Using travel time reliability measures with individual vehicle data

2016 ◽  
Author(s):  
Isaac K Isukapati ◽  
George F List
Keyword(s):  
Travel Time ◽  
Travel Time Reliability ◽  
Reliability Measures ◽  
Vehicle Data

Analyzing Travel Time Reliability from Sparse Probe Vehicle Data: A Case Study on the Effects of Spatial and Temporal Aggregation

2021 ◽  
pp. 036119812110315
Author(s):  
Markus Steinmaßl ◽  
Stefan Kranzinger ◽  
Karl Rehrl
Keyword(s):  
Travel Time ◽  
Time Of Day ◽  
Temporal Aggregation ◽  
Travel Time Reliability ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Depth Analysis ◽  
Data Source ◽  
Travel Time Index

Travel time reliability (TTR) indices have gained considerable attention for evaluating the quality of traffic infrastructure. Whereas TTR measures have been widely explored using data from stationary sensors with high penetration rates, there is a lack of research on calculating TTR from mobile sensors such as probe vehicle data (PVD) which is characterized by low penetration rates. PVD is a relevant data source for analyzing non-highway routes, as they are often not sufficiently covered by stationary sensors. The paper presents a methodology for analyzing TTR on (sub-)urban and rural routes with sparse PVD as the only data source that could be used by road authorities or traffic planners. Especially in the case of sparse data, spatial and temporal aggregations could have great impact, which are investigated on two levels: first, the width of time of day (TOD) intervals and second, the length of road segments. The spatial and temporal aggregation effects on travel time index (TTI) as prominent TTR measure are analyzed within an exemplary case study including three different routes. TTI patterns are calculated from data of one year grouped by different days-of-week (DOW) groups and the TOD. The case study shows that using well-chosen temporal and spatial aggregations, even with sparse PVD, an in-depth analysis of traffic patterns is possible.

Which Aggregation Fits Best? The Use of Linear Regression to Show the Influence of Temporal and Spatial Aggregation of Sparse Probe Vehicle Data on the Explanation of Travel Time Reliability

2021 ◽  
pp. 036119812110578
Author(s):  
Stefan Kranzinger ◽  
Markus Steinmaßl
Keyword(s):  
Linear Regression ◽  
Travel Time ◽  
Linear Regression Analysis ◽  
Spatial Aggregation ◽  
Travel Time Reliability ◽  
Road Design ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Temporal And Spatial ◽  
Design Characteristics

Aggregation of sparse probe vehicle data (PVD) is a crucial issue in travel time reliability (TTR) analysis. This study, therefore, examines the effect of temporal and spatial aggregation of sparse PVD on the results of a linear regression analysis where two different measures of TTR are analyzed as the dependent variable. Our results show that by aggregating the data to longer time intervals and coarser spatial units the linear model can explain a higher proportion of the variance in TTR. Furthermore, we find that the effects of road design characteristics in particular depend on the variable used to represent TTR. We conclude that the temporal and spatial aggregation of sparse PVD affects the results of linear regression explaining TTR.

Travel Time Reliability Measures Accommodating Scheduling Preferences of Travelers

2019 ◽  
Vol 2673 (4) ◽  
pp. 708-721 ◽  
Author(s):  
Mojtaba Rajabi-Bahaabadi ◽  
Afshin Shariat-Mohaymany ◽  
Shu Yang
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Travel Time Reliability ◽  
Time Data ◽  
Reliability Measures ◽  
Time Performance ◽  
Early Arrival ◽  
Travel Time Distribution ◽  
Travel Time Data ◽  
Schedule Adherence

Existing travel time reliability measures fail to accommodate scheduling preferences of travelers and cannot distinguish between the variability associated with early and late arrivals. This study introduces two new travel time reliability measures based on concepts from behavioral economics. The first proposed measure is an indicator of the width of travel time distribution. It considers scheduling preferences of travelers and can distinguish between early arrival and late arrival. The second measure determines the skewness of travel time distribution. To estimate the proposed measures, travel time is modeled by mixture models and closed-form expressions are derived for the expected values of early and late arrivals. In addition, real travel time data from a freeway segment is used to compare the proposed measures with the existing travel time reliability measures. The results suggest that, although there exist significant correlations between travel time reliability measures, travelers’ preferences have considerable effects on the travel time reliability as perceived by them. Furthermore, four measures are developed based on the notions of early and late arrivals to assess the on-time performance (schedule adherence) of transit vehicles at stop level. The results of this study show that the four measures can serve as complementary to the existing on-time performance indices.

