Demand-responsive transportation has been introduced in many cities around the world.However,whether it is applicable in the railway is still questionable,an exploration of passenger choice behavior between demandresp...Demand-responsive transportation has been introduced in many cities around the world.However,whether it is applicable in the railway is still questionable,an exploration of passenger choice behavior between demandresponsive trains and pre-scheduled trains is pivotal in addressing this issue.To delve into passengers’choice preferences when facing demand-responsive trains and to dissect the feasibility of implementing demandresponsive service in high-speed railways,the stated preference survey method is employed to investigate travel intention of passengers.Based on the survey data obtained in China,the heterogeneity of passengers is analyzed from three aspects:personal socio-economic characteristics,travel characteristics,and travel mode choice.Considering the situation that demand-responsive train cannot operate,the risk attributes are considered.To bolster the appeal of demand-responsive trains,personalized service product attributes are added.Mixed Logit mode,which takes into account the heterogeneous travel choice behavior of passengers,is developed,and Maximum Likelihood Estimation and the Monte Carlo method are used to calibrate model parameters.The willingness to pay in terms of different factors of passengers is determined.The results indicate that early arrival deviation time,late arrival deviation time,demand response time,and success rate of ticket purchase remarkable influence passengers’decision regarding demand-responsive train,with only the success rate of ticket purchase positively impacting train choice.Moreover,the significant difference in train ticket price is observed solely in the self-funded long distance scenario,while demand-responsive trains are found to be particularly appealing in self-funded short distance scenario.Through the Willingness To Pay(WTP)analysis,it is discovered that by shortening demand response time,enhancing the success rate of ticket purchase,and minimizing the deviation times of early arrival and late arrival of trains,the attractiveness of the demand-responsive train to passengers under three travel scenarios can be augmented.This study provides profound insights into the possibility of railway enterprises operating demand-responsive trains.展开更多
Bicycling has been actively promoted as a clean and efficient mode of commute.Besides,due to the personal and societal benefits it provides,it has been adopted by many city dwellers for short-distance trips.Despite th...Bicycling has been actively promoted as a clean and efficient mode of commute.Besides,due to the personal and societal benefits it provides,it has been adopted by many city dwellers for short-distance trips.Despite the integral role this active transport mode plays,it is unfortunately associated with a high risk of fatalities in the event of a traffic crash as they are not protected.Many studies have been conducted in several jurisdictions to examine the factors contributing to crashes involving these vulnerable road users.In the case of Louisiana which is currently experiencing increased cases of severe and fatal bicycleinvolved crashes,less attention has been paid to investigating the critical factors influencing bicyclist injury severity outcomes using more detailed data and advanced econometric modeling frameworks to help propose adequate policies to improve the safety of riders.Against this background,this study examined the key contributing factors influencing bicyclist injuries by using more detailed roadway crash data spanning 2010-2016 obtained from the state of Louisiana.The study then applies an advanced random parameter logit modeling with heterogeneity in means and variances to address the unobserved heterogeneity issue associated with traffic crash data.To overcome the imbalanced data issue,three major crash injury levels were used instead of the conventional five crash injury levels.Besides,the data groups classified under each injury level were compared for the final variable selection.The study found that distracted drivers,elderly bicyclists,careless operations,and riding in dark conditions increase the probability of having severe injuries in vehicle-bicyclist crashes.Moreover,the variables for straight-level roadways and city streets decrease the odds of severe injuries.The straight-level roadway may provide better sight distance for both drivers and bicyclists,and complex environments like city streets discourage crashes with severe injuries.展开更多
The aim of this study is to identify factors that affect injury severity levels of work zone rear-end crashes with high collision speeds(P35 miles per hour(mph,1 mph equals about 1.609344 km/h)).Using statewide crash ...The aim of this study is to identify factors that affect injury severity levels of work zone rear-end crashes with high collision speeds(P35 miles per hour(mph,1 mph equals about 1.609344 km/h)).Using statewide crash data provided by the South Carolina Department of Transportation from 2014 to 2020,a mixed binary logit model with heterogeneity in mean and variance is estimated.The model’s outcome variable is injury or non-injury(i.e.,property damage only),and the explanatory variables include information related to vehicle,collision,time,occupant,roadway,and environmental characteristics.The estima-tion results show that the interstate variable is best modeled as a random parameter at a 90%confidence level.Late-night and dawn/dusk conditions influence the mean effect,while driving under the influence affects the variance of the random parameter.Factors positively influencing injury severity include multi-vehicle involvement,airbag deploy-ment,dark conditions,and truck-involved crashes.Conversely,advanced warning area,activity area,lane shift/crossover,young and middle-aged drivers,and dawn/dusk condi-tions have negative effects on injury severity.展开更多
