A travel time reliability model of urban expressways with varying levels of service

Lei, Fangshu; Wang, Yunpeng; Lu, Guangquan; Sun, Jian

doi:10.1016/j.trc.2014.09.019

Cited by 37 publications

(14 citation statements)

References 28 publications

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“…It is noteworthy that most studies on travel time analysis employed travel time data from only one road type [3,9,10] or treated different types of roads equally [5,11], which failed to provide more detailed analysis of travel time characteristics for each specific road type. In practice, travel times of different road types are supposed to have diverse variability patterns due to different road characteristics and capacities and attractiveness for travelers.…”

Section: Journal Of Advanced Transportationmentioning

confidence: 99%

“…The fitted distributions can be categorized into three main classes, that is, single-mode distributions [9,[13][14][15][16], multimode distributions [6,[17][18][19], and truncated distributions [4,20,21].…”

Section: Travel Time Distributionmentioning

confidence: 99%

“…Nie et al [22] adopted gamma distribution to model travel time on arterial and local streets during morning peak, mid-of-day, and evening peak. Lei et al [9] compared Generalized extreme, Weibull, burr, normal, gamma, lognormal, Generalized Pareto, and other distributions for modeling travel times on urban expressways with varying levels of service and concluded that generalized extreme and Generalized Pareto are preferable fits than others. Kieu et al [16] selected burr, gamma, lognormal, normal, and Weibull to model public transport travel time and found that lognormal can be recommended as the descriptor because of its desirable performance and attractive mathematical characteristics.…”

Section: Travel Time Distributionmentioning

confidence: 99%

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Exploring Travel Time Distribution and Variability Patterns Using Probe Vehicle Data: Case Study in Beijing

Chen

Tong

et al. 2018

Journal of Advanced Transportation

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Exploring travel time distribution and variability patterns is essential for reliable route choices and sophisticated traffic management and control. State-of-the-art studies tend to treat different types of roads equally, which fails to provide more detailed analysis of travel time characteristics for each specific road type. In this study, based on a vast amount of probe vehicle data, 200 links inside the Third Ring Road of Beijing, China, were investigated. Four types of roads were covered including urban expressways, auxiliary roads of urban expressways, major roads, and secondary roads. The day-of-week distributions of unit distance travel time were first analyzed. Kolmogorov-Smirnov test, Anderson-Darling test, and chi-squared test were employed to test the goodness-of-fit of different distributions and the results showed lognormal distribution was best-fitted for different time periods and road types compared with normal, gamma, and Weibull distribution. In addition, four reliability measures, that is, unit distance travel time, coefficient of variation, buffer time index, and punctuality rate, were used to explore the day-of-week travel time variability patterns. The results indicated that urban expressways, auxiliary roads of urban expressways, and major roads have regular and distinct morning and afternoon peaks on weekdays. It is noteworthy that in daytime the travel times on auxiliary roads of urban expressways and major roads share similar variability patterns and appear relatively stable and reliable, while urban expressways have most reliable travel times at night. The results of analysis help enable a better understanding of the volatile travel time characteristics of each road type in urban network.

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Section: Journal Of Advanced Transportationmentioning

confidence: 99%

Section: Travel Time Distributionmentioning

confidence: 99%

Section: Travel Time Distributionmentioning

confidence: 99%

See 1 more Smart Citation

Exploring Travel Time Distribution and Variability Patterns Using Probe Vehicle Data: Case Study in Beijing

Chen

Tong

et al. 2018

Journal of Advanced Transportation

Self Cite

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“…In accordance with (3), the ETT is strictly decreasing monotonously with the increase of the CDC, i.e., zETT/zϕ a < 0. When road section saturation belongs to the increasing interval of DDFS, as well as g ″ (φ) ≥ 0, it obtains that sen ETT w k and ϕ a have a negative correlation according to (10). en, ϕ a is decreasing monotonously with sen ETT w k .…”

Section: Propositionmentioning

confidence: 99%

“…e reliability of the transportation system has been gaining considerable attention because of the increase in traffic demand and serious congestion. Many scholars have investigated the three aspects of traffic reliability, which includes connectivity [1][2][3], capacity [4,5], and travel time [6][7][8][9][10][11]. Among these aspects, travel time reliability has been identified as an important indicator of the performance of the transportation network [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Analytical Model for Travel Time‐Based BPR Function with Demand Fluctuation and Capacity Degradation

Zhang

Liu

Zhou

2019

Mathematical Problems in Engineering

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This study presents a stochastic model based on the link performance function of the Bureau of Public Roads to assess the reliability of travel time in the transportation network. Empirical studies have verified that the variability of travel time can be ascribed to demand fluctuation and the degradation of the capacity of the stochastic network. The mean-variance approach in previous research presented the budget model of travel time, with the capacity of the stochastic network and elastic demand as the sources of uncertainty of travel time. Previous research was devoted to the study of estimation of travel time considering a single factor or a factor independent of these two sources. Meanwhile, this study introduces the current degeneration coefficient of capacity (CDC) and the density distribution function of road section saturation (DDFS) with simultaneous network capacity and traffic demand. Sensitivity analysis method for the parameters of the proposed model is investigated theoretically using the sensitivity model of traffic capacity degradation. Results of case analysis show that the DDFS and CDC have an effect on the decision of travelers regarding the choice of route. The empirical analysis also illustrates the effectiveness of the computational approach and the proposed model.

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Estimation of travel time distributions for urban roads using GPS trajectories of vehicles: a case of Athens, Greece

Adnan

Gazder

Yasar

et al. 2020

Pers Ubiquit Comput

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A travel time reliability model of urban expressways with varying levels of service

Cited by 37 publications

References 28 publications

Exploring Travel Time Distribution and Variability Patterns Using Probe Vehicle Data: Case Study in Beijing

Exploring Travel Time Distribution and Variability Patterns Using Probe Vehicle Data: Case Study in Beijing

Analytical Model for Travel Time‐Based BPR Function with Demand Fluctuation and Capacity Degradation

Estimation of travel time distributions for urban roads using GPS trajectories of vehicles: a case of Athens, Greece

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