Comparison of travel time measurement methods along freeway and arterial facilities (original) (raw)
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Using Archived ITS Data to Generate Improved Freeway Travel Time Estimates
Accurate travel time estimation has become possible with the deployment of advanced traveler management and information systems. Dynamic message signs, websites, and handheld/in-vehicle devices are being increasingly used by public agencies to communicate important travel information to the public such as incidents, road closures and travel times. Travel time estimates are usually derived from roadway sensors, although other technologies such as cell phone matching, license plate matching, automatic vehicle identification and video detection have also been employed. In Oregon, freeway travel time estimates are generated by the Oregon Department of Transportation using data from inductive loop detectors throughout the Portland metropolitan area. These estimates are generated using a simple midpoint algorithm that extrapolates measured speeds over a freeway segment. The objective of this paper is to evaluate the accuracy of the current midpoint algorithm by comparing the derived estimates to ground truth (probe vehicle) travel times. In addition, travel time estimates from a travel time algorithm developed by Coifman were also evaluated for accuracy under varying traffic conditions. Various scenarios were tested using traffic data from both upstream and downstream detector stations. The results indicate that both the midpoint and the Coifman algorithms generate accurate travel time estimates under free flow conditions. The Coifman algorithm using data from the upstream detector provided the best estimate of travel time for a link during congestion as well as in periods after an incident occurred. TRB 2007 Annual Meeting CD-ROM Paper revised from original submittal. Kothuri, Tufte, Ahn and Bertini TRB 2007 Annual Meeting CD-ROM Paper revised from original submittal. Kothuri, Tufte, Ahn and Bertini
Method for the Annual Accuracy Assessment of Freeway Travel Time Systems
Public Works Management & Policy, 2018
Motivated by Title 23 Code of Federal Regulation 511, the U.S. Department of Transportation has recently established real-time systems management information programs to improve the consistency of traveler information provided to the public. Part of these requirements is to review the quality of the information provided to the public. Because limited information is available about current practices for meeting this new regulation, this article presents a method of evaluating real-time travel time information along metropolitan interstate freeways in the context of 23 CFR (Code of Federal Regulations) 511. This article presents a method and a case study of collecting travel time information from multiple sources (such as Google Maps and TravelMidwest.com ) and comparing them to measure accuracy. The findings suggested that the travel time information in the Chicago, Illinois, metropolitan area meets the accuracy requirements of 23 CFR 511, and this study has identified areas for futu...
Improved travel time estimation for reliable performance measure development for closed highways
Accurate travel time information not only is valuable for travelers but is critical to transportation agencies for quantifying the performance of their systems. Interest has been increasing in the development of reliable approaches for estimating travel time from various sensor data. Unlike the extensively studied estimation approaches based on point sensor measurements, the use of probe data from closed highway systems has been limited. To complement current understanding, this study developed an approach that used probe data from an electronic toll collection (ETC) system on closed freeways to estimate travel time. This approach differs from studies relying on automatic vehicle identification systems deployed on main lines as well as those estimated from point detectors. The proposed approach breaks down individual journey time into section travel time and fuses the probe data from vehicles that have used the links. The results, which are based on real-world case studies, illustrate the potential of mining ETC data for travel time estimation for both incident-free and incident conditions. In addition, the estimated results capture traffic dynamics better than instantaneous travel time estimates based on point sensor data. More accurate information is thus provided for deriving reliable performance measures to depict travel time reliability.
Assessing Highway Travel Time Reliability using Probe Vehicle Data
Transportation Research Record, 2018
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. Probe vehicle data (PVD, also known as ''floating car data''), is a method of measuring traffic speed based on time-space data transmitted from GPS receivers and navigation devices in vehicles on the road. This method of obtaining information about traffic conditions in real time is used by navigation service providers and to aid traffic management. Long-term observations can be utilized to analyze highway travel time reliability; that is, to find how often traffic congestion occurs on a particular road segment. The paper presents intermediate results of research project MOP-DZ: ''Modern methods of calculating road capacity and assessment of traffic conditions on roads outside urban areas and express roads.'' The aim of the project is to update the Polish methods for evaluating traffic performance and estimating capacity for uninterrupted flow facilities. The project is jointly financed by the General Directorate for National Roads and Motorways and the Polish National Centre for Research and Development. It is being carried out by a consortium The project uses data from various sources, including traffic volume and speed data from permanent count stations , field surveys, and PVD. This last data category proved to be very useful for illustrating the emergence, spreading, and dissipation of congested regions as well as for assessing travel time reliability. Historical data from 16 weeks in 2015/16 for all national roads and motorways in Poland were used to examine travel time variability and to analyze how often unacceptable traffic conditions occur. The paper presents detailed analysis of a 36.5 km-long segment of S6 expressway, known as the Tricity Bypass Road. The study combined PVD with traffic counts from a permanent count station located at the middle section of the expressway.
Monitoring travel time reliability on freeways
Transport & Planning, Faculty of Civil Engineering and …, 2008
This thesis is the result of a Ph.D. study carried out from November 2003 to March 2008 at Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning. The research was sponsored in the first year by the Sino-Netherlands ITS ...
Evaluation of the accuracy and automation of travel time and delay data collection methods
2010
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.
Viewing cities in this contemporary era, travel time efficiency and operational performance as well as the service quality of transport connectivity posed a strong effect on transportation networks. However, severe and unanticipated delays disrupted deliveries, program and activity schedules, operations, and other logistics. The travel time index was used to identify the determinants of the buffer time index (BTI) and planning time index (PTI) as a technique for measuring travel time consistency. This study aimed to examine highway travel time reliability measures based on BTI and PTI using a highway capacity manual (HCM) as an integral part of this work. The study focuses on the following objectives. Firstly, to identify the measures of highway travel time reliability. Secondly, to assess the effectiveness of travel time reliability measures. The researchers developed a bibliometric analysis to identify the intensity of the two variables used based on the literature. The study further provides an explanatory framework for travel time reliability through wide and broader learning under diverse works of literature. The study shows that BTI was more consistent and proven more effective in measuring TTI compared to PTI with minimum percentile. The study's findings will be useful to transportation planners, academics, and traffic engineers in their decision-making process to improve TTR.
Toward Understanding and Reducing Errors in Real-Time Estimation of Travel Times
Transportation Research Record: Journal of the Transportation Research Board, 2008
In recent years the increased deployment of intelligent transportation systems infrastructure has enabled the provision of real-time traveler information to the public. Many states as well as private contractors are providing real-time travel time estimates to commuters to help improve the quality and efficiency of their trips. Accuracy of travel time estimates is important, as inaccurate estimates can be detrimental. Improving the accuracy of real-time estimates involves identifying and understanding the sources of error. This paper reports on the errors found during the evaluation of real-time travel time estimates in Portland, Oregon and provides solutions for reducing estimation error. The midpoint algorithm used by the Oregon Department of Transportation was used to estimate travel times from speeds obtained from loop detectors. The estimates were assessed for accuracy by comparisons with ground truth probe vehicle runs. The findings from the study indicate that 85% of the travel time runs had errors below 20% and, further, that accuracy varied widely between segments. The evaluation of high-error runs revealed the main causes of errors as transition traffic conditions, failure of detectors and detector spacing. Potential solutions were identified for each source of error. In addition, a method was tested for evaluating the benefits of additional detectors by simulation of virtual detectors. The results indicate that additional detection helps in reducing the mean average percent error in most cases but the location of detectors is critical to error reduction.