Traffic congestion has become a critical issue in developing countries,as it tends to increase social costs in terms of travel cost and time,energy consumption and environmental degradation.With limited resources,redu...Traffic congestion has become a critical issue in developing countries,as it tends to increase social costs in terms of travel cost and time,energy consumption and environmental degradation.With limited resources,reducing travel demand by influencing individuals’ travel behavior can be a better long-term solution.To achieve this objective,alternate travel options need to be provided so that people can commute comfortably and economically.This study aims to identify key motives and constraints in the consideration of carpooling policy with the help of stated preference questionnaire survey that was conducted in Lahore City.The designed questionnaire includes respondents’ socioeconomic demographics,and intentions and stated preferences on carpooling policy.Factor analysis was conducted on travelers’ responses,and a structural model was developed for carpooling.Survey and modeling results reveal that social,environmental and economic benefits,disincentives on car use,preferential parking treatment for carpooling,and comfort and convenience attributes are significant determinants in promoting carpooling.However,people with strong belief in personal privacy,security,freedom in traveling and carpooling service constraints would have less potential to use thecarpooling service.In addition,pro-auto and pro-carpooling attitudes,marital status,profession and travel purpose for carpooling are also underlying factors.The findings implicate that to promote carpooling policy it is required to consider appropriate incentives on this service and disincentives on use of private vehicle along with modification of people’s attitudes and intentions.展开更多
Pedestrian level of service(PLOS)is an important measure of performance in the analysis of existing pedestrian crosswalk conditions.Many researchers have developed PLOS models based on pedestrian delay,turning vehicle...Pedestrian level of service(PLOS)is an important measure of performance in the analysis of existing pedestrian crosswalk conditions.Many researchers have developed PLOS models based on pedestrian delay,turning vehicle effect,etc.,using the conventional regression method.However,these factors may not effectively reflect the pedestrians'perception of safety while crossing the crosswalk.The conventional regression method has failed to estimate accurate PLOS because of the primary assumption of an arbitrary probability distribution and vagueness in the input data.Moreover,PLOS categories in existing studies are based on rigid threshold values and the boundaries that are not well defined.Therefore,it is an important attempt to develop a PLOS model with respect to pedestrian safety,convenience,and efficiency at signalized intersections.For this purpose,a video-graphic and user perception surveys were conducted at selected nine signalized intersections in Mumbai,India.The data such as pedestrian,traffic,and geometric characteristics were extracted,and significant variables were identified using Pearson correlation analysis.A consistent and statistically calibrated PLOS model was developed using fuzzy linear regression analysis.PLOS was categorized into six levels(A–F)based on the predicted user perception score,and threshold values for each level were estimated using the fuzzy c-means clustering technique.The developed PLOS model and threshold values were validated with the fieldobserved data.Statistical performance tests were conducted and the results provided more accurate and reliable solutions.In conclusion,this study provides a feasible alternative to measure pedestrian perception-based level of service at signalized intersections.The developed PLOS model and threshold values would be useful for planning and designing pedestrian facilities and also in evaluating and improving the existing conditions of pedestrian facilities at signalized intersections.展开更多
Rapid transit(RT)systems are becoming increasingly attractive in the developing world as they improve transportation and mobility conditions in urban areas,reduce motorization impacts and offer high quality,yet cost e...Rapid transit(RT)systems are becoming increasingly attractive in the developing world as they improve transportation and mobility conditions in urban areas,reduce motorization impacts and offer high quality,yet cost effective services to travelers.Light rail transit(LRT)and bus rapid transit(BRT)are RT systems that combine high capacity with relatively low investment costs,and as such,they are preferred in developing countries over regular metro systems.This paper investigates traveler preferences over alternative,planned rapid transit options for the city of Multan,Pakistan.The analysis is based on a household information survey with over 2300 questionnaires