Transportation Research Part C: Emerging Technologies
Dynamics of commuting decision behaviour under advanced traveller information systems
Introduction
The response of drivers to real-time information continues to be an important missing link in our ability to evaluate the effectiveness of Advanced Traveller Information Systems (ATIS), and to design beneficial information supply strategies. Due to limited deployment of ATIS technologies, it is not practical to observe actual behaviour of users under different real-time information strategies on a daily basis together with the various performance measures affecting these responses (Mahmassani and Herman, 1990). Laboratory experiments have been proposed and tested to a limited extent as an effective and practical approach to gain insights into tripmakers' decision processes under different types of ATIS-provided information (Adler et al., 1993, Bonsall and Parry, 1991, Chen and Mahmassani, 1993, Koutsopoulos et al., 1994, Vaughn et al., 1993). An interactive multi-user simulator has been developed at the University of Texas at Austin, and used in a set of laboratory experiments to examine the day-to-day commuter behaviour under real-time information and develop the mathematical models presented in this paper. Models of the decision processes that determine pre-trip departure time and route switching as well as en-route path switching as a function of the user's cumulative and recent experience with the system are developed and calibrated under a multinomial probit model framework, so as to take account of travellers' learning from past experience with the system, and to capture the serial correlation arising from repeated decisions made by the same respondent.
Section 2 describes the laboratory experiment, followed by the boundedly rational behaviour framework for commuters' day-to-day departure time and route switching models under ATIS. A brief discussion and interpretation of the model specification is presented in Section 4. The estimation results are discussed in Section 5, followed by concluding comments in Section 6.
Section snippets
The laboratory experiment
The dynamic interactive simulator developed at the University of Texas at Austin adopts the client/server modelling concept used extensively in X Window System applications (Chen and Mahmassani, 1993). At the core is a simulation-assignment model based on the corridor network version of the DYNASMART model (Jayakrishnan et al., 1994) that includes pre-trip route selection and en-route path switching. Another program controls the layout of windows displayed on the screens of a set of Macintosh
Modelling framework
The boundedly rational rule is applied to commuters' departure time and route switching behaviour under real-time information and is described in the following.
Model specification
The analysis focuses on the day-to-day dynamics of commuter pre-trip departure time and route choices as well as en-route path switching for morning commutes. Based on the preliminary analysis results (Mahmassani and Liu, 1995, Mahmassani and Liu, 1996, Mahmassani and Liu, 1997) the specifications of the departure time and route switching indifference band models consist of the following components: (1) initial band, (2) user characteristics component, (3) information reliability component, (4)
Estimation results
The model parameters were estimated using a special purpose maximum likelihood estimation procedure that relies on Monte-Carlo simulation to evaluate the MNP choice probability (Liu and Mahmassani, 1997). Based on the preliminary analysis, the contemporaneous correlation terms (c.c.t., γD2,r, and γD2,m) and the serial correlation terms for the relative indifference band (γ1r, γ2r, γ3r, γ4r) in Eq. (15) are not significant, and assumed to be zero in this study Mahmassani and Liu, 1996,
Conclusions
This paper presented both a model framework and an empirical analysis of tripmakers' indifference band for departure time and route switching behaviour in response to real-time information, based on data collected using a laboratory interactive dynamic simulator. The analysis focused on the day-to-day dynamics of commuters' departure time and route decision process in response to the supplied information. The multinomial probit (MNP) model provides a very flexible framework to model and
Acknowledgements
This paper is based on research funded by the US Department of Transportation through the Southwest Region University Transportation Center. The laboratory simulator used in this study was developed initially by Peter Chen.
References (20)
- et al.
An evaluation tool for advanced traffic information and management systems in urban networks
Transportation Research
(1994) - et al.
A driving simulator and its application for modeling route choice in the presence of information
Transportation Research
(1994) Dynamic models of commuter behavior: experimental investigation and application to the analysis of planned traffic disruptions
Transportation Research
(1990)- et al.
System performance and user response under real-time information in a congested traffic corridor
Transportation Research
(1991) - et al.
A conflict model and interactive simulator (FASTCARS) for predicting enroute driver behavior in response to real-time traffic condition information
Transportation
(1993) - et al.
Using an interactive route-choice simulator to investigate drivers' compliance with route guidance advice
Transportation Research Record
(1991) - et al.
Dynamic interactive simulator for studying commuter behavior under real-time traffic information supply strategies
Transportation Research Record
(1993) - et al.
Multinomial probit with time series data: unifying state dependence and serial correlation models
Environment and Planning A
(1982) - Liu, Y.-H., Mahmassani, H.S., 1997. Global maximum likelihood estimation procedure of multinomial probit model...
- Mahmassani, H.S., 1996. Dynamics of commuter behaviour: Recent research and continuing challenges. In: Lee-Gosselin,...
Cited by (280)
Backpressure or no backpressure? Two simple examples
2024, Transportation Research Part C: Emerging TechnologiesGlobal path preference and local response: A reward decomposition approach for network path choice analysis in the presence of visually perceived attributes
2024, Transportation Research Part A: Policy and PracticeReducing strategic uncertainty in transportation networks by personalized routing advice: A route-choice laboratory experiment
2024, Travel Behaviour and SocietyA bibliometric review of driver information processing and application studies
2023, Journal of Traffic and Transportation Engineering (English Edition)A day-to-day dynamic model for mixed traffic flow of autonomous vehicles and inertial human-driven vehicles
2023, Transportation Research Part E: Logistics and Transportation ReviewProactive route choice with real-time information: Learning and effects of network complexity and cognitive load
2023, Transportation Research Part C: Emerging Technologies