Determinants of Travel Choice Behaviour with Travel-Time Variability in the Presence of Real-Time Information

2018 ◽  
Vol 17 (1) ◽  
pp. 61-73 ◽  
Author(s):  
Fumitaka Kurauchi ◽  
Amr M. Wahaballa ◽  
Ayman M. Othman ◽  
Nobuhiro Uno ◽  
Akiyoshi Takagi
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Variability ◽  
Choice Behaviour ◽  
Travel Choice ◽  
Real Time Information ◽  
Time Information ◽  
Travel Time Variability

scholarly journals Empirical Study of Effect of Dynamic Travel Time Information on Driver Route Choice Behavior

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3257 ◽  
Author(s):  
Jinghui Wang ◽  
Hesham Rakha
Keyword(s):  
Travel Time ◽  
Choice Behavior ◽  
Route Choice ◽  
Time Variability ◽  
Route Choice Behavior ◽  
Increase Risk ◽  
The Difference ◽  
Alternative Routes ◽  
Time Information ◽  
Travel Time Variability

The objective of this paper is to study the effect of travel time information on day-to-day driver route choice behavior. A real-world experimental study is designed to have participants repeatedly choose between two alternative routes for five origin-destination pairs over multiple days after providing them with dynamically updated travel time information (average travel time and travel time variability). The results demonstrate that historical travel time information enhances behavioral rationality by 10% on average and reduces inertial tendencies to increase risk seeking in the gain domain. Furthermore, expected travel time information is demonstrated to be more effective than travel time variability information in enhancing rational behavior when drivers have limited experiences. After drivers gain sufficient knowledge of routes, however, the difference in behavior associated with the two information types becomes insignificant. The results also demonstrate that, when drivers lack experience, the faster less reliable route is more attractive than the slower more reliable route. However, with cumulative experiences, drivers become more willing to take the more reliable route given that they are reluctant to become risk seekers once experience is gained. Furthermore, the effect of information on driver behavior differs significantly by participant and trip, which is, to a large extent, dependent on personal traits and trip characteristics.

An Empirical Study of Travel Time Variability and Travel Choice Behavior

1982 ◽  
Vol 16 (4) ◽  
pp. 460-475 ◽  
Author(s):  
W. Burke Jackson ◽  
James V. Jucker
Keyword(s):  
Empirical Study ◽  
Travel Time ◽  
Choice Behavior ◽  
Time Variability ◽  
Travel Choice ◽  
Travel Time Variability

Predicting Travel Times for the South Jersey Real-Time Motorist Information System

2003 ◽  
Vol 1855 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Steven I. J. Chien ◽  
Xiaobo Liu ◽  
Kaan Ozbay
Keyword(s):  
Information System ◽  
Prediction Model ◽  
Simulation Model ◽  
Travel Time ◽  
Real Time ◽  
Travel Times ◽  
The South ◽  
Travel Time Prediction ◽  
Real Time Information ◽  
Time Information

A dynamic travel-time prediction model was developed for the South Jersey (southern New Jersey) motorist real-time information system. During development and evaluation of the model, the integration of traffic flow theory, measurement and application of collected data, and traffic simulation were considered. Reliable prediction results can be generated with limited historical real-time traffic data. In the study, acoustic sensors were installed at potential congested places to monitor traffic congestion. A developed simulation model was calibrated with the data collected from the sensors, and this was applied to emulate traffic operations and evaluate the proposed prediction model under time-varying traffic conditions. With emulated real–time information (travel times) generated by the simulation model, an algorithm based on Kalman filtering was developed and applied to forecast travel times for specific origin-destination pairs over different periods. Prediction accuracy was evaluated by the simulation model. Results show that the developed travel-time predictive model demonstrates satisfactory performance.

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