Quantifying the Impact of Weather Events on Travel Time and Reliability
It is of practical significance to understand the specific impact of weather events on the operating condition of the surface transportation system so that proactive and reactive strategies can be quickly implemented by transportation agencies to minimize the negativity resulted from adverse weather...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/8203081 |
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| author | Xu Zhang Mei Chen |
| author_facet | Xu Zhang Mei Chen |
| author_sort | Xu Zhang |
| collection | DOAJ |
| description | It is of practical significance to understand the specific impact of weather events on the operating condition of the surface transportation system so that proactive and reactive strategies can be quickly implemented by transportation agencies to minimize the negativity resulted from adverse weather events. Many studies have been conducted on quantifying such effects yet suffer from limitations such as subjectively defining a time window under uncongested conditions and not being able to account for the severe impact from weather events which result in travel time unreliability. To overcome those shortcomings in existing literature, an integrated data mining framework based on decision tree and quantile regression techniques is developed in this study. The results demonstrate that the approach is effective in characterizing time periods with different traffic characteristics and quantifying the impact of rain and snow events on both congestion and reliability aspects of the transportation system. It is observed that snow events impose more significant impact on travel times than that from rain events. In addition, the impact from weather events is even more severe on travel time reliability than average delay. The impact magnitude is directly related to the level of recurrent congestion under study. Other insights with regard to the capability of quantile regression and future improvement on the methodological design are also offered. |
| format | Article |
| id | doaj-art-75b32c1332d043048bb2248a1e1724bc |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-75b32c1332d043048bb2248a1e1724bc2025-08-20T03:35:32ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/82030818203081Quantifying the Impact of Weather Events on Travel Time and ReliabilityXu Zhang0Mei Chen1Kentucky Transportation Center, University of Kentucky, 266 Raymond Bldg, Lexington, KY 40506-0281, USADepartment of Civil Engineering, University of Kentucky, 267 Raymond Bldg, Lexington, KY 40506-0281, USAIt is of practical significance to understand the specific impact of weather events on the operating condition of the surface transportation system so that proactive and reactive strategies can be quickly implemented by transportation agencies to minimize the negativity resulted from adverse weather events. Many studies have been conducted on quantifying such effects yet suffer from limitations such as subjectively defining a time window under uncongested conditions and not being able to account for the severe impact from weather events which result in travel time unreliability. To overcome those shortcomings in existing literature, an integrated data mining framework based on decision tree and quantile regression techniques is developed in this study. The results demonstrate that the approach is effective in characterizing time periods with different traffic characteristics and quantifying the impact of rain and snow events on both congestion and reliability aspects of the transportation system. It is observed that snow events impose more significant impact on travel times than that from rain events. In addition, the impact from weather events is even more severe on travel time reliability than average delay. The impact magnitude is directly related to the level of recurrent congestion under study. Other insights with regard to the capability of quantile regression and future improvement on the methodological design are also offered.http://dx.doi.org/10.1155/2019/8203081 |
| spellingShingle | Xu Zhang Mei Chen Quantifying the Impact of Weather Events on Travel Time and Reliability Journal of Advanced Transportation |
| title | Quantifying the Impact of Weather Events on Travel Time and Reliability |
| title_full | Quantifying the Impact of Weather Events on Travel Time and Reliability |
| title_fullStr | Quantifying the Impact of Weather Events on Travel Time and Reliability |
| title_full_unstemmed | Quantifying the Impact of Weather Events on Travel Time and Reliability |
| title_short | Quantifying the Impact of Weather Events on Travel Time and Reliability |
| title_sort | quantifying the impact of weather events on travel time and reliability |
| url | http://dx.doi.org/10.1155/2019/8203081 |
| work_keys_str_mv | AT xuzhang quantifyingtheimpactofweathereventsontraveltimeandreliability AT meichen quantifyingtheimpactofweathereventsontraveltimeandreliability |