Latest revision as of 11:50, 25 January 2021

Abstract

Traffic information systems have become a major issue in many countries as a modern technology for alleviating traffic congestion in urban areas. Pre-trip or en-route real-time travel information regarding traffic conditions can enhance drivers’ knowledge of the situation in road networks and may assist in drivers’ decisions such as the choice of departure time, route, and destination. In facts, several papers have shown that traffic information yields benefits to drivers such as travel-time reduction and the avoidance of traffic accidents, among others. In considering the potential benefits of alternative driver information systems, it is also necessary to evaluate the potential of adverse impacts that improved information may have. Ben-Akiva et al. (1991) explained this phenomenon in terms of three elements: oversaturation; overreaction; and concentration. Among them, overreaction and concentration are the principal causes of adverse effects. Overreaction occurs when drivers’ reactions to traffic information cause congestion to transfer from one road to another. It may also generate fluctuations in road usage. Overreaction may occur if drivers respond too sensitively to information on current traffic conditions. Concentration may occur when drivers choose a specific route in a very short period. In order to implement the strategies of an Intelligent Transportation System (ITS), it is necessary to predict the temporal evolution of the traffic pattern on a congested transportation network, where travel demands and travel costs vary over time and space. For urban areas, dynamic models are mainly considered as they describe how commuters adjust their travel decisions concerning routes and departure times. Moreover, to model the impact of information provision by an ITS, it is necessary to develop a multi-class model given there are different classes of users in a transportation network, who respond in differing ways to traffic information. In this chapter, a multiple-user-class dynamic stochastic assignment (MDSA) model is introduced to reflect drivers who have varying perceptual errors and varying dynamic traffic behaviors. MDSA is an extended version of a static single-user-class assignment. The driver's route-choice mechanism is based on his/her past experience of the road traffic conditions during prior days of travel. Some information-provision strategies that are involved in a route-guidance system are also introduced for the effective use of the systems.

Original document

The different versions of the original document can be found in:

• http://www.intechopen.com/articles/show/title/multiple-user-class-dynamic-stochastic-assignment-for-a-route-guidance-strategy

• https://www.intechopen.com/citation-pdf-url/10983 under the license cc-by-nc-sa

• https://www.intechopen.com/books/convergence-and-hybrid-information-technologies/multiple-user-class-dynamic-stochastic-assignment-for-a-route-guidance-strategy,

https://cdn.intechopen.com/pdfs-wm/10983.pdf,

https://academic.microsoft.com/#/detail/1524939259

• https://www.intechopen.com/download/pdf/10983,

http://dx.doi.org/10.5772/9648