Understanding the temporal stability in the factors influencing drivers' injury severity in single-vehicle collisions would help evaluating the effectiveness of implementing different safety treatments so that res...Understanding the temporal stability in the factors influencing drivers' injury severity in single-vehicle collisions would help evaluating the effectiveness of implementing different safety treatments so that researchers could understand whether any safety improvements,observed after applying a certain safety treatment, are attributed to the specific treatment or simply attributed to the temporal instability of the factors being addressed. This study investigates the temporal stability of the factors affecting drivers' injury severity in singlevehicle collisions involving light-duty vehicles. The study is based on utilizing ordinal regression modeling to analyze the severity of drivers' injuries in all police-reported lightduty single-vehicle collisions that occurred in North Carolina from January 1, 2007, to December 31, 2013. A separate regression model was estimated for each year so that statistical significance of each risk factor may be compared over the years. The study also estimated random-parameter(mixed) ordered logit models to explore the heterogeneity in data. The most significant factor that was found to increase the severity of drivers' injuries in light-duty single-vehicle collisions is driving under the influence of alcohol or illicit drugs. Other significant factors, in decreasing order in terms of their significance, include driving on a highway curve, exceeding speed limit, lighting conditions, the age of the driver, and the age of the vehicle. In contrast, there were six factors that were found to be significant in only some years and not in all years. These six temporally unstable factors include the use of seatbelt, driver's gender, rural highways, undivided highways, the type of the light-duty vehicle, and weather and road surface conditions. These same factors were found by other previous research studies to be significant and stable predictors of drivers' injury severity in single-vehicle collisions.展开更多
The effect of sealed or unsealed road pavements on motorist’s injury severities has not been extensively explored.This study collected a four-year crash dataset(2015–2018)from South Australia to explore this issue.T...The effect of sealed or unsealed road pavements on motorist’s injury severities has not been extensively explored.This study collected a four-year crash dataset(2015–2018)from South Australia to explore this issue.The data shows 3,812 and 1,086 crashes at sealed and unsealed pavement surfaces,respectively,during those years.This study examines the consequence of sealed and unsealed pavements on driver injury severity outcomes of motor vehicle crashes.A mixed logit model was developed by accounting for heterogeneity in means and variances of the random parameters.The variables were distributed among several categories:driver,temporal,spatial,roadway characteristics,crash type,vehicle type,and vehicle movement.Four random parameters were observed in the sealed model,whereas five parameters were in the unsealed one.Moreover,the sealed pavements model showed substantial heterogeneity in means of four of the random parameters,while the unsealed pavements model has some heterogeneity in both means and variances of some of the random parameters.Marginal effect results indicate that two indicator variables have enlarged the likelihood of driver severe injury consequences in sealed,alcohol involvement and posted speed limit>100 km/hr.Additionally,four other significant variables sustain the probability of severe injury outcomes at unsealed pavement like male drivers,middle-aged drivers,rollover crash types,and crashes at straight roads.Based on these variables,various countermeasures were recommended to enhance the safety of both types of pavements.展开更多
This research explores the various factors influencing the severity of injuries motorcyclists sustain across different collision scenarios.The study considers the types of vehicles involved,including motorcycle(MC),ca...This research explores the various factors influencing the severity of injuries motorcyclists sustain across different collision scenarios.The study considers the types of vehicles involved,including motorcycle(MC),cars,pickup trucks,vans,and trucks.The study is grounded in an analysis of road crashes in Thailand from 2016 to 2019.Recognizing the unique characteristics inherent in each collision type,the study categorizes crashes into six distinct models for a comprehensive analysis.Each model is constructed using the random parameter logit with unobserved heterogeneity in means.Notably,all models incorporate random parameters,with the exception of the MC vs.truck model.Despite some consistent factors across most models,there are noteworthy variations in parameters when comparing different vehicle types.In the context of single-motorcycle crashes,speed limit violation emerges as a critical factor.For the MC vs.MC model,crashes happening from midnight to early morning are significant.The presence of a passenger(pillion)is a key determinant in the MC vs.car model.Meanwhile,in the MC vs.pickup truck model,crashes occurring under poor light conditions from midnight to early morning are of particular importance.The MC vs.van model notably highlights the involvement of male riders.Lastly,the MC vs.truck model draws attention to crashes happening on weekends.By creating specific crash models for diverse vehicle types,this study enhances our understanding of motorcycle crashes.The findings provide valuable insights to inform the development of policies,the design of safety campaigns,the creation of training programs,and the evaluation of road safety.展开更多
基金supported by the National Natural Science Foundation of China(No.72471023,71971019)the Fundamental Research Funds for the Central Universities(No.2024QYBS025).
