Grant: Alliance Grants
Toronto Metropolitan University
$25,000.00
Using surrogate safety measures to make roads safer for pedestrians and cyclists
EF 2023-24
Résumé
Safety of cyclists and pedestrians and cyclists is a world-wide concern, with some 27% of the 1.24 million annual road deaths globally involving these vulnerable road users (VRUs). On Canadian roads, there are some 325 VRU deaths and 1,500 seriously injured VRUs per year resulting from largely preventable crashes. This collaborative project between Toronto Metropolitan University (TMU) and Lund University, Sweden will have two objectives with the aim of improving the knowledge base for reducing this toll of deaths and injuries. Although VRUs are key targets of Vision Zero programs around the globe, crashes involving them are relatively rare, so there are limits to the insights that can be gained in understanding and improving VRU safety with crash-based analyses. On the other hand, surrogate safety measures (SSMs), which are based on "near misses" when a crash is narrowly avoided, can provide those insights because of the high frequency of such encounters. Therefore, both objectives will be addressed with SSMs. For the first objective, advanced modeling techniques will be developed and applied to data collected using a questionnaire and a bicycle simulator at Lund University, Sweden. The expected outcome is a better understanding of the behavioral aspects affecting the performance of riders they relate to design characteristics of the road environment and the bicycles. For the second objective, advanced statistical methods will be applied to data collected from videos to better understand the factors related to the near miss encounters that are most likely to lead to pedestrian crashes.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
586256-2023
Name nom
Persaud, Bhagwant BN
Department département
Civil Engineering
Organization id
43
Institution établissement
Toronto Metropolitan University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2023
Award amount
25000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1200
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Advancement of knowledge
Area of application fr
Avancement des connaissances
Research subject code
3004
Research subject group en
Statistics and probability
Research subject group fr
Applied statistics
Research subject en
Statistique et probabilité
Research subject fr
Statistique appliquée
Application title
Using surrogate safety measures to make roads safer for pedestrians and cyclists
Keyword
bicycle simulators; crash data modelling; cyclist safety; machine learning; pedestrian safety; road safety; structural equation modelling; surrogate safety measures; video analytics
Application summary
Safety of cyclists and pedestrians and cyclists is a world-wide concern, with some 27% of the 1.24 million annual road deaths globally involving these vulnerable road users (VRUs). On Canadian roads, there are some 325 VRU deaths and 1,500 seriously injured VRUs per year resulting from largely preventable crashes. This collaborative project between Toronto Metropolitan University (TMU) and Lund University, Sweden will have two objectives with the aim of improving the knowledge base for reducing this toll of deaths and injuries. Although VRUs are key targets of Vision Zero programs around the globe, crashes involving them are relatively rare, so there are limits to the insights that can be gained in understanding and improving VRU safety with crash-based analyses. On the other hand, surrogate safety measures (SSMs), which are based on "near misses" when a crash is narrowly avoided, can provide those insights because of the high frequency of such encounters. Therefore, both objectives will be addressed with SSMs. For the first objective, advanced modeling techniques will be developed and applied to data collected using a questionnaire and a bicycle simulator at Lund University, Sweden. The expected outcome is a better understanding of the behavioral aspects affecting the performance of riders they relate to design characteristics of the road environment and the bicycles. For the second objective, advanced statistical methods will be applied to data collected from videos to better understand the factors related to the near miss encounters that are most likely to lead to pedestrian crashes.