Multi-criteria analysis of optimal signal plans using microscopic traffic models

Ma, Xiaoliang; Jin, Junchen; Lei, Wei Ning · 2014 · OpenAlex-citations

DOI: 10.1016/j.trd.2014.06.013

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the growing need for sustainable traffic management by integrating environmental and energy efficiency goals into traffic signal optimization. While traditional signal planning focuses primarily on mobility metrics like travel delay, current practices often neglect the significant environmental impacts of road traffic, including greenhouse gas emissions and fuel consumption. The study aims to demonstrate a model-based framework that quantifies these environmental impacts and evaluates optimal signal plans for isolated intersections, balancing mobility performance with sustainability measures. The methodology employs a computational engine that integrates microscopic traffic simulation with micro-scale emission modeling. Two traffic simulators, VISSIM and SUMO, were calibrated using field data collected from an intersection in Wuhan, China, to ensure simulation fidelity. Driver behavior parameters were tuned using observed traffic flow rates and speed distributions. These simulators were coupled with the Comprehensive Modal Emission Model (CMEM), which estimates second-by-second tailpipe emissions and fuel consumption based on vehicle dynamics. A Genetic Algorithm (GA) was implemented as a stochastic optimization engine to search for optimal signal parameters. The GA utilized adaptive mutation probabilities and parallel computing to handle the high computational costs of running multiple simulation replications with different random seeds. The study optimized two control schemes: Fixed Time (FT) and Vehicle Actuated (VA). The optimization targeted various policy goals, including minimizing travel delay, reducing stop-and-go maneuvers, improving fuel economy, and lowering emissions (CO, HC, and NOx). An integrated performance index was also used to find compromises between conflicting objectives. Results from both VISSIM and SUMO were consistent, showing that optimal signal plans derived for specific sustainability goals significantly outperformed the baseline fixed-time plan. Optimizing for fuel efficiency or emissions resulted in measurable reductions in these metrics compared to the baseline, while also generally improving mobility indicators. The GA operator tests identified a specific combination of tournament selection, uniform crossover, and bit-flip mutation as the most efficient for convergence. The significance of this work lies in providing a validated, general software framework for evaluating traffic signal plans against multiple sustainability criteria. It demonstrates that microscopic simulation integrated with emission modeling can effectively guide traffic management policies toward ecological transport. By showing that signal optimization can simultaneously improve mobility and reduce environmental impacts, the study offers a practical tool for planners to prioritize policy goals in environment and energy, addressing the urgent need for sustainable urban traffic solutions in rapidly developing regions.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• regulatory evaluation