Abstract Manager

Urban traffic safety is crucial for preventing accidents, reducing violations, and protecting all road users, including pedestrians and cyclists. Automated Speed Enforcement (ASE) systems play a key role in deterring speeding, lowering traffic-related deaths, and improving overall community safety. ASE cameras provide consistent and unbiased monitoring, which enhances law enforcement efforts and promotes a culture of compliance among drivers. Additionally, by targeting high-risk areas, ASE systems help to create safer environments around schools, parks, and residential neighborhoods. This study examines the economic aspect of deploying ASE cameras, using a data-driven approach to optimize their placement while considering associated costs. It introduces an interactive tool that integrates datasets such as traffic volume, speed data, collision reports, citation records, and infrastructure information to identify Community Safety Zones (CSZs) and high-priority road segments requiring intervention. By applying adjustable scoring criteria, the model evaluates and ranks road segments based on factors like speed patterns and proximity to sensitive locations, such as schools, assigning them to CSZs. An optimization model is integrated to efficiently deploy and manage ASE cameras within these CSZs, maximizing their impact on traffic safety.

ASE camera planning involves addressing financial and budgeting requirements to ensure economic feasibility and program sustainability. This study and its developed tool consider labor costs, camera expenses, and processing fees, integrating them in addition to the optimization model to provide a complete financial evaluation. We use a prediction model to forecast reductions in speed violations and expected citations, allowing for the calculation of final ticket revenue. By comparing these revenues with the deployment costs, the tool offers net cost and profit estimates to evaluate the financial impact of ASE deployments. Additionally, the tool displays key metric such as frequency distributions of cameras across wards and cost related metrics, to make it easier to compare and make informed budgeting decisions for camera placement and relocation.

This study and its tool enhance ASE program efficiency and feasibility by combining economic analysis with practical analytics tailored to each city's unique characteristics, advancing urban traffic safety management. By streamlining the identification, assessment, and implementation of ASE measures, the tool allows urban planners to perform comprehensive, economically informed analyses and decisions quickly and effectively.