Driver speed compliance following automatic incident detection: Insights from a naturalistic driving study

Varotto, S.F.; Jansen, Reinier J.; Bijleveld, F D; Nes, Nicole van

doi:10.1016/j.aap.2020.105939

Cited by 13 publications

(4 citation statements)

References 40 publications

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“…There are a wide range of tests available for determining the probability distribution that best fits some given data. For example, Wang et al ( 6 ) used the Akaike Information Criterion (AIC) to determine the distribution of cut-in lead and lag gaps, and Varotto et al ( 21 ) used the Kolmogorov Smirnov test to check that two data sets come from the same distribution. Wax and Kailath ( 22 ) demonstrated that the Minimum Description Length (MDL) criterion is an asymptotically unbiased measurement, as opposed to the AIC.…”

Section: Methodsmentioning

confidence: 99%

Predictive Models of Driver Deceleration and Acceleration Responses to Lead Vehicle Cutting In and Out

Lin

Vega

et al. 2022

Transportation Research Record: Journal of the Transportation R

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A common maneuver drivers perform and experience on the road is changing lanes. Autonomous vehicles are required to engage a lane change safely and to react to the other road users’ lane changes. To develop autonomous vehicles that change lanes or respond to the lead vehicle’s lane changes in a safe and human-like way, one should investigate the factors that affect human driver responses. By reviewing the literature to identify potential factors, this study extracted these factors from a naturalistic driving data set and associated them with driver deceleration and acceleration responses to the lead vehicle’s cut-in and cut-out to develop predictive models for the impact of the events on traffic flow. After the events were verified as accurate, the variables associated with the events, including range, range rate, speed, lateral position in the lane, and average acceleration were analyzed using logistic regression, support vector machines (SVM), and two forms of decision trees. In total, 799 cut-in events and 684 cut-out events with the necessary variables were applied for analysis. The significant variables influencing driver behavior were found, and using these variables, the predictive models achieved around 80% accuracy for cut-ins, and 73% accuracy for cut-outs on test data. These results will assist in the future design of autonomous vehicle control to minimize detrimental effects on traffic when changing lanes and safe longitudinal control when responding to a lead vehicle’s lane changes, allowing for safe integration with human drivers, and better design of driver assistance systems.

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Section: Methodsmentioning

confidence: 99%

Predictive Models of Driver Deceleration and Acceleration Responses to Lead Vehicle Cutting In and Out

Lin

Vega

et al. 2022

Transportation Research Record: Journal of the Transportation R

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“…Indeed, from the study it was found that users tended to disregard the imposed speed limits when the driver did not perceive a danger such that he or she had to reduce his or her speed and when the VSL system was adopted continuously over days, which generated a habit of ignoring the presence of such a signal. This second condition is particularly alarming because it highlights the effectiveness of the strategy only when used for limited times and under conditions of special need, which also emerged in [9].…”

Section: State Of Artmentioning

confidence: 99%

Integrated Variable Speed Limits and User Information Strategy

Cipriani,

Giannantoni,

Mannini

2023

Sustainability

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This paper deals with the study of variable speed limits (VSLs) for traffic control and their integration with user information strategies. As few studies have addressed the integrated VSL and user information strategy, we focus on comparing the adoption of the latter with the VSL alone strategy application and the no-control case, highlighting the benefits the integration brings. The integrated strategy is able to smooth the severity of congestion, shifting its occurrence in a section of the mainstream mostly suited to vehicle accumulation. An application on a real network is carried out. The traffic congestion conditions along the real highway are simulated by means of Dynameq simulation software and the METANET macroscopic model. The VSLs are applied in a control area aiming to evaluate the potential and the limitations of the strategy on a real network as well as the integration of variable speed limits and user information strategies. Two different cases of road congestion caused by the presence of on-ramps are studied. Results show that the integration of the two strategies leads to a redistribution of flows, achieving a reduction in the total travel time spent in the network and an increase in the traveled distances, i.e., reducing the overall network time despite the increase in assigned flows. However, an integrated strategy requires adequate transportation supply and mainly crossing demand.

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“…To reduce vehicle speeds and increase compliance, many measures exist, such as enhancing the design of roads and using physical or automated police enforcement instruments such as speed cameras ( 18 , 19 ). In the case of physical enforcement, police officers stop drivers who drive above the speed limit using a measurement from a visible or invisible speed radar unit.…”

Section: Speed Limit Enforcement Techniquesmentioning

confidence: 99%

Identifying Optimal Locations for Speed Enforcement Cameras

Mohammad

Eleimat²

2022

Transportation Research Record: Journal of the Transportation R

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Speed enforcement cameras are used to improve traffic safety. These cameras are usually placed in areas with significant crashes related to speeding. However, in many countries, detailed crash data are not available or are of poor quality. This study aims to propose and explore three simple ranking methods for identifying optimal locations for speed enforcement cameras that do not depend on crash records. The first method is based on the Star Rating Scores and the traffic volume data. The second method relies on developing a data-driven model for predicting speeding violation rate (VR) based on road geometry aspects and comparing it with available road segment crash risk. The third method uses a combination of the first two methods. The proposed methods and prediction models were applied to a case study and successfully identified the optimal location for speed enforcement cameras. The developed prediction models suggest that speed limit, junction spacing, and the number of access points per kilometer are the most common variables to estimate speed VRs for both urban and rural roads. Availability of service roads and the number of lanes were variables that affect the speed VR on urban roads only. The proposed methods are considered valuable simple tools for road safety practitioners to effectively prepare speed enforcement plans for different road networks.

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Driver speed compliance following automatic incident detection: Insights from a naturalistic driving study

Cited by 13 publications

References 40 publications

Predictive Models of Driver Deceleration and Acceleration Responses to Lead Vehicle Cutting In and Out

Predictive Models of Driver Deceleration and Acceleration Responses to Lead Vehicle Cutting In and Out

Integrated Variable Speed Limits and User Information Strategy

Identifying Optimal Locations for Speed Enforcement Cameras

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