Gdansk, , Gdynia ia, Sopot Ruth Bergel-Hayat(*) Wojciech Kustra, - - PowerPoint PPT Presentation

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Gdansk, , Gdynia ia, Sopot Ruth Bergel-Hayat(*) Wojciech Kustra, - - PowerPoint PPT Presentation

ICTCT workshop, Olomouc, Czech Republic, October 26-27th, 2017 Traffic safety and the City" - How to enhance traffic safety and liveability in urban areas? ________________________________________________________________________ In


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In Incle lement weather effect on road casualties in in urban areas: : the case of the metropolitan area Gdansk, , Gdynia ia, Sopot

Wojciech Kustra, Joanna Zukowska

Highway & Transportation Engineering Department Faculty of Civil & Environmental Engineering Gansk University of Technology, Poland

ICTCT workshop, Olomouc, Czech Republic, October 26-27th, 2017 “Traffic safety and the City" - How to enhance traffic safety and liveability in urban areas? ________________________________________________________________________


Ruth Bergel-Hayat(*)

RBH Conseil & Formation, France

Nathalie Focant

VIAS Institute, Belgium

__________________________________________ (*)University Paris-Est, IFSTTAR GRETTIA for a part of the work

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Plan of the presentation

  • 1. Context of the research
  • 2. Tricity metropolitan area
  • 3. Casualty data
  • 4. Inclement weather data
  • 5. Modelling
  • 6. Model’s outputs
  • 7. Discussion
  • 8. Conclusion
  • 9. References

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The context

  • The relationship between weather conditions and road safety outputs has been questionned in

2010 (specially cold spells over Europe)

  • Researchers invested this topic intensively (SafetyNet, 2004-2008)
  • The IRTAD group of OCDE initiated a formal reflexion for analysis and short-term forecasting

purposes (from 2014 onwards)

  • The emergence of the reflexion about road safety and cities leads to raise this question for urban

areas.

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Tricity

Gdansk – 464.000 inhabitants 262 km2 Sopot – 37.500 inhabitants 17,3 km2 Gdynia – 245.000 inhabitants 135 km2

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Casualty data

  • Road safety data for Gdansk-Gdynia-Sopot Metropolitan Area has been extracted from

the Polish Police Database

  • The casualty numbers, for drivers+passengers, are disaggregated according to :
  • the severity (killed and serious injuried, slight injured);
  • the user type ( VRU among which pedestrians mainly, vehicle users among which

car users mainly)

  • For the period 2000-2005, the total of victims in cars was uncomplete but this could be

successfully corrected.

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Casualty data

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Casualty data

Total number of victims: Disaggregation by severity type: 96,4% are injured,:slight injured (85,7%), seriously injured (10,69%), 3,6% are fatalities Disaggregation by road user type 48,4% are VRU: Pedestrians (34,4%), bicyclists (9,3%) and motorised 2 wheelers (4,6%) 51,6% are Vehicles: Car + lvc users (45,9%), hvl (1,5%), Bus + Trolleys (2,8%), Trams (1,2%)

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Inclement weather data

  • The weather data have been gathered from the Agri4cast website
  • The weather data are measured by grids
  • The center of grid nr 118132 with Latitude 54,30 and Longitude 18,70) is the nearest

grid center to the Tricity (Gdansk itself having Latitude 54,37 and Longitude 18,63)

  • The weather data, averaged over all grid centers in Poland, are used for measuring the

average weather for the whole of Poland

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Weather data: 3 types of days with inclement weather

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rainy and cold days (13%) , dry and cold days (13%), rainy and warm days (33%), and normal days (41%)

33% 33% 13% 16% 13% 14% 41% 37% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Gdansk/Tricity Poland normal dry_cold rain_cold rain_only

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Modelling

  • State space methods
  • A locally linear trend (stochastic level and slope) and seasonal
  • Dependant variable: Number of victims in the month;
  • Independant (explanatory) variables: numbers of 3 different types of days with

inclement weather in the month;

  • With interventions
  • On a monthly basis (January 2000 - December 2016)
  • Main output
  • Independant variables coefficients’ estimates (3 per model)
  • Using Stamp (in Oxmetrics)
  • or other softwares (such as the SAS system, proc UCM)

