A Proactive Approach to Evaluating Intersection Safety Using Hard-Braking Data

Hunter, Margaret; Saldivar-Carranza, Enrique; Desai, Jairaj; Mathew, Jijo K.; Li, Howell; Bullock, Darcy M.

doi:10.1007/s42421-021-00039-y

Cited by 37 publications

(18 citation statements)

References 24 publications

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“…The objective of this study is to determine if lane width measurements from sensors onboard production vehicles can be used to screen public road networks for sections that have less than prescribed lane widths. Previous studies have found many uses for connected vehicle data, including pavement marking evaluation, traffic signal performance, and crash mitigation through surrogate safety measures and hard braking [ 17 , 18 , 19 , 20 , 21 ]. The benefit of using production vehicle data is that it can be crowdsourced and provide ubiquitous coverage across the state without resource-intensive deployments and data collection.…”

Section: Objectivementioning

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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Section: Objectivementioning

confidence: 99%

Measuring Roadway Lane Widths Using Connected Vehicle Sensor Data

Mahlberg

Cheng

et al. 2022

Sensors

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“…A study conducted on 23 construction work zones that covered approximately 150 centerline miles of Indiana interstate roadway in the summer of 2019, found that there was approximately 1 crash for every 147 hard-braking events in and around a construction site with an R 2 value of 0.85 [23]. Thus, the new commercially available hard-braking data sources provide an opportunity for agencies to follow up with mitigation measures addressing emerging problems much quicker than typical practices that rely on 3 -5 years of crash data [24]. This study presents a methodology using these hard-braking events as a surrogate for crash incidents for impact evaluation of queue warning trucks and digital alerts.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Sakhare¹,

Desai²,

Mahlberg³

et al. 2021

JTTs

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Back of queue crashes on Interstates are a major concern for all state transportation departments. In 2020, Indiana DOT begin deploying queue warning trucks with message boards, flashers and digital alerts that could be transmitted to navigation systems such as Waze. This study reports on the deployment and impact evaluation of digital alerts on motorist's assistance patrols and 19 Queue trucks in Indiana. The motorist assistance patrol evaluation is provided qualitatively. A novel analysis of queue warning trucks equipped with digital alerts was conducted during the months of May-July in 2021 using connected vehicle data. This new data set reports locations of anonymous hard-braking events from connected vehicles on the Interstate. Hard-braking events were tabulated for when queueing occurred with and without the presence of a queue warning truck. Approximately 370 hours of queueing with queue trucks present and 58 hours of queueing without queue trucks present were evaluated. Hard-braking events were found to decrease approximately 80% when queue warning trucks were used to alert motorists of impending queues.

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“…With the emergence and improvement of commercially available connected vehicle (CV) data, new techniques have been developed to assess operational and safety performance at intersections without the need for costly infrastructure investments. CV hard-braking events have been proven to be a surrogate of crashes [18]. Vehicle trajectories have been used to estimate queue lengths [19] [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Diverging Diamond Interchange Performance Measures Using Connected Vehicle Data

Saldivar-Carranza¹,

Li²,

Bullock³

2021

JTTs

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Since the first Diverging Diamond Interchange (DDI) implementation in 2009, most of the performance studies developed for this type of interchange have been based on simulations and historical crash data, with a small number of studies using Automated Traffic Signal Performance Measures (ATSPM). Simulation models require considerable effort to collect volumes and to model actual controller operations. Safety studies based on historical crashes usually require from 3 to 5 years of data collection. ATSPMs rely on sensing equipment. This study describes the use of connected vehicle trajectory data to analyze the performance of a DDI located in the metropolitan area of Fort Wayne, IN. An extension of the Purdue Probe Diagram (PPD) is proposed to assess the levels of delay, progression, and saturation. Further, an additional PPD variation is presented that provides a convenient visualization to qualitatively understand progression patterns and to evaluate queue length for spillback in the critical interior crossover. Over 7000 trajectories and 130,000 GPS points were analyzed between the 7 th and the 11 th of June 2021 from 5:00 AM to 10:00 PM to estimate the DDI's arrivals on green, level of service, split failures, and downstream blockage. Although this technique was demonstrated for weekdays, the ubiquity of connected vehicle data makes it very easy to adapt these techniques to analysis during special events, winter storms, and weekends. Furthermore, the methodologies presented in this paper can be applied by any agency wanting to assess the performance of any DDI in their jurisdiction.

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A Proactive Approach to Evaluating Intersection Safety Using Hard-Braking Data

Cited by 37 publications

References 24 publications

Measuring Roadway Lane Widths Using Connected Vehicle Sensor Data

Measuring Roadway Lane Widths Using Connected Vehicle Sensor Data

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

Diverging Diamond Interchange Performance Measures Using Connected Vehicle Data

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