers selecting routes in terms of their driving attitude.  Driver decision making can be supported by an in-vehicle system that estimate drivers ’ attitude from past route choice.

6. Theoretical Contribution Supporting that the range of travel time influences drivers ’ risk attitude. Supporting that the range of travel time influences drivers ’ risk attitude. Estimating drivers ’ travel time heuristically inside the range that fits the relatively complex data quite well by using a simple heuristic model. Estimating drivers ’ travel time heuristically inside the range that fits the relatively complex data quite well by using a simple heuristic model.

7. Appropriate Methodologies Variable used for this experiment is the drivers route choices by given route information. Variable used for this experiment is the drivers route choices by given route information. Each scenario involved two routes, reference and alternative route. Participants spoke aloud their route choice on a variable massage sign, and the experiment recorded it. Each scenario involved two routes, reference and alternative route. Participants spoke aloud their route choice on a variable massage sign, and the experiment recorded it. A repeated-measures analysis of variance is conducted to test the hypotheses A repeated-measures analysis of variance is conducted to test the hypotheses All participants were presented with the same route choice scenario. In all scenarios, Er = 100 min. The two levels of Ea (95,100), three levels of Rr (0,30,60), and six levels of Ra (0, 20,30,40,50, 60) were crossed to produce 36 scenarios. All participants were presented with the same route choice scenario. In all scenarios, Er = 100 min. The two levels of Ea (95,100), three levels of Rr (0,30,60), and six levels of Ra (0, 20,30,40,50, 60) were crossed to produce 36 scenarios. (E= Expected travel time, R = Range, r = Reference route, a = Alternative route) (E= Expected travel time, R = Range, r = Reference route, a = Alternative route) The order of participants was randomized and the participants selected the route by not their previous experience but given route information. The order of participants was randomized and the participants selected the route by not their previous experience but given route information.

8. Statistical Analysis and Assumptions The data was analyzed using a 2(Ea) x 3(Rr) x 6(Ra) repeated-measures analysis of variance. The data was analyzed using a 2(Ea) x 3(Rr) x 6(Ra) repeated-measures analysis of variance. Assumed that the use of simulator is sufficient for the collection of data and the previous driving experiences do not influence the choices. Assumed that the use of simulator is sufficient for the collection of data and the previous driving experiences do not influence the choices. The data collected through verbal means, and route choice recorded by experimenter. The data collected through verbal means, and route choice recorded by experimenter.

9. Results Drivers diverted significantly less as the alternative expectation (Ea) increased. Drivers diverted significantly less as the alternative expectation (Ea) increased. The effect of alternative range (Ra) depended on the level of the alternative expectation (Ea). The effect of alternative range (Ra) depended on the level of the alternative expectation (Ea). This interaction in turn depended on the level of the reference range (Rr). This interaction in turn depended on the level of the reference range (Rr). Er was kept at a constant 100 Er was kept at a constant 100 When Ea = 95, drivers were risk averse for Rr=0 Ra When Ea = 95, drivers were risk averse for Rr=0 Ra When Ea = 105, drivers are risk seeking for Rr =0 Ra When Ea = 105, drivers are risk seeking for Rr =0 Ra

10. Conclusions When the reference range is smaller than the alternative range, the results replicates those of Katsikopoulos et al. (2000) for risk aversion for gains (in Ea) and risk seekingness for losses (in Ea). When the reference range is smaller than the alternative range, the results replicates those of Katsikopoulos et al. (2000) for risk aversion for gains (in Ea) and risk seekingness for losses (in Ea). The results are consistent with the hypothesis of risk seekingness for gains and risk aversion for losses when the reference range is larger than the alternative range. The results are consistent with the hypothesis of risk seekingness for gains and risk aversion for losses when the reference range is larger than the alternative range.

11. Future work/research Research possible in implementation of results for road side Variable Message Sign’s (VMS’s). Research possible in implementation of results for road side Variable Message Sign’s (VMS’s). Implementation of results in driver decision support. Implementation of results in driver decision support.