Data-Driven Mobility Strategies for Multi-Modal Transportation

Yao-Jan Wu, University of Arizona


  • Xianfeng Yang, University of Utah
  • Sirisha Kothuri, Portland State University


By using various modes (e.g., walking, cycling, automobile, public transit, etc.), multi-modal transportation systems are effective in increasing people’s travel flexibility and reducing congestion. Hence, it is critical to understand how roadway speed management strategies would affect people’s mode choices. Additionally, with advanced technology, such as connected autonomous vehicle systems, we are now facing a transition from traditional urban planning to developing smart cities. To support multimodal transportation planning, this project will pave a bridge to connect speed management strategies of conventional signalized arterial to connected vehicle corridor. The research outcomes will help decision-makers understand the data and infrastructure needs in supporting future multimodal planning tasks and speed management. Multiple data resources, such as Pems and ATSPM from UDOT and traffic sensor data from PCDOT, will be used for this study. Our research team, from U of Utah and U or Arizona, will develop data-driven approaches to achieve three primary objectives. The first objective of this project is to evaluate arterial speed management plans and investigate the impact of deploying speed feedback signs. We will explore the relationship among speed feedback signs, posted speed limit enforcement, and intersection capacity, and investigate how these features may impact multi-modal transportation mobility and safety. Particularly, we will study how buses, vehicles, pedestrians, and bicyclists are affected by the current speed management strategies. The second objective is to understand the role of speed management strategies in supporting smart city operational functions. Starting from 2016, UDOT has launched a project to build a full Dedicated-Short-Range-Communications (DSRC) corridor for CV technology testing. In this project, our team will work closely with UDOT for studying the impact of multi-modal speed management plans on the CV corridor. The last objective is to utilize big data to understand the interrelations among speed management, safety, congestion, travelers’ route choice. The research findings will help the cities be prepared for the coming of shared self-driving cars.

Project Details

Project Type:
Project Status:
In Progress
End Date:
January 31,2021
UTC Grant Cycle:
NITC 16 Round 3
UTC Funding:

Other Products

  • Zhang, Zhao, & Yang, Xianfeng. (2020). Freeway Traffic Speed Estimation by Regression Machine-Learning Techniques Using Probe Vehicle and Sensor Detector Data. Journal of Transportation Engineering, Part A, 146(12), Journal of transportation engineering, Part A, 2020-12-01, Vol.146 (12). (PUBLICATION)
  • Zhang, Z., Yuan, Y., & Yang, X. F. A Hybrid Machine Learning Approach for Freeway Traffic Speed Estimation. Transportation Research Record. doi:10.1177/0361198120935875 (PUBLICATION)
  • Wang, Q. Z., Yang, X. F., Huang, Z. T., & Yuan, Y. (2020). Multi-Vehicle Trajectory Design During Cooperative Adaptive Cruise Control Platoon Formation. Transportation Research Record, 2674(4), 30-41. doi:10.1177/0361198120913290 (PUBLICATION)