Simulation-based joint optimization framework for congestion mitigation in multimodal urban network: a macroscopic approach

  • Takao Dantsuji
  • Daisuke Fukuda
  • Nan ZhengEmail author


Travel demand management (TDM) is an important measure that will aid in the realization of efficient and sustainable transportation systems. However, in cities where the most serious traffic congestion occurs, implementation of a single TDM measure might not be enough to reduce congestion, because the congestion mechanism in this case is highly complex and involves different transportation modes interacting with each other. Implementation of multiple TDM measures has rarely been discussed in the literature. Therefore, in this study, we propose a simulation-based joint optimization framework composed of dedicated bus lanes and vehicular congestion pricing. The objective of the optimization process is to minimize the congestion cost based on an advanced macroscopic flow theory called the multimodal macroscopic fundamental diagram (mMFD), which can capture the macroscopic traffic dynamics of multimodal transportation systems. In the proposed framework, we develop mMFD-based congestion pricing scheme and incorporate traveler’s behavioral model (i.e. joint departure time and mode choices) with the microscopic traffic simulator. We consider the Tokyo central area as a case study. The simulation results indicate that space allocation of 4.7% for the dedicated bus lanes would be optimal for Tokyo’s network, while the optimal congestion pricing scheme indicates that charges of 900 JPY between 7:30 and 8:00 AM and 300 JPY between 8.00 and 8:30 AM should be levied.


Road space allocation Congestion pricing Simulation-based optimization 3D-MFD 



This study was supported by JSPS KAKENHI Grant Number JP 18J15178, the Committee on Advanced Road Technology (CART), Ministry of Land, Infrastructure, Transport, and Tourism, Japan, and partially by JSPS Overseas Challenge Program for Young Researchers. The majority of the work was done during the doctoral study of the first author at Tokyo Institute of Technology. The authors would like to thank anonymous reviewers for their constructive comments that helped improve the details of this manuscript substantially.


