Over-speeding is a pivotal factor in fatal traffic crashes globally,necessitating robust speed management strategies to augment road safety.In 2021,the National Highway Traffic Safety Administration reported over 1200...Over-speeding is a pivotal factor in fatal traffic crashes globally,necessitating robust speed management strategies to augment road safety.In 2021,the National Highway Traffic Safety Administration reported over 12000 speed-related fatalities in the United States alone.Previous studies aggregated over-speeding tendencies;however,the extent of over-speeding has a significant implication on the crash outcome.This study delves into the prevalence and magnitude of over-speeding in various scenarios,utilizing data from traffic cameras in Edmonton,Canada,and employing a negative binomial statistical model for analysis.The model elucidates the significance and likelihood of over-speeding tenden-cies by incorporating temporal and built environment variables,i.e.,year,month,number of lanes,dwelling unit types,school-related,and open green space.Study results indicated that the aggregation of the over-speeding data tends to underestimate the influence of var-ious factors.Notably,the estimated impact of the posted speed limit for the disaggregated models is up to over two times that for the aggregated model.Further,the summer months exhibit a roughly 25%uptick in speed limit violations for aggregated models while about a 40%uptick in the speed limit violations for disaggregated approaches.Conversely,a dis-cernible decline in over-speeding tendencies is observed with camera enforcement,show-casing a 25%reduction over four years.Built environment variables presented mixed results,with one-unit dwellings associated with a 12%increase in over-speeding,while proximity to schools indicated a 10%decrease.These pivotal findings provide policymakers and practitioners with valuable insights to formulate targeted interventions and counter-measures to curtail speed limit violations and bolster overall road safety conditions.展开更多
Electric vehicles(EVs)are gaining popularity across the globe.Various initiatives are being implemented to ensure that most of the operating vehicles on public roadways are EVs by 2050.Such initiatives include the con...Electric vehicles(EVs)are gaining popularity across the globe.Various initiatives are being implemented to ensure that most of the operating vehicles on public roadways are EVs by 2050.Such initiatives include the construction of charging stations to improve EV charging accessibility.The utilization of the charging stations has not been explored to a great extent,despite its importance in future installations in various cities.This study evaluated the EV station utilization across eleven cities in three countries:the United States,Canada,and Scotland.The Negative Binomial(NB)regression model was applied to understand the influence of the spatial–temporal factors on the daily utilization of EV charging stations.In addition to the overall analysis,countryspecific analyses were also performed.It was revealed that there is a great variation in daily EV utilization across the cities in different countries and within the country.In fact,only stations in Crieff,Scotland,showed lower predicted daily utilization,while cities in the United States had over two times predicted daily utilization compared to stations in Aberfeldy,Scotland.Furthermore,the longer the station has been in service,the higher the daily utilization,although there was significant variation across cities.Further,the day of the week and months of the year depicted consistent utilization patterns for Scotland and the United States but showed mixed findings for Canada.The study findings can help planners and policymakers improve the allocation of EV charging stations.展开更多
文摘Over-speeding is a pivotal factor in fatal traffic crashes globally,necessitating robust speed management strategies to augment road safety.In 2021,the National Highway Traffic Safety Administration reported over 12000 speed-related fatalities in the United States alone.Previous studies aggregated over-speeding tendencies;however,the extent of over-speeding has a significant implication on the crash outcome.This study delves into the prevalence and magnitude of over-speeding in various scenarios,utilizing data from traffic cameras in Edmonton,Canada,and employing a negative binomial statistical model for analysis.The model elucidates the significance and likelihood of over-speeding tenden-cies by incorporating temporal and built environment variables,i.e.,year,month,number of lanes,dwelling unit types,school-related,and open green space.Study results indicated that the aggregation of the over-speeding data tends to underestimate the influence of var-ious factors.Notably,the estimated impact of the posted speed limit for the disaggregated models is up to over two times that for the aggregated model.Further,the summer months exhibit a roughly 25%uptick in speed limit violations for aggregated models while about a 40%uptick in the speed limit violations for disaggregated approaches.Conversely,a dis-cernible decline in over-speeding tendencies is observed with camera enforcement,show-casing a 25%reduction over four years.Built environment variables presented mixed results,with one-unit dwellings associated with a 12%increase in over-speeding,while proximity to schools indicated a 10%decrease.These pivotal findings provide policymakers and practitioners with valuable insights to formulate targeted interventions and counter-measures to curtail speed limit violations and bolster overall road safety conditions.
文摘Electric vehicles(EVs)are gaining popularity across the globe.Various initiatives are being implemented to ensure that most of the operating vehicles on public roadways are EVs by 2050.Such initiatives include the construction of charging stations to improve EV charging accessibility.The utilization of the charging stations has not been explored to a great extent,despite its importance in future installations in various cities.This study evaluated the EV station utilization across eleven cities in three countries:the United States,Canada,and Scotland.The Negative Binomial(NB)regression model was applied to understand the influence of the spatial–temporal factors on the daily utilization of EV charging stations.In addition to the overall analysis,countryspecific analyses were also performed.It was revealed that there is a great variation in daily EV utilization across the cities in different countries and within the country.In fact,only stations in Crieff,Scotland,showed lower predicted daily utilization,while cities in the United States had over two times predicted daily utilization compared to stations in Aberfeldy,Scotland.Furthermore,the longer the station has been in service,the higher the daily utilization,although there was significant variation across cities.Further,the day of the week and months of the year depicted consistent utilization patterns for Scotland and the United States but showed mixed findings for Canada.The study findings can help planners and policymakers improve the allocation of EV charging stations.
