Time-Variant Travel Time Distributions and Reliability Metrics and Their Utility in Reliability Assessments

Alvarez, Patricio; Hadi, Mohammed

doi:10.3141/2315-09

Cited by 13 publications

(10 citation statements)

References 5 publications

(4 reference statements)

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“…For line 2 we propose the following: 6:00 to 6:30, 6:30 to 7:30, 7:30 to 8:00, and 8:00 to 8:30. Indeed, Alvarez and Hadi (2013) show that planning periods of 30 minutes lead to more accurate deterministic travel times.…”

Section: Introductionmentioning

confidence: 98%

Multiperiod Bus Timetabling

Ibarra-Rojas

Lopez-Irarragorri

Ríos-Solís

2016

Transportation Science

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The timetabling subproblem of bus transit network planning determines the departure times for all trips of the lines along the entire day. Most of the public transport networks consider planning periods identical for all lines. In this study we drop this strong assumption by introducing specific periods for each line, which is more realistic. Thus, we propose the multiperiod synchronization bus timetabling (MSBT) problem, which specifies the departure times of the trips of all lines where each line has its own planning periods along the day, with the objective of optimizing synchronization events: maximize passenger transfers and minimize bus bunching along the network. We propose an integer linear programming formulation for the MSBT problem and analyze the structural properties of this formulation by a constraint propagation methodology. These properties are the basis for different operators that lead to the design of efficient metaheuristics for solving the problem. We empirically obtain high-quality feasible solutions for real size instances and show that by considering a multiperiod approach, synchronization events of trips belonging to different planning periods are not ignored, as it is the case when several single period timetables are merged.

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Section: Introductionmentioning

confidence: 98%

Multiperiod Bus Timetabling

Ibarra-Rojas

Lopez-Irarragorri

Ríos-Solís

2016

Transportation Science

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“…Moylan [25] considered the buffer time, planning time index, failure rate, and frequency of congestion of freeway segments. Alvarez and Hadi [29] measured the failure rate and misery index of general purpose lanes and high-occupancy toll lanes. The Florida Department of Transportation [30] used the percentage of travel time less than the median travel time plus a certain acceptable additional time such as the percentage of 5%, 10%, 15%, and 20% above the expected travel time to estimate TTV.…”

Section: Variability Patternsmentioning

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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“…Spectral clustering, a method that allows clustering using fewer dimensions, was used to analyze the traffic state variation based on the quantitative speed data [8]. Other studies that used clustering include Oh, Tok, & Ritchie [9] and Alvarez & Hadi [10].…”

Section: Review Of Clustering Applicationsmentioning

confidence: 99%

Pattern Recognition Using Clustering Analysis to Support Transportation System Management, Operations, and Modeling

Saha

Tariq

Hadi

et al. 2019

Journal of Advanced Transportation

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There has been an increasing interest in recent years in using clustering analysis for the identification of traffic patterns that are representative of traffic conditions in support of transportation system operations and management (TSMO); integrated corridor management; and analysis, modeling, and simulation (AMS). However, there has been limited information to support agencies in their selection of the most appropriate clustering technique(s), associated parameters, the optimal number of clusters, clustering result analysis, and selecting observations that are representative of each cluster. This paper investigates and compares the use of a number of existing clustering methods for traffic pattern identifications, considering the above. These methods include the K-means, K-prototypes, K-medoids, four variations of the Hierarchical method, and the combination of Principal Component Analysis for mixed data (PCAmix) with K-means. Among these methods, the K-prototypes and K-means with PCs produced the best results. The paper then provides recommendations regarding conducting and utilizing the results of clustering analysis.

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Time-Variant Travel Time Distributions and Reliability Metrics and Their Utility in Reliability Assessments

Cited by 13 publications

References 5 publications

Multiperiod Bus Timetabling

Multiperiod Bus Timetabling

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

Pattern Recognition Using Clustering Analysis to Support Transportation System Management, Operations, and Modeling

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