Reducing Congestion by Using Integrated Corridor Management Technology to Divert Vehicles to Park-and-Ride Facilities

Status

Complete

Project Timeline

April 1, 2022 - March 30, 2023

Principal Investigator

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience Intelligent Transportation Systems, Emerging Technologies, & Big Data

Campus(es)

Electrical Engineering and Computer Science

Project Summary

Considerable advancements have been made in traffic management strategies over the past few decades to enhance user mobility on freeways. Still, the continuous growth of metropolitan regions and increasing mobility needs tend to impede such progress. Pre-pandemic, the recent Urban Mobility Report showed that the total cost of traffic delay in the top urban areas in the U.S.has grown by almost 48 percent over the past decade. In many of these regions (such as in California), freeways experience a great deal of traffic congestion, arising from bottlenecks at which high-volume, free-flowing traffic transforms into tightly packed clusters of low-speed vehicles. As such, transportation planners have given special attention to the notion of integrated corridor management (ICM), which encourages the adoption of global traffic management strategies. In practice, this involves consolidating the various traffic components deployed along the corridor (e.g., ramp meter controllers) into a single interconnected system with a global view of traffic condition along the entire corridor, allowing upstream traffic components to be tuned to relieve downstream bottlenecks. In other words, an ICM strategy can coordinate various traffic control units to optimize their operations along the entire freeway, rather than just on pre-specified settings or in localized areas.

Connected Vehicles (CV) technology offers significant potential for managing traffic congestion and improving mobility along transportation corridors. This project presents a novel approach using integrated corridor management (ICM) technology to divert CVs to underutilized park-and-ride facilities where drivers can park their vehicle and access public transportation. Using vehicle-to-infrastructure (V2I) communication protocols, the system collects data on downstream traffic and sends messages regarding available park-and-ride options to upstream traffic. A deep reinforcement learning (DRL) program controls the messaging, with the objective of maximizing traffic throughput and minimizing CO2 emissions and travel time. The ICM strategy is simulated on a realistic model of Interstate 5 using Veins simulation software. The results show marginal improvement in throughput, freeway travel time, and CO2 emissions, but increased travel delay for drivers choosing to divert to a park-and-ride facility to take public transportation for a portion of their travel.