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An Efficient Bundle-Like Algorithm for Data-Driven Multi-objective Bi-level Signal Design for Traffic Networks with Hazardous Material Transportation

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Data Science and Digital Business

Abstract

A data-driven multi-objective bi-level signal design for urban network with hazmat transportation is considered in this chapter. A bundle-like algorithm for a min-max model is presented to determine generalized travel cost for hazmat carriers under uncertain risk. A data-driven bi-level decision support system (DBSS) is developed for robust signal control under risk uncertainty. Since this problem is generally non-convex, a data-driven bounding strategy is developed to stabilize solutions and reduce relative gap between iterations. Numerical comparisons are made with other data-driven risk-averse models. The trade-offs between maximum risk exposure and travel costs are empirically investigated. As a result, the proposed model consistently exhibits highly considerable advantage on mitigation of public risk exposure whilst incurred less cost loss as compared to other data-driven risk models.

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References

  1. Allsop, R. E. (1974). Some possibilities for using traffic control to influence trip distribution and route choice. In: D. J. Buckley (Ed.), Proceedings of the Sixth International Symposium on Transportation and Traffic Theory, pp. 345–374. New York: Elsevier.

    Google Scholar 

  2. Allsop, R. E. (1992). Evolving application of mathematical optimisation in design and operation of individual signal-controlled road junctions. In J. D. Griffths (Ed.), Mathematics in Transport Planning and Control (pp. 1–25). Oxford: Oxford University Press.

    Google Scholar 

  3. Allsop, R. E., & Charlesworth, J. A. (1977). Traffic in a signal-controlled road network: An example of different signal timings inducing different routeings. Traffic Engineering Control, 18, 262–264.

    Google Scholar 

  4. Bell, M. G. H. (2007). Mixed routing strategies for hazardous materials: decision-making under complete uncertainty. International Journal on Sustainable Transportation, 1, 133–142.

    Article  Google Scholar 

  5. Bianco, L., Caramia, M., Giordani, S., & Piccialli. V. (2013). Operations research models for global route planning in hazardous material transportation. In: R. Batta, & C. Kwon (Eds.), Handbook of OR/MS models in hazardous materials transportation. International Series in Operations Research and Management Science (Vol. 193, pp. 49–101). New York: Springer Science+Business Media.

    Google Scholar 

  6. Ceylan, H., & Bell, M. G. H. (2004). Traffic signal timing optimisation based on genetic algorithm approach including drivers’ routing. Transportation Research Part B, 38, 329–342.

    Article  Google Scholar 

  7. Chiou, S.-W. (2003). TRANSYT derivatives for area traffic control optimisation with network equilibrium flows. Transportation Research Part B, 37, 263–290.

    Article  Google Scholar 

  8. Chiou, S.-W. (2005). Joint optimization for area traffic control and network flow. Computers and Operations Research, 32(11), 2821–2841.

    Article  Google Scholar 

  9. Chiou, S.-W. (2005). Bilevel programming for the continuous transport network design problem. Transportation Research Part B, 39(4), 361–383.

    Article  Google Scholar 

  10. Chiou, S.-W. (2010). Optimization of a nonlinear area traffic control system with elastic demand. Automatica, 46, 1626–1635.

    Article  Google Scholar 

  11. Chiou, S.-W. (2015). A bi-level decision support system for uncertain network design with equilibrium flow. Decision Support Systems, 69, 50–58.

    Article  Google Scholar 

  12. Clarke, F. (1983). Optimization and nonsmooth analysis. New York: Wiley.

    Google Scholar 

  13. Clegg, J., Smith, M. J., Xiang, Y., & Yarrow, R. (2001). Bilevel programming applied to optimizing urban transportation. Transportation Research Part B, 35(1), 41–70.

    Article  Google Scholar 

  14. Dahal, K., Almejalli, K., & Hossain, M. A. (2013). Decision support for coordinated road traffic control actions. Decision Support Systems, 54, 962–975.

    Article  Google Scholar 

  15. Dempe, S. (2003). Annotated bibliography on bilevel programming and mathematical programs with equilibrium constraints. Optimization, 52, 333–359.

    Article  Google Scholar 

  16. Erkut, E., & Verter, V. (1998). Modeling of transport risk for hazardous materials. Operations Research, 46, 625–664.

    Article  Google Scholar 

  17. Erkut, E., & Ingolfsson, A. (2000). Catastrophe avoidance models for hazardous materials route planning. Transportation Science, 34, 165–179.

    Article  Google Scholar 

  18. Erkut, E., & Alp, O. (2007). Designing a road network for hazardous materials shipments. Computers and Operations Research, 34, 1389–1405.

    Article  Google Scholar 

  19. Erkut, E., & Gzara, F. (2008). Solving the hazmat transport network design problem. Computers and Operations Research, 35, 2234–2247.

