Active traffic management strategies for expressways based on crash risk prediction of moving vehicle groups

Ma, Wanjing; He, Ziliang; Wang, Ling; Abdel-Aty, Mohamed; Yu, Chunhui

doi:10.1016/j.aap.2021.106421

Cited by 20 publications

(5 citation statements)

References 52 publications

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“…Traffic management is not limited to the management of people and roads but expands to the management of vehicle driving [122], such as optimal vehicle scheduling in road intersections [123]. By taking vehicles as an analysis object, active traffic management can be better realized to reduce accident risk and improve traffic operation safety [124].…”

Section: Resultsmentioning

confidence: 99%

Trend analysis of traffic management based on literature data mining and graph analysis tools

Ding

Liu

Wang

et al. 2023

IET Intelligent Trans Sys

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Studites on traffic management is crucial for the development of intelligent transportation systems and smart cities. However, identifying the development stages of traffic management field based on bibliometric analysis is still lacking. In this study, CiteSpace and VOSviewer software are used to explore “traffic management” field by summarizing development process and predicting future research trend. A total of 3,028 relevant documents over the past 40 years were collected from Web of Science. Results show that (1) studies on traffic management were mainly published by researchers from USA (30.55%), China (20.90%), and some European countries; (2) the key traffic management research contents can be classified into four categories, that is, background requirements, traffic problems, method models, and control strategies; (3) the evolution process can be divided into four stages, that is, budding stage (1990–1994), development stage (1995–2003), calm stage (2004–2010), and maturation stage (2011–); (4) machine learning, deep learning and other intelligent algorithms have played more important roles in recent years, and connected vehicle management is also a potential development trend. Results suggest that cooperative vehicle‐infrastructure systems or machine learning‐based studies might be the hotspots on traffic management studies.

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Section: Resultsmentioning

confidence: 99%

Trend analysis of traffic management based on literature data mining and graph analysis tools

Ding

Liu

Wang

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…Analyses based on individual vehicles primarily consider front and rear vehicles and disregard the effects of interactions among surrounding vehicles on driving safety. erefore, driving safety has been analyzed from the perspective of vehicle groups [42], focusing on the correlation between the main vehicle and its surrounding vehicles. is correlation considers the preceding and following cars in the same lane as well as other surrounding vehicles.…”

Section: Modern Traffic Safety Analysis Methodsmentioning

confidence: 99%

Risk Measurement Model for Vehicle Group Based on Temporal and Spatial Similarities

Wen

Chen

Zhao

2022

Journal of Advanced Transportation

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Vehicle rear-end collisions are primarily caused by tight car following in a continuous traffic flow, as well as a driver’s incorrect perception of the traffic environment ahead and delayed response. To facilitate an investigation pertaining to rear-end collision mechanisms and accurately measure the risk, the concept of a vehicle group is introduced herein. A risk measurement model for a vehicle group (RMVG) based on temporal and spatial similarities is proposed. First, vehicles are categorized based on their temporal and spatial similarities. Risk measurement metrics are defined based on the traffic composition, movement state, and conflict extent. Subsequently, vehicle group risk identification and risk measurement models based on an isolation forest are established. the rear-end collision risk of the vehicle groups is analyzed both qualitatively and quantitatively. Finally, the RMVG is tested using the vehicle trajectory data set of Longpan South Road, Nanjing City, Jiangsu Province, China, and the results are compared with those of a support vector machine and local outlier factor. The results show that the accuracy of the RMVG is higher than those of other models: its accuracy rate and specificity are 95.68% and 88.89%, respectively, whereas its false alarm rate is only 3.47%.

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“…The study focused on speed, travel time, and density, as well as conflict types for the safety analysis. Several similar simulations have been implemented to study the traffic flow and safety impacts of various ATM approaches (25)(26)(27)(28)(29)(30)(31).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Evaluation Methodology for Active Traffic Management Systems Utilizing Sparse Speed Data

Robbennolt

Hourdos

2023

Transportation Research Record: Journal of the Transportation R

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Many active traffic management systems and transportation systems management and operations strategies have been evaluated for safety based on crash reduction over time. These long-term studies are effective in showing the safety benefits of new systems, but do not often quantify other factors such as travel time, the extent of congestion, or the environmental impacts. Building on previous research into spatiotemporal interpolation of speed data, this methodology developed a mathematical representation of the speed and acceleration potential of the traffic stream given the sparse speed data from point sensors. This high-resolution estimate of traffic state could be used to construct trajectories of vehicles the could include data on vehicle speed and acceleration at each location in space and point in time. This methodology is general, and the trajectories could be used to evaluate traffic flow in several different ways. In the case study provided, trajectories were used to evaluate the ability of a queue warning algorithm to detect and warn drivers about unsafe conditions. Other potential applications include utilizing these trajectories to calculate fuel consumption, travel times, and speed variability to determine how new systems affect fundamental traffic characteristics.

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Active traffic management strategies for expressways based on crash risk prediction of moving vehicle groups

Cited by 20 publications

References 52 publications

Trend analysis of traffic management based on literature data mining and graph analysis tools

Trend analysis of traffic management based on literature data mining and graph analysis tools

Risk Measurement Model for Vehicle Group Based on Temporal and Spatial Similarities

Data-Driven Evaluation Methodology for Active Traffic Management Systems Utilizing Sparse Speed Data

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