Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach |
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| Institution: | 1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;2. Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT 84112-0561, USA;3. School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA;1. Center for Environmental Research & Technology, University of California, Riverside, 1084 Columbia Avenue, Riverside, CA 92507, USA;2. Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, 110 Eighth Street, 4034 Jonsson Engineering Center, Troy, NY 12180-3590, USA;3. Department of Computer Science, University of Southern California, 900 W 34th Street, Los Angeles, CA 90089, USA;4. Department of Electrical and Systems Engineering, Rensselaer Polytechnic Institute, 110 Eighth Street, 7004 Jonsson Engineering Center, Troy, NY 12180-3590, USA;1. Center for Sustainable Urban Development (CEDEUS), Pontificia Universidad Católica de Chile, Chile;2. Department of Transport Engineering and Logistics, Pontificia Universidad Católica de Chile, Chile;1. Delft University of Technology, Stevinweg 1, 2628CN, Delft, The Netherlands;2. IFSTTAR, LICIT, F-69675, Bron Cedex, France |
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| Abstract: | This study focuses on how to use multiple data sources, including loop detector counts, AVI Bluetooth travel time readings and GPS location samples, to estimate macroscopic traffic states on a homogeneous freeway segment. With a generalized least square estimation framework, this research constructs a number of linear equations that map the traffic measurements as functions of cumulative vehicle counts on both ends of a traffic segment. We extend Newell’s method to solve a stochastic three-detector problem, where the mean and variance estimates of cell-based density and flow can be analytically derived through a multinomial probit model and an innovative use of Clark’s approximation method. An information measure is further introduced to quantify the value of heterogeneous traffic measurements for improving traffic state estimation on a freeway segment. |
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| Keywords: | Three-detector problem Kinematic wave method Probit model Clark’s approximation Traffic state estimation |
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