Visualising corridor performance with probe vehicle speed and crash data

Both geometric changes and new technologies are being implemented throughout the United States with the potential to significantly improve the operational capabilities and safety of underperforming corridors. Despite the potential upsides, there are significant up-front costs that can prohibit the widespread adoption of new technologies. This research explores the use of anonymous probe vehicle (APV) speed data and crash data to visualise and rank intersection performance. A methodology was created to produce visually intuitive performance metrics to highlight underperforming road segments. Spearman’s rank correlation (SRC) is utilised to determine road segments with a significant correlation between the observed congestion and the number of crashes, using 360 million APV speed records and 12 000 crash records along a 32 mile (51.5 km) segment of US Route 1 in New Jersey, USA. The study proposes a methodology to analyse the corridor response to crash incidents using vectors to visualise changes in speeds around an incident. With the new visualisation technique and SRC, capital improvement decisions are supported by data-driven funding allocation methods.