Traffic Probe Data Processing for Full-Scale Deployment of Vehicle-Infrastructure Integration

Shladover, Steven E; Kühn, Thomas

doi:10.3141/2086-14

Cited by 23 publications

(7 citation statements)

References 9 publications

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“…Similar to Demers' approach to a more wide scale probe vehicle-based study, Shladover [13] developed a data sampling process as part of vehicle-infrastructure integration (VII). Each VII-equipped vehicle on the road serves as a probe for data transmission.…”

Section: Related Workmentioning

confidence: 99%

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

Suarez¹,

Faghri²,

Li³

2014

JTTs

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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.

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Section: Related Workmentioning

confidence: 99%

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

Suarez¹,

Faghri²,

Li³

2014

JTTs

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“…This high sampling frequency of 1 second is feasible for current GPS‐enabled cell phones, because of the high bandwidth of the new cellular networks (i.e., 3 G and 4 G networks) and the rapid improvements in the servers' capabilities. Further analysis should be conducted in the future to examine the standard sampling frequency as indicated for the connected vehicles' purposes, in the order of 5 seconds and lower, conforming to the proposed Society of Automotive Engineers J2735 standards and as adopted in the connected vehicles' probe data studies conducted by Shladover and Kuhn and Dion et al . .…”

Section: Description Of the Simulation Analysismentioning

confidence: 99%

Comparative analysis of probe‐based ramp metering with detector‐based and pretimed ramp metering

Kattan

Saidi

2012

J of Advced Transportation

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SUMMARY In this paper, a probe‐based adaptive ramp metering approach has been developed and compared with a detector‐based and pretimed ramp metering approaches using PARAMICS microsimulation model made by Quadstone Limited. Both examined probe‐based and detector‐based ramp metering approaches adopt the same problem formulation; however, they differ by the data source that provides estimates of their input parameters. The probe‐based approach takes as main input space mean speed extracted from vehicle probes moving constantly on the entire freeway. The detector‐based approach takes the density readings directly from point detectors placed upstream and downstream of each on‐ramp. The developed approach formulates the ramp metering rates to simultaneously reduce freeway travel time and decrease delays on ramps. A dynamic weighting scheme is adopted in this formulation to give different weighting schemes to the various freeway sections and the on‐ramps. These weights are able to adjust dynamically based on prevailing and forecasted traffic conditions to give priority in each step to the sections of the freeways or/and on‐ramps experiencing critical traffic conditions. The comparative analysis of the three ramp metering approaches was conducted on an 8‐km freeway section on Highway 2 in Calgary, Alberta, Canada. The results of the analysis indicate that the probe‐based algorithm consistently outperformed the two other approaches in terms of all performance measures. The probe‐based approach is shown to exceed the performance of the detector‐based approach with a market share of vehicle probes as low as 3%. The results of the analysis are promising and show the effectiveness of developing ramp metering control algorithms taking the main input parameters from vehicle probes on the freeway. Copyright © 2012 John Wiley & Sons, Ltd.

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“…However, many factors affect the quality and quantity of probe vehicle data offered by VII; these data include market penetration rate, the generation and uploading process of VII snapshots, communication delays, roadside unit (RSU) configurations (i.e. the number and position of RSUs), RSU communication range (Li et al 2008;Shladover et al 2008) etc. The placement of RSUs in a network is one of the most important considerations for VII deployment because this poses significant implications for VII traffic monitoring applications.…”

Section: Introductionmentioning

confidence: 99%

Rsu Localization Model and Simulation Optimization for Vii Network

Sun

Yang

2012

Transport

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While facing the needs for Vehicle Infrastructure Integration (VII) applications in traffic management, the paper deals with the problem of locating Road Side Units (RSU) for VII deployment. After analyzing the difference between traditional problems of locating traffic information detector and the problem of RSU location, a significance ranking model for RSU localization and three kinds of Significance Degree (SD) computing strategies are put forward. A VII simulation environment for the purpose of RSU localization optimization within VISSIM microscopic traffic simulation software is established developing add-on functions using VISSIM's Component Object Model (COM). A VII test bed of the Olympic Park network in Beijing is taken as an example to evaluate the performance of RSU localization model. The results of simulation experiments indicate that the mixed SD strategy considering both speed and route monitoring is superior to the other two SD strategies. Then, the impact of RSU number and OBE market penetration rate on the evaluation measures of traffic monitoring are studied with reference to the proposed mixed SD strategy. In this case, the evaluation measures of optimized RSU configurations generated by the ranking algorithm are always better than those of random RSU configurations. In addition, the benefits of optimized RSU configurations increase along with RSU number and market penetration rate while the benefits of random RSU configurations are more fluctuant.

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Traffic Probe Data Processing for Full-Scale Deployment of Vehicle-Infrastructure Integration

Cited by 23 publications

References 9 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

Comparative analysis of probe‐based ramp metering with detector‐based and pretimed ramp metering

Rsu Localization Model and Simulation Optimization for Vii Network

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