Driver Behavior Models for Heavy Vehicles and Passenger Cars at a Work Zone

Mahmood, Bawan; Kianfar, Jalil

doi:10.3390/su11216007

Cited by 8 publications

(6 citation statements)

References 27 publications

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“…Work zone safety issues have been examined under different conditions and utilizing a range of crash-influencing factors. For example, the frequency and severity of rear-end crashes in work zones were analyzed by Qi et al ( 8 ), and the impact of heavy vehicles on work zone safety was assessed by Mahmood and Kianfar ( 19 ). The effects of environmental conditions, such as time of day and weather conditions, on safety have also been examined for work zone safety ( 20 , 21 ).…”

Section: Related Literaturementioning

confidence: 99%

Examination of Crash Rates and Injury Severity Before, During, and After Roadworks at High-Speed Regional Roads

McClure

Siriwardene

Debnath

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Although work zones pose risks to roadworkers and motorists, undertaking roadworks is important to maintain and upgrade road networks. Many researchers have examined the associations between safety levels and roadwork activities and have produced inconsistent findings. Whereas some researchers have compared safety levels before and during roadwork activities, few compared the safety levels at three stages of roadworks: before, during, and after roadworks, to obtain a comprehensive understanding of the associations. In addition to the inconsistent findings, another significant gap exists in the literature: existing research on all three stages is dated and scarce. To address this important gap, this paper aims to examine the associations between safety levels and three stages of roadworks. Using police-reported crash data from twenty-four high-speed road segments in the regional areas of New South Wales, Australia, this paper presents findings from a statistical analysis of the data to understand how crash rates and injury severity levels changed across the three stages. Results showed that crash rates for all crashes, as well as for fatal and injury crashes, reduced significantly from the before period to the during period, and then further to the after period. Greater reductions in crash rates were also observed for crashes that did not involve a heavy vehicle than the heavy vehicle-involved crashes, crashes that were not related to speeding or fatigue issues than those with such issues, crashes that occurred during weekdays than weekends, and crashes that occurred at straight sections than at curved sections.

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Section: Related Literaturementioning

confidence: 99%

Examination of Crash Rates and Injury Severity Before, During, and After Roadworks at High-Speed Regional Roads

McClure

Siriwardene

Debnath

et al. 2023

Transportation Research Record: Journal of the Transportation R

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show abstract

“…A multilayer feed-forward artificial neural network (ANN) model was developed by Du et al [20] to estimate the work zone delay and their results indicated that the ANN model outperformed traditional deterministic queuing models in terms of travel delay estimation accuracy. With respect to multi-model simulation, Mahmood et al [21] proposed separate driver behavior model parameters for heavy vehicles and passenger vehicles of work zone simulation, where the desired time headway was found to be 2.31 s for heavy vehicles and 1.53 s for passenger cars.…”

Section: Literature Review 21 Impact Assessment Of Work Zonesmentioning

confidence: 99%

Influence Range and Traffic Risk Analysis of Moving Work Zones on Urban Roads

Fang

2021

Sustainability

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There is a body of literature on the influence range and traffic risk of fixed work zones. However, relatively few studies have examined the effect of ubiquitous moving operating vehicles, such as road cleaners, on urban roads. The influence of low speed moving work zones on road traffic flow and traffic risk is still unclear. In this work, we used simulations to establish an urban expressway three lanes VISSIM model, and selected the road traffic volume and speed of the moving work zone as the independent variables. We analyzed the range of influence of the moving work zone on the rear vehicles in the left, middle and right lanes of the urban expressway and the traffic risk variation law caused by the moving work zone. The results show that the left lane was indirectly affected by the moving work zone when the traffic volume reached 2000 pcu/h. The influence of the moving work zone on the middle lane was controlled by the traffic volume and the speed of the moving work zone. Both the left and middle lanes were mainly impacted by vehicles changing lane from the right lane. Regardless of the traffic volume and the speed of the moving work zone change, the vehicles 200 m behind a moving work zone will be directly affected in the right lane. Furthermore, the average traffic risk is the highest within 50 m of the moving work zone in the right lane. When the traffic volume decreases and the speed of the moving work zone increases, the average traffic risk decreases gradually. These results provide a scientific basis for the operation and management of moving working vehicles on urban roads.

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“…changing the car's acceleration based on the speed and distance of the vehicle ahead (car-following models, [19], [20], [21]), or the event of lane changing based on the relative speed and available space in the overtaking lane (lane-changing models, [22]). However, more complex models are being developed to describe more complicated events, such as driver behaviour during unexpected lane closures [23].…”

Section: B Parametric Traffic Modelsmentioning

confidence: 99%

Traffic Swarm Behaviour: Machine Learning and Game Theory in Behaviour Analysis

Hollósi¹,

Lukovszki²,

Bancsics³

et al. 2021

Infocommunications journal

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High density traffic on highways and city streets consists of endless interactions among participants. These interactions and the corresponding behaviours have great impact not only on throughput of traffic but also on safety, comfort and economy. Because of this, there is a great interest in deeper understanding of these interactions and concluding the impacts on traffic participants. This paper explores and maps the world of traffic behaviour analysis, especially researches focusing on groups of vehicles called traffic swarm, while presents the state-of-the-art methods and algorithms. The conclusion of this paper states that there are special areas of traffic behaviour analysis which have great research potential in the near future to describe traffic behaviour in more detail than present methods.

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Driver Behavior Models for Heavy Vehicles and Passenger Cars at a Work Zone

Cited by 8 publications

References 27 publications

Examination of Crash Rates and Injury Severity Before, During, and After Roadworks at High-Speed Regional Roads

Examination of Crash Rates and Injury Severity Before, During, and After Roadworks at High-Speed Regional Roads

Influence Range and Traffic Risk Analysis of Moving Work Zones on Urban Roads

Traffic Swarm Behaviour: Machine Learning and Game Theory in Behaviour Analysis

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