A model for weather-related traffic variations and accident probabilities on roads

<p>Weather conditions affect both road traffic volume and the probability of road accidents. The aim of this study is improve the understanding of both effects as well as their interactions. In a first step, we develop generalized linear models for hourly road traffic counts at 1400 traffic stations on German federal roads and highways. It is distinguished between different vehicle types, including motorbikes, cars, delivery vans and trucks. Different meteorological variables are derived from reanalysis and radar data. The impacts of these variables on the predictive skill of the models is analyzed. In particular models for motorbike counts show large improvements, if meteorological predictors are added to the model. At weekends in the afternoon the mean squared errors of modeled motorbike counts are reduced by up to 60%. Temperatures around 25&#176;C, no precipitation, low cloud cover and low wind speeds lead to the highest motorbike counts. In a second step, the information derived from the traffic models is used to improve models for hourly probabilities of road accidents. These models are based on police reports, which are available at the level of administrative districts, and can now explicitly take traffic volume into account. It is shown that in particular winter conditions like precipitation and freezing temperatures lead to a significant increase in accident probability. Especially the probabilities of roadway departures show an increase under such conditions. The models presented in this study are suitable for the integration in risk-based warning systems and have the potential to improve risk perception and behavior of warning recipients.</p>

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Traffic Calming Measures: An Evaluation of the Effect on Driving Speed

PROMET - Traffic&Transportation ◽

10.7307/ptt.v29i3.2265 ◽

2017 ◽

Vol 29 (3) ◽

pp. 275-285 ◽

Cited By ~ 6

Author(s):

Audrius Vaitkus ◽

Donatas Čygas ◽

Vilma Jasiūnienė ◽

Laura Jateikienė ◽

Tadas Andriejauskas ◽

...

Keyword(s):

Traffic Safety ◽

Road Traffic ◽

Control Measures ◽

Road Accidents ◽

The Road ◽

Road Traffic Safety ◽

Traffic Calming ◽

Road Users ◽

Accident Probability ◽

Driving Speed

Road accidents are one of the leading causes of death in the world, particularly among young people. Excessive speed is one of the main risk factors in road traffic safety, increasing accident probability and affecting accident severity. Experimental research of the traffic calming measures allocation effect on the driving speed is presented in this paper. The research has been carried out on two aspects. The first one with respect to the mean speed and the second one regarding instantaneous speed. However, the paper is not only restricted by the above research. Standardized survey interview and questioning, a survey of public opinion, was carried out to find out the road users’ opinions about the need for traffic calming measures and speed control measures. Finally, the authors presented their insights and recommendations for the installation of speed humps and gateways and their optimum spacing.

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Fast post-Doppler STAP with road map for traffic monitoring: overview and first results

Advances in Radio Science ◽

10.5194/ars-17-137-2019 ◽

2019 ◽

Vol 17 ◽

pp. 137-143

Author(s):

André B. C. da Silva ◽

Stefan V. Baumgartner ◽

Alberto Moreira

Keyword(s):

Road Traffic ◽

A Priori ◽

Weather Conditions ◽

Radar Data ◽

Traffic Monitoring ◽

Hardware Complexity ◽

Real Time Processing ◽

Road Map ◽

First Results ◽

Elevation Model

Abstract. Synthetic aperture radar (SAR) is an efficient solution for road traffic monitoring due to its high spatial resolution and independence from daylight and weather conditions. In this sense, a number of ground moving target indication (GMTI) algorithms have been developed, whereas their robustness is often achieved with high costs, increased hardware complexity and high computational burden. This paper presents a fast GMTI processor that blends the powerful post-Doppler space-time adaptive processing (PD STAP) with an a priori known road map and digital elevation model (DEM). The algorithm presents great potential for real-time processing, decreased hardware complexity and low costs compared to state-of-the-art systems. It is tested using real 4-channel X-band radar data acquired with the DLR's airborne F-SAR.