  1. Ampountolas, K., Zheng, N., Geroliminis, N.: Macroscopic modelling and robust control of bi-modal multi-region urban road networks. Transp. Res. Part B: Methodol. 104, 616–637 (2017)CrossRefGoogle Scholar
  2. Arnott, R.: A bathtub model of downtown traffic congestion. J. Urban Econ. 76, 110–121 (2013)CrossRefGoogle Scholar
  3. Arnott, R., De Palma, A., Lindsey, R.: A structural model of peak-period congestion: a traffic bottleneck with elastic demand. Am. Econ. Rev. 83(1), 161–179 (1993)Google Scholar
  4. Basso, L.J., Guevara, C.A., Gschwender, A., Fuster, M.: Congestion pricing, transit subsidies and dedicated bus lanes: efficient and practical solutions to congestion. Transp. Policy 18(5), 676–684 (2011)CrossRefGoogle Scholar
  5. Basso, L.J., Silva, H.E.: Efficiency and substitutability of transit subsidies and other urban transport policies. Am. Econ. J.: Econ. Policy 6(4), 1–33 (2014). CrossRefGoogle Scholar
  6. Bequette, B.W.: Process Control: Modeling, Design, and Simulation. Prentice Hall, Upper Saddle River (2010)Google Scholar
  7. Boyac, B., Geroliminis, N.: Estimation of the network capacity for multimodal urban systems. Procedia Soc. Behav. Sci. 16, 803–813 (2011)CrossRefGoogle Scholar
  8. Black, J.A., Lim, P.N., Kim, G.H.: A traffic model for the optimal allocation of arterial road space: a case study of Seoul’s first experimental bus lane. Transp. Plan. Technol. 16(3), 195–207 (1992)CrossRefGoogle Scholar
  9. Carey, M., Srinivasan, A.: Externalities, average and marginal costs, and tolls on congested networks with time-varying flows. Oper. Res. 41(1), 217–231 (1993)CrossRefGoogle Scholar
  10. Castrillon, F., Laval, J.: Impact of buses on the macroscopic fundamental diagram of homogeneous arterial corridors. Transp. B: Transp. Dyn. 6(4), 286–301 (2018)Google Scholar
  11. Chen, X.M., Xiong, C., He, X., Zhu, Z., Zhang, L.: Time-of-day vehicle mileage fees for congestion mitigation and revenue generation: a simulation-based optimization method and its real-world application. Transp. Res. Part C: Emerg. Technol. 63, 71–95 (2016)CrossRefGoogle Scholar
  12. Chen, X., Zhang, L., He, X., Xiong, C., Zhu, Z.: Simulation-based pricing optimization for improving network-wide travel time reliability. Transp. A: Transp. Sci. 14(1–2), 155–176 (2018)Google Scholar
  13. Chiabaut, N.: Evaluation of a multimodal urban arterial: the passenger macroscopic fundamental diagram. Transp. Res. Part B: Methodol. 81, 410–420 (2015)CrossRefGoogle Scholar
  14. Chiabaut, N., Barcet, A.: Demonstration and evaluation of an intermittent bus lane strategy. Public Transp. (2019). CrossRefGoogle Scholar
  15. Chong, L., Osorio, C.: A simulation-based optimization algorithm for dynamic large-scale urban transportation problems. Transp. Sci. 52(3), 637–656 (2017)CrossRefGoogle Scholar
  16. Cleveland, W.S., Grosse, E., Shyu, W.M.: Local regression models. In: Hastie, T.J. (ed.) Statistical Models in S, pp. 309–376. Routledge, Abingdon (1992) CrossRefGoogle Scholar
  17. Dakic, I., Ambühl, L., Schümperlin, O., Menendez, M.: On the modeling of passenger mobility for stochastic bi-modal urban corridors. Transp. Res. Part C: Emerg. Technol. (2019) (in press) Google Scholar
  18. Dantsuji, T., Fukuda, D., Zheng, N.: A macroscopic approach for optimizing road space allocation of bus lanes in multimodal urban networks through simulation analysis: an application to the Tokyo CBD network. Paper Presented at IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 945–952 (2017)Google Scholar
  19. de Palma, A., Lindsey, R.: Traffic congestion pricing methodologies and technologies. Transp. Res. Part C: Emerg. Technol. 19(6), 1377–1399 (2011)CrossRefGoogle Scholar
  20. de Palma, A., Kilani, M., Lindsey, R.: Congestion pricing on a road network: a study using the dynamic equilibrium simulator METROPOLIS. Transp. Res. Part A: Policy Pract. 39(7–9), 588–611 (2005)Google Scholar
  21. Fosgerau, M.: Congestion in the bathtub. Econ. Transp. 4(4), 241–255 (2015)CrossRefGoogle Scholar
  22. Fu, H., Tang, X., Wang, Y., Zheng, N., Geroliminis, N.: Emprical analysis of large-scale multimodal traffic with multi-sensor data: a case study in the Shenzhen network. Paper Presented at the 97th Annual Meeting of the Transportation Research Board, Washington, DC (2018)Google Scholar
  23. Geroliminis, N., Daganzo, C.F.: Existence of urban-scale macroscopic fundamental diagrams: some experimental findings. Transp. Res. Part B: Methodol. 42(9), 759–770 (2008)CrossRefGoogle Scholar
  24. Geroliminis, N., Zheng, N., Ampountolas, K.: A three-dimensional macroscopic fundamental diagram for mixed bi-modal urban networks. Transp. Res. Part C: Emerg. Technol. 42, 168–181 (2014)CrossRefGoogle Scholar
  25. Gonzales, E.J.: Coordinated pricing for cars and transit in cities with hypercongestion. Econ. Transp. 4(1–2), 64–81 (2015)CrossRefGoogle Scholar
  26. Gu, Z., Shafiei, S., Liu, Z., Saberi, M.: Optimal distance-and time-dependent area-based pricing with the network fundamental diagram. Transp. Res. Part C: Emerg. Technol. 95, 1–28 (2018)CrossRefGoogle Scholar
  27. Haitao, H., Yang, K., Liang, H., Menendez, M., Guler, S.I.: Providing public transport priority in the perimeter of urban networks: a bimodal strategy. Transp. Res. Part C: Emerg. Technol. 107, 171–192 (2019)CrossRefGoogle Scholar
  28. Ji, Y., Geroliminis, N.: On the spatial partitioning of urban transportation networks. Transp. Res. Part B: Methodol. 46(10), 1639–1656 (2012)CrossRefGoogle Scholar
