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Eco-Friendly Agent Based Advanced Traffic Management Techniques in a Connected Vehicle Environment

  • Author(s): Jin, Qiu
  • Advisor(s): Barth, Matthew
  • et al.
Abstract

Transportation is responsible for one third of greenhouse gases (GHG), as well as a major source of other pollutants including hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOX). Existing transportation systems are facing numerous issues resulting from the increased travel demands and limited capacities of roadway infrastructure. As wireless communication advances, agent-based techniques provide a new perspective to advanced traffic management systems. In both urban arterial and highway networks, vehicles and road infrastructure interact with each other as individual intelligent agents in an integrated environment, which can significantly improve the overall traffic performance in terms of safety, mobility and environmental sustainability, due to knowledge sharing and system-wide decision-making. In this dissertation, we propose a variety of environmentally- friendly agent-based advanced traffic management technologies in a connected vehicle environment.

For agent-based arterial traffic management, we developed an agent-based hierarchical structure for signal-less intersection management system. From the perspective of IMAs, they receive probe vehicle data from VAs, dynamically schedule VAs’ arrival times (by potentially grouping VAs in platoons), reserve intersection time-space occupancies for VAs, and communicate arrival time advices back to VAs. Furthermore, an optimal lane selection algorithm for agent-based traffic management system is developed, which could provide guidance on determining optimal target lanes for individual vehicle agent in order to better regulate traffic flow, thus achieving a system-wide optimal solution in terms of maintaining desired traffic speeds.

On the other hand, vehicle agents use advices to plan their trajectories in order to further minimize energy consumption and pollutant emissions. Firstly, an Eco-Approach and Departure algorithm is introduced and field test has been conducted in Turner Fairbank Highway Research Center on automated vehicle. Secondly, a power-based approach is used to develop an optimal vehicle longitudinal control algorithm for individual vehicles by considering vehicle dynamics (e.g., engine efficiency map), roadway grade and other constraints (e.g., traffic signal status).

For freeway traffic management, a driving simulator study is conducted with truck drivers to evaluate the energy and emissions benefits as well as study the behavioral impact eco-driving may have on truck drivers.

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