Reconstructing maximum likelihood trajectory of probe vehicles between sparse updates |
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| Institution: | 1. Department of Mechanical Engineering, Clemson University, Clemson, SC 29634-0921, United States;2. BMW Group Information Technology Research Center, Greenville, SC, United States;1. Department of Civil and Construction Engineering, Western Michigan University, 4601 Campus Drive, Room G-242, Kalamazoo, MI 49008-5316, USA;2. Center for Environmental Research & Technology, University of California, Riverside, 1084 Columbia Avenue, Riverside, CA 92507, USA;3. Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute (RPI), 110 Eighth Street, Room JEC 4034, Troy, NY 12180-3590, USA;4. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 10084, China;5. Department of Automotive Engineering, Tsinghua University, Beijing 10084, China;1. Institute of Transportation Studies, 4000 Anteater Instruction and Research Bldg, University of California, Irvine, CA 92697-3600, United States;2. Department of Civil and Environmental Engineering, California Institute for Telecommunications and Information Technology, Institute of Transportation Studies, 4000 Anteater Instruction and Research Bldg, University of California, Irvine, CA 92697-3600, United States;3. Department of Civil and Environmental Engineering, Institute of Transportation Studies, 4000 Anteater Instruction and Research Bldg, University of California, Irvine, CA 92697-3600, United States;1. Department of Civil and Environmental Engineering, University of Michigan, United States;2. University of Michigan Transportation Research Institute, United States |
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| Abstract: | Data from connected probe vehicles can be critical in estimating road traffic conditions. Unfortunately, current available data is usually sparse due to the low reporting frequency and the low penetration rate of probe vehicles. To help fill the gaps in data, this paper presents an approach for estimating the maximum likelihood trajectory (MLT) of a probe vehicle in between two data updates on arterial roads. A public data feed from transit buses in the city of San Francisco is used as an example data source. Low frequency updates (at every 200 m or 90 s) leaves much to be inferred. We first estimate travel time statistics along the road and queue patterns at intersections from historical probe data. The path is divided into short segments, and an Expectation Maximization (EM) algorithm is proposed for allocating travel time statistics to each segment. Then the trajectory with the maximum likelihood is generated based on segment travel time statistics. The results are compared with high frequency ground truth data in multiple scenarios, which demonstrate the effectiveness of the proposed approach, in estimating both the trajectory while moving and the stop positions and durations at intersections. |
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| Keywords: | Probe vehicular data Expectation Maximization Maximum likelihood Trajectory estimation Transit bus |
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