A comparative assessment of the LWR-IM traffic model using linear regression
Traffic models used for predictions of traffic parameters needs to be validated. This paper presents a comparative assessment to validate the predictive ability of the Lighthill-Witham-Richards - Integrated Model (LWR-IM) traffic model in simulating average delays in urban arterials using linear reg...
Published in: | 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 |
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2016
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Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018184158&doi=10.1109%2fICAEES.2016.7888122&partnerID=40&md5=381b727b5445d3e59321ee5095f0f09d |
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2-s2.0-85018184158 Ng K.M.; Reaz M.B.I.; Ali M.A.M.; Razak N.A. A comparative assessment of the LWR-IM traffic model using linear regression 2016 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 10.1109/ICAEES.2016.7888122 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018184158&doi=10.1109%2fICAEES.2016.7888122&partnerID=40&md5=381b727b5445d3e59321ee5095f0f09d Traffic models used for predictions of traffic parameters needs to be validated. This paper presents a comparative assessment to validate the predictive ability of the Lighthill-Witham-Richards - Integrated Model (LWR-IM) traffic model in simulating average delays in urban arterials using linear regression. For this purpose, the LWR-IM, TRANSYT, CTM and HCM 2000 are applied to a test intersection. Average delays are simulated by these models based on 20 different traffic scenarios. Average delays simulated by these traffic models are analyzed using the linear regression to assess how closely fitted the delays simulated by the LWR-IM with delays simulated by TRANSYT, CTM and HCM 2000. The regression reveals high degrees of correspondence with R2 exceeding 0.9 for linear regression of average delays from the LWR-IM with predictions from the other traffic models. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. English Conference paper |
author |
Ng K.M.; Reaz M.B.I.; Ali M.A.M.; Razak N.A. |
spellingShingle |
Ng K.M.; Reaz M.B.I.; Ali M.A.M.; Razak N.A. A comparative assessment of the LWR-IM traffic model using linear regression |
author_facet |
Ng K.M.; Reaz M.B.I.; Ali M.A.M.; Razak N.A. |
author_sort |
Ng K.M.; Reaz M.B.I.; Ali M.A.M.; Razak N.A. |
title |
A comparative assessment of the LWR-IM traffic model using linear regression |
title_short |
A comparative assessment of the LWR-IM traffic model using linear regression |
title_full |
A comparative assessment of the LWR-IM traffic model using linear regression |
title_fullStr |
A comparative assessment of the LWR-IM traffic model using linear regression |
title_full_unstemmed |
A comparative assessment of the LWR-IM traffic model using linear regression |
title_sort |
A comparative assessment of the LWR-IM traffic model using linear regression |
publishDate |
2016 |
container_title |
2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 |
container_volume |
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container_issue |
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doi_str_mv |
10.1109/ICAEES.2016.7888122 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018184158&doi=10.1109%2fICAEES.2016.7888122&partnerID=40&md5=381b727b5445d3e59321ee5095f0f09d |
description |
Traffic models used for predictions of traffic parameters needs to be validated. This paper presents a comparative assessment to validate the predictive ability of the Lighthill-Witham-Richards - Integrated Model (LWR-IM) traffic model in simulating average delays in urban arterials using linear regression. For this purpose, the LWR-IM, TRANSYT, CTM and HCM 2000 are applied to a test intersection. Average delays are simulated by these models based on 20 different traffic scenarios. Average delays simulated by these traffic models are analyzed using the linear regression to assess how closely fitted the delays simulated by the LWR-IM with delays simulated by TRANSYT, CTM and HCM 2000. The regression reveals high degrees of correspondence with R2 exceeding 0.9 for linear regression of average delays from the LWR-IM with predictions from the other traffic models. © 2016 IEEE. |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
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language |
English |
format |
Conference paper |
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scopus |
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Scopus |
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1809678486707634176 |