Project: #590 Enhanced Crash Risk Estimation in Urban Environments: Integrating Multi-Source Data and Advanced Modeling Approaches for the City of Pittsburgh Progress Report - Reporting Period Ending: Aug. 18, 2026 Principal Investigator: Sean Qian Status: Overdue Project Start Date: July 1, 2025 End Date: June 30, 2026 Research Type: None Grant Type: Research Advanced Grant Program: US DOT BIL, Safety21, 2023 - 2028 (4811) Grant Cycle: Safety21 : 25-26 Progress Report (Last Updated: Aug. 18, 2026, 2:39 p.m.) % Project Completed to Date: None % Grant Award Expended: None % Match Expended & Document: None USDOT Requirements Accomplishments Crash risk estimation faces several important challenges. First, crash risk estimation is a data-intensive task. As multiple factors may affect crash risk on the streets, it is important to aggregate the datasets of those influential factors in the modeling process. Traditional risk estimation methods use observed crash data on major roads only to generate risk maps (e.g. the risk maps created based on PennDOT crash data), but it may not effectively reflect the true risky location or time as a result of highly sporadic and random nature of crashes. This is evident by inconsistency between the City of Pittsburgh crash risk maps and anecdotally reported 311 calls regarding citizens’ risk assessment and witnesses near-miss occurrences. Second, the estimation performance is heavily depended on the modeling approach. Different modeling approaches may perform variously on different datasets and the types of crash risk (e.g., motorized and non-motorized). Finally, crash risk estimation is subject to selection bias. Since crash risk is usually fluctuating along the time, it would be possible that the selected period of crashes for modeling is located in high-crash-frequency or low-crash-frequency years, which may bias the estimation. To address these challenges, this research project applies multiple datasets, multiple modeling approaches, and long-term historical crash data (including near-misses estimation) to estimate the crash risk of the street segments in the city of Pittsburgh. Impacts Improve the process of estimating High Injury Network for more informed decision making, particularly related to safety action plans. Other New methods, and analytical models. Outcomes New Partners n/a Issues n/a