Eco-Friendly Intelligent Transportation System Technology for Freight Vehicles

Kari, David; Wu, Guoyuan; Barth, Matthew · 2017 · ROSA P / National Center for Sustainable Transportation (NCST) (UTC)

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Summary

This research report addresses the disproportionate environmental impact of heavy-duty freight vehicles, which contribute significantly to greenhouse gas emissions despite representing a small fraction of vehicle miles traveled. The study aims to develop an eco-friendly Intelligent Transportation System (ITS) application for arterial roadways by improving traffic signal control. Conventional adaptive signal control strategies rely on point detection sensors, such as inductive loop detectors, which provide limited information and often result in suboptimal performance under varying traffic conditions. To overcome these limitations, the authors propose an agent-based online adaptive signal control (ASC) strategy utilizing Connected Vehicle (CV) technology. This approach leverages wireless vehicle-to-infrastructure communications to access real-time, comprehensive traffic data, enabling dynamic optimization of signal phase and timing to reduce travel delay and energy consumption. The methodology employs a multi-agent system architecture consisting of Vehicle Agents (VA) and an Intersection Management Agent (IMA). The VAs transmit ego-information, such as location and speed, to the IMA, which determines optimal signal timing based on a selected Measure of Effectiveness (MOE). The authors selected queue length as the primary MOE due to its computational tractability, accuracy, and privacy benefits. To prevent "green starvation" and ensure fairness, the optimizer incorporates aging factors that weight queue lengths based on the time elapsed since a phase was last served. The system utilizes a flexible dual-ring controller model, allowing for independent operation of signal phases rather than fixed sequences. The proposed CV-ASC strategy was evaluated using microscopic traffic simulations in PARAMICS, integrated with EPA’s MOVES software for environmental metrics. The study tested the strategy at both an isolated intersection and a corridor of intersections, comparing it against baseline methods including fixed-phase timing, Webster’s formula, and Highway Capacity Manual (HCM) based methods. Sensitivity analyses were conducted across various traffic volumes and demand profiles. The results indicate that the proposed CV-ASC strategy outperforms conventional HCM-based methods in both isolated intersection and corridor contexts. The system demonstrated robustness to variations in traffic demand, effectively reducing travel delay and energy consumption. By optimizing signal timing in real-time based on actual vehicle presence and speed, the strategy mitigates the inefficiencies associated with pre-timed or point-detection-based controls. The flexible state machine approach allowed for dynamic adjustments, such as green extensions and phase insertions, which improved overall traffic flow. The significance of this work lies in providing a computationally attractive and structurally flexible framework for online adaptive signal control that specifically addresses the environmental impact of freight vehicles. The study demonstrates that CV technology can enhance the efficiency of existing roadway infrastructure without the need for costly physical expansions. The proposed system offers a scalable solution for urban areas facing growing congestion and emission challenges, providing a pathway for integrating ITS applications to support sustainable transportation goals. The findings suggest that leveraging real-time CV data for signal optimization is a viable method for reducing the disproportionate emissions associated with heavy-duty freight traffic.

Key finding

The Connected Vehicle Adaptive Signal Control strategy outperforms Highway Capacity Manual-based methods and fixed timing strategies in reducing travel delay and energy consumption for both isolated intersections and corridors.

Methodology

simulator

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• traffic density