Real-time traffic state estimation in urban corridors from heterogeneous data

Nantes, Alfredo, Ngoduy, Dong, Bhaskar, Ashish, Miska, Marc, & Chung, Edward (2016) Real-time traffic state estimation in urban corridors from heterogeneous data. Transportation Research Part C: Emerging Technologies, 66, pp. 99-118.

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In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.

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ID Code: 85946
Item Type: Journal Article
Refereed: Yes
Keywords: Urban traffic estimation, Incremental extended Kalman filter, Multi-source data fusion, First order traffic model
DOI: 10.1016/j.trc.2015.07.005
ISSN: 0968-090X
Divisions: Current > Schools > School of Civil Engineering & Built Environment
Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Research Centres > Smart Transport Research Centre
Copyright Owner: Copyright 2015 Elsevier
Copyright Statement: This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Deposited On: 22 Jul 2015 22:46
Last Modified: 19 May 2016 05:13

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