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Massive GNSS data for road safety analysis: Comparing crash models for several Canadian cities and data sources
- Source :
- Accident Analysis & Prevention. 159:106232
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Mobile sensors are a useful data source with applications in several transportation fields. Though cost of collection, transmission, and storage has limited studies on driving data and safety, this can be overcome through usage-based insurance (UBI). In UBI programs, drivers are monitored, and their premiums are adjusted based on driver-level surrogate safety measures (SSMs) related to exposure and driving style. Contextual link-level SSMs (volume, speed, or density) could further improve discount calibration. This study quantifies relationships between contextual SSMs and crashes and includes the validation of previous results (correlations between SSMs and crashes and statistical models estimated using smartphone-collected data from Quebec City) and the comparison of three Canadian cities (using UBI data from Quebec City, Montreal, and Ottawa). Extracted SSMs were compared to large volumes of historical crash frequency data using Spearman’s Rank Correlation Coefficient and then implemented into spatial Bayesian crash models. Results from the UBI data generally matched those from the previous study, with observed correlations mirroring previous results in direction (braking, congestion, and speed variation are positively associated with crash frequency while mean speed is negatively associated) while correlation strength was slightly higher. Furthermore, these results were consistent between cities. For the crash modelling, repeatability of previous results in Quebec City was moderately good for the UBI data. Importantly for large-scale implementation, models estimated using UBI data were largely consistent between cities. This work provides an important contribution to the existing literature, clearly demonstrating how contextual safety measures could be applied to benefit UBI practices.
- Subjects :
- Automobile Driving
Canada
Calibration (statistics)
Computer science
Bayesian probability
Information Storage and Retrieval
Human Factors and Ergonomics
Crash
Correlation
0502 economics and business
Statistics
Humans
0501 psychology and cognitive sciences
Cities
Safety, Risk, Reliability and Quality
050107 human factors
Rank correlation
050210 logistics & transportation
Models, Statistical
Mathematical model
05 social sciences
Accidents, Traffic
Public Health, Environmental and Occupational Health
Bayes Theorem
Statistical model
GNSS applications
Safety
Subjects
Details
- ISSN :
- 00014575
- Volume :
- 159
- Database :
- OpenAIRE
- Journal :
- Accident Analysis & Prevention
- Accession number :
- edsair.doi.dedup.....7325a0e9ec5b53d813091892630ba1c0