Travel Time Variability and Congestion Assessment for Origin–Destination Clusters through the Experience of Mobility Companies

Reliability is a measure of network performance that reflects the ability of the network to provide predictable travel times. Deviations from planned travel times can increase travel costs for users. To improve the system's performance, it is crucial to identify sources of unreliability, particularly the location on the network of unreliable performance. The large amount of travel time data recorded by Transportation Network Providers (TNPs) in recent years has enabled researchers to study the performance of entire networks. In this study, a real-world dataset provided by TNPs in Chicago is used to determine time of day, and day of week distribution of travel time per unit distance for origin–destination (OD) pairs. Eight measures of reliability are calculated for OD pairs in the network. Standard deviation (SD), planning time index (PTI), and on-time measure (PR) are used for a network-wide comparison of reliability performance. K-means clustering is performed on more than 21.3 million trips to divide 3,450 eligible OD pairs in the Chicago network into three groups with low, medium, and high intensity of each reliability metric. Lastly, metrics in each cluster of SD, PTI, and PR are compared. The results show that ranking PTI and PR is not sufficient for identifying unreliable/congested OD pairs in the network. Approaches for comparing reliability performance over different periods of the day for the same segment and over different segments in the network are discussed, along with network-wide measures of reliability.

[1]  Alexander Skabardonis,et al.  Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies , 2012 .

[2]  Timothy J Lomax,et al.  SELECTING TRAVEL RELIABILITY MEASURES , 2003 .

[3]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[4]  Din J. Wasem,et al.  Mining of Massive Datasets , 2014 .

[5]  Wenjing Pu Analytic Relationships between Travel Time Reliability Measures , 2011 .

[6]  Shu Yang,et al.  Origin–Destination-Based Travel Time Reliability , 2017 .

[7]  Pablo Montero,et al.  TSclust: An R Package for Time Series Clustering , 2014 .

[8]  Nagui M. Rouphail,et al.  Detailed Analysis of Travel Time Reliability Performance Measures from Empirical Data , 2013 .

[9]  Kyriacos C. Mouskos,et al.  Analysis of Travel Time Reliability in New York City Based on Day-of-Week and Time-of-Day Periods , 2012 .

[10]  P. Sopp Cluster analysis. , 1996, Veterinary immunology and immunopathology.

[11]  Peter Dalgaard,et al.  R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .

[12]  Mark W Burris,et al.  Using Empirical Data to Find the Best Measure of Travel Time Reliability , 2015 .

[13]  Huizhao Tu,et al.  Travel time unreliability on freeways: Why measures based on variance tell only half the story , 2008 .

[14]  Hani S. Mahmassani,et al.  Characterizing Travel Time Variability in Vehicular Traffic Networks , 2012 .

[15]  Richard Margiotta,et al.  Incorporating travel time reliability into the Highway Capacity Manual. , 2014 .