Impact of Advanced Driver Assistance Systems on Urban Traffic Network Conditions

One of the most significant problems of modern cities is undoubtedly traffic congestion, a well-known and sufficiently documented issue (Bhargab et al, 1999; NRC, 1995). Several approaches have been adopted towards providing adequate solutions, or at least mitigating its impact in the everyday life of the citizens. Supply related solutions like the construction of new roads are not economical and do not really solve the problem as, by providing additional capacity, they often result in the generation of new demand. Similarly, demand related approaches, such as the improvement of the rider-ship of public transit modes, congestion pricing and flexible work-hours are difficult to implement or have low public acceptance. One promising direction is the introduction of intelligent in-vehicle (or cooperative invehicle—infrastructure-based) systems that assist the driver in his/her various tasks. A large variety of such systems, collectively referred to as Advanced Driver Assistance Systems (ADAS) are being developed by various parties, including system developers, car manufacturers, and scientists worldwide (Brand et al, 1997; Davison et al, 1997; Heijer et al, 2000). Several attempts have been made to categorize these systems (Heijer et al, 2000). One of the key considerations in the design and development of ADA systems is the improvement of vehicles’ traffic dynamics, thus improving the overall network efficiency. Other considerations include fuel consumption and related emission reduction, and safety improvements (Penttinen et al, 2000). As ADA systems are a rapidly evolving area of research and development, it would be helpful to obtain intuition into the potential benefits from the implementation of these systems prior to their actual deployment. Research in the area has so far been mostly experimental, using either actual ADAS-equipped vehicles or properly configured driving simulators (Duynstee et al, 2000, Ishida et al, 2000, Regan et al, 2000). A methodology for the assessment of Dynamic Speed Adaptation on driver behaviour has been proposed by Braban-Ledoux et al (2000), who also propose a set of microscopic indicators for characterising driver behaviour. A few simulation efforts have also been reported. For example, Misener et al (2000) describe the adaptation of a microscopic simulator, SmartAHS, for the evaluation of ADAS and present simulation results for an Adaptive Cruise Control and Stop and Go system. Furthermore, Neunzig et al (2000) present an analysis of the impact of Adaptive Cruise Control on the fuel consumption of equipped vehicles. Given the relatively short history of advanced driver assistance systems their impact on traffic conditions started recently to be investigated, mainly at microscopic level (Hoogerndoorn et al. 2001, Stevens et al. 2001, Niittymäki et al. 2001) and the first results concerning specific types of road sections and traffic situations are available. However, no attempt is recorded for the identification of ADAS impact at network level as the low market penetration level of these systems makes very difficult the execution of any experiment in real conditions. In order to fill in this gap, the objective of this paper is the investigation of the advanced driver assistance systems impact on traffic network conditions through the use of a traffic network simulation model.

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