Active learning for multi-objective optimal road congestion pricing considering negative land use effect

Shaopeng Zhong, Yunhai Gong, Zhijian Zhou, Rong Cheng, Feng Xiao*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


The road congestion pricing policy is implemented to alleviate traffic congestion and improve the efficiency of the transportation system during peak hours. However, the negative land use effect caused by this policy could not be ignored. How to design the optimal congestion toll that can not only ensure its positive effect on the transportation system but also reduce its negative effect on land use is an urgent problem to be solved. Given this, this paper proposed a multi-objective bi-level programming road congestion pricing model based on the integrated land use and transportation model to optimize the regional average accessibility, regional average land use diversity, and regional total flow time. Since the proposed problem is NP-hard, this paper innovatively proposed an active learning optimization algorithm based on multi-objective Bayesian optimization, which improves the computation efficiency of the bi-level programming model by automatically finding the next sampling point (candidate solution) according to the probability information. An empirical analysis of Jiangyin City demonstrated the effectiveness of the proposed approach in coordinating the relationship between land use and transportation and alleviating the negative land use effect caused by road congestion pricing. Moreover, the algorithm proposed in this paper can also be used to solve other transportation-related black box problems with high computation complexity.
Original languageEnglish
Article number103002
JournalTransportation Research Part C: Emerging Technologies
Publication statusPublished - 2021


  • Road congestion pricing
  • Negative land use effect
  • Integrated land use and transportation model
  • Probabilistic surrogate model
  • Multi-objective Bayesian optimization
  • Simulation-based optimization


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