Dependable and Historic Computing

Volume 6875 of the series Lecture Notes in Computer Science pp 93-117

Using Real-Time Road Traffic Data to Evaluate Congestion

  • Jean BaconAffiliated withComputer Laboratory, University of Cambridge
  • , Andrei Iu. BejanAffiliated withComputer Laboratory, University of Cambridge
  • , Alastair R. BeresfordAffiliated withComputer Laboratory, University of Cambridge
  • , David EvansAffiliated withComputer Laboratory, University of Cambridge
  • , Richard J. GibbensAffiliated withComputer Laboratory, University of Cambridge
  • , Ken MoodyAffiliated withComputer Laboratory, University of Cambridge

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Providing citizens with accurate information on traffic conditions can encourage journeys at times of low congestion, and uptake of public transport. The TIME project (Transport Information Monitoring Environment) has focussed on urban traffic, using the city of Cambridge as an example. We have investigated sensor and network technology for gathering traffic data, and have designed and built reusable software components to distribute, process and store sensor data in real time. Instrumenting a city to provide this information is expensive and potentially invades privacy. Increasingly, public transport vehicles are equipped with sensors to provide arrival time estimates at bus stop displays in real-time. We have shown that these data can be used for a number of purposes. Firstly, archived data can be analysed statistically to understand the behaviour of traffic under a range of “normal” conditions at different times, for example in and out of school term. Secondly, periods of extreme congestion resulting from known incidents can be analysed to show the behaviour of traffic over time. Thirdly, with such analyses providing background information, real-time data can be interpreted in context to provide more reliable and accurate information to citizens. In this paper we present some of the findings of the TIME project.


static sensor mobile sensor traffic monitoring middleware bus probe data journey times large scale data analysis quantile regression spline interpolation