The Palm distribution of traffic conditions and its application to accident risk assessment

Norros, Ilkka; Kuusela, Pirkko; Innamaa, Satu; Pilli-Sihvola, Eetu; Rajamäki, Riikka

doi:10.1016/j.amar.2016.10.002

Cited by 20 publications

(20 citation statements)

References 17 publications

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“…Between 1994 and 2012 on the United States, 16% of the annual fatalities caused by road accidents reportedly happened during adverse meteorological conditions [1]. Heavy rain and moderate or heavy snowfall increasingly raise driving accident risk [2,3] and injury risk due to such accidents [2], as they both modify the paving conditions and the general visibility. Wind speed magnitude has also been identified as a significant variable for car crash severity prediction, in addition to rainfall intensity during the 15 minutes previous to an accident [4].…”

Section: Introductionmentioning

confidence: 99%

“…Wind speed magnitude has also been identified as a significant variable for car crash severity prediction, in addition to rainfall intensity during the 15 minutes previous to an accident [4]. Moreover, some results from [3] suggested that air temperature below a certain level may cause a relevant increase in car crash risk, although it is acknowledged that the number of results are not enough to be statistically significant. Harsh weather conditions also modulate the effects of light conditions on driving speeds [5], adding an indirect component to its influence on accident risks.…”

Section: Introductionmentioning

confidence: 99%

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Comparison between Geostatistical Interpolation and Numerical Weather Model Predictions for Meteorological Conditions Mapping

et al. 2020

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Mapping of meteorological conditions surrounding road infrastructures is a critical tool to identify high-risk spots related to harsh weather. However, local or regional data are not always available, and researchers and authorities must rely on coarser observations or predictions. Thus, choosing a suitable method for downscaling global data to local levels becomes essential to obtain accurate information. This work presents a deep analysis of the performance of two of these methods, commonly used in meteorology science: Universal Kriging geostatistical interpolation and Weather Research and Forecasting numerical weather prediction outputs. Estimations from both techniques are compared on 11 locations in central continental Portugal during January 2019, using measured data from a weather station network as the ground truth. Results show the different performance characteristics of both algorithms based on the nature of the specific variable interpolated, highlighting potential correlations to obtain the most accurate data for each case. Hence, this work provides a solid foundation for the selection of the most appropriate tool for mapping of weather conditions at the local level over linear transport infrastructures.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison between Geostatistical Interpolation and Numerical Weather Model Predictions for Meteorological Conditions Mapping

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Tab. [8], [10], [11], [12], [13], [14], [15], [16], [18], [27], [34], [35], [37], [41], [43], [45], [47], [60] 2. RA2 2 [3], [6] 3.…”

Section: Strategic Risk Assessment In Road Transportmentioning

confidence: 99%

Risk Assessment in Road Transport – Strategic and Business Approach

Tubis

2018

Journal of KONBiN

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In the presented paper, author focus on the issues related to risk assessment of transport processes performance. The author presented the characteristics of the traditional approach to risk assessment in transport, which is popular in the literature. This approach addresses the strategic risk assessment for the road transport system and it focuses primarily on aspects related to the transport safety. The business approach to risk assessment from the perspective of the managers of the transport process is presented in the next part of the article. In this case, the subject of the analysis is the operational risk, which focuses on the risks affecting the implementation of the transport process and the achievement of the company’s operational objective. The article concludes with a summary, including directions for further research.

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“…(Hill and Boyle, 2007). It is worth mentioning, that traffic, weather and road conditions have been also linked to accident occurrence (Norros et al, 2016), which can be an outcome of the increased demands of the driving task in some occasions. Moreover, time urgency and congestion levels have been identified as two factors influencing drivers' stress (Hennessy and Wiesenthal, 1999).…”

Section: Introductionmentioning

confidence: 99%

Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour

Paschalidis

Choudhury

Hess

2019

Analytic Methods in Accident Research

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Car-following models, which are used to predict the acceleration-deceleration decisions of drivers in the presence of a closely spaced lead vehicle, are critical components of traffic microsimulation tools and useful for safety evaluation. Existing car-following models primarily account for the effects of surrounding traffic conditions on a driver's decision to accelerate or decelerate. However, research in human factors and safety has demonstrated that driving decisions are also significantly affected by individuals' characteristics and their emotional states like stress, fatigue, etc. This motivates us to develop a car-following model where we explicitly account for the stress level of the driver and quantify its impact on acceleration-deceleration decisions. An extension of the GM stimulus-response model framework is proposed in this regard, where stress is treated as a latent (unobserved) variable, while the specification also accounts for the effects of drivers' sociodemographic characteristics. The proposed hybrid models are calibrated using data collected with the University of Leeds Driving Simulator where participants are deliberately subjected to stress in the form of aggressive surrounding vehicles, slow leaders and/or time pressure while driving in a motorway setting. Alongside commonly used variables, physiological measures of stress (i.e. heart rate, blood volume pulse, skin conductance) are collected with a non-intrusive wristband. These measurements are used as indicators of the latent stress level in a hybrid model framework and the model parameters are estimated using Maximum Likelihood Technique. Estimation results indicate that car-following behaviour is significantly influenced by stress alongside speed, headway and drivers' characteristics. The findings can be used to improve the fidelity of simulation tools and designing interventions to improve safety.

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The Palm distribution of traffic conditions and its application to accident risk assessment

Cited by 20 publications

References 17 publications

Comparison between Geostatistical Interpolation and Numerical Weather Model Predictions for Meteorological Conditions Mapping

Comparison between Geostatistical Interpolation and Numerical Weather Model Predictions for Meteorological Conditions Mapping

Risk Assessment in Road Transport – Strategic and Business Approach

Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour

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