Active Demand Management

For decades, the most widely applied solution to traffic congestion has been building more roads to better accommodate traffic demand, which turns out to be of limited effect and not sustainable. The increasingly considered strategy called Active Traffic and Demand Management (ATDM), on the contrary, aims at a better balance between the need to travel a particular route at a particular time and the capacity of available facilities to handle this demand efficiently. ATDM is being considered by the Federal Highway Administration (FHWA) as market-ready technologies and innovative operational approaches that are becoming available for managing traffic congestion within the existing infrastructure.

Metropia, A startup company
An incentive-based Active Traffic and Demand Management system called “Metropia,” was a University spinoff that I spent quite some time on. It predicts future traffic conditions, applies an advanced routing algorithm to produce the best route and future departure times, providing travelers with multiple departure time choices, with each choice associated with predicted travel time as well as its respective reward. The goal of this ATDM system is to effectively alleviate traffic congestion in cities by incentivizing travelers to trigger the behavior change and avoid traffic congestion. The level of rewards provided by the system depends on the travelers’ behavioral change degree and the contribution to traffic congestion alleviation. The field study results of such system in Los Angeles, Calif., USA, shows promising traveler behavior changes and travel time savings. ​

I was a founding team member and has played various roles at Metropia between 2011 and 2017 (Student, programmer, algorithm developer, system architect, the person who managed cloud server, the guy who developed API interfaces, the routing guy, project manager, product manager) . By the time I left, my “official” title was Director of R&D and General Manager of its subsidiary company in China. It was a beautiful journey. I enjoyed, learned, suffered, and am thankful for the unique experience that I was able to be part of.

Why Does Traffic Congestion Happen?

What is Metropia?

How to Use Metropia App?

Usage Based Insurance

Traditional insurance models are based on a combination of static factors such as the driver’s socio-demographic profile and vehicle information in conjunction with driving history. With the advent of new mobile technologies, some insurance companies have begun exploring a new auto insurance model known as Usage-Based-Insurance [UBI] or Pay-As-You-Drive-And-You-Save [PAYDAYS], which aims to incorporate individualized, real-world dynamic driving patterns into actuarial pricing. While most existing UBI or PAYDAYS efforts can be considered a major leap forward, the majority of researchers and insurers currently rely solely on user GPS trajectories, which only measure the specific driver’s performance–such as the number of miles driven, travelling speeds, and hardness of braking–without considering other critical risk factors in the surrounding environment that may also contribute to crash risk, i.e. the contextual-sensitive risk factors.

The research team proposed that the consideration of these contextual risk factors would offset risk for automobile insurers providing PAYDAYS coverage and lead to greater savings for low-risk drivers operating under such a policy. In September of 2013, The Federal Highway Administration contracted a research project to prove their PAYDAYS theory and study the implications of such an approach to insurance companies’ premium calculation model. In this research project, we collected individualized driving behavior data from the smartphone GPS module, combined with geographical network information and dynamic traffic conditions, to identify driving risk factors and evaluate driving behaviors under various contexts.

Executive Summary Video

For more details of this PAYDAYS project please click here

Relevant Publications​

  1. Chen, C.#, Hu, X.*, Li, Y.#, Tang, Q.# (2023). Optimization of Privacy Budget Allocation In Differential Privacy-Based Public Transit Trajectory Data Publishing for Smart Mobility Applications. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2023.3309783
  2. Song, Y.#, Hu, X. *, Lu, J., Zhou, X. (2022). Analytical Approximation and Calibration of Roundabout Capacity: A Merging State Transition-based Modeling Approach. Transportation Research Part B: Methodological. https://doi.org/10.1016/j.trb.2022.07.006
  3. Tang, Q.#, Hu, X.*, Lu, J., Zhou, X. (2021). Analytical characterization of multi-state effective discharge rates for bus-only lane conversion scheduling problem. Transportation Research Part B: Methodological, 148, 106-131. https://doi.org/10.1016/j.trb.2021.04.008
  4. Xianbiao Hu*, Xiaoyu Zhu, Yi-Chang Chiu, Qing Tang#. Will Information and Incentive Affect Traveler’s Day-to-Day Departure Time Decisions? –  An Empirical Study of Decision Making Evolution Process. International Journal of Sustainable Transportation. 2020. https://doi.org/10.1080/15568318.2019.1570402
  5. Qing Tang, Xianbiao Hu*. Triggering behavior changes with information and incentives: An active traffic and demand management-oriented review. Advances in Transport Policy and Planning, Academic Press. 2019. https://doi.org/10.1016/bs.atpp.2019.05.002
  6. Xianbiao Hu*, Yifei Yuan, Xiaoyu Zhu, Hong Yang, Kun Xie. Behavioral Responses to Pre-Planned Road Capacity Reduction Based on Smartphone GPS Trajectory Data – A Functional Data Analysis Approach. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations. 2018. https://doi.org/10.1155/2020/8836511
  7. Yuluen Ma, Xiaoyu Zhu, Xianbiao Hu*, Yi-Chang Chiu. The Use of Context-Sensitive Insurance Telematics Data in Auto Insurance Rate Making. Transportation Research Part A: Policy and Practice. Volume 113, July 2018, Pages 243-258. https://doi.org/10.1016/j.tra.2018.04.013
  8. Zhu, Xiaoyu, Yifei Yuan, Xianbiao Hu, Yi-Chang Chiu, and Yu-Luen Ma. “A Bayesian Network model for contextual versus non-contextual driving behavior assessment.” Transportation Research Part C: Emerging Technologies 81 (2017): 172-187. https://doi.org/10.1016/j.trc.2017.05.015
  9. Xianbiao Hu*, Yi-Chang Chiu, Jorge A. Villalobos, and Eric Nava. “A Sequential Decomposition Framework and Method for Calibrating Dynamic Origin-Destination Demand in a Congested Network.” IEEE Transactions on Intelligent Transportation Systems. 2017. https://doi.org/10.1109/TITS.2017.2661751
  10. Xianbiao Hu*, Yi-Chang Chiu, Jeff Shelton. Development of a Behaviorally Induced System Optimal Travel Demand Management System. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations. 2016. https://doi.org/10.1080/15472450.2016.1171151
  11. Xianbiao Hu*, Yi-Chang Chiu, Lei Zhu. Behavior Insights for Incentive-Based Active Demand Management Platform. International Journal of Transportation Science and Technology, vol. 4 no. 2 2015, pp. 119-134. https://doi.org/10.1260/2046-0430.4.2.119
  12. Xianbiao Hu*, Yi-Chang Chiu. A Constrained Time-Dependent K Shortest Paths Algorithm Addressing Overlap and Travel Time Deviation. International Journal of Transportation Science and Technology, vol. 4 no. 4, 2015, pp. 371-394. https://doi.org/10.1016/S2046-0430(16)30169-1
  13. An, Kang, Yi-Chang Chiu, Xianbiao Hu, and Xiaohong Chen. “A Network Partitioning Algorithmic Approach for Macroscopic Fundamental Diagram-Based Hierarchical Traffic Network Management.” IEEE Transactions on Intelligent Transportation Systems. 2017. https://doi.org/10.1109/TITS.2017.2713808