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.

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.

Application of LiDAR and Connected Vehicle Data to Evaluate the Impact of Work Zone Geometry on Freeway Traffic Operations

2018 ◽  
Vol 2672 (16) ◽  
pp. 1-13 ◽  
Author(s):  
Michelle M. Mekker ◽  
Yun-Jou Lin ◽  
Magdy K. I. Elbahnasawy ◽  
Tamer S. A. Shamseldin ◽  
Howell Li ◽  
...  
Keyword(s):  
Case Studies ◽  
Work Zone ◽  
Extensive Literature ◽  
Vehicle Speed ◽  
Connected Vehicle ◽  
Traffic Operations ◽  
Work Zones ◽  
Geometric Data ◽  
Vehicle Data ◽  
The Impact

Extensive literature exists regarding recommendations for lane widths, merging tapers, and work zone geometry to provide safe and efficient traffic operations. However, it is often infeasible or unsafe for inspectors to check these geometric features in a freeway work zone. This paper discusses the integration of LiDAR (Light Detection And Ranging)-generated geometric data with connected vehicle speed data to evaluate the impact of work zone geometry on traffic operations. Connected vehicle speed data can be used at both a system-wide (statewide) or segment-level view to identify periods of congestion and queueing. Examples of regional trends, localized incidents, and recurring bottlenecks are shown in the data in this paper. A LiDAR-mounted vehicle was deployed to a variety of work zones where recurring bottlenecks were identified to collect geometric data. In total, 350 directional miles were covered, resulting in approximately 360 GB of data. Two case studies, where geometric anomalies were identified, are discussed in this paper: a short segment with a narrow lane width of 10–10.5 feet and a merging taper that was about 200 feet shorter than recommended by the Manual on Uniform Traffic Control Devices. In both case studies, these work zone features did not conform to project specifications but were difficult to assess safely by an inspector in the field because of the high volume of traffic. The paper concludes by recommending the use of connected vehicle data to systematically identify work zones with recurring congestion and the use of LiDAR to assess work zone geometrics.

scholarly journals Food consumption of women during holidays: fit, healthy, or relaxed?

2020 ◽  
Vol 10 (1, 2 e 3) ◽  
Author(s):  
Caner Çalişkan ◽  
Çiğdem Sabbağ ◽  
Bekir Bora Dedeoğlu
Keyword(s):  
Food Consumption ◽  
Planning Process ◽  
Social Roles ◽  
Important Data ◽  
Face To Face ◽  
Data Source ◽  
Role Of Women ◽  
The Impact

During holidays, what, why, and how do women consume? Women’s attitudes and thoughts about food consumption should be researched in terms of their social roles. However, the important role of women in holiday planning process makes women's food consumption preferences and also behaviours an important data source especially for tourism managers. This study focused on the impact of holidays on women’s food consumption. For this purpose, face-to-face interviews were carried out with 15 women participants, who spent two holiday periods during the previous year – summer and winter – in Adıyaman, Turkey. According to the results of the survey, women’s food consumption preferences and behaviours change during the summer and winter holiday periods.

scholarly journals Investigation of superstorm Sandy 2012 in a multi-disciplinary approach

2013 ◽  
Vol 1 (2) ◽  
pp. 625-679 ◽  
Author(s):  
M. Kunz ◽  
B. Mühr ◽  
T. Kunz-Plapp ◽  
J.E. Daniell ◽  
B. Khazai ◽  
...  
Keyword(s):  
United States ◽  
Hurricane Katrina ◽  
The United States ◽  
Hurricane Sandy ◽  
Economic Losses ◽  
Weather System ◽  
Cascading Effects ◽  
The Us ◽  
The Caribbean ◽  
The Impact

