Quantifying Distribution Shift in Traffic Signal Control with Histogram-Based GEH Distance

TL;DR

This paper presents a histogram-based GEH distance method to quantify distribution shift in traffic signal control, enabling better prediction of performance degradation across different traffic scenarios.

Contribution

It introduces a novel, interpretable, policy-independent approach to measure distribution shift using demand histograms and GEH distance in traffic signal control.

Findings

Larger scenario distances correlate with increased travel time.

The method predicts performance degradation more effectively than previous techniques.

Strong explanatory power for learning-based traffic control systems.

Abstract

Traffic signal control algorithms are vulnerable to distribution shift, where performance degrades under traffic conditions that differ from those seen during design or training. This paper introduces a principled approach to quantify distribution shift by representing traffic scenarios as demand histograms and comparing them with a GEH-based distance function. The method is policy-independent, interpretable, and leverages a widely used traffic engineering statistic. We validate the approach on 20 simulated scenarios using both a NEMA actuated controller and a reinforcement learning controller (FRAP++). Results show that larger scenario distances consistently correspond to increased travel time and reduced throughput, with particularly strong explanatory power for learning-based control. Overall, this method can predict performance degradation under distribution shift better than previously published techniques. These findings highlight the utility of the proposed framework for benchmarking, training regime design, and monitoring in adaptive traffic signal control.