Using velocity fields in evaluating urban traffic congestion via sparse public transport data and crowedsourced maps
Seminar Room 1, Newton Institute
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
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.