A dynamic merge assistance method based on the concept of instantaneous virtual trajectory for vehicle-to-infrastructure connected vehicles

Xiaowen Jiang, Peter J. Jin, Yizhou Wang

Research output: Contribution to journalArticle

Abstract

Lane-changing activities near freeway merging, diverging, and weaving sections are one of the major factors leading to recurrent bottleneck congestion. In recent years, microscopic dynamic merging assistance (DMA) methods are found efficient to improve mobility and safety in merging areas. The article proposes a solution, the connected vehicle (CV) vehicle-to-infrastructure (V2I)-based dynamic merge assistance (DMA) method, analyzing vehicle trajectory data collected at CV roadside units (RSU) and implements a “vehicle-gap” pairing process for vehicle-gap synchronized control. The vehicle-gap pairing is determined by dynamically predicting their merging potential per the relationship between the “instantaneous virtual trajectories” (IVTs) of mainline and onramp vehicles. The IVTs are generated according to the prevailing speed profiles on both ramp and mainline through lane. Other than those automated control-based methods, this article focuses on providing a realistic speed instruction to human drivers through CV communication. A Cooperative Adaptive Cruise Control model is adopted to calculate such speed instruction. The paired onramp/mainline vehicles who follow the speed instruction can form a putative platoon so that their speed and location can be synchronized all through the merging area to ensure the gap-opening and catching-up. The proposed method is evaluated within a simulation network based on the field data collected on Interstate 35 at Austin, TX. The proposed method is tested in a microscopic traffic simulation environment using VISSIM external driver model Application Programing Interface. The simulation results indicated significant reduced average travel time at merge sections during congestion and increased time-to-collision during merging compared with other existing microscopic merge control models.

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All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Automotive Engineering
  • Aerospace Engineering
  • Computer Science Applications
  • Applied Mathematics

Keywords

  • connected vehicle
  • dynamic merge control
  • instantaneous virtual trajectory
  • vehicle-to-infrastructure (V2I) application

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