Abstract. At the end of October 2012, Hurricane Sandy moved from the Caribbean Sea into the Atlantic Ocean and entered the United States not far from New York. Along its track, Sandy caused more than 200 fatalities and severe losses in Jamaica, Bahamas, Haiti, Cuba, and the US. This paper demonstrates the capability and potential for near-real time analysis of catastrophes. It is shown that the impact of Sandy was driven by the superposition of different extremes (high wind speeds, storm surge, heavy precipitation) and by cascading effects. In particular the interaction between Sandy and an extra-tropical weather system created a huge storm that affected large areas in the US. It is examined how Sandy compares to historic hurricane events, both from a hydro-meteorological and impact perspective. The distribution of losses to different sectors of the economy is calculated with simple input-output models as well as government estimates. Direct economic losses are estimated about 4.2 billion US$ in the Caribbean and between 78 and 97 billion US$ for the US. Indirect economic losses from power outages is estimated in the order of 16.3 billion US$. Modelling sector-specific dependencies, quantifies total business interruption losses between 10.8 and 15.5 billion US$. Thus, seven years after the record impact of Hurricane Katrina in 2005, Hurricane Sandy is the second costliest hurricane in the history of the United States.

Road Closures and Freight Diversion

2012 ◽  
Vol 2269 (1) ◽  
pp. 51-57 ◽  
Author(s):  
David Pierce ◽  
Jeffrey Short
Keyword(s):  
Performance Measures ◽  
The United States ◽  
Valuable Insight ◽  
Data Set ◽  
Sensing Technology ◽  
Global Positioning ◽  
Data Points ◽  
System Data ◽  
The Impact

The FHWA-sponsored Freight Performance Measures (FPM) program generates and monitors a series of performance measures related to the freight transportation system of the United States. The primary information analyzed by the FPM program is a data set consisting of billions of Global Positioning System data points from trucks. These data can be used to demonstrate empirically changes in truck travel patterns and freight performance independent of the availability of roadside sensing technology. A case study that was based on the flooding closure of Arkansas Interstate 40 in May 2011 was presented to show how FPM data can be used to analyze diversion behavior around road closures. This type of empirical analysis is in contrast to the majority of current diversion analyses, which rely on modeling to generate results. Not only do FPM data provide a viable alternative to modeling for studying past events, but the data may provide valuable insight into the underlying assumptions of future models designed to predict the impact of disaster scenarios. By understanding more fully how previous events unfolded, planners can prepare better for the next disaster.

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.

scholarly journals Investigation of superstorm Sandy 2012 in a multi-disciplinary approach

2013 ◽  
Vol 13 (10) ◽  
pp. 2579-2598 ◽  
Author(s):  
M. Kunz ◽  
B. Mühr ◽  
T. Kunz-Plapp ◽  
J. E. Daniell ◽  
B. Khazai ◽  
...  
Keyword(s):  
United States ◽  
Hurricane Katrina ◽  
The United States ◽  
Hurricane Sandy ◽  
Economic Losses ◽  
Weather System ◽  
Cascading Effects ◽  
The Us ◽  
The Caribbean ◽  
The Impact

Abstract. At the end of October 2012, Hurricane Sandy moved from the Caribbean Sea into the Atlantic Ocean and entered the United States not far from New York. Along its track, Sandy caused more than 200 fatalities and severe losses in Jamaica, The Bahamas, Haiti, Cuba, and the US. This paper demonstrates the capability and potential for near-real-time analysis of catastrophes. It is shown that the impact of Sandy was driven by the superposition of different extremes (high wind speeds, storm surge, heavy precipitation) and by cascading effects. In particular the interaction between Sandy and an extra-tropical weather system created a huge storm that affected large areas in the US. It is examined how Sandy compares to historic hurricane events, both from a hydro-meteorological and impact perspective. The distribution of losses to different sectors of the economy is calculated with simple input-output models as well as government estimates. Direct economic losses are estimated about USD 4.2 billion in the Caribbean and between USD 78 and 97 billion in the US. Indirect economic losses from power outages is estimated in the order of USD 16.3 billion. Modelling sector-specific dependencies quantifies total business interruption losses between USD 10.8 and 15.5 billion. Thus, seven years after the record impact of Hurricane Katrina in 2005, Hurricane Sandy is the second costliest hurricane in the history of the United States.

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