Estimation of travel time using temporal and spatial relationships in sparse data
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Estimation of travel time using temporal and spatial relationships in sparse data
DE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary Research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering an Estimation of travel time using temporal and spatial relationships in sparse data nd MediaEstimation of Travel Time using Temporal and Spatial Relationships in Sparse DataAuthor:Luong Huy VuSupervisors:Dr. Benjamin N. PassowDr. Daniel PaluszczyszynProf. Yingjie YangDr. Lipika Deka/1 thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy43405Abst Estimation of travel time using temporal and spatial relationships in sparse data ractTravel time is a basic measure upon which c.g. traveller information systems, traffic management systems, public transportation planning ami otherEstimation of travel time using temporal and spatial relationships in sparse data
intelligent transport systems are developed. Collecting travel time information in a large and dynamic road network is essential to managing the tranDE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary Research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering an Estimation of travel time using temporal and spatial relationships in sparse data echniques arc employed to ut ilise data collected for the major roads and traffic network structure to approximate travel times for minor links.Although many methodologies have been proposed, they have not yet adequately solved many challenges associated with travel time, in particular, travel time Estimation of travel time using temporal and spatial relationships in sparse data estimation for all links in a large and dynamic urban traffic network. Typically focus is placed on major roads such as motorways and main city arteriEstimation of travel time using temporal and spatial relationships in sparse data
es but there is an increasing need to know accurate travel times for minor urban roads. Such information is crucial for tackling air quality problems,DE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary Research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering an Estimation of travel time using temporal and spatial relationships in sparse data tioned challenges by introducing a methodology able to estimate travel times in near-real-time by using historical sparse travel time data. To this end, an investigation of temporal and spatial dependencies between travel time of traffic links in the datasets is carefully conducted. Two novel method Estimation of travel time using temporal and spatial relationships in sparse data ologies arc proposed, Neighbouring Link Inference method (NLIM) and Similar Model Searching method (SMS). The NLIM learns the temporal and spatial relEstimation of travel time using temporal and spatial relationships in sparse data
ationship between the travel time of adjacent links and uses the relation to estimate travel time of the target cd link. For this purpose, several macDE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary Research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering an Estimation of travel time using temporal and spatial relationships in sparse data r similar NLIM models from which to utilise data in order to improve the performance of a selected N’LIM model. NLIM and SMS incorporates an additional novel application for travel time outlier detection and removal. By adapting a multivariate Gaussian mixture model, an improvement in travel t ime e Estimation of travel time using temporal and spatial relationships in sparse data stimation is achieved.Both introduced methods arc evaluated on four distinct datasets and compared against benchmark techniques adopted from literaturEstimation of travel time using temporal and spatial relationships in sparse data
e. They efficiently perform the task of travel time estimation in near-real-time of a target link using models learnt from adjacent traffic links. TheDE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary Research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering an Estimation of travel time using temporal and spatial relationships in sparse data nks to support the high variability of urban travel time and high data sparsity.X cknowledgementsI would firstly like to thank Dr Benjamin N. Passow and Dr Daniel Paluszczyszyn for their non-stop support in every part of my PhD journey alongside the rest of my supervisory team, Prof. Yingjic Yang, D Estimation of travel time using temporal and spatial relationships in sparse data r Lipika Deka and Prof. Eric Goodycr who assisted in supporting my ciforts.1 would also like to thank members within the De Montfort University InterdEstimation of travel time using temporal and spatial relationships in sparse data
isciplinary' research Group in Intelligent Transport Systems (DIGITS) who offered assistance to my work, both technical and inspirational.I would likeDE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary Research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering an Estimation of travel time using temporal and spatial relationships in sparse data without, her love and sharing every moment, in t his journey, 1 would not have been able to finish this research.I gratefully acknowledge the Ministry of Education and Training of Vietnam funding me with the threo-ycar scholarship for my study.ii Estimation of travel time using temporal and spatial relationships in sparse data DE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary Research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering anGọi ngay
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