Reliable route guidance: A case study from Chicago

Nie, Yu; Wu, Xing; Dillenburg, John; Nelson, Peter

doi:10.1016/j.tra.2011.10.008

Cited by 39 publications

(36 citation statements)

References 27 publications

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“…For example, Emam and Al-Deek [15] tested the lognormal, gamma, Weibull, and exponential distributions for representing travel time data of a freeway from weekdays and found that a lognormal distribution was bestfitted. 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.…”

Section: Travel Time Distributionmentioning

confidence: 99%

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: Travel Time Distributionmentioning

confidence: 99%

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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“…It indicates the probability that product is unable to complete the function under the specified time and conditions. F(t j ) is the distribution function of T [13].…”

Section: Survival Functionmentioning

confidence: 99%

Reliability analysis of subway vehicles based on the data of operational failures

Yin

Wang

Qin

et al. 2017

J Wireless Com Network

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A large quantity of failure data for subway vehicles was collected from long-term field investigations and technical exchanged. These failure data has a guiding significance for preserving subway system. By preprocessing (screening, refining, and classification) the original data and statistical analysis, we establish some selected model, then we use A-D test to verify the degree of fitting in selected model so that we can determine the optimal failure distribution model, and then the reliability characteristic quantities could be calculated by the optimal failure distribution model. These reliability characteristic quantities can predict failure rate, failure number, etc. It can be used to assist proper maintenance scheduling to reduce the occurrence of accidents and significant to important practical guiding.

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“…Several formulations have been proposed in the literature, associating one or many criteria for the optimal path selection. We cite for example, -minimizing the expected travel-time [15,28], -maximizing the expected utility [19,29], -maximizing the probability of arriving at the destination on time [9,36], -minimizing the expected travel-time while ensuring a pre-specified probability of arriving by a given deadline [33,3,32,31,34], and minimizing the sum of expected travel-time [24,33,4,40]. The stochastic shortest path problem solutions are either a priori paths or adaptive strategies.…”

Section: Introductionmentioning

confidence: 99%

Robust routing, its price, and the tradeoff between routing robustness and travel time reliability in road networks

Manseur

Farhi

Phu

et al. 2020

European Journal of Operational Research

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We propose in this article an adaptive algorithm for optimal and robust guidance for the users of the road networks. The algorithm is based on the Stochastic On Time Arrival (SOTA) family of routing algorithms, which is appropriate for taking into account the variability of travel times through the road networks. The SOTA approach permits the derivation of the maximum cumulative probability distribution of the time arrival toward a given destination in the network. Those distributions allow the selection of the most reliable origin-destination paths under given travel time budgets. We investigate here the introduction of robustness against link and path failures in the criterion of the guidance strategy selection. Our algorithm takes into account the reliability of itinerary travel times, since it is based on a SOTA approach. In addition, the algorithm takes into account itinerary robustness, by favoring itineraries with possible and reliable alternative diversions, in case of link failures, with respect to itineraries without or with less reliable alternatives. We first analyze the algorithm in its static version, without considering the traffic dynamics, and show some interesting properties. We then combine the robust guidance algorithm with a dynamic traffic model by using the traffic simulator SUMO (Simulation of Urban Mobility), and illustrate its effectiveness in some dynamic scenarios.

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Reliable route guidance: A case study from Chicago

Cited by 39 publications

References 27 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

Reliability analysis of subway vehicles based on the data of operational failures

Robust routing, its price, and the tradeoff between routing robustness and travel time reliability in road networks

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