IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v46y2019i9p1684-1705.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2399808318763368
    Download Restriction: no
    File URL: https://libkey.io/10.1177/2399808318763368?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ermagun, Alireza & Tajik, Nazanin & Janatabadi, Fatemeh & Mahmassani, Hani, 2023. "Uncertainty in vulnerability of metro transit networks: A global perspective," Journal of Transport Geography, Elsevier, vol. 113(C).
    2. Junseo Bae & Kunhee Choi, 2021. "A land-use clustering approach to capturing the level-of-service of large urban corridors: A case study in downtown Los Angeles," Environment and Planning B, , vol. 48(7), pages 2093-2109, September.
    3. Luan, Mingye & Waller, S.Travis & Rey, David, 2023. "A non-additive path-based reward credit scheme for traffic congestion management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    4. Almotahari, Amirmasoud & Yazici, Anil, 2021. "A computationally efficient metric for identification of critical links in large transportation networks," Reliability Engineering and System Safety, Elsevier, vol. 209(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ampol Karoonsoontawong & Dung-Ying Lin, 2015. "Combined Gravity Model Trip Distribution and Paired Combinatorial Logit Stochastic User Equilibrium Problem," Networks and Spatial Economics, Springer, vol. 15(4), pages 1011-1048, December.
    2. Shanjiang Zhu & David Levinson, 2011. "A Portfolio Theory of Route Choice," Working Papers 000096, University of Minnesota: Nexus Research Group.
    3. Voltes-Dorta, Augusto & Rodríguez-Déniz, Héctor & Suau-Sanchez, Pere, 2017. "Vulnerability of the European air transport network to major airport closures from the perspective of passenger delays: Ranking the most critical airports," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 119-145.
    4. Almotahari, Amirmasoud & Yazici, Anil, 2021. "A computationally efficient metric for identification of critical links in large transportation networks," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    5. Fan, Yinchao & Ding, Jianxun & Liu, Haoxiang & Wang, Yu & Long, Jiancheng, 2022. "Large-scale multimodal transportation network models and algorithms-Part I: The combined mode split and traffic assignment problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    6. Mengying Cui & David Levinson, 2018. "Accessibility analysis of risk severity," Transportation, Springer, vol. 45(4), pages 1029-1050, July.
    7. Ji, Xiangfeng & Chu, Yanyu, 2020. "A target-oriented bi-attribute user equilibrium model with travelers’ perception errors on the tolled traffic network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    8. Du, Muqing & Tan, Heqing & Chen, Anthony, 2021. "A faster path-based algorithm with Barzilai-Borwein step size for solving stochastic traffic equilibrium models," European Journal of Operational Research, Elsevier, vol. 290(3), pages 982-999.
    9. Sun, Mingmei, 2023. "A day-to-day dynamic model for mixed traffic flow of autonomous vehicles and inertial human-driven vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    10. Farahani, Reza Zanjirani & Miandoabchi, Elnaz & Szeto, W.Y. & Rashidi, Hannaneh, 2013. "A review of urban transportation network design problems," European Journal of Operational Research, Elsevier, vol. 229(2), pages 281-302.
    11. Jiayang Li & Zhaoran Wang & Yu Marco Nie, 2023. "Wardrop Equilibrium Can Be Boundedly Rational: A New Behavioral Theory of Route Choice," Papers 2304.02500, arXiv.org, revised Feb 2024.
    12. Yang, Chao & Chen, Anthony, 2009. "Sensitivity analysis of the combined travel demand model with applications," European Journal of Operational Research, Elsevier, vol. 198(3), pages 909-921, November.
    13. Dong-Joon Kang & Su-Han Woo, 2017. "Liner shipping networks, port characteristics and the impact on port performance," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(2), pages 274-295, June.
    14. Paramet Luathep & Agachai Sumalee & H. Ho & Fumitaka Kurauchi, 2011. "Large-scale road network vulnerability analysis: a sensitivity analysis based approach," Transportation, Springer, vol. 38(5), pages 799-817, September.
    15. Zhou, Zhong & Chen, Anthony & Wong, S.C., 2009. "Alternative formulations of a combined trip generation, trip distribution, modal split, and trip assignment model," European Journal of Operational Research, Elsevier, vol. 198(1), pages 129-138, October.
    16. S. F. A. Batista & Ludovic Leclercq, 2019. "Regional Dynamic Traffic Assignment Framework for Macroscopic Fundamental Diagram Multi-regions Models," Transportation Science, INFORMS, vol. 53(6), pages 1563-1590, November.
    17. Yao, Jia & Chen, Anthony & Ryu, Seungkyu & Shi, Feng, 2014. "A general unconstrained optimization formulation for the combined distribution and assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 137-160.
    18. Bombelli, Alessandro & Santos, Bruno F. & Tavasszy, Lóránt, 2020. "Analysis of the air cargo transport network using a complex network theory perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    19. Tocchi, Daniela & Sys, Christa & Papola, Andrea & Tinessa, Fiore & Simonelli, Fulvio & Marzano, Vittorio, 2022. "Hypergraph-based centrality metrics for maritime container service networks: A worldwide application," Journal of Transport Geography, Elsevier, vol. 98(C).
    20. García, Ricardo & Marín, Angel, 2005. "Network equilibrium with combined modes: models and solution algorithms," Transportation Research Part B: Methodological, Elsevier, vol. 39(3), pages 223-254, March.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envirb:v:46:y:2019:i:9:p:1684-1705. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.