Copula-Based Travel Time Distribution Estimation Considering Channelization Section Spillover
Travel time is inherently uncertain in urban networks due to volatile traffic flows, signal controls, bus stops and disturbances from pedestrians. An effective way to characterize such uncertainty is by estimating Travel Time Distribution (TTD). However, conventional TTD models are lack of consideri...
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doaj-768b9303d45547d79be78ccba16b35fa2021-06-17T23:00:09ZengIEEEIEEE Access2169-35362020-01-018328503286110.1109/ACCESS.2020.29705308976161Copula-Based Travel Time Distribution Estimation Considering Channelization Section SpilloverYi Yu0https://orcid.org/0000-0002-5062-5071Mengwei Chen1Hongsheng Qi2Dianhai Wang3Department of Civil Engineering and Architecture, Zhejiang University, Hangzhou, ChinaCollege of Civil Engineering and Architecture, Zhejiang University of Technology, Hangzhou, ChinaDepartment of Civil Engineering and Architecture, Zhejiang University, Hangzhou, ChinaDepartment of Civil Engineering and Architecture, Zhejiang University, Hangzhou, ChinaTravel time is inherently uncertain in urban networks due to volatile traffic flows, signal controls, bus stops and disturbances from pedestrians. An effective way to characterize such uncertainty is by estimating Travel Time Distribution (TTD). However, conventional TTD models are lack of considering the interactions between turning movements within the link. Whereas the phenomenon of the Channelization Section Spillover (CSS) is very common, leading to strong interactions between turning movements. In this study, by incorporating the correlation of turning movements in the TTD model, that is, considering CSS, copula-based link-level and path-level TTD models are built. First, based on the empirical data, the correlations of turning movements are analyzed, and then the applicability of the various copula models are examined. The marginal distribution of each turning movement is described using parametric and non-parametric regression analysis, respectively. Then, the best-fitting copula is determined based on correlation parameters and the goodness-of-fit tests. As a case study, the chosen model is applied in estimating link-level and path-level TTD in an arterial road in Hangzhou, China, and compared with the model that did not consider the CSS before. Both results indicate that the copula-based approach can precisely capture the positively correlated relationship between turning movements during peak hours. Furthermore, higher TTD estimation accuracy demonstrates the significance to consider CSS, particularly in peak hours.https://ieeexplore.ieee.org/document/8976161/Travel time distributionturning movements correlationchannelization section spillovercopula |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yi Yu Mengwei Chen Hongsheng Qi Dianhai Wang |
spellingShingle |
Yi Yu Mengwei Chen Hongsheng Qi Dianhai Wang Copula-Based Travel Time Distribution Estimation Considering Channelization Section Spillover IEEE Access Travel time distribution turning movements correlation channelization section spillover copula |
author_facet |
Yi Yu Mengwei Chen Hongsheng Qi Dianhai Wang |
author_sort |
Yi Yu |
title |
Copula-Based Travel Time Distribution Estimation Considering Channelization Section Spillover |
title_short |
Copula-Based Travel Time Distribution Estimation Considering Channelization Section Spillover |
title_full |
Copula-Based Travel Time Distribution Estimation Considering Channelization Section Spillover |
title_fullStr |
Copula-Based Travel Time Distribution Estimation Considering Channelization Section Spillover |
title_full_unstemmed |
Copula-Based Travel Time Distribution Estimation Considering Channelization Section Spillover |
title_sort |
copula-based travel time distribution estimation considering channelization section spillover |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Travel time is inherently uncertain in urban networks due to volatile traffic flows, signal controls, bus stops and disturbances from pedestrians. An effective way to characterize such uncertainty is by estimating Travel Time Distribution (TTD). However, conventional TTD models are lack of considering the interactions between turning movements within the link. Whereas the phenomenon of the Channelization Section Spillover (CSS) is very common, leading to strong interactions between turning movements. In this study, by incorporating the correlation of turning movements in the TTD model, that is, considering CSS, copula-based link-level and path-level TTD models are built. First, based on the empirical data, the correlations of turning movements are analyzed, and then the applicability of the various copula models are examined. The marginal distribution of each turning movement is described using parametric and non-parametric regression analysis, respectively. Then, the best-fitting copula is determined based on correlation parameters and the goodness-of-fit tests. As a case study, the chosen model is applied in estimating link-level and path-level TTD in an arterial road in Hangzhou, China, and compared with the model that did not consider the CSS before. Both results indicate that the copula-based approach can precisely capture the positively correlated relationship between turning movements during peak hours. Furthermore, higher TTD estimation accuracy demonstrates the significance to consider CSS, particularly in peak hours. |
topic |
Travel time distribution turning movements correlation channelization section spillover copula |
url |
https://ieeexplore.ieee.org/document/8976161/ |
work_keys_str_mv |
AT yiyu copulabasedtraveltimedistributionestimationconsideringchannelizationsectionspillover AT mengweichen copulabasedtraveltimedistributionestimationconsideringchannelizationsectionspillover AT hongshengqi copulabasedtraveltimedistributionestimationconsideringchannelizationsectionspillover AT dianhaiwang copulabasedtraveltimedistributionestimationconsideringchannelizationsectionspillover |
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1721373564748169216 |