Assessing the Impact of Weather on Traffic Intensity

Cools, Mario; Moons, Elke; Wets, Geert

doi:10.1175/2009wcas1014.1

Cited by 174 publications

(107 citation statements)

References 21 publications

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“…This 14% reduction refers to all road weather information together, not only warnings. As mentioned earlier, Cools et al [26] showed that adverse weather tends to reduce traffic volume (roughly by −3% to −7% in the case of Belgium). As the model presented here is based on realized accidents, the estimated (and observed) increase in accident rates includes the just mentioned response effects.…”

Section: Resultsmentioning

confidence: 76%

“…Road traffic demand decreases during adverse weather [26], which attenuates the rise in accidents, while the extent of the decrease depends on the purpose of the trip (work, shopping, holiday, etc.). The structure of road traffic, the composition of drivers (by skill level) in the traffic, and the average degree of time pressure varies between days.…”

Section: Approaches In Analysis Of Road Traffic Sensitivity To Weathementioning

confidence: 99%

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Weather Conditions, Weather Information and Car Crashes

Perrels

Votsis

Nurmi

et al. 2015

IJGI

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Road traffic safety is the result of a complex interaction of factors, and causes behind road vehicle crashes require different measures to reduce their impacts. This study assesses how strongly the variation in daily winter crash rates associates with weather conditions in Finland. This is done by illustrating trends and spatiotemporal variation in the crash rates, by showing how a GIS application can evidence the association between temporary rises in regional crash rates and the occurrence of bad weather, and with a regression model on crash rate sensitivity to adverse weather conditions. The analysis indicates that a base rate of crashes depending on non-weather factors exists, and some combinations of extreme weather conditions are able to substantially push up crash rates on days with bad weather. Some spatial causation factors, such as variation of geophysical characteristics causing systematic differences in the distributions of weather variables, exist. Yet, even in winter, non-spatial factors are normally more significant. GIS data can support optimal deployment of rescue services and enhance in-depth quantitative analysis by helping to identify the most appropriate spatial and temporal resolutions. However, the supportive role of GIS should not be inferred as existence of highly significant spatial causation.

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Section: Resultsmentioning

confidence: 76%

Section: Approaches In Analysis Of Road Traffic Sensitivity To Weathementioning

confidence: 99%

Weather Conditions, Weather Information and Car Crashes

Perrels

Votsis

Nurmi

et al. 2015

IJGI

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“…traffic safety, reliability of travel times (Papinski et al, 2009)), and situational variables (e.g. weather conditions (Cools et al, 2010a, Cools et al, 2010c, holiday effects (Cools et al, 2009, Cools et al, 2010b and traffic information (Zhang and Levinson, 2008)). Moreover, future research should extent to other transport modes such as walking, bicycle use, public transport and carpooling.…”

Section: Discussionmentioning

confidence: 99%

Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data

et al. 2013

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The aim of this research is to identify the relationship between activity patterns and route choice decisions. The focus is twofold: on the one hand, the relationship between the purpose of a trip and the road categories used for the relocation is investigated; on the other hand, the relationship between the purpose of a trip and the deviation from the shortest path is studied. The data for this study were collected in 2006 and 2007 in Flanders, the Dutch speaking and northern part of Belgium. To estimate the relationship between the primary road category travelled on and the corresponding activity-travel behaviour a multinomial logit model is developed. To estimate the relationship between the deviation from the shortest path and the corresponding activity-travel behaviour a Tobit model is developed. The results of the first model point out that route choice is a function of multiple factors, not just travel time or distance. Crucial for modelling route choices or in general for traffic assignment procedures is the conclusion that activity patterns have a clear influence on the road category primarily driven on. Particularly, it was shown that the likelihood of taking primarily through roads is highest for work trips and lowest for leisure trips. The second model shows a significant relationship between the deviation from the shortest path and the purpose of the trip. Furthermore, next to trip-related attributes (trip distance), also socio-demographic variables and geographical differences play an important role. These results certainly suggest that traffic assignment procedures should be developed that explicitly take into account an activity-based segmentation. In addition, it was shown that route choices were similar during peak and off-peak periods. This is an indication that car drivers are not necessarily utility maximizers, or that classical utility functions in the context of route choices are omitting important explanatory variables.

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“…Furthermore, a seasonality pattern in road accidents was expected, as a number of accidents in winter may be due to snow and slippery roads (6,17). However, we have experienced a relatively long period of drought, which has led to winters with low precipitation and blizzards, which in turn has reduced the occurrence of snow and blizzards.…”

Section: Discussionmentioning

confidence: 95%

Assessment of Trend and Seasonality in Road Accident Data: An Iranian Case Study

Razzaghi

Bahrampour

Baneshi

et al. 2013

Int J Health Policy Manag

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Background:Road traffic accidents and their related deaths have become a major concern, particularly in developing countries. Iran has adopted a series of policies and interventions to control the high number of accidents occurring over the past few years. In this study we used a time series model to understand the trend of accidents, and ascertain the viability of applying ARIMA models on data from Taybad city. Methods: This study is a cross-sectional study. We used data from accidents occurring in Taybad between 2007 and 2011. We obtained the data from the Ministry of Health (MOH) and used the time series method with a time lag of one month. After plotting the trend, non-stationary data in mean and variance were removed using Box-Cox transformation and a differencing method respectively. The ACF and PACF plots were used to control the stationary situation. Results: The traffic accidents in our study had an increasing trend over the five years of study. Based on ACF and PACF plots gained after applying Box-Cox transformation and differencing, data did not fit to a time series model. Therefore, neither ARIMA model nor seasonality were observed. Conclusion: Traffic accidents in Taybad have an upward trend. In addition, we expected either the AR model, MA model or ARIMA model to have a seasonal trend, yet this was not observed in this analysis. Several reasons may have contributed to this situation, such as uncertainty of the quality of data, weather changes, and behavioural factors that are not taken into account by time series analysis.

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Assessing the Impact of Weather on Traffic Intensity

Cited by 174 publications

References 21 publications

Weather Conditions, Weather Information and Car Crashes

Weather Conditions, Weather Information and Car Crashes

Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data

Assessment of Trend and Seasonality in Road Accident Data: An Iranian Case Study

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