International Conference on Transportation and Development 2020
Dynamic Vehicle OD Flow Estimation for Urban Road Network Using Multi-Source Heterogeneous Data
Publication: International Conference on Transportation and Development 2020
ABSTRACT
Dynamic OD flow plays an important role in transportation planning and management. In this paper, a dynamic vehicle OD flow estimation model of urban road network was developed using multi-source heterogeneous traffic flow data. First, in order to improve the accuracy of OD demand allocation, the GPS data, road topology, and land use attributes were considered to construct the network-level traffic zones. Second, the ALPR data and GPS data were combined to increase the accuracy of observable vehicle OD flow. Third, a Kalman filter model with linear state constraint was proposed to estimate the unobservable vehicle OD flow. Specifically, the state transition equation was developed using random walks. The observation equation with dynamic mapping relationship between OD flow and link flow was developed based on dynamic traffic flow distribution theory using data collected from microwave detectors and ALPR sensors. The linear state constraint was formulated with observed traffic demand of network-level traffic zones using ALPR data. Finally, the performance of the model was evaluated and analyzed with the field data of Kunshan, China. The results showed that the proposed model estimated link flows accurately and performed better than standard Kalman filter model.
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REFERENCES
Caceres N, Wideberg J P, Benitez F G. (2007). “Deriving origin destination data from a mobile phone network”. IET Intelligent Transport Systems, 1(1): 15-26.
Cao P, Miwa T, Yamamoto T, et al. (2013). “Bilevel generalized least squares estimation of dynamic origin-destination matrix for urban network with probe vehicle data”. Transportation Research Record: Journal of the Transportation Research Board, (2333): 66–73.
Cascetta E, Inaudi D, Marquis G.(1993). “Dynamic estimators of origin-destination matrices using traffic counts”. Transportation science, 27(4): 363-373.
Dixon M P, Rilett L R. (2002). “Real-time OD Estimation Using Automatic Vehicle Identification and Traffic Count Data”. Computer-Aided Civil and Infrastructure Engineering, 17(1): 7-21.
Frederix R, Viti F, Tampère C M J (2013). “Dynamic origin–destination estimation in congested networks: theoretical findings and implications in practice”. Transportmetrica A: Transport Science, 9(6): 494-513.
Hazelton M L (2008). “Statistical inference for time varying origin–destination matrices”. Transportation Research Part B: Methodological, 42(6): 542-552.
Jiang J R, Huang H W, Liao J H, et al. (2014). “Extending Dijkstra’s shortest path algorithm for software defined networking” Network Operations and Management Symposium (APNOMS), 2014 16th Asia-Pacific. IEEE, 2014: 1-4.
K. Ashok, M. E. Ben-Akiva. (2000). “Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin-Destination Flows”. Transportation Science, 34(1):21-36.
Lu Z, Rao W, Wu Y J, et al. (2015). “A Kalman filter approach to dynamic OD flow estimation for urban road networks using multi-sensor data”. Journal of Advanced Transportation, 49(2): 210-227.
Simon D. (2010). “Kalman filtering with state constraints: a survey of linear and nonlinear algorithms”. Iet Control Theory & Applications, 4(8):1303-1318.
Xie C, Kockelman K M, Waller S T (2011). “A maximum entropy-least squares estimator for elastic origin–destination trip matrix estimation”. Transportation Research Part B: Methodological, 45(9): 1465-1482.
Zhou D, Hong R, Xia J. (2018). “Identification of taxi pick-up and drop-off hotspots using the density-based spatial clustering method”. Proceedings of the 17th COTA International Conference of Transportation Professionals. 196-204.
Zhou X, Mahmassani H S. (2006). “Dynamic origin-destination demand estimation using automatic vehicle identification data”. IEEE Transactions on intelligent transportation systems, 7(1): 105-114.
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Published In
International Conference on Transportation and Development 2020
Pages: 161 - 172
Editor: Guohui Zhang, Ph.D., University of Hawaii
ISBN (Online): 978-0-7844-8316-9
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Aug 31, 2020
Published in print: Aug 31, 2020
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