Context-aware long-term prediction of incident hotspots

Traffic incidents, such as crashes, hazards and vehicle breakdowns, significantly impact road network efficiency. Existing systems lack prediction capabilities for these events, resulting in reactive emergency responses. Long-term prediction of incident hotspots can facilitate proactive resource allocation and help improve emergency response efficiency.

Due to the complex spatiotemporal correlations of incidents across different regions, traditional time-series forecasting methods fall short as they are unable to effectively model spatial dependencies or integrate multi-scale, multimodal information.

This project will use advanced AI approaches that fuse incident records with contextual data (e.g., weather, road topology, special events) to predict long-term incident hotspots, improving strategic planning and resource allocation

Participants

• Transport for NSW
• University of Technology Sydney

Project background

Traffic incidents such as crashes, hazards, and vehicle breakdowns significantly affect traffic flow and road safety. Emergency response to traffic incidents remains largely reactive due to the lack of reliable predictive capabilities.

Building on the cleaned and consolidated incident records from iMOVE project 1-093 (Streamlining and integrating incident data), this project aims to develop an AI-driven predictive system to forecast long-term incident hotspots.

By integrating historical incident data with contextual factors such as weather conditions, road topology, and special events, the system will enhance proactive traffic management.

Traditional time-series forecasting methods are insufficient for this task, as they fail to capture the complex spatiotemporal relationships between incidents across different regions or incorporate multi-scale, multimodal data.

This project will leverage advanced AI techniques, such as graph neural networks and transformer-based models, to analyse these dependencies and generate predictive hotspot maps. By doing so, it will enable proactive resource allocation and optimised emergency response planning.

The expected outcomes include enhanced public safety, reduced incident response times, and improved road user experience. Ultimately, this research seeks to shift traffic incident management from a reactive to a proactive and predictive approach, contributing to smarter and more resilient transportation systems.

Project objective

The project objective is to develop an AI-driven predictive system to forecast long-term (i.e., monthly and quarterly) incident hotspots and visualise them and their dynamics on the map.

Please note …

This page will be a living record of this project. As it matures, hits milestones, etc., we’ll continue to add information, links, images, interviews and more. Watch this space!