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Model’s output: Tricity

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  • 8%
  • 7%
  • 6%
  • 5%
  • 4%
  • 3%
  • 2%
  • 1%

0% 1% 2% Lvictims LvictimsCARS LvictimsVRU LvictimsPed LvictimsBic LvictimsMot rain_cold dry_cold rain_only

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Model’s outputs: All cities in Poland

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  • 8%
  • 7%
  • 6%
  • 5%
  • 4%
  • 3%
  • 2%
  • 1%

0% 1% 2% Lvictims LvictimsCARS LvictimsVRU LvictimsPed LvictimsBic LvictimsMot rain_cold dry_cold rain_only

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Model’s outputs: estimates’ interpretation - Tricity

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  • A rainy and cold day is linked with:

a decrease in the numbers of victims in the month, regarding the total of VRU

  • A dry and cold is linked with:

a decrease in the numbers of victims in the month, regarding the total of VRU

  • A rainy day without cold is linked with:

a decrease in the numbers of victims in the month, regarding the total of VRU a increase in the numbers of victims in the month, regarding car users Unfortunately, no estimates could be computed regarding each type of VRU.

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Model’s outputs: estimates’ interpretation - all cities in Poland

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  • A rainy and cold day is linked with:

a decrease in the numbers of victims in the month, regarding all VRU: pedestrians, bicyclists and motorized 2W (-1%, -7% to -8% resp.)

  • A dry and cold day is linked with:

a decrease in the numbers of victims in the month, regarding all VRU: pedestrians, bicyclists and motorized 2W ( -1,5%, -4% to -6% resp.)

  • A rainy day without cold is linked with:

a decrease in the numbers of victims in the month, regarding 2 Wheelers: bicycles and motorized 2W ( around -2% each) a increase in the numbers of victims in the month, regarding pedestrians (+0,8%) and also, but less significantly regarding car users (+0,4%).

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Discussion

  • Choose between estimates’ significancy and data disaggregation
  • Significant coefficients’ estimates can only be provided by aggregate (Gaussian) models when

the victim numbers are large enough

  • Other models are more appropriate for disagreggated victim numbers
  • Choose according to the final aim
  • Analysis of inclement weather impact on specific victim types data
  • Analysis of (aggregated) weather impact on aggregated victim data
  • Analysis or correction for the weather impact ?

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Conclusions

  • The significant results highlight the relevance of taking 3 inclement weather

configurations into account in road safety trend analysis.

  • Information related to rain, snow and frost, appropriately aggregated at urban area-level

and at the scale of the month, enrich the short-term trend analysis performed at the monthly level.

  • A relevant output of the analysis is that it provides correlations between weather

variables and types of road user's casualty variables aggregated at urban area-level.

  • The work done on the Tricity area provides comparisons with the total of casualty

variables in urban areas in Poland.

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Outlook

  • Comparisons not only on other (the total of ) casualty datasets in urban areas in Poland

but also on the same type of road user's casualty datasets in urban areas in other countries.

  • These results could serve to improve road safety management (alert levels by inclement

weather are to be set), and to improve road safety trend assessment as well (short-term changes in the trend, due to atypical inclement meteorology are to be corrected for, which highlights the changes imputable to other factors such as road safety policies).

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Some more information ?

  • A very large number of papers on the topic ‘Weather and road safety’:

Among which: Bergel-Hayat, R., Debbarh, M., Antoniou, C., Yannis, G. (2013) Explaining the road accident risk: Weather effects. Accident Analysis & Prevention nr 60, 456-465. Bergel-Hayat R., Zukowska J. (2015) Road safety trends at national level in Europe: a review of time-series analysis performed during the period 2000-2012. Transport Reviews: A Transnational Transdisciplinary Journal. 35(5), 650-671

  • Factsheet N° 3 of the IRTAD Forecasting Subgroup, chaired by Klaus Machata (KfV,

Austria)

Bergel-Hayat, R., Focant, N. (2017) Inclement weather effects on road casualty at national level- Methodology and application to a number of European countries. 6th IRTAD Conference, 12 October 2017, Marrakech, Morocco

Thank you for your attention !

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