Estimating Travel Time Distribution under Different Traffic Conditions

Guessous, Younes; Aron, Maurice; Bhouri, Neïla; Cohen, Simon

doi:10.1016/j.trpro.2014.10.014

Cited by 58 publications

(22 citation statements)

References 12 publications

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“…The work in regards to travel time-related contexts is vast, varying from distribution fitting (Rahmani et al, 2015;Ma et al, 2017a;Fu et al, 2019;Ma et al, 2016;Westgate et al, 2016;Yang and Wu, 2016;Chalumuri and Yasuo, 2014;Guessous et al, 2014;Woodard et al, 2017); simulation-based studies (Chen and Chien, 2000;Ojeda-Cabral and Chorus, 2016;Zhang et al, 2018;Samandar, Williams, and Ahmed, 2018;Lv et al, 2019); and the value of travel time and travel time reliability (Athira et al, 2016;Truong and Hensher, 1985;Ettema and Timmermans, 2006;Devarasetty et al, 2012;Carrion and Levinson, 2012;Soriguera, 2014;Hensher, 2001;Engelson and Fosgerau, 2016;Kou et al, 2017;Xiao et al, 2017;Rich and Vandet, 2019;Bouscasse and Lapparent, 2019). However, of the travel time-related studies, the most related to the current work is travel time prediction or forecasting through various statistical methods.…”

Section: Related Workmentioning

confidence: 97%

Understanding Factors Affecting Arterial Reliability Performance Metrics

Anderson¹,

Sirupa²,

Kothuri³

et al. 2019

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The contents of this report reflect the views of the authors, who are solely responsible for the facts and the accuracy of the material and information presented herein. This document is disseminated under the sponsorship of the U.S. Department of Transportation University Transportation Centers Program in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. The contents do not necessarily reflect the official views of the U.S. Government. This report does not constitute a standard, specification, or regulation.

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Section: Related Workmentioning

confidence: 97%

Understanding Factors Affecting Arterial Reliability Performance Metrics

Anderson¹,

Sirupa²,

Kothuri³

et al. 2019

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“…Most existing studies on the travel time distribution (TTD) have put significant efforts into identifying the best fitting model. The fitted distributions can be classified as single-mode distributions [11][12][13][14][15][16][17][18] and other distributions (multi-mode distribution and truncated distribution) [19][20][21]. Herman and Lam [4] suggested gamma or log-normal distribution for TTD.…”

Section: Literature Reviewmentioning

confidence: 99%

Calculation of Urban Transportation Network Reliability Considering Correlation Among the Links Comprising a Route

2020

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Recently, the researchers in the field of transportation network planning have become increasingly interested in network reliability, publishing research works focused on the calculation of various types of network reliability. Calculation of network reliability has led the transportation network optimizers toward new approaches where the maximization of reliability has been considered as an objective. Travel time reliability is among the most important reliabilities investigated when analyzing urban transportation networks, with various approaches based on different assumptions proposed for calculating it. In the present research, the uncertainty associated with the demand for travel and the flows passing across links and also the correlations among the links comprising a route were considered to calculate the travel time for each of the network links. Moreover, it was shown that this process follows shifted log-normal distribution. These calculations are expected to serve as a basis for the employment of travel time reliability within the modeling of an transportation system, so as to increase the accuracy and reliability of the simulations. Finally, in order to validate, an urban network with 12 nodes, 21 links, and 4 origin-destination pairs was subjected to the travel time reliability assessment by calculating the travel time over all forming links.

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“…Others suggested less common distributions to represent the travel time variability of urban roads [37][38][39]. Aron et al [40] made a comparison of six statistical distributions concluding that the Gamma mixture and the Normal mixture showed the best fit for the variation of travel times. In this research, we use real failure data to find the reliability function of different roads in Colombia proposing the use of the Poisson Zero Inflated Distribution (ZIP), a relatively unknown distribution that to the best of our knowledge has not been used in reliability studies.…”

Section: Performance Reliabilitymentioning

confidence: 99%

Analysis of transportation networks subject to natural hazards – Insights from a Colombian case

Muriel-Villegas

Álvarez-Uribe

Patiño-Rodríguez

et al. 2016

Reliability Engineering & System Safety

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This study provides an applied framework to derive the connectivity reliability and vulnerability of interurban transportation systems under network disruptions. The proposed model integrates statistical reliability analysis to find the reliability and vulnerability of transportation networks. Most of the modern research in this field has focused on urban transportation networks where the primary concerns are guaranteeing predefined standards of capacity and travel time. However, at a regional and national level, especially in developing countries, the connectivity of remote populations in the case of disaster is of utmost importance. The applicability of the framework is demonstrated with a case study in the state of Antioquia, Colombia, using historical records from the 2010-2011 rainy season, an aspect that stands out and gives additional support compared to previous studies that considers simulated data from assumed distributions. The results provide significant insights to practitioners and researchers for the design and management of transportation systems and route planning strategies under this type of disruptions.

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Estimating Travel Time Distribution under Different Traffic Conditions

Cited by 58 publications

References 12 publications

Understanding Factors Affecting Arterial Reliability Performance Metrics

Understanding Factors Affecting Arterial Reliability Performance Metrics

Calculation of Urban Transportation Network Reliability Considering Correlation Among the Links Comprising a Route

Analysis of transportation networks subject to natural hazards – Insights from a Colombian case

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