Car Park Queue Prediction

Tender description

The Greater Cambridge Partnership is aiming to ease congestion and prioritise greener and active travel, making it easier for people to travel by bus, rail, cycle or on foot and to improve average journey time (4.87 minutes per mile in the peak hour in 2015 as well as reducing Congestion in peak-time traffic levels in Cambridge by 10-15% based on a 2011 baseline by 2031). One of the ways that modal shift can be achieved is by giving travellers better information which can nudge behavior. Currently it's difficult for travellers to be able to accurately compare the difference in journey times between bus trips through services like the Park and Ride and travel times by car. This is made particularly difficult at busy times when cars can spend a significant amount of time queueing to get into car parks. By being able to accurately predict the time it will take for a driver to get into a car park and thus the amount of time spent queueing we can; • Give a more realistic comparison of public versus non-public transport • Be able to manage routing to car parks better - look to manage queues which can cause delays on roads better • Support other work looking at Air Quality to understand the impact of standing traffic The supplier will conduct a trial for a max period of a year at the Grand Arcade Car Park to create a model which gives accurate predictions of queue times but at a cost that is sustainable in the long term. This information will then be used along with travel time information to give accurate travel time messages on the way into town via VMS screens at a point where drivers can make a choice to use a Park and Ride service instead. It is anticipate that the supplier will need to deploy data collection infrastructure and this would be enabled by the GCP. As part of the project we will look to put in place a robust methodology for measuring the impact that these messages have on behavior in both a quantitative and qualitative way. The project will • address a widely recognized piece of information that is currently missing, and the solution would support wider GCP objectives - • If the pilot at Grand Arcade is successful, it provides a model which can be rolled out to other areas of the city. • It would provide an 'exemplar' of how sensors can be used for public good and a visualisation of the data we hold. This may serve to encourage other similar projects in future.