  29. Kato, H., Fukuda, D., Yamashita, Y., Iwakura, S., Yai, T.: Latest urban rail demand forecast model system in the Tokyo metropolitan area. Transp. Res. Rec.: J. Transp. Res. Board 2668, 60–77 (2017)CrossRefGoogle Scholar
  30. Liu, W., Geroliminis, N.: Doubly dynamics for multi-modal networks with park-and-ride and adaptive pricing. Transp. Res. Part B: Methodol. 102, 162–179 (2017)CrossRefGoogle Scholar
  31. Loder, A., Ambühl, L., Menendez, M., Axhausen, K.W.: Empirics of multi-modal traffic networks—using the 3D macroscopic fundamental diagram. Transp. Res. Part C: Emerg. Technol. 82, 88–101 (2017)CrossRefGoogle Scholar
  32. Loder, A., Dakic, I., Bressan, L., Ambühl, L., Bliemer, M.C., Menendez, M., Axhausen, K.W.: Capturing network properties with a functional form for the multi-modal macroscopic fundamental diagram. Transp. Res. Part B: Methodol. 129, 1–19 (2019)CrossRefGoogle Scholar
  33. Mesbah, M., Sarvi, M., Ouveysi, I., Currie, G.: Optimization of transit priority in the transportation network using a decomposition methodology. Transp. Res. Part C: Emerg. Technol. 19(2), 363–373 (2011)CrossRefGoogle Scholar
  34. Mesbah, M., Sarvi, M., Currie, G.: New methodology for optimizing transit priority at the network level. Transp. Res. Rec.: J. Transp. Res. Board 2089, 93–100 (2008)CrossRefGoogle Scholar
  35. Nourinejad, M., Ramezani, M. Ride-Sourcing modeling and pricing in non-equilibrium two-sided markets. Transp. Res. Part B: Methodol. (2019) (in press)Google Scholar
  36. Osorio, C., Bierlaire, M.: A simulation-based optimization framework for urban transportation problems. Oper. Res. 61(6), 1333–1345 (2013)CrossRefGoogle Scholar
  37. Osorio, C., Atastoy, B.: Efficient Simulation-Based Toll Optimization for Large-Scale Networks. Technical Report, Massachusetts Institute of Technology (2017)Google Scholar
  38. PACIFIC Exchange Rate Service. Foreign Currency Unit per 1 U.S. Dollar, 1950–2018. Retrieved 6 Nov 2019, from
  39. Radwan, A.E., Benevelli, D.A.: Bus priority strategy: justification and environmental aspects. J. Transp. Eng. 109(1), 88–106 (1983)CrossRefGoogle Scholar
  40. Ramezani, M., Nourinejad, M.: Dynamic modeling and control of taxi services in large-scale urban networks: a macroscopic approach. Transp. Res. Part C: Emerg. Technol. 94, 203–219 (2018)CrossRefGoogle Scholar
  41. Small, K.A.: The scheduling of consumer activities: work trips. Am. Econ. Rev. 72(3), 467–479 (1982)Google Scholar
  42. Truong, L.T., Sarvi, M., Currie, G.: Exploring multiplier effects generated by bus lane combinations. Transp. Res. Rec.: J. Transp. Res. Board 2533, 68–77 (2015)CrossRefGoogle Scholar
  43. TSS: Aimsun 8.2 Microsimulator User’s Manual. Transportation Simulation Systems (2011)Google Scholar
  44. Xu, Z., Yin, Y., Zha, L.: Optimal parking provision for ride-sourcing services. Transp. Res. Part B: Methodol. 105, 559–578 (2017)CrossRefGoogle Scholar
  45. Yang, H., Huang, H.J.: Analysis of the time-varying pricing of a bottleneck with elastic demand using optimal control theory. Transp. Res. Part B: Methodol. 31(6), 425–440 (1997)CrossRefGoogle Scholar
  46. Yang, H., Huang, H.J.: Mathematical and Economic Theory of Road Pricing. Elsevier, Oxford (2005)CrossRefGoogle Scholar
  47. Yang, K., Menendez, M., Zheng, N.: Heterogeneity aware urban traffic control in a connected vehicle environment: A joint framework for congestion pricing and perimeter control. Transp. Res. Part C: Emerg. Technol. 105, 439–455 (2019)CrossRefGoogle Scholar
  48. Yildirimoglu, M., Ramezani, M.: Demand management with limited cooperation among travellers: a doubly dynamic approach. Transp. Res. Part B: Methodol. (2019) (in press)Google Scholar
  49. Yoshii, T., Kuwahara, M.: SOUND: a traffic simulation model for oversaturated traffic flow on urban expressways. In: Preprint at 7th World Conference on Transportation Research, Sydney, Australia (1995)Google Scholar
  50. Zhang, F., Zheng, N., Yang, H., Geroliminis, N.: A systematic analysis of multimodal transport systems with road space distribution and responsive bus service. Transp. Res. Part C: Emerg. Technol. 96, 208–230 (2018)CrossRefGoogle Scholar
  51. Zhang, F., Liu, W.: Responsive bus dispatching strategy in a multi-modal and multi-directional transportation system: a doubly dynamical approach. Transp. Res. Part C: Emerg. Technol. (2019) (in press)Google Scholar
  52. Zheng, N., Waraich, R.A., Axhausen, K.W., Geroliminis, N.: A dynamic cordon pricing scheme combining the macroscopic fundamental diagram and an agent-based traffic model. Transp. Res. Part A: Policy Pract. 46(8), 1291–1303 (2012)Google Scholar
  53. Zheng, N., Geroliminis, N.: On the distribution of urban road space for multimodal congested networks. Transp. Res. Part B: Methodol. 57, 326–341 (2013)CrossRefGoogle Scholar
  54. Zheng, N., Rérat, G., Geroliminis, N.: Time-dependent area-based pricing for multimodal systems with heterogeneous users in an agent-based environment. Transp. Res. Part C: Emerg. Technol. 62, 133–148 (2016)CrossRefGoogle Scholar
  55. Zheng, N., Dantsuji, T., Wang, P., Geroliminis, N.: Macroscopic approach for optimizing road space allocation of bus lanes in multimodal urban networks through simulation analysis. Transp. Res. Rec.: J. Transp. Res. Board 2651, 42–51 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringTokyo Institute of TechnologyTokyoJapan
  2. 2.Department of Civil Engineering, Institute of Transport StudiesMonash UniversityMelbourneAustralia
  3. 3.Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijingChina

Personalised recommendations