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Advanced ITS with data fusion

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Advanced ITS with data fusion

Build more efficient intelligent transportation system using data fusion technology

Traffic demand estimation

Data-driven human mobility modeling has recently experienced rapid developments with the wider availability of banknote data, mobile phone data, floating car data, and smartcard data. Yet, there are still lacking approaches to capture real-time human mobility in a sustainable and economical manner.

In this study, we develop a novel real-time human mobility model through combining the advantages of mobile phone signaling data (i.e., comprehensive penetration in a population) and transportation data (i.e., continuous collection and high accuracy). Using the proposed mobility model, travel demands during each 1-hour time window were estimated for the city of Shenzhen, China. Strikingly, the estimated travel demands not only preserved the distribution of ground-truth travel demands, but also captured real-time bursts of mobility fluxes during large crowding events. Finally, based on the proposed mobility model, a predictive model is deployed to predict crowd gatherings that usually cause severe traffic jams.

Estimating real-time mobility flows between OD pairs with different ranges of scaling factors β.

Related publications:

  • Huang, Z., Ling, X., Wang, P., Zhang, F., Mao, Y., Lin, T., Wang, F., 2018. Modeling real-time human mobility based on mobile phone and transportation data fusion. Transp. Res. Part C 96, 251–269.

Traffic state estimation

Framework to estimate traffic speed based on three GPS datasets

Related publications:

  • Huang, Z., Wang, P., Lai, J., Liu, Y., Lin, T., 2019. A data fusion framework to improve traffic speed estimation in urban road networks. Submitted to IEEE Trans. Intell. Transp. Syst.

  • Huang, Z., Wang, P., Liu, Y., Lin, T., 2019. Identifying the transportation modes of mobile phone GPS trajectories. Submitted to IET Intell. Transp. Syst.

Traffic flow estimation

Related publications:

  • Wang, P., Lai, J., Huang, Z., Tan, Q., Lin, T., 2019. A Data-Driven Approach to Estimate Traffic Flow in Large Road Networks. Submitted to IEEE Trans. Intell. Transp. Syst.

Traffic control strategy

A multi-source data-driven traffic control approach is developed to alleviate traffic overload at bottleneck road segments. In the proposed approach, the high-penetration feature of mobile-phone signalling data and the real-time feature of taxi global-positioning-system data are combined to simulate traffic flows in the road network of Shenzhen, a major city of southern China. The road intersections for implementing traffic control schemes are selected by locating the major vehicle sources of the bottleneck road segments, and a genetic algorithm was used to solve the dynamic traffic control schemes. Two important bottleneck road segments in Shenzhen were used as case studies to test the effectiveness of the proposed approach.

Flowchart of traffic simulation method

Related publications:

Zhiren Huang
Postdoctoral researcher

My research interests include network science, machine learning and transportation engineering.


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