Alice Billot-Grasset. Vivian Viallon Emmanuelle Amoros



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Transcription:

Alice Billot-Grasset Vivian Viallon Emmanuelle Amoros Martine Hours Ifsttar/TS2/Umrestte 1

Cycling safety Cycling is promoted because of health benefits & for modal shift on utilitarian trips. http://inhabitat.com/infographic-how-biking-to-work-can-help-you-save-money-lose-weight-and-reduce-co2/ 2

Cycling safety Safety is a concern for many stakeholders because of an increasing cycling modal share in many major cities. Bicycle modal share in some French cities https://phillymotu.files.wordpress.com/2013/11/bicyclec ommuting-011.png?w=630&h=630 3

Cycling safety Risk of accident: The risk to be injured is 8 times higher for cyclists than for car occupants per hour (Blaizot et al., 2012). 4

Cycling safety Cyclist-only accidents are up to 70% of all cases but they are hard to analyze because most of data come from police forces (underreporting + biased on accident type) Collisions Cyclist-only accidents More generally, only a few typologies of bicycle crashes exist. 5

Cycling accident ROAD USER VEHICLE ENVIRONMENT Accidents (injuries) 6

Objectives Improve knowledge on cyclist accidents thanks to a typology containing: Collisions and cyclist-only accidents; All type of practises (utilitarian, sport, leisure). 7

By : Objectives collecting data through a survey; constructing a typology using classification; identifying contribution of accidents factors to accidents types; understanding the impact of cyclist behaviours; analysing gender effects. 8

Available data in France Études Détaillées d Accident (EDA) About 30 cycling accidents out of 1000 EDA, Case studies collection Official data from police forces (BAAC) Incomplete and many biases, 4500 cycling accidents per year in France and 150 in the Rhône department. Rhône Trauma Registry 1300 cycling accidents per year in the Rhône department, 260 care services on this territory, almost exhaustive, biases on injuries severity AIS 1, design for collecting additional data. 9

Available data in France Études Détaillées d Accident (EDA) About 30 cycling accidents out of 1000 EDA, Case studies collection Official data from police forces (BAAC) Incomplete and many biases, 4500 cycling accidents per year in France and 150 in the Rhône department. Rhône Trauma Registry 1300 cycling accidents per year in the Rhône department, 260 care services on this territory, almost exhaustive, biases on injuries severity AIS 1, design for collecting additional data. 10

Material and methods Postal survey sent to cyclists identified from the Rhône Road Trauma Registry (medical database) and injured in 2009-2011. n=1078 respondents 3337 questionnaires sent 797 False address (24%) 1455 (57%) non respondents 1085 (43%) RESPONDENTS Respondents : Older, more often women, more often cycling in urbanised area, more often involved in collisions and more often sustaining serious injuries; than non respondents. 11

Methods Trade-off between automatic methods and experts decisions: 35 relevant variables selected (state of art + descriptive statistic from the Registry), unsupervised classification (clustering to reveal data natural structure), ultimate decision for the total number of clusters made by expertise or SoA outcomes. Variables selection Hierarchical Ascendant Classification (HAC) Clustering Ultimate decision for total cluster number Typology of cyclist accidents MCA : checking and refining analyses. 12

Statistical approach Hierarchical Ascendant Classification (HAC) Partionning Around Medoids (PAM) Clustering individuals using a distance for binary variables, to obtain a indicative number of clusters. Clustering individuals using the same distance. As a result of a robust method we obtain a typology of 17 clusters. 13 Dendrogram (Jaccard ; Ward) Clustering (Jaccard)

Typology of Cyclist Accidents Accident factors : 7 configurations on utilitarian trips Commuting Utilitarian (except commuting) Leisure Sport 14

Example of an accident type N 1 - Avoidance of another road user (n = 51) Often, a cyclist avoiding another road user (82 vs. 10%) on a dedicated bicycle facility (49 vs. 16%). Usually, there is a trajectory conflict (67 vs. 27%), while commuting (57 vs. 23%). Most of the time on a straight road between intersections. 15

Multiple Correspondence Analysis (MCA)

Type of trip: utilitarian sport leisure MCA (2)

MCA (3) Type of trip: utilitarian sport leisure Accident type: cyclist only collision

MCA (4) Type of trip: utilitarian sport leisure Accident type: cyclist only collision Behavioral variables 19

Summary Survey : rigorous methodology, RR=43% (1078 respondents), estimation of differences between respondents and nonrespondents. Typology of cyclists accidents : 17 configurations (7 on utilitarian trips, 10 sport and leisure), identification of accidents factors for each configuration, validation of the typology quality and connection between cyclist behaviours and cyclist only accidents. 20

Application Proposals for action: preventing parking on bike path (conf. n 1), improving obstacle conspicuity (conf. n 5 et 7), increasing road facilities to make them convenient for cyclist, i.e. predictable trajectory, eye-contact with other road users (conf. n 3 et 6), growing awarness of cyclist behavior impact among cyclistes themselves to encourage conspicuity clothing and bike devices, appropriate speed, alcool consumption (conf n 2-4 - 5 7). Proposed approach: pinpoint key actors to diffuse prevention (cycling school instructor, association, medias ), 21

Alice Billot-Grasset IFSTTAR / TS2 / UMRESTTE billot.aliceg@gmail.com Thanks are due to the ARVAC Founding: InVS, Bertrand Thélot

Contribution of variables Table 2 Significance Level of the typology variables P-value Crash.Opponent 1.12e-236 Collide.Opponent 8.40e-204 Commuting 2.62e-194 Senior 1.05e-168 Sport 1.47e-153 Traj..Conflict 5.07e-149 Adult 3.74e-116 Collide.Object 4.13e-105 Object 7.02e-99 Not.been.seen 6.22e-93 Leisure 1.77e-82 Off.Road 5.84e-78 Avoide.Opponent 1.43e-77 Week-End 3.87e-70 Tricky.Road 4.25e-70 Intersection 1.71e-65 Friends 3.89e-61 Invisible 1.14e-55 Not.Expert Slip Teenager Not.see Young Dark Speed Bad.Weather MAIS_2 No.Manoeuvre Cycle.Infra MAIS_sup3 Pedestrian Men Public.Transport. Alcohol Meca..Fail. 3.45e-53 2.60e-48 1.47e-47 8.52e-44 2.98e-42 1.05e-40 1.30e-34 3.09e-33 2.05e-31 3.11e-31 1.08e-28 4.86e-07 1.35e-04 4.56e-04 1.85e-03 2.31e-03 1.94e-02 23