Characterizing Bridge Functional Obsolescence Using Congestion Performance Measures Determined from Anonymous Probe-Vehicle Data

2016 ◽  
Vol 30 (2) ◽  
pp. 04015027 ◽  
Author(s):  
Andrew J. Bechtel ◽  
Thomas M. Brennan ◽  
Jhenifer Mesquita de Araujo
Keyword(s):  
Performance Measures ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Bridge Functional

Performance Measures for Characterizing Regional Congestion using Aggregated Multi-Year Probe Vehicle Data

2018 ◽  
Vol 2672 (42) ◽  
pp. 170-179 ◽  
Author(s):  
Thomas M. Brennan ◽  
Mohan M. Venigalla ◽  
Ashley Hyde ◽  
Anthony LaRegina
Keyword(s):  
Performance Measures ◽  
The United States ◽  
Hurricane Sandy ◽  
Vehicle Speed ◽  
Important Data ◽  
Probe Vehicle ◽  
Special Events ◽  
Vehicle Data ◽  
Data Source ◽  
The Impact

Probe vehicle speed data has become an important data source for evaluating the congestion performance of highways and arterial roads. Pre-defined spatially located segments known as traffic message channels (TMCs) are linked to commercially available, temporal anonymous probe vehicle speed data. These data have been used to develop agency-wide performance measures to better plan and manage infrastructure assets. Recent research has analyzed individual as well as aggregated TMC links on roadway systems to identify congested areas along spatially defined routes. By understanding the typical congestion of all TMCs in a region as indicated by increased travel times, a broader perspective of the congestion characteristics can be gained. This is especially important when determining the impact of such occurrences in the region as a major crash event, special events, or during extreme conditions such as a natural or human-made disaster. This paper demonstrates how aggregated probe speed data can be used to characterize regional congestion. To demonstrate the methodology, an analysis of vehicle speed data during Hurricane Sandy, the second costliest hurricane in the United States, is used to show the regional impact in 2012. Further, the analysis results are compared and contrasted with comparable periods of increased congestion in 2013, 2014, and 2016. The analysis encompasses 614 TMCs, within 10 miles of the New Jersey coast. Approximately 90 million speed records covering five counties are analyzed in the study.

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.

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.

Approach to Quantify the Impact of Disruptions on Traffic Conditions using Dynamic Weighted Resilience Metrics of Transport Networks

2021 ◽  
pp. 036119812199866 ◽  
Author(s):  
Elise Henry ◽  
Angelo Furno ◽  
Nour-Eddin El Faouzi
Keyword(s):  
Three Dimensional ◽  
Traffic Load ◽  
Transport Networks ◽  
Traffic Conditions ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Network Operations ◽  
Network States ◽  
The Impact ◽  
Proper Operation

Transport networks are essential for societies. Their proper operation has to be preserved to face any perturbation or disruption. It is therefore of paramount importance that the modeling and quantification of the resilience of such networks are addressed to ensure an acceptable level of service even in the presence of disruptions. The paper aims at characterizing network resilience through weighted degree centrality. To do so, a real dataset issued from probe vehicle data is used to weight the graph by the traffic load. In particular, a set of disrupted situations retrieved from the study dataset is analyzed to quantify the impact on network operations. Results demonstrate the ability of the proposed metrics to capture traffic dynamics as well as their utility for quantifying the resilience of the network. The proposed methodology combines different metrics from the complex networks theory (i.e., heterogeneity, density, and symmetry) computed on temporal and weighted graphs. Time-varying traffic conditions and disruptions are analyzed by providing relevant insights on the network states via three-dimensional maps.

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.

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