Assessing Highway Travel Time Reliability using Probe Vehicle Data

2018 ◽  
Vol 2672 (15) ◽  
pp. 118-130 ◽  
Author(s):  
Piotr Olszewski ◽  
Tomasz Dybicz ◽  
Kazimierz Jamroz ◽  
Wojciech Kustra ◽  
Aleksandra Romanowska
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Time Of Day ◽  
Travel Time Reliability ◽  
Floating Car Data ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Reliability Indicator ◽  
Reliability Performance ◽  
Road Segments

Probe vehicle data (also known as “floating car data”) can be used to analyze travel time reliability of an existing road corridor in order to determine where, when, and how often traffic congestion occurs at particular road segments. The aim of the study is to find the best reliability performance measures for assessing congestion frequency and severity based on probe data. Pilot surveys conducted on A2 motorway in Poland confirm the usefulness and reasonable accuracy of probe data for measuring speed variation in both congested and free-flowing traffic. Historical probe vehicle data and traditional traffic counts from Polish S6 expressway were used to analyze travel time reliability on its 24 road sections. Travel time indexes and reliability ratings for the whole year 2016 were calculated to identify segments with lower reliability and higher expected delay. It is concluded that unlike the HCM-6 method, travel times obtained from probe data should be averaged in 1-hour intervals. Delay index is proposed as a new reliability indicator for road segments. Delay map diagrams are recommended for showing how the congestion spots move in space and with time of day.

Improving Interstate Freeway Travel Time Reliability Analysis by Clustering Travel Time Distributions

2021 ◽  
pp. 036119812110120
Author(s):  
Xiaoxiao Zhang ◽  
Mo Zhao ◽  
Justice Appiah ◽  
Michael D. Fontaine
Keyword(s):  
Travel Time ◽  
Cumulative Distribution ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Clustering Methods ◽  
Time Data ◽  
Dissimilarity Matrix ◽  
Model Based ◽  
Probe Vehicle ◽  
Vehicle Data

Travel time reliability quantifies variability in travel times and has become a critical aspect for evaluating transportation network performance. The empirical travel time cumulative distribution function (CDF) has been used as a tool to preserve inherent information on the variability and distribution of travel times. With advances in data collection technology, probe vehicle data has been frequently used to measure highway system performance. One challenge with using CDFs when handling large amounts of probe vehicle data is deciding how many different CDFs are necessary to fully characterize experienced travel times. This paper explores statistical methods for clustering CDFs of travel times at segment level into an optimal number of homogeneous clusters that retain all relevant distributional information. Two clustering methods were tested, one based on classic hierarchical clustering and the other used model-based functional data clustering, to find out their performance on clustering distributions using travel time data from Interstate 64 in Virginia. Freeway segments and those within interchange areas were clustered separately. To find the proper data format as clustering input, both scaled and original travel times were considered. In addition, a non-data-driven method based on geometric features was included for comparison. The results showed that for freeway segments, clustering using travel times and the Anderson–Darling dissimilarity matrix and Ward’s linkage had the best performance. For interchange segments, model-based clustering provided the best clusters. By clustering segments into homogenous groups, the results of this study could improve the efficiency of further travel time reliability modeling.

Incorporation of Travel Time Reliability in Regional Travel Model

2018 ◽  
Vol 2672 (49) ◽  
pp. 46-57 ◽  
Author(s):  
Surabhi Gupta ◽  
Peter Vovsha ◽  
Arup Dutta ◽  
Vladimir Livshits ◽  
Wang Zhang ◽  
...  
Keyword(s):  
Travel Time ◽  
Partial Correlation ◽  
Practical Method ◽  
Vehicle Speed ◽  
Travel Time Reliability ◽  
Reliability Measures ◽  
The Third ◽  
The Core ◽  
Sensitivity Tests ◽  
Fourth Step

The paper presents a practical method for incorporation of travel time reliability in a regional travel model. The discussion includes five consecutive steps. First it describes how the vehicle speed dataset for the metropolitan area of Phoenix, AZ, termed HERE, was processed and link-level volume–delay–reliability functions were estimated, and then how link-level reliability measures can be applied for network path building. The third step describes how trip origin–destination (OD) reliability measures can be constructed out of the link-level reliability measures. The fourth step involves implementation of the link-level and OD-level reliability measures in highway assignment, mode choice, and other travel models. The fifth step includes model validation and sensitivity tests. The paper addresses several long-standing issues associated with incorporation of travel time reliability in operational travel models in practice. These issues include construction of OD reliability measures with the recognition that the core link-level reliability measures such as standard deviation or variance are not additive in a general case, accounting for a partial correlation between travel time distributions for different links, and incorporation of travel time reliability in a standard static assignment.

Sign in / Sign up

Export Citation Format

Share Document