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|Title: ||Developing travel time estimation methods using sparse GPS data|
|Authors: ||Sanaullah, Irum|
Quddus, Mohammed A.
Enoch, Marcus P.
|Keywords: ||Travel time estimation|
Vehicle penetration rate
|Issue Date: ||2016|
|Publisher: ||© Taylor & Francis|
|Citation: ||SANAULLAH, I., QUDDUS, M.A. and ENOCH, M.P., 2016. Developing travel time estimation methods using sparse GPS data. Journal of Intelligent Transportation Systems: technology, planning, and operations, 20 (6), pp. 532-544.|
|Abstract: ||Existing methods of estimating travel time from GPS data are not able to simultaneously take account of the issues related to uncertainties associated with GPS and spatial road network data. Moreover, they typically depend upon high frequency data sources from specialist data providers which can be expensive and are not always readily available. The study reported here therefore sought to better estimate travel time using ‘readily available’ vehicle trajectory data from moving sensors such as buses, taxis and logistical vehicles equipped with GPS in ‘near’ real-time. To do this, accurate locations of vehicles on a link were first map-matched to reduce the positioning errors associated with GPS and digital road maps. Two mathematical methods were then developed to estimate link travel times from map-matched GPS fixes, vehicle speeds and network connectivity information with a special focus on sampling frequencies, vehicle penetration rates and time window lengths. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-2
Berkeley’s Mobile Century Project, and these were used to evaluate several travel time estimation methods, the results of which were then validated against reference travel time data collected from high resolution video cameras. The results indicate that vehicle penetration rates, data sampling frequencies, vehicle coverage on the links and time window lengths all influence the accuracy of link travel time estimation. The performance was found to be best in the 5 minute time window length and for a GPS sampling frequency of 60 seconds.|
|Description: ||This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Intelligent Transportation Systems on 8th March 2016, available online: http://www.tandfonline.com/10.1080/15472450.2016.1154764.|
|Version: ||Accepted for publication|
|Publisher Link: ||http://dx.doi.org/10.1080/15472450.2016.1154764|
|Appears in Collections:||Published Articles (Architecture, Building and Civil Engineering)|
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