Demand estimation and spatio-temporal clustering for urban road networks
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Date
2020
Publication Type
Conference Paper
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yes
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Abstract
Urban road networks play a key role in mobility amongst the critical infrastructure of city, which is a strong time-variant system with uncertainty. In this paper, for the purpose of understanding the traffic congestion propagation patterns, demand estimation and spatio-temporal clustering was performed with a case study on the central area of Nanjing, China. Firstly, a four-hour time-dependent origin-destination traffic demand is calibrated by utilizing the Adaptive Fine-tuning (AFT) algorithm, and aiming at minimizing the error between microscopic simulated results by SUMO and real-world Radio Frequency Identification (RFID) data. Then, spatio-temporal clustering was performed to illustrate the dynamic feature on congestion propagation by implementing the spectral clustering approach. Results demonstrate that the calibrated dynamic origin-destination matrix can illustrate a good match with RFID data, and the proposed spectral clustering approach is fast and feasible for the partitioning of urban road network. --> Urban road networks play a key role in mobility amongst the critical infrastructure of city, which is a strong time-variant system with uncertainty. In this paper, for the purpose of understanding the traffic congestion propagation patterns, demand estimation and spatio-temporal clustering was performed with a case study on the central area of Nanjing, China. Firstly, a four-hour time-dependent origin-destination traffic demand is calibrated by utilizing the Adaptive Fine-tuning (AFT) algorithm, and aiming at minimizing the error between microscopic simulated results by SUMO and real-world Radio Frequency Identification (RFID) data. Then, spatio-temporal clustering was performed to illustrate the dynamic feature on congestion propagation by implementing the spectral clustering approach. Results demonstrate that the calibrated dynamic origin-destination matrix can illustrate a good match with RFID data, and the proposed spectral clustering approach is fast and feasible for the partitioning of urban road network.
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published
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Publisher
European Association for Research in Transportation
Event
9th Symposium of the European Association for Research in Transportation (hEART 2020)
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Subject
Spatio-temporal; Spectral clustering; Urban road network
Organisational unit
08686 - Gruppe Strassenverkehrstechnik
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG
Notes
Conference postponed due to the Corona virus (COVID-19).
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Is part of: https://transp-or.epfl.ch/heart/2020.php