DAE

Abstract

It is widely recognised that congestion in urban areas causes financial loss to business and increased use of energy compared with free owing traffic. Providing one with accurate information on traffic conditions can encourage journeys at times of low congestion and uptake of public transport. Installing a static measurement infrastructure in a city to provide this information may be an expensive option and potentially invade privacy. Increasingly, public transport vehicles are equipped with sensors to provide realtime arrival time estimates, but these data are fl eet specific and sparse. The recent work with colleagues from the Cambridge University Computer Laboratory showed how to overcome data mining issues and use this kind of data to statistically analyse journey times experienced by road users generally (i.e. journey durations experienced by public transport users as well as individual car drivers) and in uence of various factors (e.g. time of day, school/out of school term effects, etc)[Be10, Ba11]. Furthermore, we showed how the specifics of these location data may be used in conjunction with other sources of data, such as crowdsourced maps, in order to recover speed information from the sparse movement data and reconstruct information on transport traffic fl ow dynamics in terms of velocity fields on road networks[Be11]. In my short talk I will present a number of snapshots illustrating this analysis and some results and introduce the problem of comparing/classifying velocity fields and early spotting of accidents and their consequences for the traffic and road users.