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.

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.

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.

Performance Measures to Characterize Corridor Travel Time Delay Based on Probe Vehicle Data

2015 ◽  
Vol 2526 (1) ◽  
pp. 39-50 ◽  
Author(s):  
Thomas M. Brennan ◽  
Stephen M. Remias ◽  
Lucas Manili
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
The United States ◽  
Flow Speed ◽  
Performance Measure ◽  
Vehicle Speed ◽  
Travel Time Reliability ◽  
Highway Capacity Manual ◽  
Probe Vehicle ◽  
Vehicle Data

Anonymous probe vehicle data are being collected on roadways throughout the United States. These data are incorporated into local and statewide mobility reports to measure the performance of highways and arterial systems. Predefined spatially located segments, known as traffic message channels (TMCs), are spatially and temporally joined with probe vehicle speed data. Through the analysis of these data, transportation agencies have been developing agencywide travel time performance measures. One widely accepted performance measure is travel time reliability, which is calculated along a series of TMCs. When reliable travel times are not achieved because of incidents and recurring congestion, it is desirable to understand the time and the location of these occurrences so that the corridor can be proactively managed. This research emphasizes a visually intuitive methodology that aggregates a series of TMC segments based on a cursory review of congestion hotspots within a corridor. Instead of a fixed congestion speed threshold, each TMC is assigned a congestion threshold based on the 70th percentile of the 15-min average speeds between 02:00 and 06:00. An analysis of approximately 90 million speed records collected in 2013 along I-80 in northern New Jersey was performed for this project. Travel time inflation, the time exceeding the expected travel time at 70% of measured free-flow speed, was used to evaluate each of the 166 directional TMC segments along 70 mi of I-80. This performance measure accounts for speed variability caused by roadway geometry and other Highway Capacity Manual speed-reducing friction factors associated with each TMC.

Combining Life Cycle Assessment and Linear Regression Analysis to Determine Significant Design Characteristics

2012 ◽  
Author(s):  
Ashley DeVierno ◽  
Brian Thorn ◽  
Andres L. Carrano
Keyword(s):  
Regression Analysis ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Linear Regression ◽  
Environmental Impact ◽  
End Of Life ◽  
Significant Relationship ◽  
Linear Regression Analysis ◽  
Cellular Phones ◽  
Design Characteristics

For designers it is difficult to pin-point the design characteristics that could be changed to reduce the environmental impact of their products. This paper describes a method for determining the design characteristics that have a significant relationship with environmental impact that arises at product end-of-life. In this method, Life Cycle Assessment (LCA) and Linear Regression Analysis (LRA) are combined. LCA is used to quantify the environmental impact of products from the extraction of their raw materials to their disposal. LRA is used to determine the design characteristics that have the most significant relationship with environmental impact. Combining LCA and LRA gives the designer the ability to (1) establish a relationship between design characteristics and their environmental impact, (2) determine the most significant design characteristics that influence environmental impact, and (3) validate design changes with their influence on product environmental impact. In the case study described here, the design characteristic, Volume, is shown to have significant relationship with the end-of-life environmental impact of cellular phones. This trend is consistent with the results of the one-phase end-of-life disposition assessments that evaluated disassembled cellular phones. With the results of this method, designers can focus their sustainable design efforts on modifying and improving the design characteristics that have the strongest relationship with environmental impact.

scholarly journals Urban Link Travel Time Estimation Based on Low Frequency Probe Vehicle Data

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Xiyang Zhou ◽  
Zhaosheng Yang ◽  
Wei Zhang ◽  
Xiujuan Tian ◽  
Qichun Bing
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Time Window ◽  
Low Frequency ◽  
Time Estimation ◽  
Estimation Accuracy ◽  
Travel Time Estimation ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Link Travel Time

To improve the accuracy and robustness of urban link travel time estimation with limited resources, this research developed a methodology to estimate the urban link travel time using low frequency GPS probe vehicle data. First, focusing on the case without reporting points for the GPS probe vehicle on the target link in the current estimation time window, a virtual report point creation model based on theK-Nearest Neighbour Rule was proposed. Then an improved back propagation neural network model was used to estimate the link travel time. The proposed method was applied to a case study based on an arterial road in Changchun, China: comparisons with the traditional artificial neural network method and the spatiotemporal moving average method revealed that the proposed method offered a higher estimation accuracy and better robustness.

Third-Party Data Fusion to Estimate Freeway Performance Measures

2021 ◽  
pp. 036119812110242
Author(s):  
Sakib Mahmud Khan ◽  
Anthony David Patire
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
Traditional Method ◽  
Estimation Method ◽  
Performance Measure ◽  
Third Party ◽  
Traffic Demand ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Transportation Agencies

Transportation agencies monitor freeway performance using various measures such as VMT (vehicle-miles traveled), VHD (vehicle-hours of delay), and VHT (vehicle-hours traveled). They typically rely on data from point detectors to estimate these freeway performance measures. Point detectors such as inductive loops cannot capture the travel time for a corridor, leading to inaccurate performance measure estimation. This research develops a hybrid method, which estimates freeway performance measures using a mix of probe vehicle data provided by third-party vendors and data from traditional point detectors. Using a simulated model of a freeway (Interstate-210), the overall framework using multiple data sources is evaluated and compared with the traditional point detector-based estimation method. In the traditional method, point speeds are estimated with the flow and occupancy values using g-factors. Data from 5% of the total vehicles are used to generate the third-party provided travel time data. The analysis is conducted for multiple scenarios, including peak and off-peak periods. Results suggest that fusing probe vehicle data from third-party vendors with data from point detectors can help transportation agencies estimate performance measures better than the traditional method, in scenarios that have noticeable traffic demand on freeways.

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