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Adhesion of car tires to the road surface during reconstruction of road accidents

E3S Web of Conferences ◽

10.1051/e3sconf/202016403022 ◽

2020 ◽

Vol 164 ◽

pp. 03022

Author(s):

Stanislav Evtukov ◽

Egor Golov

Keyword(s):

Road Traffic ◽

Field Research ◽

Weather Conditions ◽

Road Transport ◽

Road Surface ◽

Road Accidents ◽

The Road ◽

Use Values ◽

On The Road ◽

The Impact

Road and weather conditions have a significant impact on the occurrence of road accidents and their development as events. According to these circumstances, the system “Driver – Car – Road – Environment” identifies certain types of expertise that are engaged in determining the presence and magnitude of the parameters of the road environment that affect the development of the road transport situation. When conducting relevant investigations, the experts calculate the speed and length of the stopping distance of the vehicle, using coefficients that determine the impact of road conditions on the road traffic situation under study. One of these important indicators is the coefficient of adhesion of car tires to the road surface. Due to the lack of technical capability to experimentally determine the coefficient of adhesion at the place of an accident, many experts are forced to use values from the reference literature. This study is devoted to checking the agreement of reference values of this indicator with actual values that correspond to the conditions of driving on Russian roads. To solve this problem, full-scale measurements were made of the coefficient of adhesion of tires with a coating on more than 2000 km of roads in different regions of Russia (with different climatic and topographic conditions) and the results of field research were analyzed.

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Predictive modeling of hourly probabilities for weather-related road accidents

Natural Hazards and Earth System Science ◽

10.5194/nhess-20-2857-2020 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2857-2871

Author(s):

Nico Becker ◽

Henning W. Rust ◽

Uwe Ulbrich

Keyword(s):

Traffic Management ◽

Model Performance ◽

Weather Conditions ◽

Reanalysis Data ◽

Road Maintenance ◽

Road Accidents ◽

Logistic Regression Models ◽

Weather Forecasts ◽

Accident Probability ◽

Adverse Weather

Abstract. Impacts of weather on road accidents have been identified in several studies with a focus mainly on monthly or daily accident counts. This study investigates hourly probabilities of road accidents caused by adverse weather conditions in Germany on the spatial scale of administrative districts using logistic regression models. Including meteorological predictor variables from radar-based precipitation estimates, high-resolution reanalysis and weather forecasts improves the prediction of accident probability compared to models without weather information. For example, the percentage of correctly predicted accidents (hit rate) is increased from 30 % to 70 %, while keeping the percentage of wrongly predicted accidents (false-alarm rate) constant at 20 %. When using ensemble weather forecasts up to 21 h instead of radar and reanalysis data, the decline in model performance is negligible. Accident probability has a nonlinear relationship with precipitation. Given an hourly precipitation sum of 1 mm, accident probabilities are approximately 5 times larger at negative temperatures compared to positive temperatures. The findings are relevant in the context of impact-based warnings for road users, road maintenance, traffic management and rescue forces.

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COVID Lockdowns, Social Distancing, and Fatal Car Crashes: More Deaths on Hobbesian Highways?

Cambridge Journal of Evidence-Based Policing ◽

10.1007/s41887-020-00059-8 ◽

2020 ◽

Vol 4 (3-4) ◽

pp. 238-259 ◽

Cited By ~ 2

Author(s):

Marshall W. Meyer

Keyword(s):

World War Ii ◽

Traffic Safety ◽

Motor Vehicle ◽

Road Traffic ◽

Research Question ◽

Traffic Volume ◽

Similar Data ◽

World War ◽

Social Distancing ◽

The Usa

Abstract Research Question What happened to US traffic safety during the first US COVID-19 lockdown, and why was the pattern the opposite of that observed in previous sudden declines of traffic volume? Data National and local statistics on US traffic volume, traffic fatalities, injury accidents, speeding violations, running of stop signs, and other indicators of vehicular driving behavior, both in 2020 and in previous US economic recessions affecting the volume of road traffic. Methods Comparative analysis of the similarities and differences between the data for the COVID-19 lockdown in parts of the USA in March 2020 and similar data for the 2008–2009 global economic crisis, as well as other US cases of major reductions in traffic volume. Findings The volume of traffic contracted sharply once a COVID-19 national emergency was declared and most states issued stay-at-home orders, but motor vehicle fatality rates, injury accidents, and speeding violations went up, and remained elevated even as traffic began returning toward normal. This pattern does not fit post-World War II recessions where fatality rates declined with the volume of traffic nor does the 2020 pattern match the pattern during World War II when traffic dropped substantially with little change in motor vehicle fatality rates. Conclusions The findings are consistent with a theory of social distancing on highways undermining compliance with social norms, a social cost of COVID which, if not corrected, poses potential long-term increases in non-compliance and dangerous driving.