completed via personal interviews.Intention to pay for improved PT services and choice between LRT and BRT systems are investigated,using appropriate econometric models.Findings of this study can assist in better understanding the factors and their effect on choice between BRT and LRT in developing countries.Results show that potential travelers,who prefer LRT are willing to pay more for better public transport services.On the other hand,commuters and elders express a taste towards BRT implementation.Based on model outputs policy makers can develop targeted marketing policies in order to promote BRT/LRT implementation andattract candidate travelers from different groups,improving the possibility that users would support a BRT or LRT project.展开更多
Speeding is one of the most common aberrant driving behaviors among the driving population.Although research on speeding behavior among drivers has increased over the decades,little is known about the motivating facto...Speeding is one of the most common aberrant driving behaviors among the driving population.Although research on speeding behavior among drivers has increased over the decades,little is known about the motivating factors associated with speeding behavior among long-haul truck drivers(LHTDs),especially in developing nations like India.This study aims to develop a prediction model for speeding behavior and to identify the contributory factors and their influential patterns underlying speeding behavior among LHTDs in India.A cross-sectional study was conducted among LHTDs in Salem City,Tamil Nadu,India.The data were collected through face-to-face interviews using a questionnaire encompassing socio-demographic,work,vehicle,health-related lifestyle,and speeding-related characteristics.A total of 756 valid samples were collected and utilized for analysis purposes.While conventional statistical methods like binary logit technique lacked prediction capabilities,machine learning(ML)algorithms including decision tree(DT),random forest(RF),adaptive boosting(AdaBoost),and extreme gradient boosting(XGBoost)were employed to model speeding behavior among LHTDs.The analysis results showed that RF demonstrated superior performance in predicting speeding behavior over other competing algorithms with accuracy(0.80),F1 score(0.77),and AUROC(0.81).From the befitting RF model,the importance of factors contributing to speeding behavior among LHTDs was determined through the variable importance plot.Pressured delivery of goods,sleeping duration per day,age of truck,size of truck,monthly income,driving experience,driving duration per day,and age of the driver were identified as the eight topmost critical factors contributing to speeding behavior among LHTDs.Based on the developed RF model,the hidden relationships behind identified critical factors in relation to the speeding behavior were investigated using partial dependence plots(PDPs).The outcomes of this research will be useful for road safety authorities and Indian trucking industries to frame suitable policies and to introduce effective strategies for mitigating speeding behavior among LHTDs to promote road safety.展开更多
基金conducted at University of Engineering and Technology Lahore with support of Department of Transportation Engineering and Management Department
文摘Traffic congestion has become a critical issue in developing countries,as it tends to increase social costs in terms of travel cost and time,energy consumption and environmental degradation.With limited resources,reducing travel demand by influencing individuals’ travel behavior can be a better long-term solution.To achieve this objective,alternate travel options need to be provided so that people can commute comfortably and economically.This study aims to identify key motives and constraints in the consideration of carpooling policy with the help of stated preference questionnaire survey that was conducted in Lahore City.The designed questionnaire includes respondents’ socioeconomic demographics,and intentions and stated preferences on carpooling policy.Factor analysis was conducted on travelers’ responses,and a structural model was developed for carpooling.Survey and modeling results reveal that social,environmental and economic benefits,disincentives on car use,preferential parking treatment for carpooling,and comfort and convenience attributes are significant determinants in promoting carpooling.However,people with strong belief in personal privacy,security,freedom in traveling and carpooling service constraints would have less potential to use thecarpooling service.In addition,pro-auto and pro-carpooling attitudes,marital status,profession and travel purpose for carpooling are also underlying factors.The findings implicate that to promote carpooling policy it is required to consider appropriate incentives on this service and disincentives on use of private vehicle along with modification of people’s attitudes and intentions.
