Journal of Advanced Transportation

Recent comprehensive reviews on traveler behavior in transportation networks have been given by Asakura (1999), Alder (2001), Chatterjee et al. (2002), Bell (2002), Arentze and Timmermans (2005). However, the expansion of transportation networks and the emergence of new technologies have generated an urgent need for advanced models and solution algorithms so as to provide better understanding of the traveler behavior on networks in response to various changes. This special issue on "Behavior in Networks" is a collection of selected papers presented at the International Workshop on Behavior in Networks held on 22nd - 23rd July, 2004. The workshop placed greater emphasis on discussions of how new models and advanced methods can be used for giving better insights on travel behaviors in transportation networks particularly in view of the recent advancement of technologies. The workshop brought together experts in a behavior side as well as a transportation network side worldwide to discuss both recent research and future directions in this important field. The selected papers cover the important aspects of behavior in networks to enhance the realism of modeling in transport.

Contents of this Special Journal

The first paper by Lee et. al. considers a multi-step ahead prediction algorithm of link travel speeds using a Kalman filtering technique in order to calculate a dynamic shortest path. The main point is the comparison of the one-step and the multi-step ahead link travel time prediction models. The results show that the multi-step ahead algorithm is compared more favorably for searching the dynamic shortest time path than the other algorithm.

The second paper by Chen and Ji examines three definitions of optimality for finding the optimal path under an uncertain environment. These three stochastic path finding models are formulated as the expected value model, dependent-chance model, and chance-constrained model using different criteria to hedge against the travel time uncertainty. The main focus of this paper is to demonstrate the features of these stochastic path finding models.

The third paper by Park, Nam and Khamkongkhun examines a mode choice decision by considering not only travel time but also reliability of its modes. In order to estimate value of time, and value of reliability, the Multinomial and Nested Logit models are adopted for investigating their effects on mode choice problem. The analysis results revealed that reliability is an important factor affecting mode choice decisions.

The fourth paper by Lim, Heydecker and Lee considers the continuous network design problem under a stochastic environment. This paper formulates this problem for road expansion based on Stackelberg game where leader and follower exist, and allows for variety of travellers' behaviour in choosing their routes.

The fifth paper by Wong et. al. considers the micro-searching behavior for urban taxi services. They develop a mathematical model that is based on the absorbing Markov chain approach to describe taxi movements, taking into account the stochastic searching processes of taxis in a network. This paper provides a novel and potentially useful formulation for describing the urban taxi services in a network.

The last paper by Asakura and Iryo focuses on tracking travelers' data using mobile phone for monitoring and analyzing individual travel behavior. In particular, this paper shows a location positioning method of a mobile object when the location data of base stations are not available. Instead of base stations, signal strength vectors (reference vectors) are used for calculating the location position of a mobile object.

A better understanding of the traveler choice behavior in networks would enable us to design more economical and efficient transportation networks as demanded by the travelers. We sincerely hope that these selected papers in this special issue would provide a useful reference and bring the subject of behavior in networks to the attention of the researchers and practitioners. Finally, we would like to thank all the authors and reviewers for the publication of this special issue.

References

Adler, J.L. (2001). Investigation the learning effects of route guidance and traffic advisories on route choice behavior. Transportation Research, 9C, 1-14.
Arentze, T. and Timmermans, H. (2005). Modelling learning and adaptation in transportation contexts. Transportmetrica, 1, 13-22.
Asakura, Y. (1999). Evaluation of network reliability using stochastic user equilibrium. Journal of Advanced Transportation, 33, 147-158.
Bell, M. G. H. (2002). The future for in-vehicle information systems: the technology and its impacts. Journal of Advanced Transportation, 36, 231-242.
Chatterjee, K., Hounsell, N., Firmin, P. and Bonsall, P. (2002). Driver response to variable message sign information in London. Transportation Research, 10C, 149-169.