Practical Procedure to Collect Arterial Travel Time Data Using GPS-Instrumented Test Vehicles

Hunter, Michael; Wu, Seung Kook; Kim, Hoe Kyoung

doi:10.1177/0361198106197800120

Cited by 8 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many of these experiments offer new ways of organizing and automating the data collection procedure. In one such study, Hunter developed the Travel Run Intersection Passing Time Identification (TRIPTI) algorithm [11] for the collection and analysis of GPS-based travel time data. The TRIPTI algorithm first checks each data point location against the known location of each intersection to determine which intersections were traversed.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of the Accuracy and Automation of Travel Time and Delay Data Collection Methods

Suarez¹,

Faghri²,

Li³

2014

JTTs

View full text Add to dashboard Cite

Travel time and delay are among the most important measures for gauging a transportation system's performance. To address the growing problem of congestion in the US, transportation planning legislation mandated the monitoring and analysis of system performance and produced a renewed interest in travel time and delay studies. The use of traditional sensors installed on major roads (e.g. inductive loops) for collecting data is necessary but not sufficient because of their limited coverage and expensive costs for setting up and maintaining the required infrastructure. The GPS-based techniques employed by the University of Delaware have evolved into an automated system, which provides more realistic experience of a traffic flow throughout the road links. However, human error and the weaknesses of using GPS devices in urban settings still have the potential to create inaccuracies. By simultaneously collecting data using three different techniques, the accuracy of the GPS positioning data and the resulting travel time and delay values could be objectively compared for automation and statistically compared for accuracy. It was found that the new technique provided the greatest automation requiring minimal attention of the data collectors and automatically processing the data sets. The data samples were statistically analyzed by using a combination of parametric and nonparametric statistical tests. This analysis greatly favored the GeoStats GPS method over the rest methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Evaluation of the Accuracy and Automation of Travel Time and Delay Data Collection Methods

Suarez¹,

Faghri²,

Li³

2014

JTTs

View full text Add to dashboard Cite

show abstract

“…Schuessler and Axhausen (10) described a postprocessing procedure for cleaning and smoothing raw GPS data and identified trip activity and trip modes automatically by using fuzzy logic. Hunter et al (11) used GPS-instrumented test vehicles and developed an algorithm that identified the traversal time between intersections for a GPS device mounted in a probe vehicle to calculate travel times on urban arterial streets. Zheng et al (12) used GPS to monitor transit vehicle movements along signalized arterial corridors to evaluate a transit signal priority system deployed in Snohomish County in Washington State.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because GPS devices record a range of stops, a methodology was needed to differentiate between traffic-based (unintended) stops at intersections or in congestion and intended stops that corresponded to a truck's origin and destination. Several studies that have analyzed GPS truck data have attempted to separate traffic stops from O-D stops on the basis of the stop duration (i.e., dwell time) (4,(9)(10)(11). In these studies, the origins and destinations identified by stop duration were also manually examined to identify any unusual situations or problems.…”

Section: Identification Of Origins and Destinationsmentioning

confidence: 99%

Processing Commercial Global Positioning System Data to Develop a Web-Based Truck Performance Measures Program

McCormack

Wang

2011

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Although trucks move larger volumes of goods than other modes of transportation, public agencies know little about their travel patterns and how the roadway network performs for trucks. Trucking companies use data from the Global Positioning System (GPS) provided by commercial vendors to dispatch and track their equipment. This research collected GPS data from approximately 2,500 trucks in the Puget Sound, Washington, region and evaluated the feasibility of processing these data to support a statewide network performance measures program. The program monitors truck travel time and system reliability and will guide freight investment decisions by public agencies. While other studies have used a limited number of project-specific GPS devices to collect frequent location readings, which permit a fine-grained analysis of specific roadway segments, this study used data that involved less frequent readings but that were collected from a larger number of trucks for more than a year. Automated processing was used to clean and format the data, which encompassed millions of data points. Because a performance measurement program ultimately monitored trips generated by trucks as they travel between origins and destinations, an algorithm was developed to extract this information and geocode each truck's location to the roadway network and to traffic analysis zones. Measures were developed to quantify truck travel characteristics and performance between zones. To simplify the process and provide a better communications platform for the analysis, the researchers developed a Google Maps–based online system to compute the measures and show the trucks’ routes graphically.

show abstract

“…Intersection-to-intersection travel times were then calculated from the intersection passing times and saved in databases for later analyses. The details of the data reduction procedure can be found in (Hunter et al, 2006).…”

Section: Gps Data Processingmentioning

confidence: 99%

Evaluation of traffic signal control system using a system-wide performance measure under two-fluid model theory

Hunter

Lee

et al. 2011

KSCE Journal of Civil Engineering

Self Cite

View full text Add to dashboard Cite

This study compared the performance of an adaptive traffic control system and a semi-actuated coordinated control system using trip time and stop time relation under the two-fluid model theory. Using the test vehicle data collected for the evaluation of SCATS in Cobb County, Georgia, the parameters were estimated and interpreted. At the initial interpretation, potentially conflicting recommendations could be drawn from the individual route-period group level comparisons and the two-fluid model results. Therefore, a new interpretation combining the two approaches was presented. The new approach resulted in curves representing restricted ranges of the trip time versus stop time relationship. Using the curves and a two-fluid model assumption, the fraction of moving vehicles in the network can be obtained. Unlike the measures adopted in the previous traffic signal system evaluation studies, such as mean travel time and speed, this measure is defined by all time periods and routes data, allowing for a system-wide performance measure. Application of this measure showed that under SCATS approximately 10% more vehicles were moving on end-to-end type routes when the network was most congested and 15% to 20% less vehicles were moving on side-street to side-street type routes over different traffic concentration levels.

show abstract

Practical Procedure to Collect Arterial Travel Time Data Using GPS-Instrumented Test Vehicles

Cited by 8 publications

References 4 publications

Evaluation of the Accuracy and Automation of Travel Time and Delay Data Collection Methods

Evaluation of the Accuracy and Automation of Travel Time and Delay Data Collection Methods

Processing Commercial Global Positioning System Data to Develop a Web-Based Truck Performance Measures Program

Evaluation of traffic signal control system using a system-wide performance measure under two-fluid model theory

Contact Info

Product

Resources

About