Risk in our midst: Centrelines, perceived risk, and speed choice

Charlton, Samuel G.; Starkey, Nicola J.

doi:10.1016/j.aap.2016.07.019

Cited by 41 publications

(17 citation statements)

References 17 publications

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“…Its validity against on-road performance has been tested in several different kinds of experimental scenarios, including those with a focus on speed choice (e.g. [4,5]).…”

Section: Simulator and The Speed Warning Systemmentioning

confidence: 99%

Speedometer monitoring when driving with a speed warning system

Lehtonen

Malhotra

Starkey

et al. 2020

Eur. Transp. Res. Rev.

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Aim: The study investigated how a speed warning system influence drivers' speedometer monitoring, and how drivers respond when they receive a speed warning. With a speed warning system, drivers may pay more attention to the exact speed, which would be expected to increase glances to the speedometer. Methods: Nineteen participants drove in a driving simulator, either with or without a speed warning system implemented on a smartphone, which alerted them if they exceeded the set speed limit. Their glances to the speedometer and speed warning system's display were recorded with an eye tracker. Results: Drivers sped less with the warning system. The system did not significantly change the speedometer monitoring. Speeding was preceded by fewer speedometer glances, indicating the speeding was linked to participants' momentary inattention to their speed control. After a warning, the frequency of speedometer glances increased. Interestingly, drivers often started to glance at the speedometer and release the accelerator a couple of seconds before a warning, suggesting that drivers learn to anticipate warnings. The results suggest that drivers do not only react to warnings, but they also actively try to avoid them. Understanding drivers' speed control with speed warning system may help to create adaptive ISA systems.

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“…Its validity against on-road performance has been tested in several different kinds of experimental scenarios, including those with a focus on speed choice (e.g. [4,5]).…”

Section: Simulator and The Speed Warning Systemmentioning

confidence: 99%

Speedometer monitoring when driving with a speed warning system

Lehtonen

Malhotra

Starkey

et al. 2020

Eur. Transp. Res. Rev.

Self Cite

View full text Add to dashboard Cite

show abstract

“…To be effective, speed limits need to be creditable to drivers. Studies of rural roads in New Zealand (Charlton and Starkey, 2016), urban roads in Canada (Gargoum, El-Basyouny and Kim, 2016) and both road types in the UK (Yao, Carsten and Hibberd, 2020) show that road characteristics play a large role in compliance with speed limits. Road conditions that signal the potential to do higher speeds than posted such as wide or multilane roads or where the limit is higher than drivers prefer such as roads containing hazards like parked cars, pedestrians or cyclists both create problems for drivers and reduce compliance.…”

Section: Speed Managementmentioning

confidence: 99%

“…Approaches to encourage compliance are mostly linked to enforcement by police through financial or pointbased penalties. Evidence shows that police enforcement produces lower and compliant speeds (Gayah et al, 2018) and even presence of police cars lowers speeds (Charlton and Starkey, 2016). Nevertheless, enforcement of speed limits is not entirely supported by the driving community.…”

Section: Speed Managementmentioning

confidence: 99%

Why do we make safe behaviour so hard for drivers?

Williamson

2021

JRS

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Despite significant improvements in road safety in Australia and developed countries over some decades, the downward trend in fatalities and serious injuries has slowed markedly, and even stalled. New strategies are needed to turn this trend around. Current road safety philosophy, the Safe System, has been effective, but needs broadening to increase the scope of solutions. The Safe System accepts that road users make errors and that the road system should be forgiving of those errors. This leads to countermeasures that emphasise limiting consequences of crashes like lowered speeds, crashworthy vehicles and roads. The problem is that conceptualising road-user error as inevitable ignores the fact that many road-user errors are caused by poor design of the road system including roads, vehicles and road rules. It means road safety overlooks productive avenues for prevention of road-user error and crashes. This paper discusses this issue with Safe System and provides examples of poor road system design that make it difficult for road users to behave safely. This includes poor road rules like inappropriate speed limits, inadequate road design such as poor signage and confusing lane-marking, inadequate vehicle design that limits vision or provides false visual information, as well as problems with driver-assistive technologies: cruise control, automated driving and warning systems. In each case the paper discusses how poor design fails to account for human capacities making it hard for road-users to behave safely. Importantly the paper looks at solutions to these problems and provides some new principles for Safe System.

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“…In order to fully characterize the feature of road geometry, objective characteristics of the road alignment and drivers' subjective perception in a 3D real space should both be considered [11,12]. Driving speed serves as the most representative variable due to a clear correlation between drivers' perception of the road and behavior change [13][14][15][16]. Meanwhile, studies which adopted sight distance as the input of the speed prediction model found that the index is highly correlated to driver's operating speed, as well as driving safety [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Speed Distribution Prediction of Freight Vehicles on Mountainous Freeway Using Deep Learning Methods

Chen

2020

Journal of Advanced Transportation

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Driving speed is one of the most critical indicators in safety evaluation and network monitoring in freight transportation. Speed prediction model serves as the most efficient method to obtain the data of driving speed. Current speed prediction models mostly focus on operating speed, which is hard to reveal the overall condition of driving speed on the road section. Meanwhile, the models were mostly developed based on the regression method, which is inconsistent with natural driving process. Recurrent neural network (RNN) is a distinctive type of deep learning method to capture the temporary dependency in behavioral research. The aim of this paper is to apply the deep learning method to predict the general condition of driving speed in consideration of the road geometry and the temporal evolutions. 3D mobile mapping was applied to obtain road geometry information with high precision, and driving simulation experiment was then conducted with the help of the road geometry data. Driving speed was characterized by the bimodal Gauss mixture model. RNN and its variants including long short-term memory (LSTM) and RNN and gated recurrent units (GRUs) were utilized to predict speed distribution in a spatial-temporal dimension with KL divergence being the loss function. The result proved the applicability of the model in speed distribution prediction of freight vehicles, while LSTM holds the best performance with the length of input sequence being 400 m. The result can be related to the threshold of drivers’ information processing on mountainous freeway. Multiple linear regression models were constructed to be a contrast with the LSTM model, and the results showed that LSTM was superior to regression models in terms of the model accuracy and interpretability of the driving process and the formation of vehicle speed. This study may help to understand speed change behavior of freight vehicles on mountainous freeways, while providing the feasible method for safety evaluation or network efficiency analysis.

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Risk in our midst: Centrelines, perceived risk, and speed choice

Cited by 41 publications

References 17 publications

Speedometer monitoring when driving with a speed warning system

Speedometer monitoring when driving with a speed warning system

Why do we make safe behaviour so hard for drivers?

Speed Distribution Prediction of Freight Vehicles on Mountainous Freeway Using Deep Learning Methods

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