Connected Vehicle Data-driven Robust Optimization for Traffic Signal Timing: Modeling Traffic Flow Variability and Errors

TL;DR

This paper introduces a robust optimization framework for traffic signal timing using connected vehicle data, explicitly accounting for traffic flow variability and estimation errors to improve traffic management.

Contribution

It proposes a novel CV data-driven robust optimization model that incorporates traffic flow uncertainties without relying on error-prone arrival rate estimates.

Findings

CV-RO outperforms traditional models across various scenarios

Effective at low CV penetration rates and high traffic fluctuations

Provides a deterministic mixed-integer linear programming solution

Abstract

Recent advancements in Connected Vehicle (CV) technology have prompted research on leveraging CV data for more effective traffic management. Despite the low penetration rate, such detailed CV data has demonstrated great potential in improving traffic signal performance. However, existing studies share a common shortcoming in that they all ignore traffic flow estimation errors in their modeling process, which is inevitable due to the sampling observation nature of CVs. This study proposes a CV data-driven robust optimization framework for traffic signal timing accounting for both traffic flow variability and estimation errors. First, we propose a general CV data-driven optimization model that can be widely applied to various signalized intersection scenarios including under-/over-saturated and fixed-/real-time. Then, we propose a novel data-driven uncertainty set of arrival rates based on the bounds information derived from CVs, which circumvents the error-prone arrival rate estimation process. Finally, a CV data-driven robust optimization model (CV-RO) is formulated to explicitly handle arrival rate uncertainties. By means of the robust counterpart approach, this robust optimization problem can be equalized to a deterministic mixed-integer linear programming problem with an exact solution. The evaluation results highlight the superior performance of the CV-RO model compared to the deterministic model and traditional methods across various scenarios: different penetration rates, traffic demands, and control types. Notably, the CV-RO model demonstrates its excellence at lower CV penetration rates and in the presence of different traffic flow fluctuation levels, affirming its effectiveness and robustness.