Lanzaro, Gabriel (University of British Columbia), Sayed, Tarek (University of British Columbia)

Optimizing Pedestrian Safety in Real-Time: An Extreme Value Theory-Based Reinforcement Learning Framework

Scheduled for presentation during the Regular Session "S06b-Safety, Sensing, and Infrastructure Design for Vulnerable Road Users" (WE-EA-T6), Wednesday, November 19, 2025, 14:30−14:50, Surfers Paradise 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Abstract

As cities continue to encourage active transportation, increased focus has been placed on pedestrian safety given their vulnerability and proneness to more severe crashes in environments where they coexist with other road users. Many cities have introduced pedestrian-friendly designs, such as raised crosswalks, narrowed lanes, and signal timing changes like Leading Pedestrian Intervals (LPIs), which allow pedestrians to enter intersections ahead of vehicles. This improves visibility, minimizes the number of unsafe interactions, and reduces the crash risk overall. Simultaneously, cities have been preparing for the massive introduction of connected and autonomous vehicles (CAVs), which will use vehicle-to-everything (V2X) communications to interact with infrastructure and enable real-time traffic optimization through Actuated Traffic Signal Controls (ATSCs). Despite these advances, few studies have focused on incorporating pedestrian safety metrics into the operation and optimization of advanced ATSCs. This work develops a novel framework that integrates real-time crash risk metrics using Extreme Value Theory (EVT) with a Reinforcement Learning (RL) controller that dynamically adjusts signal timings based on real-time traffic conditions. The proposed system selects between introducing an LPI or maintaining the standard phase sequence at each signal cycle by using a multi-objective function that considers both safety and mobility. Case studies at two intersections in Vancouver, Canada, show that the approach can substantially improve pedestrian safety while preserving acceptable traffic performance levels for all users. This is especially useful for locations with high pedestrian activity, where the algorithm is able to improve their safety without compromising mobility.