Evaluation of the Impact of Queue Trucks with Navigation Alerts Using Connected Vehicle Data

Sakhare, Rahul Suryakant; Desai, Jairaj; Mahlberg, Justin; Mathew, Jijo K.; Kim, Woo-Sung; Li, Howell; McGregor, John D.; Bullock, Darcy M.

doi:10.4236/jtts.2021.114035

Cited by 10 publications

(8 citation statements)

References 18 publications

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“…Several studies have also shown the applicability of CV data for queue detections and incident management [18] [19] [20]. A CV data-driven analysis from May-July 2021 in Indiana found that a combination of queue warning trucks and digital alerts to Waze reduced hard-braking events by 80% ahead of impending queues at work zones [21]. The Pennsylvania Turnpike equipped more than 150 maintenance and service patrol vehicles with the ability to broadcast emergency alerts and found that roadside crashes reduced from thirty in 2018 to zero in 2020 [22].…”

Section: Literature Reviewmentioning

confidence: 99%

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Quantifying the Impact of In-Cab Alerts on Truck Speed Reductions in Ohio

Desai,

Mathew,

Bullock

2024

JTTs

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In-cab alerts warn commercial vehicle drivers of upcoming roadway incidents, slowdowns and work zone construction activities. This paper reports on a study evaluating the driver response to in-cab alerts in Ohio. Driver response was evaluated by measuring the statistical trends of vehicle speeds after the in-cab alerts were received. Vehicle speeds pre and post in-cab alert were collected over a 47 day period in the fall of 2023 for trucks traveling on interstate roadways in Ohio. Results show that approximately 22% of drivers receiving Dangerous Slowdown alerts had reduced their speeds by at least 5 mph 30 seconds after receiving such an alert. Segmenting this analysis by speed found that of vehicles traveling at or above 70 mph at the time of alerting, 26% reduced speeds by at least 5 mph. These speed reductions suggest drivers taking actional measures after receiving alerts. Future studies will involve further analysis on the impact of the types of alerts shown, roadway characteristics and overall traffic conditions on truck speeds passing through work zones.

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Section: Literature Reviewmentioning

confidence: 99%

“…Locations where rapidly forming congestion creates an unexpected slowdown raise the risk of a back of queue collision. Recent research with high resolution trajectories and other alerting techniques have demonstrated that hard-braking events in these areas can be reduced by up to 80% [21].…”

Section: In-cab Alerts Datamentioning

confidence: 99%

Quantifying the Impact of In-Cab Alerts on Truck Speed Reductions in Ohio

Desai,

Mathew,

Bullock

2024

JTTs

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“…The recent wide-scale availability of high-resolution connected vehicle traffic data presents a unique opportunity to measure shock wave characteristics which previously have only been represented by models. More recently, trajectory-based granular vehicle data have been utilized for a wide variety of applications including operational performance gains [15], intersection safety, systemwide identification of signal retiming opportunities, work zone safety and management [16], winter weather maintenance, asset management, etc., aiding transportation agencies.…”

Section: Literature Reviewmentioning

confidence: 99%

Methodology for the Identification of Shock Wave Type and Speed in a Traffic Stream Using Connected Vehicle Data

Sakhare,

Li,

Bullock

2023

Future Transportation

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The concept of traffic shock waves was first theorized by Lighthill and Whitham in 1955. The identification of shock wave type and speed in a traffic stream provides critical information about the queue formation and its dissipation. This information can be utilized by various stakeholders for traffic management, emergency response, etc. Such information can also be integrated into the travel time prediction models and real-time route diversions for navigation. Past efforts at identifying shock waves used simulation or analysis based on location-based sensors such as loop detectors. This paper describes scalable methodologies for measuring shock wave propagation using Connected Vehicle (CV) data. The techniques to identify the six different types of shock waves are illustrated through case studies from Indiana highways that use both CV data and the corresponding surveillance camera images. The shock wave speeds for each event are estimated using the linear regression model, with most shock wave speed estimates having a coefficient of determination (R2) of 0.9 or better. Although shock wave speeds vary by traffic flow rates and geometry, the typical backward forming shock wave speeds ranged from 1.75 to 11.76 mph whereas the backward recovery shock wave speeds were observed to be between 5.78 and 16.54 mph. These techniques can be adapted for real-time use to assist traffic management centers with estimating upstream propagation and recovery time. A case study with a car fire is used to illustrate how this shock wave speed data can be used to frame discussions with first responders regarding how reducing incident clearance time can reduce the risk of secondary crashes.

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“…The spatiotemporal heatmap in Figure 4 shows vehicle speeds at this location by linear referenced 0.1 mile segments. The heatmaps have been extensively used in past research to visualize congested conditions [ 13 , 14 , 15 , 16 ]. The horizontal axis on the speed profile heatmaps represents the time of day while the vertical axis represents mile marker location.…”

Section: Motivationmentioning

confidence: 99%

Measuring Roadway Lane Widths Using Connected Vehicle Sensor Data

Mahlberg

Cheng

et al. 2022

Sensors

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The United States has over three trillion vehicle miles of travel annually on over four million miles of public roadways, which require regular maintenance. To maintain and improve these facilities, agencies often temporarily close lanes, reconfigure lane geometry, or completely close the road depending on the scope of the construction project. Lane widths of less than 11 feet in construction zones can impact highway capacity and crash rates. Crash data can be used to identify locations where the road geometry could be improved. However, this is a manual process that does not scale well. This paper describes findings for using data from onboard sensors in production vehicles for measuring lane widths. Over 200 miles of roadway on US-52, US-41, and I-65 in Indiana were measured using vehicle sensor data and compared with mobile LiDAR point clouds as ground truth and had a root mean square error of approximately 0.24 feet. The novelty of these results is that vehicle sensors can identify when work zones use lane widths substantially narrower than the 11 foot standard at a network level and can be used to aid in the inspection and verification of construction specification conformity. This information would contribute to the construction inspection performed by agencies in a safer, more efficient way.

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Evaluation of the Impact of Queue Trucks with Navigation Alerts Using Connected Vehicle Data

Cited by 10 publications

References 18 publications

Quantifying the Impact of In-Cab Alerts on Truck Speed Reductions in Ohio

Quantifying the Impact of In-Cab Alerts on Truck Speed Reductions in Ohio

Methodology for the Identification of Shock Wave Type and Speed in a Traffic Stream Using Connected Vehicle Data

Measuring Roadway Lane Widths Using Connected Vehicle Sensor Data

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