    Article  Google Scholar 

  20. Fernandes, S., Captivo, M. E., & Climaco, J. (2014). A DSS for bicriteria location problems. Decision Support Systems, 57, 224–244.

    Article  Google Scholar 

  21. Kara, B. Y., & Verter, V. (2004). Designing a road network for hazardous materials transportation. Transportation Science, 38, 188–196.

    Article  Google Scholar 

  22. Luo, Z., Pang, J.-S., & Ralph, D. (1996). Mathematical programs with equilibrium constraints. New York: Cambridge University Press.

    Book  Google Scholar 

  23. Marcotte, P., Mercier, A., Savard, G., & Verter, V. (2009). Toll policies for mitigating hazardous materials transport risk. Transportation Science, 43, 228–243.

    Article  Google Scholar 

  24. Outrata, J., Kocvara, M., & Zowe, J. (1998). Nonsmooth approach to optimization problems with equilibrium constraints. Dordrecht: Kluwer Academic Publishers.

    Book  Google Scholar 

  25. Papageorgiou, M., Diakaki, C., Dinopoulou, V., Kotsialos, A., & Wang, Y. (2003). Review of road traffic control strategies. Proceedings of the IEEE, 91, 2043–2067.

    Article  Google Scholar 

  26. Patriksson, M., & Rockafellar, R. T. (2003). Sensitivity analysis of aggregated variational inequality problems, with application to traffic equilibrium. Transportation Science, 37, 56–68.

    Article  Google Scholar 

  27. Power, D. J. (2008). Decision support systems: A historical overview. In F. Burstein & C. W. Holsapple (Eds.), Handbook on decision support systems 1 basic themes (pp. 141–162). Berlin: Springer-Verlag.

    Google Scholar 

  28. Power, D. J., & Sharda, R. (2007). Model-driven decision support systems: Concepts and research directions. Decision Support Systems, 43, 1044–1061.

    Article  Google Scholar 

  29. Power, D. J. (2016). Computerized decision support case study research: Concepts and suggestions. In: J. Papathanasiou, N. Ploskas, & I. Linden (Eds.), Real-world decision support systems case studies. Integrated Series in Information Management (IS2). Berlin: Springer.

    Google Scholar 

  30. Samanlioglu, F. (2013). A multi-objective mathematical model for the industrial hazardous waste location-routing problem. European Journal of Operational Research, 226, 332–340.

    Article  Google Scholar 

  31. Smith, M. J., & Van Vuren, T. (1993). Traffic equilibrium with responsive traffic control. Transportation Science, 27(2), 118–132.

    Article  Google Scholar 

  32. Tan, N. H., Gershwin, S. B., & Athans, M. (1979). Hybrid optimization in urban transport networks. Laboratory for Information and Decision Systems Technical Report DOT-TSC-RSPA-97-7, published by MIT, Cambridge, MA.

    Google Scholar 

  33. Veter, V., & Kara, B. Y. (2008). A path-based approach for hazardous transport network design. Management Science, 54, 29–40.

    Article  Google Scholar 

  34. Vincent, R. A., Mitchell, A. I., & Robertson, D. I. (1980). User guide to TRANSYT. TRRL Report, LR888, Crowthorne: Transport and Road Research Laboratory.

    Google Scholar 

  35. Wong, S. C. (1995). Derivatives of the performance index for the traffic model from TRANSYT. Transportation Research Part B, 29(5), 303–327.

    Article  Google Scholar 

  36. Yang, H., & Yagar, S. (1995). Traffic assignment and signal control in saturated road networks. Transportation Research Part A, 29(2), 125–139.

    Article  Google Scholar 

  37. Zhou, H., Bouyekhf, R., & Moudui, A. El. Modeling and entropy based control of urban transportation network. C.

    Google Scholar 

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Acknowledgements

The author is grateful to editors for their kind comments in earlier version of this manuscript. The work reported has been supported by grant MOST 104-2221-E-259-029-MY3 from Taiwan National Science Council.

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Authors and Affiliations

  1. Department of Information Management, National Dong Hwa University, Hualien County, Taiwan

    Suh-Wen Chiou

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  1. Suh-Wen Chiou
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Correspondence to Suh-Wen Chiou .

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Editors and Affiliations

  1. ETSI Industriales de Ciudad Real, University of Castilla-La Mancha, Ciudad Real, Spain

    Fausto Pedro García Márquez

  2. LeBow College of Business, Drexel University, Philadelphia, PA, USA

    Benjamin Lev

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Chiou, SW. (2019). An Efficient Bundle-Like Algorithm for Data-Driven Multi-objective Bi-level Signal Design for Traffic Networks with Hazardous Material Transportation. In: García Márquez, F., Lev, B. (eds) Data Science and Digital Business. Springer, Cham. https://doi.org/10.1007/978-3-319-95651-0_11

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