In view of the problem that the requirements of travel demand management and traffic policy-sensitivity are ignored during the establishment process of the travel demand forecasting model, a discrete-choice-based trav...In view of the problem that the requirements of travel demand management and traffic policy-sensitivity are ignored during the establishment process of the travel demand forecasting model, a discrete-choice-based travel demand forecasting model is proposed to demonstrate its applicability to travel demand management. A car-bus discrete choice model is established, including three variables, i. e,, individual socioeconomic characteristics, time, and cost, and the traffic policy-sensitivity is evaluated through two kinds of traffic policies： parking charges and bus priorities. The empirical results show that travel choice is insensitive to the policy of parking charges as 88. 41% of the travelers are insensitive to parking charges; travel choice is, however, sensitive to the policy of bus priorities as 67.70% of the car travelers and 77.02% of the bus travelers are sensitive to bus priorities. The discrete-choice-based travel demand forecasting model is quite policy-sensitive and also has a good adaptability for travel demand management when meeting the basic functions of the demand forecasting model.展开更多
Among the fast growing states in the USA,the States of Washington and Oregon have enacted legislative land use and transportation concurrency/balancing planning policies for orderly urban growth management since 1990 ...Among the fast growing states in the USA,the States of Washington and Oregon have enacted legislative land use and transportation concurrency/balancing planning policies for orderly urban growth management since 1990 and 1991,respectively.Regional or urban travel demand forecasting models play an instrumental role in implementing the Washington GMA(Growth Management Act)and the Oregon TPR(Transportation Planning Rule).Both program-and project-level modeling approaches to urban land use/transportation system management are evaluated through the selected cities in Washington and Oregon.展开更多
Most current Travel Demand Management(TDM)programs such as vanpooling,ridesharing,or transit focus on managing travel demand of specific groups of commuters but are limited in effectively managing demand for automobil...Most current Travel Demand Management(TDM)programs such as vanpooling,ridesharing,or transit focus on managing travel demand of specific groups of commuters but are limited in effectively managing demand for automobile drivers,who are unable or unwilling to participate in such programs.This paper highlights results from a pilot field study conducted in a large west coast city experiencing major traffic congestion,and documents results of the use of an incentive-based active demand management(ADM)system focusing on automobile commuters.The system,called“Metropia,”predicts future traffic conditions,applies a proprietary routing algorithm to find time-dependent shortest paths for different departure times,and,based on user request,provides automobile travelers with multiple departure times and route choices.Each of these travel choices are assigned points values,with higher points(and thus more valuable rewards)available for travelling during off-peak times and less congested routes,and lower points available for peak traffic travel times.The goal of this ADM system is to improve traffic flow and commuter travel times citywide,alleviating heavily congested areas without the use of new roadway construction by incentivizing travelers to change their travel behavior and avoid traffic congestion.The level of rewards points available to users(commuters)by the system depends on the travelers’behavioral change degree and their contributions to traffic congestion alleviation.This system was implemented in Los Angeles,Calif.,USA,as a small scale pilot field study carried out beginning April 2013 and lasting for 10 weeks.Results from this field study show the system is able to accurately predict travel time with Relative Mean Absolute Error(RMAE)as low as 15.20%.Significant travel behavior changes were observed which validate the concept of using incentives to influence people’s travel behavior.Furthermore,field study results show 20%travel time can be saved for people who changed their travel behavior.展开更多
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
文摘In view of the problem that the requirements of travel demand management and traffic policy-sensitivity are ignored during the establishment process of the travel demand forecasting model, a discrete-choice-based travel demand forecasting model is proposed to demonstrate its applicability to travel demand management. A car-bus discrete choice model is established, including three variables, i. e,, individual socioeconomic characteristics, time, and cost, and the traffic policy-sensitivity is evaluated through two kinds of traffic policies： parking charges and bus priorities. The empirical results show that travel choice is insensitive to the policy of parking charges as 88. 41% of the travelers are insensitive to parking charges; travel choice is, however, sensitive to the policy of bus priorities as 67.70% of the car travelers and 77.02% of the bus travelers are sensitive to bus priorities. The discrete-choice-based travel demand forecasting model is quite policy-sensitive and also has a good adaptability for travel demand management when meeting the basic functions of the demand forecasting model.
文摘Among the fast growing states in the USA,the States of Washington and Oregon have enacted legislative land use and transportation concurrency/balancing planning policies for orderly urban growth management since 1990 and 1991,respectively.Regional or urban travel demand forecasting models play an instrumental role in implementing the Washington GMA(Growth Management Act)and the Oregon TPR(Transportation Planning Rule).Both program-and project-level modeling approaches to urban land use/transportation system management are evaluated through the selected cities in Washington and Oregon.
文摘Most current Travel Demand Management(TDM)programs such as vanpooling,ridesharing,or transit focus on managing travel demand of specific groups of commuters but are limited in effectively managing demand for automobile drivers,who are unable or unwilling to participate in such programs.This paper highlights results from a pilot field study conducted in a large west coast city experiencing major traffic congestion,and documents results of the use of an incentive-based active demand management(ADM)system focusing on automobile commuters.The system,called“Metropia,”predicts future traffic conditions,applies a proprietary routing algorithm to find time-dependent shortest paths for different departure times,and,based on user request,provides automobile travelers with multiple departure times and route choices.Each of these travel choices are assigned points values,with higher points(and thus more valuable rewards)available for travelling during off-peak times and less congested routes,and lower points available for peak traffic travel times.The goal of this ADM system is to improve traffic flow and commuter travel times citywide,alleviating heavily congested areas without the use of new roadway construction by incentivizing travelers to change their travel behavior and avoid traffic congestion.The level of rewards points available to users(commuters)by the system depends on the travelers’behavioral change degree and their contributions to traffic congestion alleviation.This system was implemented in Los Angeles,Calif.,USA,as a small scale pilot field study carried out beginning April 2013 and lasting for 10 weeks.Results from this field study show the system is able to accurately predict travel time with Relative Mean Absolute Error(RMAE)as low as 15.20%.Significant travel behavior changes were observed which validate the concept of using incentives to influence people’s travel behavior.Furthermore,field study results show 20%travel time can be saved for people who changed their travel behavior.
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.
文摘奥运期间小汽车限行政策的实施,有效缓解了北京道路交通拥堵.本文通过措施实施前后收集的居民入户调查数据,定量分析了限行措施对居民出行特征的影响.研究发现:限行政策实施后,居民总体出行率由1.88次/日降低到1.70次/日,上下学、接送人和工作外出等出行比例明显降低;私人机动车出行强度降低0.07次/(天.辆),乘载率提高0.10人/车;公务车出行强度增加0.70次/(天.辆),乘载率提高0.32人/车;地铁、公交和小汽车出行时耗分别缩短了12 min,8 min和8 min.
