A closed-form estimation of the travel time percentile function for characterizing travel time reliability

Zang, Zhaoqi; Xu, Xiangdong; Yang, Chao; Chen, Anthony

doi:10.1016/j.trb.2018.10.012

Cited by 22 publications

(10 citation statements)

References 60 publications

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“…According to [17], the key premise of the indicators presented in Table 1 is to fit the distribution of the flight time and find the most suitable distribution function. They also discussed some of the commonly used distribution fitting functions which are Lognormal, Gamma, Weibull and normal distribution.…”

Section: A Travel Time Reliabilitymentioning

confidence: 99%

“…Figure 7 is the nine Different fit distribution of different flight taxi-in time. The fitting results are shown from Table 4 to 7 in terms of three metrics which are Mean squared error (MSE), the sum of squares due to error (SSE), and coefficient of determination (R 2 ) [17], [25]. Figure 8 is the best fit distribution function as well as the coefficients of flight block time, flight air time, flight taxi-out time and flight taxi-in time.…”

Section: Flight Time Distributionmentioning

confidence: 99%

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Assessment of Flight Block Time Reliability Under Different Delay Time Windows: A Case Study

Tian

Wang

et al. 2020

IEEE Access

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In transportation field, reliability is the probability of reaching the destination from the starting point within the expected time. While the concept of reliability, including travel time reliability model and its evaluation indicators have been extensively studied in ground transportation, they are relatively very scarce in air transportation. Flight block time reliability not only exert a strong influence on passenger behavior but also greatly affect aerodromes, airlines and air traffic management, thus affecting the entire air transportation. This paper proposes a methodology for evaluating the flight block time under different delay time windows. We applied the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method to set windows which reflects different delays. We proposed a probabilistic model of flight block time based on a series system. A case study is performed to compare flight block time, flight air time, flight taxi-in time, and flight taxi-out time at different time reliability metrics with different delays. In particular, we found the best fit for each flight time segment of three delay time windows among nine well-known distribution functions to calculate the flight block time reliability indexes. Based on our analysis, we find that the reliability of delay time window 3 from 22:20-23:00 to be relatively less compared to the other delay time windows. The results from the reliability estimation demonstrate that the model is efficient in estimating the reliability of flight block time in different delay time windows. INDEX TERMS Flight operations, flight block time reliability, flight time distribution, DBSCAN, flight delay.

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Section: A Travel Time Reliabilitymentioning

confidence: 99%

Section: Flight Time Distributionmentioning

confidence: 99%

Assessment of Flight Block Time Reliability Under Different Delay Time Windows: A Case Study

Tian

Wang

et al. 2020

IEEE Access

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“…in Zang et al (2018), Li (2019), andLi et al (2020)), and for traffic flow predictions in Dutreix and Coogan (2017).…”

Section: Quantiles and Expectilesmentioning

confidence: 99%

Directional Assessment of Traffic Flow Extremes

Osipenko¹

2020

Preprint

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We analyze extremes of traffic flow profiles composed of traffic counts over a day. The data is essentially curves and determining which trajectory should be classified as extreme is not straight forward.To assess the extremes of the traffic flow curves in a coherent way, we use a directional definition of extremeness and apply the dimension reduction technique called principal component analysis (PCA) in an asymmetric norm. In the classical PCA one reduces the dimensions of the data by projecting it in the direction of the largest variation of the projection around its mean. In the PCA in an asymmetric norm one chooses the projection directions, such that the asymmetrically weighted variation around a tail index -an expectile -of the data is the largest possible. Expectiles are tail measures that generalize the mean in a similar manner as quantiles generalize the median. Focusing on the asymmetrically weighted variation around an expectile of the data, we find the appropriate projection directions and the low dimensional representation of the traffic flow profiles that uncover different patterns in their extremes. Using the traffic flow data from the roundabout on Ernst-Reuter-Platz in the city center of Berlin, Germany, we estimate, visualize and interpret the resulting principal expectile components. The corresponding directional extremes of the traffic flow profiles are simple to identify and to connect to their location-and time-related specifics. Their shapes are driven by their scores on each principal expectile component which is useful for extracting and analyzing traffic patterns. Our approach to dimensionality reduction towards the directional extremes of traffic flow extends the related methodological basis and gives promising results for subsequent analysis, prediction and control of traffic flow patterns.

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“…Such data points need to be filtered out to obtain accurate reliability metrics. This section explains the outlier detection algorithm implemented in this work (Zang et al, 2018). In this method, all the observations lying outside the bounds defined by Eq.…”

Section: Outlier Detectionmentioning

confidence: 99%

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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A closed-form estimation of the travel time percentile function for characterizing travel time reliability

Cited by 22 publications

References 60 publications

Assessment of Flight Block Time Reliability Under Different Delay Time Windows: A Case Study

Assessment of Flight Block Time Reliability Under Different Delay Time Windows: A Case Study

Directional Assessment of Traffic Flow Extremes

Understanding Factors Affecting Arterial Reliability Performance Metrics

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