A comparative assessment of the LWR-IM traffic model using linear regression

K. M. Ng, Md. Mamun Ibne Reaz, M. A.M. Ali, N. A. Razak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages626-630
Number of pages5
ISBN (Electronic)9781509028894
DOIs
Publication statusPublished - 27 Mar 2017
Externally publishedYes
Event2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 - Putrajaya, Malaysia
Duration: 14 Nov 201616 Nov 2016

Other

Other2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016
CountryMalaysia
CityPutrajaya
Period14/11/1616/11/16

Fingerprint

Linear regression
traffic
regression analysis
predictions
intersections

Keywords

  • LWR
  • LWR-IM
  • Rakha Model
  • Traffic Model
  • VCPN

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Biomedical Engineering
  • Control and Systems Engineering
  • Hardware and Architecture
  • Computer Networks and Communications
  • Instrumentation

Cite this

Ng, K. M., Ibne Reaz, M. M., Ali, M. A. M., & Razak, N. A. (2017). A comparative assessment of the LWR-IM traffic model using linear regression. In 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 (pp. 626-630). [7888122] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAEES.2016.7888122

A comparative assessment of the LWR-IM traffic model using linear regression. / Ng, K. M.; Ibne Reaz, Md. Mamun; Ali, M. A.M.; Razak, N. A.

2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 626-630 7888122.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ng, KM, Ibne Reaz, MM, Ali, MAM & Razak, NA 2017, A comparative assessment of the LWR-IM traffic model using linear regression. in 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016., 7888122, Institute of Electrical and Electronics Engineers Inc., pp. 626-630, 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016, Putrajaya, Malaysia, 14/11/16. https://doi.org/10.1109/ICAEES.2016.7888122
Ng KM, Ibne Reaz MM, Ali MAM, Razak NA. A comparative assessment of the LWR-IM traffic model using linear regression. In 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 626-630. 7888122 https://doi.org/10.1109/ICAEES.2016.7888122
Ng, K. M. ; Ibne Reaz, Md. Mamun ; Ali, M. A.M. ; Razak, N. A. / A comparative assessment of the LWR-IM traffic model using linear regression. 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 626-630
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