Insight

Harnessing citizens and sensors for traffic management

The advent of real-time traffic streaming offers users the opportunity to visualise current traffic conditions and congestion information. However, real-time information highlighting the underlying reason for tail-backs remains largely unexplored.

Broken traffic lights, an accident, a large concert, or road-works reveal important information for citizens and traffic operators alike. Providing such information in real time requires intelligent mechanisms and user interfaces in order to

As huge numbers of sensors relay real-time traffic measurements on city roads, real-time information management systems need to be in place that sieve relevant from irrelevant information. This is particularly important in mission critical applications. For example traffic operators need to be informed about potential anomalies caused by road traffic accidents or similar events in order to deploy timely mitigation strategies.

We investigate a new method that elevates anomalies visually to an operator by using a combination of change-detection analytics and auxiliary information from human-sensors (e.g., through Twitter). We identify operational regimes of constant behaviour and change points that bound a regime. This allows us to account for drifts that are not captured in a model.

This has the advantage over cumulative sum (CUSUM) algorithms that no threshold parameter needs to be specified upfront and relaxes the assumption of stationarity. Stationarity assumptions do not hold in traffic systems, as network effects take hold and traffic signal control intervenes when local traffic states are deteriorating.

Contact

Jia Yuan Yu
Elizabeth Daly
Dominik Dahlem

 


Related link

INSIGHT EU Project

 

 


Related papers

 

Challenge
Solution
Benefits

Preliminary results

Time series and corresponding change vector for a single detector on a day of an event.

The two top rows show the degree of saturation and flow of a given detector.

The two bottom rows show the change vector with piece-wise linear regimes and the angle, between those regimes, respectively.

 

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Application areas

Smarter water
Smarter energy
Smarter healthcare
Smarter transportation
Smarter cities