文摘Demand-responsive transportation has been introduced in many cities around the world.However,whether it is applicable in the railway is still questionable,an exploration of passenger choice behavior between demandresponsive trains and pre-scheduled trains is pivotal in addressing this issue.To delve into passengers’choice preferences when facing demand-responsive trains and to dissect the feasibility of implementing demandresponsive service in high-speed railways,the stated preference survey method is employed to investigate travel intention of passengers.Based on the survey data obtained in China,the heterogeneity of passengers is analyzed from three aspects:personal socio-economic characteristics,travel characteristics,and travel mode choice.Considering the situation that demand-responsive train cannot operate,the risk attributes are considered.To bolster the appeal of demand-responsive trains,personalized service product attributes are added.Mixed Logit mode,which takes into account the heterogeneous travel choice behavior of passengers,is developed,and Maximum Likelihood Estimation and the Monte Carlo method are used to calibrate model parameters.The willingness to pay in terms of different factors of passengers is determined.The results indicate that early arrival deviation time,late arrival deviation time,demand response time,and success rate of ticket purchase remarkable influence passengers’decision regarding demand-responsive train,with only the success rate of ticket purchase positively impacting train choice.Moreover,the significant difference in train ticket price is observed solely in the self-funded long distance scenario,while demand-responsive trains are found to be particularly appealing in self-funded short distance scenario.Through the Willingness To Pay(WTP)analysis,it is discovered that by shortening demand response time,enhancing the success rate of ticket purchase,and minimizing the deviation times of early arrival and late arrival of trains,the attractiveness of the demand-responsive train to passengers under three travel scenarios can be augmented.This study provides profound insights into the possibility of railway enterprises operating demand-responsive trains.
文摘Bicycling has been actively promoted as a clean and efficient mode of commute.Besides,due to the personal and societal benefits it provides,it has been adopted by many city dwellers for short-distance trips.Despite the integral role this active transport mode plays,it is unfortunately associated with a high risk of fatalities in the event of a traffic crash as they are not protected.Many studies have been conducted in several jurisdictions to examine the factors contributing to crashes involving these vulnerable road users.In the case of Louisiana which is currently experiencing increased cases of severe and fatal bicycleinvolved crashes,less attention has been paid to investigating the critical factors influencing bicyclist injury severity outcomes using more detailed data and advanced econometric modeling frameworks to help propose adequate policies to improve the safety of riders.Against this background,this study examined the key contributing factors influencing bicyclist injuries by using more detailed roadway crash data spanning 2010-2016 obtained from the state of Louisiana.The study then applies an advanced random parameter logit modeling with heterogeneity in means and variances to address the unobserved heterogeneity issue associated with traffic crash data.To overcome the imbalanced data issue,three major crash injury levels were used instead of the conventional five crash injury levels.Besides,the data groups classified under each injury level were compared for the final variable selection.The study found that distracted drivers,elderly bicyclists,careless operations,and riding in dark conditions increase the probability of having severe injuries in vehicle-bicyclist crashes.Moreover,the variables for straight-level roadways and city streets decrease the odds of severe injuries.The straight-level roadway may provide better sight distance for both drivers and bicyclists,and complex environments like city streets discourage crashes with severe injuries.
文摘The aim of this study is to identify factors that affect injury severity levels of work zone rear-end crashes with high collision speeds(P35 miles per hour(mph,1 mph equals about 1.609344 km/h)).Using statewide crash data provided by the South Carolina Department of Transportation from 2014 to 2020,a mixed binary logit model with heterogeneity in mean and variance is estimated.The model’s outcome variable is injury or non-injury(i.e.,property damage only),and the explanatory variables include information related to vehicle,collision,time,occupant,roadway,and environmental characteristics.The estima-tion results show that the interstate variable is best modeled as a random parameter at a 90%confidence level.Late-night and dawn/dusk conditions influence the mean effect,while driving under the influence affects the variance of the random parameter.Factors positively influencing injury severity include multi-vehicle involvement,airbag deploy-ment,dark conditions,and truck-involved crashes.Conversely,advanced warning area,activity area,lane shift/crossover,young and middle-aged drivers,and dawn/dusk condi-tions have negative effects on injury severity.
