Empirical analysis of large-scale multimodal traffic with multi-sensor data

Hui Fu, Yefei Wang, Xianma Tang, Nan Zheng, Nikolaos Geroliminis

Research output: Contribution to journalArticleResearchpeer-review

45 Citations (Scopus)


Recent advances in the network-level traffic flow modelling provide an efficient tool for analyzing traffic performance of large-scale networks. A relationship between density and flow at the network level is developed and widely studied, namely the macroscopic fundamental diagram (MFD). Nevertheless, few empirical studies have been dedicated on the empirical evidence on the properties of the MFD for multiple modes of transport and to the best knowledge not yet at the scale of a megacity. This work combines rich, but incomplete data from multiple sources to investigate the vehicle and passenger MFDs for cars and buses in the road network of Shenzhen. A novel algorithm is proposed for partitioning bimodal network considering the homogeneous distribution of link-level car speeds and bus speeds. Furthermore, this paper sheds light on the passenger MFD for mixed car-bus networks. We propose an algorithm to estimate alighting passenger flow and passenger density on bus, by fusing smart card data (i.e. records for boarding passengers) and bus GPS data. We analyze the complexities of passenger flow and the impact of weather on traffic demand and bus occupancy. The results provide an empirical knowledge on multimodal traffic performance with respect to passenger flow. The existence of double hysteresis loops in bus passenger MFD is observed and the causes are explained by considering the influence of service operational features. The three-dimensional vehicle and passenger MFDs are also presented for revealing the complex dynamics characteristics of bimodal road network.

Original languageEnglish
Article number102725
Number of pages16
JournalTransportation Research Part C: Emerging Technologies
Publication statusPublished - Sept 2020


  • Bimodal network partition
  • Macroscopic fundamental diagram
  • Penetration rate
  • Public transport
  • Taxi GPS data
  • Traffic heterogeneity

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