Correlation Analysis for Exploring the Relationship Between Probe Vehicle Data and Event-Based Traffic Signal Performance Measures

Emtenan, A. M. Tahsin; Day, Christopher M.

doi:10.1177/03611981221084684

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…These simpler models can be useful when adequate field data are not available. Finally, a multiple variable model was developed for each category with the best set of predictors ( 15 ). Stepwise regression was used to select variables for inclusion in the model.…”

Section: Methodsmentioning

confidence: 99%

Exploratory Regression Models for Estimating Right-Turn-on-Red Volume on Exclusive Right-Turn Lanes at Signalized Intersections

Emtenan

Haghighat

Shields³

et al. 2022

Transportation Research Record: Journal of the Transportation R

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The Highway Capacity Manual (HCM) 2016 recommends using right-turn-on-red (RTOR) volume for capacity and level of service analyses but provides very limited guidance for predicting the volume in the absence of field data. The signalized intersection methodology in the HCM computes capacity using only the green portion of the cycle, ignoring the additional capacity that may result from allowing RTOR. This leads to an overestimation of delay, and consequently a determination of level of service that is likely to appear worse than the actual performance. Therefore, it is essential to develop proper RTOR volume and capacity estimation models for correct estimation of the level of service at signalized intersections. In this study, regression models for RTOR volume were developed for intersections where the subject RTOR movement occurs from an exclusive right-turn lane. Data from 128 intersections across the United States was used for the analysis. Four different categories of regression model with three types of model under each category were developed and their performances were compared using a testing data set. Tobit regression performed better than other types of regression in predicting the RTOR volume. The model will be useful in predicting RTOR volumes in the absence of field data for accurate estimation of delay and level of service at a signalized intersection.

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Section: Methodsmentioning

confidence: 99%

Exploratory Regression Models for Estimating Right-Turn-on-Red Volume on Exclusive Right-Turn Lanes at Signalized Intersections

Emtenan

Haghighat

Shields³

et al. 2022

Transportation Research Record: Journal of the Transportation R

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“…It was concluded that all data sources are capable of locating coordination challenges, with ATSPMs and CV-based vehicle trajectory data providing more detailed insights. Tahsin Emtenan and Day ( 31 ) used regression analysis to determine that probe vehicle average speed with 1-min resolution was highly correlated with four common ATSPMs on two signalized intersections in Iowa. Kim and Coifman ( 32 ) compared probe speed data with concurrent loop detector stations and identified challenges.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Arrivals on Green Estimations from Vehicle Detection and Connected Vehicle Data

Saldivar-Carranza

Gayen

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Many agencies use Automated Traffic Signal Performance Measures (ATSPMs), which require vehicle detection and communication equipment, to evaluate traffic signal efficiency. ATSPMs usually rely on projections based on spatially limited vehicle trajectory samples to estimate performance. Recently, connected vehicle (CV) data has become available that provides entire vehicle trajectories that can be used to generate accurate signal performance measures without the need for projections or infrastructure upgrades. This paper analyzes over 50 intersections in Utah to evaluate how traditional detector-based arrivals on green (AOG) calculations compare with their CV-based calculations. The effects that saturation and queue- lengths have on estimations are analyzed. In general, there is close correlation between computations when queues are short and undersaturated conditions exist. However, if queues extend past the advance detector, detector-based calculations tend to overestimate AOG since vehicles are detected during green but may stop before. This impact is particularly large when the approach is oversaturated, which led to overestimations of around 40% in this study. In addition, detector-based estimations can underestimate AOG in undersaturated scenarios with short queues if vehicles reach the advance detector on red but reduce their speed afterwards, allowing them to not stop. In some cases, the detector-based technique underestimated AOG by over 50%. The findings can help practitioners understand how detector-based estimations vary by traffic conditions. This is particularly important as the industry moves toward a hybrid blend of detector- and CV-based signal performance measures. It is recommended that CV trajectories be used to measure AOG during periods with long queues, oversaturated conditions, or both.

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“…A probe vehicle is a motor vehicle equipped with GPS data acquisition equipment and wireless communication devices to realize real-time transmission of vehicle time and location information. Probe vehicle data refer to ID number, longitude and latitude, speed, and other data obtained by GPS transceiver installed on taxis or buses, which are widely used to evaluate the trafc operational condition at signalized intersections [10]. Please also note that the existing probe vehicle system mainly selects taxis, whose daily driving distance is much longer than that of ordinary private cars; so, there are no privacy concerns [11].…”

Section: Introductionmentioning

confidence: 99%

Queue Length Estimation Based on Probe Vehicle Data at Signalized Intersections

Luo

Deng

Jia³

2023

Journal of Advanced Transportation

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Queue length is one of the important indexes to evaluate the operation efficiency of signalized intersection and also the key parameter of intersection signal control optimization. Traditional queue length estimation models are mostly based on fixed detection equipment, and the models assumptions are too harsh; there are certain limitations. Based on the probe vehicle data, this paper establishes a model of queue length estimation for signalized intersection based on shockwave theory. First, based on the speed and location data of the probe vehicle, the vehicle density is calculated to estimate the intersection stop line. A real-time calculation method of vehicle arrival rate is proposed to improve the applicability of the model. Then, based on the shockwave theory, the meeting time of the queue forming wave and the queue discharging wave are calculated after the green light is on. Finally, the queue length is summed in sections, including the distance between the last queued probe vehicle and the stop line during the red light period, the length of the subsequent vehicles arriving during the residual red light time, and the newly increased queue length within the queue discharging time. This paper uses the VISSIM software to simulate the actual intersection. The simulation results show that when the penetration of probe vehicle is 50%, 25%, and 10%, their corresponding mean absolute relative error are 11.27%, 27.77%, and 39.12%, respectively. It can be seen that with the increase of penetration, the error gradually decreases. The average absolute relative error is within the acceptable range. After analyzing the existing similar methods, although the accuracy of the method proposed in this paper does not reach the highest level, it has the advantages of simple operation, less computation, and good real-time computation. Relevant research results can provide support for traffic control at signalized intersections.

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Correlation Analysis for Exploring the Relationship Between Probe Vehicle Data and Event-Based Traffic Signal Performance Measures

Cited by 6 publications

References 17 publications

Exploratory Regression Models for Estimating Right-Turn-on-Red Volume on Exclusive Right-Turn Lanes at Signalized Intersections

Exploratory Regression Models for Estimating Right-Turn-on-Red Volume on Exclusive Right-Turn Lanes at Signalized Intersections

Comparison of Arrivals on Green Estimations from Vehicle Detection and Connected Vehicle Data

Queue Length Estimation Based on Probe Vehicle Data at Signalized Intersections

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