基金financially supported by a Science and Engineering Research Grant provided by the Emirates Foundation
文摘Understanding the temporal stability in the factors influencing drivers' injury severity in single-vehicle collisions would help evaluating the effectiveness of implementing different safety treatments so that researchers could understand whether any safety improvements,observed after applying a certain safety treatment, are attributed to the specific treatment or simply attributed to the temporal instability of the factors being addressed. This study investigates the temporal stability of the factors affecting drivers' injury severity in singlevehicle collisions involving light-duty vehicles. The study is based on utilizing ordinal regression modeling to analyze the severity of drivers' injuries in all police-reported lightduty single-vehicle collisions that occurred in North Carolina from January 1, 2007, to December 31, 2013. A separate regression model was estimated for each year so that statistical significance of each risk factor may be compared over the years. The study also estimated random-parameter(mixed) ordered logit models to explore the heterogeneity in data. The most significant factor that was found to increase the severity of drivers' injuries in light-duty single-vehicle collisions is driving under the influence of alcohol or illicit drugs. Other significant factors, in decreasing order in terms of their significance, include driving on a highway curve, exceeding speed limit, lighting conditions, the age of the driver, and the age of the vehicle. In contrast, there were six factors that were found to be significant in only some years and not in all years. These six temporally unstable factors include the use of seatbelt, driver's gender, rural highways, undivided highways, the type of the light-duty vehicle, and weather and road surface conditions. These same factors were found by other previous research studies to be significant and stable predictors of drivers' injury severity in single-vehicle collisions.
文摘The effect of sealed or unsealed road pavements on motorist’s injury severities has not been extensively explored.This study collected a four-year crash dataset(2015–2018)from South Australia to explore this issue.The data shows 3,812 and 1,086 crashes at sealed and unsealed pavement surfaces,respectively,during those years.This study examines the consequence of sealed and unsealed pavements on driver injury severity outcomes of motor vehicle crashes.A mixed logit model was developed by accounting for heterogeneity in means and variances of the random parameters.The variables were distributed among several categories:driver,temporal,spatial,roadway characteristics,crash type,vehicle type,and vehicle movement.Four random parameters were observed in the sealed model,whereas five parameters were in the unsealed one.Moreover,the sealed pavements model showed substantial heterogeneity in means of four of the random parameters,while the unsealed pavements model has some heterogeneity in both means and variances of some of the random parameters.Marginal effect results indicate that two indicator variables have enlarged the likelihood of driver severe injury consequences in sealed,alcohol involvement and posted speed limit>100 km/hr.Additionally,four other significant variables sustain the probability of severe injury outcomes at unsealed pavement like male drivers,middle-aged drivers,rollover crash types,and crashes at straight roads.Based on these variables,various countermeasures were recommended to enhance the safety of both types of pavements.
基金Suranaree University of Technology(SUT)Thailand Science Research and Innovation(TSRI)National Science,Research and Innovation Fund(NRSF)(Grant number:Full-time 61/14/2565)。
文摘This research explores the various factors influencing the severity of injuries motorcyclists sustain across different collision scenarios.The study considers the types of vehicles involved,including motorcycle(MC),cars,pickup trucks,vans,and trucks.The study is grounded in an analysis of road crashes in Thailand from 2016 to 2019.Recognizing the unique characteristics inherent in each collision type,the study categorizes crashes into six distinct models for a comprehensive analysis.Each model is constructed using the random parameter logit with unobserved heterogeneity in means.Notably,all models incorporate random parameters,with the exception of the MC vs.truck model.Despite some consistent factors across most models,there are noteworthy variations in parameters when comparing different vehicle types.In the context of single-motorcycle crashes,speed limit violation emerges as a critical factor.For the MC vs.MC model,crashes happening from midnight to early morning are significant.The presence of a passenger(pillion)is a key determinant in the MC vs.car model.Meanwhile,in the MC vs.pickup truck model,crashes occurring under poor light conditions from midnight to early morning are of particular importance.The MC vs.van model notably highlights the involvement of male riders.Lastly,the MC vs.truck model draws attention to crashes happening on weekends.By creating specific crash models for diverse vehicle types,this study enhances our understanding of motorcycle crashes.The findings provide valuable insights to inform the development of policies,the design of safety campaigns,the creation of training programs,and the evaluation of road safety.
