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Development and application of the network weight matrix to predict traffic flow for congested and uncongested conditions

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  • Alireza Ermagun
  • David M Levinson

Abstract

To capture network dependence between traffic links, we introduce two distinct network weight matrices ( W j , i ), which replace spatial weight matrices used in traffic forecasting methods. The first stands on the notion of betweenness centrality and link vulnerability in traffic networks. To derive this matrix, we use an unweighted betweenness method and assume all traffic flow is assigned to the shortest path. The other relies on flow rate change in traffic links. For forming this matrix, we use the flow information of traffic links and employ user equilibrium assignment and the method of successive averages algorithm to solve the network. The components of the network weight matrices are a function not simply of adjacency, but of network topology, network structure, and demand configuration. We test and compare the network weight matrices in different traffic conditions using the Nguyen–Dupuis network. The results lead to a conclusion that the network weight matrices operate better than traditional spatial weight matrices. Comparing the unweighted and flow-weighted network weight matrices, we also reveal that the assigned flow network weight matrices perform two times better than a betweenness network weight matrix, particularly in congested traffic conditions.

Suggested Citation

  • Alireza Ermagun & David M Levinson, 2019. "Development and application of the network weight matrix to predict traffic flow for congested and uncongested conditions," Environment and Planning B, , vol. 46(9), pages 1684-1705, November.
  • Handle: RePEc:sae:envirb:v:46:y:2019:i:9:p:1684-1705
    DOI: 10.1177/2399808318763368
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    1. Spiess, Heinz & Florian, Michael, 1989. "Optimal strategies: A new assignment model for transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 23(2), pages 83-102, April.
    2. Shaun Larcom & Ferdinand Rauch & Tim Willems, 2017. "The Benefits of Forced Experimentation: Striking Evidence from the London Underground Network," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(4), pages 2019-2055.
    3. Chaisak Suwansirikul & Terry L. Friesz & Roger L. Tobin, 1987. "Equilibrium Decomposed Optimization: A Heuristic for the Continuous Equilibrium Network Design Problem," Transportation Science, INFORMS, vol. 21(4), pages 254-263, November.
    4. Jenelius, Erik & Koutsopoulos, Haris N., 2013. "Travel time estimation for urban road networks using low frequency probe vehicle data," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 64-81.
    5. Latora, Vito & Marchiori, Massimo, 2002. "Is the Boston subway a small-world network?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 314(1), pages 109-113.
    6. Whittaker, Joe & Garside, Simon & Lindveld, Karel, 1997. "Tracking and predicting a network traffic process," International Journal of Forecasting, Elsevier, vol. 13(1), pages 51-61, March.
    7. Okutani, Iwao & Stephanedes, Yorgos J., 1984. "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 1-11, February.
    8. Sheffi, Yosef & Powell, Warren, 1981. "A comparison of stochastic and deterministic traffic assignment over congested networks," Transportation Research Part B: Methodological, Elsevier, vol. 15(1), pages 53-64, February.
    9. César Ducruet & Sung-Woo Lee & Adolf Ng, 2010. "Centrality and vulnerability in liner shipping networks : revisiting the Northeast Asian port hierarchy," Post-Print hal-03246966, HAL.
    10. Berdica, Katja, 2002. "An introduction to road vulnerability: what has been done, is done and should be done," Transport Policy, Elsevier, vol. 9(2), pages 117-127, April.
    11. Ji, Yuxuan & Geroliminis, Nikolas, 2012. "On the spatial partitioning of urban transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1639-1656.
    12. Riccardo Gallotti & Armando Bazzani & Sandro Rambaldi & Marc Barthelemy, 2016. "A stochastic model of randomly accelerated walkers for human mobility," Nature Communications, Nature, vol. 7(1), pages 1-7, November.
    13. Xu, Hongli & Lou, Yingyan & Yin, Yafeng & Zhou, Jing, 2011. "A prospect-based user equilibrium model with endogenous reference points and its application in congestion pricing," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 311-328, February.
    14. Lam, William H. K. & Huang, Hai-Jun, 1992. "A combined trip distribution and assignment model for multiple user classes," Transportation Research Part B: Methodological, Elsevier, vol. 26(4), pages 275-287, August.
    15. César Ducruet & Sung-Woo Lee & Adolf K.Y. Ng, 2010. "Centrality and vulnerability in liner shipping networks: revisiting the Northeast Asian port hierarchy," Maritime Policy & Management, Taylor & Francis Journals, vol. 37(1), pages 17-36, January.
    16. Jenelius, Erik & Petersen, Tom & Mattsson, Lars-Göran, 2006. "Importance and exposure in road network vulnerability analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(7), pages 537-560, August.
    17. Shanjiang Zhu & David Levinson, 2015. "Do People Use the Shortest Path? An Empirical Test of Wardrop’s First Principle," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-18, August.
    18. Henry Liu & Xiaozheng He & Bingsheng He, 2009. "Method of Successive Weighted Averages (MSWA) and Self-Regulated Averaging Schemes for Solving Stochastic User Equilibrium Problem," Networks and Spatial Economics, Springer, vol. 9(4), pages 485-503, December.
    19. Hani S. Mahmassani & Gang-Len Chang, 1987. "On Boundedly Rational User Equilibrium in Transportation Systems," Transportation Science, INFORMS, vol. 21(2), pages 89-99, May.
    20. Tao Cheng & James Haworth & Jiaqiu Wang, 2012. "Spatio-temporal autocorrelation of road network data," Journal of Geographical Systems, Springer, vol. 14(4), pages 389-413, October.
    21. Sang Nguyen & Clermont Dupuis, 1984. "An Efficient Method for Computing Traffic Equilibria in Networks with Asymmetric Transportation Costs," Transportation Science, INFORMS, vol. 18(2), pages 185-202, May.
    22. Carlos F. Daganzo & Yosef Sheffi, 1977. "On Stochastic Models of Traffic Assignment," Transportation Science, INFORMS, vol. 11(3), pages 253-274, August.
    23. David Levinson & Ramachandra Karamalaputi, 2003. "Induced Supply: A Model of Highway Network Expansion at the Microscopic Level," Journal of Transport Economics and Policy, University of Bath, vol. 37(3), pages 297-318, September.
    24. Anas, Alex, 1983. "Discrete choice theory, information theory and the multinomial logit and gravity models," Transportation Research Part B: Methodological, Elsevier, vol. 17(1), pages 13-23, February.
    25. Alireza Ermagun & Snigdhansu Chatterjee & David Levinson, 2017. "Using temporal detrending to observe the spatial correlation of traffic," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-21, May.
    26. Lam, William H. K. & Huang, Hai-Jun, 1992. "Calibration of the combined trip distribution and assignment model for multiple user classes," Transportation Research Part B: Methodological, Elsevier, vol. 26(4), pages 289-305, August.
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    More about this item

    Keywords

    Traffic flow; spatial weight matrix; vulnerability; traffic forecasting; network analysis; competitive links;
    All these keywords.

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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