文摘Pedestrian level of service(PLOS)is an important measure of performance in the analysis of existing pedestrian crosswalk conditions.Many researchers have developed PLOS models based on pedestrian delay,turning vehicle effect,etc.,using the conventional regression method.However,these factors may not effectively reflect the pedestrians'perception of safety while crossing the crosswalk.The conventional regression method has failed to estimate accurate PLOS because of the primary assumption of an arbitrary probability distribution and vagueness in the input data.Moreover,PLOS categories in existing studies are based on rigid threshold values and the boundaries that are not well defined.Therefore,it is an important attempt to develop a PLOS model with respect to pedestrian safety,convenience,and efficiency at signalized intersections.For this purpose,a video-graphic and user perception surveys were conducted at selected nine signalized intersections in Mumbai,India.The data such as pedestrian,traffic,and geometric characteristics were extracted,and significant variables were identified using Pearson correlation analysis.A consistent and statistically calibrated PLOS model was developed using fuzzy linear regression analysis.PLOS was categorized into six levels(A–F)based on the predicted user perception score,and threshold values for each level were estimated using the fuzzy c-means clustering technique.The developed PLOS model and threshold values were validated with the fieldobserved data.Statistical performance tests were conducted and the results provided more accurate and reliable solutions.In conclusion,this study provides a feasible alternative to measure pedestrian perception-based level of service at signalized intersections.The developed PLOS model and threshold values would be useful for planning and designing pedestrian facilities and also in evaluating and improving the existing conditions of pedestrian facilities at signalized intersections.
基金part of a consultancy project funded by the Government of Punjab and the Punjab Metrobus Authority(www.pma.punjab.gov.pk)。
文摘Rapid transit(RT)systems are becoming increasingly attractive in the developing world as they improve transportation and mobility conditions in urban areas,reduce motorization impacts and offer high quality,yet cost effective services to travelers.Light rail transit(LRT)and bus rapid transit(BRT)are RT systems that combine high capacity with relatively low investment costs,and as such,they are preferred in developing countries over regular metro systems.This paper investigates traveler preferences over alternative,planned rapid transit options for the city of Multan,Pakistan.The analysis is based on a household information survey with over 2300 questionnaires completed via personal interviews.Intention to pay for improved PT services and choice between LRT and BRT systems are investigated,using appropriate econometric models.Findings of this study can assist in better understanding the factors and their effect on choice between BRT and LRT in developing countries.Results show that potential travelers,who prefer LRT are willing to pay more for better public transport services.On the other hand,commuters and elders express a taste towards BRT implementation.Based on model outputs policy makers can develop targeted marketing policies in order to promote BRT/LRT implementation andattract candidate travelers from different groups,improving the possibility that users would support a BRT or LRT project.
文摘Speeding is one of the most common aberrant driving behaviors among the driving population.Although research on speeding behavior among drivers has increased over the decades,little is known about the motivating factors associated with speeding behavior among long-haul truck drivers(LHTDs),especially in developing nations like India.This study aims to develop a prediction model for speeding behavior and to identify the contributory factors and their influential patterns underlying speeding behavior among LHTDs in India.A cross-sectional study was conducted among LHTDs in Salem City,Tamil Nadu,India.The data were collected through face-to-face interviews using a questionnaire encompassing socio-demographic,work,vehicle,health-related lifestyle,and speeding-related characteristics.A total of 756 valid samples were collected and utilized for analysis purposes.While conventional statistical methods like binary logit technique lacked prediction capabilities,machine learning(ML)algorithms including decision tree(DT),random forest(RF),adaptive boosting(AdaBoost),and extreme gradient boosting(XGBoost)were employed to model speeding behavior among LHTDs.The analysis results showed that RF demonstrated superior performance in predicting speeding behavior over other competing algorithms with accuracy(0.80),F1 score(0.77),and AUROC(0.81).From the befitting RF model,the importance of factors contributing to speeding behavior among LHTDs was determined through the variable importance plot.Pressured delivery of goods,sleeping duration per day,age of truck,size of truck,monthly income,driving experience,driving duration per day,and age of the driver were identified as the eight topmost critical factors contributing to speeding behavior among LHTDs.Based on the developed RF model,the hidden relationships behind identified critical factors in relation to the speeding behavior were investigated using partial dependence plots(PDPs).The outcomes of this research will be useful for road safety authorities and Indian trucking industries to frame suitable policies and to introduce effective strategies for mitigating speeding behavior among LHTDs to promote road safety.