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Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction

Applied Sciences ◽

10.3390/app11136030 ◽

2021 ◽

Vol 11 (13) ◽

pp. 6030

Author(s):

Daljeet Singh ◽

Antonella B. Francavilla ◽

Simona Mancini ◽

Claudio Guarnaccia

Keyword(s):

Machine Learning ◽

Linear Models ◽

Prediction Models ◽

Road Traffic ◽

Traffic Noise ◽

Machine Learning Techniques ◽

Coefficient Of Determination ◽

Road Traffic Noise ◽

Noise Prediction ◽

Volume Percentage

A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. Leq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.

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Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

Remote Sensing ◽

10.3390/rs13122329 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2329

Author(s):

Elżbieta Macioszek ◽

Agata Kurek

Keyword(s):

Travel Behavior ◽

Traffic Management ◽

Road Traffic ◽

Traffic Volume ◽

The Road ◽

Traffic Characteristics ◽

Annual Average Daily Traffic ◽

On The Road ◽

The Impact ◽

The City

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics.

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Modeling the Vehicle-Bicycle Collision - The Analysis of the Projection Distance and Acceleration at the Cyclist's Head

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.880.177 ◽

2018 ◽

Vol 880 ◽

pp. 177-182 ◽

Cited By ~ 2

Author(s):

Oana Victoria Oţăt ◽

Ilie Dumitru ◽

Victor Oţăt ◽

Lucian Matei

Keyword(s):

Traffic Accidents ◽

Road Traffic ◽

Road Accidents ◽

Speed Variation ◽

Close Analysis ◽

Collision Type ◽

Projection Distance ◽

Negative Effect ◽

Head Area ◽

Road Traffic Networks

The ever-growing demand for transportation and the need to carry both people and goods has led to increased congestions of road traffic networks. Subsequently, the main negative effect is the multiplication of serious road accidents. Of the total number of serious road accidents, a significant increase has been registered among cyclists, with 13.9% in 2014 of total vehicles involved in traffic accidents, compared to 6.6% in 2010. The present paper underpins a close analysis of the kinematic and dynamic parameters in the event of a vehicle - bicycle – cyclist assembly – collision type. To study the vehicle-bicycle-collision type, we carried out a comparative analysis with regard to the distance the cyclist is thrown away following the collision, the speed variation of the vehicle and of the bicycle, and the speed variation in the cyclist’s head area, as well as the variation of the acceleration recorded on the vehicle, the bicycle and the cyclist’s head area. Hence, we modelled and simulated the vehicle – bicycle collision for two distinct instances, i.e. a frontal vehicle – rear bicycle collision and a frontal vehicle - frontal bicycle collision.

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Retrofit of Aerodynamic Cable Instability on a Cable-Stayed Bridge: Case Study

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1740-08 ◽

2000 ◽

Vol 1740 (1) ◽

pp. 61-67 ◽

Cited By ~ 3

Author(s):

Niket M. Telang ◽

Charles M. Minervino ◽

Paul G. Norton

Keyword(s):

Large Amplitude ◽

Weather Conditions ◽

Mitigation Measures ◽

Mobile Bay ◽

Department Of Transportation ◽

Light Rain ◽

Wind Speeds ◽

Cable Stays ◽

Large Amplitude Vibrations

Elegantly poised over the Mobile River, the twin pylons and the semi-harped cable stays of the Cochrane Bridge subtly complement the vast and undulating landscape of the Mobile Bay as the bridge carries US Route 90 over the Mobile River in Alabama. In February 1998, light rain drizzled on the bridge, and a weather station nearby recorded wind speeds of about 48 km/h (30 mph). Under these seemingly mild weather conditions, the normally immobile cable stays started to vibrate, and within moments, these nascent vibrations reached amplitudes of more than 1.2 m (4 ft). Alarmed by this event, the Alabama Department of Transportation (ALDOT) took immediate action to ensure the continued safety and serviceability of the bridge. A team of consultants was selected by ALDOT to investigate mitigation measures for the large-amplitude cable-stay vibrations. The fast-tracked comprehensive program planned and implemented to inspect, test, document, and evaluate the effects of the large-amplitude vibrations and the recommendation of retrofit measures that would limit future occurrences of such cable-stay vibrations on the Cochrane Bridge are described in detail.

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