This paper addresses the modeling of traffic flows with intelligent cars and intelligent roads. It will describe the modeling approach MIXIC and review the results for different ADA systems: Adaptive Cruise Control, a special lane for Intelligent Vehicles, cooperative following and external speed assistance. In general there are clear indications for an improvement in traffic safety. It also shows that traffic efficiency impacts are limited and in specific conditions even negative. Given the increasing artention to more advanced driver assistance system, the paper proposes new research directions Assistance' and advanced modeling techniques toward 'Integrated Driver Assistance ' . Context and problem statementSince the early nineties, there has been an increasing interest in the application of Advanced Driver Assistance (ADA) functions in cars and roads in order to make traffic safer and more efficient. ADA systems support or take over a drivers' task, e.g. to maintain a safe speed or distance, to maintain the right heading and to avoid collisions. Adaptive Cruise Control was one of the earliest systems under study: it controls the speed of a vehicle while maintaining a proper distance to its predecessor. It has a limited operational range: it can not be used at very high or low speeds and has a limited deceleration range.Nowadays, ACC is commercially available and car manufacturers are studying and developing new systems. Examples of directions of research are enhanced detection techniques, using image processing and combinations of radar and vision based systems; vehicle-vehicle communication to exchange in real-time data on the motion of cars; vehicle road-side communication on traffic and road conditions; combinations of functions such as ACC, Lane Departure Waming and Drowsiness detection. This new generation of ADA systems is referred to in this paper as Integrated Driver Assistance.Apart from the technical questions related to ADA systems, there is an important challenge to show the actual contribution of ADA systems to improved traffic safety and throughput. In order to address this issue, we need to h o w how drivers are going to use the systems, how the systems in a vehicle are going to interact with other vehicles and road-side equipment. This paper addresses the issue of modeling traffic flows with intelligent cars and intelligent roads. It will describe the modeling approach MIXIC and review the results for different ADA systems. It will relate this work to other studies that have been conducted. Finally, the paper will outline a generic approach to model Integrated Driver Assistance, given the gained experience, the development toward Integrated Driver Assistance and advanced modeling techniques. MIXIC Modeling approachIn order to study the potential impact of ADA systems, the modeling approach should be suitable for analyzing different assumptions for ADA system functionality, roadside systems, driver behavior and vehicle dynamics.Further, it should be capable of assessing impacts on traffic perfor...
Introduction This paper describes the modification and development of methodologies to assess the impacts of Intelligent Transport Systems (ITS) applications for Vulnerable Road users (VRUs) in the domains of safety, mobility and comfort. This effort was carried out in the context of the VRUITS project whose aim was to produce results at the EU-28 level. Methods An existing safety methodology was modified to take into account specific VRU aspects. The mobility and comfort assessments methodologies were developed in the project. Results The safety, mobility and comfort methodologies were applied to ten ITS applications for VRUs. The first innovation determined how the nine safety mechanisms for ex-ante analysis of ITS applications, including direct and indirect effects, can incorporate the important characteristics of the VRU groups (pedestrians, cyclists and Power-Two-Wheeler riders) in the analysis. The second innovation developed a conceptual model for mobility and comfort. Thirdly, the estimation of quantitative effects, using literature, empirical findings and expert judgement, was developed. Conclusions The new safety, mobility and comfort assessment methodologies were applied to calculate the respective effects for VRUs using ITS. These results are ex-ante findings, as very few to no empirical results for ITS applications for VRUs are available. In order to improve the accuracy of the estimates, there is a need for better standardized data and at the European level. Finally, validation of the methods could be done in the future field operational tests focusing on measuring user behavior.
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