This case study outlines how to use connected vehicle data to understand impacts to driver behaviour and travel times caused by road closures.
In September 2023, Halton Borough Council in the UK closed the Daresbury Expressway for road works. As a result of the closure, instead of vehicles travelling off Central Expressway and turning right onto Daresbury Expressway, they had to take alternative routes. Specifically, the Borough wanted to understand:
- How did the Daresbury Expressway closure impact journey times?
- Which alternative routes where drivers using?
- Did these closures increase congestion?
The borough analysed origin-destination data at possible detour sites during September 2023, starting from the Mersey Gateway, leading into the Central Expy, and ending in Chester Rd.
Data showed that the closure of Daresbury Expy increased journey times to 10 minutes - an ~8 minute increase. Once the expressway re-opened in November 2023, average journey times decreased to 1 minute and 40 seconds.
Data also showed that most drivers took the same alternative route through the surrounding expressways, indicated by a lack of other smaller routes highlighted on the map
As a result, the council was able to understand how road closures impacted journey times and see the most popular alternate routes used by drivers. A data science dashboard also revealed how trip pathing changed before and during the closure.
Trips before the closure
Trips during the closure; there are no trips visible through the closed section of the road.
We've recorded a video tutorial on how to use Road Intelligence to get these results. View it here.