How instantaneous driving behavior contributes to crashes at intersections: Extracting useful information from connected vehicle message data

Arvin, Ramin; Kamrani, Mohsen; Khattak, Asad J.

doi:10.1016/j.aap.2019.01.014

Cited by 94 publications

(27 citation statements)

References 44 publications

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“…From the safety perspective, driving behavior is considered to be a leading cause of crashes ( 40 ). Studies have shown that driving volatility is an appropriate measure to capture driving behavior and safety performance of the network ( 41 ). Driving volatility is defined as “the extent of variations in driving, especially hard accelerations/braking, and frequent switching between different driving regimes” ( 42 , 43 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Previous studies have used diverse measures to quantify driving volatilities ( 42 – 45 ). Several studies have used vehicle kinematics to measure volatile behavior or aggressive behavior as a measure of safety ( 41 , 43 , 46 , 47 ). Vehicle speed is used to quantify driving volatility ( 41 ) as a term of driving behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several studies have used vehicle kinematics to measure volatile behavior or aggressive behavior as a measure of safety ( 41 , 43 , 46 , 47 ). Vehicle speed is used to quantify driving volatility ( 41 ) as a term of driving behavior. Driving volatility can be studied at different levels including vehicle level or vehicle fleet level.…”

Section: Literature Reviewmentioning

confidence: 99%

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Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control

Mahdinia

Arvin

Khattak

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Connected and automated vehicle technologies have the potential to significantly improve transportation system performance. In particular, advanced driver-assistance systems, such as adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC), may lead to substantial improvements in performance by decreasing driver inputs and taking over control of the vehicle. However, the impacts of these technologies on the vehicle- and system-level energy consumption, emissions, and safety have not been quantified in field tests. The goal of this paper is to study the impacts of automated and cooperative systems in mixed traffic containing conventional, ACC, and CACC vehicles. To reach this goal, experimental data based on real-world conditions are collected (in tests conducted by the Federal Highway Administration and the U.S. Department of Transportation) with presence of ACC, CACC, and conventional vehicles in a vehicle platoon scenario and a cooperative merging scenario. Specifically, a platoon of five vehicles with different vehicle type combinations is analyzed to generate new knowledge about potential safety, energy efficiency, and emission improvement from vehicle automation and cooperation. Results show that adopting the CACC system in a five-vehicle platoon substantially reduces the driving volatility and reduces the risk of rear-end collision which consequently improves safety. Furthermore, it decreases fuel consumption and emissions compared with the ACC system and manually-driven vehicles. Results of the merging scenario show that while the cooperative merging system slightly reduces the driving volatility, the fuel consumption and emissions can increase because of sharper accelerations of CACC vehicles compared with manually-driven vehicles.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control

Mahdinia

Arvin

Khattak

et al. 2020

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

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“…The derived information can be employed to estimate the likelihood of an individual vehicle’s involvement in conflicts with other vehicles. For example, studies from Arvin and Kamrani quantified and used about 30 measures of driving volatility by using speed, longitudinal and lateral acceleration, and yaw-rate, extracted from BSMs at signalized intersections( 22 , 23 ). These volatilities were then used to explain crash frequencies at intersections.…”

Section: Potential Applicationsmentioning

confidence: 99%

Data Infrastructure for Connected Vehicle Applications

Wang

Shen

Bezzina

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Ann Arbor Connected Vehicle Test Environment (AACVTE) is the world’s largest operational, real-world deployment of connected vehicles (CVs) and connected infrastructure, with over 2,500 vehicles and 74 infrastructure sites, including intersections, midblocks, and highway ramps. The AACVTE generates a massive amount of data on a scale not seen in the traditional transportation systems, which provides a unique opportunity for developing a wide range of connected vehicle (CV) applications. This paper introduces a data infrastructure that processes the CV data and provides interfaces to support real-time or near real-time CV applications. There are three major components of the data infrastructure: data receiving, data pre-processing, and visualization including the performance measurements generation. The data processing algorithms include signal phasing and timing (SPaT) data compression, lane phase mapping identification, trajectory data map matching, and global positioning system (GPS) coordinates conversion. Simple performance measures are derived from the processed data, including the time–space diagram, vehicle delay, and observed queue length. Finally, a web-based interface is designed to visualize the data. A list of potential CV applications including traffic state estimation, traffic control, and safety, which can be built on this connected data infrastructure is discussed.

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“…Both scholars and transit operators are concentrating on the development of connected vehicles (CVs), along with rapid technological developments and emerging innovative mobility methods. CVs' most e x pec ted adv a nt ages a re i mprov i ng traffic safety levels and the performance of the transportation network (Arvin et al, 2019;Dowling et al, 2016;Ghiasi et al, 2019;Kidando et al, 2018;Li et al, 2016). The Internet of Things (IoT), as one of the most remarkable technological developments in the recent decade, provides connectivity for all mobile applicants and fixed infrastructures at any time and any location.…”

Section: Introductionmentioning

confidence: 99%

Willingness to Pay for Connected Vehicles: An Alternative-Specific Mixed Logit Regression Approach

2020

IJTTE

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The study of connected vehicles (CVs) has become a hot topic in recent years. Understanding the characteristics that lead consumers to relate to CVs motivates researchers to conduct market analysis studies. The current research investigated the socio-demographic attributes that may contribute to the individual preferences for purchasing CVs. Researchers constructed a series of Alternative-Specific Mixed Logit models to examine the associations between individual preferences of respondents and their willingness to pay for CV features in their future vehicle. The results indicate that hours spent driving play a privileged role among sociodemographic characteristics and driving behavior attributes of respondents. People who drive longer hours tended to purchase CV features. Also, the factor of age had a noticeable effect as the results showed that older people are more likely to purchase CV features.

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How instantaneous driving behavior contributes to crashes at intersections: Extracting useful information from connected vehicle message data

Cited by 94 publications

References 44 publications

Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control

Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control

Data Infrastructure for Connected Vehicle Applications

Willingness to Pay for Connected Vehicles: An Alternative-Specific Mixed Logit Regression Approach

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