首页 | 本学科首页   官方微博 | 高级检索  
     检索      


A phase-based smoothing method for accurate traffic speed estimation with floating car data
Institution:1. Electrical Engineering Department, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia;2. Department of Civil, Architectural and Environmental Engineering, University of Texas Austin, USA;1. Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, Irvine, CA, 92697, USA;2. Department of Civil and Environmental Engineering, California Institute for Telecommunications and Information Technology,Institute of Transportation Studies Irvine, CA, 92697-3600, USA
Abstract:In this paper, a novel freeway traffic speed estimation method based on probe data is presented. In contrast to other traffic speed estimators, it only requires velocity data from probes and does not depend on any additional data inputs such as density or flow information. In the first step the method determines the three traffic phases free flow, synchronized flow, and Wide Moving Jam (WMJ) described by Kerner et al. in space and time. Subsequently, reported data is processed with respect to the prevailing traffic phase in order to estimate traffic velocities. This two-step approach allows incorporating empirical features of phase fronts into the estimation procedure. For instance, downstream fronts of WMJs always propagate upstream with approximately constant velocity, and downstream fronts of synchronized flow phases usually stick to bottlenecks. The second step assures the validity of measured velocities is limited to the extent of its assigned phase. Effectively, velocity information in space-time can be estimated more distinctively and the result is therefore more accurate even if the input data density is low.The accuracy of the proposed Phase-Based Smoothing Method (PSM) is evaluated using real floating car data collected during two traffic congestions on the German freeway A99 and compared to the performance of the Generalized Adaptive Smoothing Method (GASM) as well as a naive algorithm. The quantitative and qualitative results show that the PSM reconstructs the congestion pattern more accurately than the other two. A subsequent analysis of the computational efficiency and sensitivity demonstrates its practical suitability.
Keywords:Traffic state estimation  Floating car data  Three-phase traffic theory  Generalized adaptive smoothing method  Congestion patterns
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号