# TuSeRACT: Turn-Sample-Based Real-Time Traffic Signal Control

## TL;DR

TuSeRACT is a novel distributed traffic signal control method that uses turn sampling to better handle turn-induced uncertainty, significantly reducing vehicle waiting times compared to previous approaches like SURTRAC.

## Contribution

It introduces a sample-based scheduling approach for real-time traffic control that improves delay minimization under turn uncertainty, addressing limitations of existing methods.

## Key findings

- TuSeRACT reduces mean vehicular waiting times significantly.
- It outperforms SURTRAC in synthetic traffic network evaluations.
- Sample-based scheduling enhances real-time responsiveness and accuracy.

## Abstract

Real-time traffic signal control is a challenging problem owing to constantly changing traffic demand patterns, limited planning time and various sources of uncertainty (e.g., turn movements, vehicle detection) in the real world. SURTRAC (Scalable URban TRAffic Control) is a recently developed traffic signal control approach which computes delay-minimizing and coordinated (across neighbouring traffic lights) schedules of oncoming vehicle clusters in real time. To ensure real-time responsiveness in the presence of turn-induced uncertainty, SURTRAC computes schedules which minimize the delay for the expected turn movements as opposed to minimizing the expected delay under turn-induced uncertainty. This approximation ensures real-time tractability, but degrades solution quality in the presence of turn-induced uncertainty. To address this limitation, we introduce TuSeRACT (Turn Sample based Real-time trAffic signal ConTrol), a distributed sample-based scheduling approach to traffic signal control. Unlike SURTRAC, TuSeRACT computes schedules that minimize expected delay over sampled turn movements of observed traffic, and communicates samples of traffic outflows to neighbouring intersections. We formulate this sample-based scheduling problem as a constraint program and empirically evaluate our approach on synthetic traffic networks. Our approach provides substantially lower mean vehicular waiting times relative to SURTRAC.