The fund budget of multipurpose transit smart card systems is studied by stochastic programming to assign limited funds to different applications reasonably. Under the constraints of a gross fund, models of chance-con...The fund budget of multipurpose transit smart card systems is studied by stochastic programming to assign limited funds to different applications reasonably. Under the constraints of a gross fund, models of chance-constrained and dependentchance for the fund budget of multipurpose transit smart card systems are established with application scale and social demand as random variables, respectively aiming to maximize earnings and satisfy the service requirements the furthest; and the genetic algorithm based on stochastic simulation is adopted for model solution. The calculation results show that the fund budget differs greatly with different system objectives which can cause the systems to have distinct expansibilities, and the application scales of some applications may not satisfy user demands with limited funds. The analysis results indicate that the forecast of application scales and application future demands should be done first, and then the system objective is determined according to the system mission, which can help reduce the risks of fund budgets.展开更多
The automated fare collection(AFC) system,also known as the transit smart card(SC) system,has gained more and more popularity among transit agencies worldwide.Compared with the conventional manual fare collection syst...The automated fare collection(AFC) system,also known as the transit smart card(SC) system,has gained more and more popularity among transit agencies worldwide.Compared with the conventional manual fare collection system,an AFC system has its inherent advantages in low labor cost and high efficiency for fare collection and transaction data archival.Although it is possible to collect highly valuable data from transit SC transactions,substantial efforts and methodologies are needed for extracting such data because most AFC systems are not initially designed for data collection.This is true especially for the Beijing AFC system,where a passenger's boarding stop(origin) on a flat-rate bus is not recorded on the check-in scan.To extract passengers' origin data from recorded SC transaction information,a Markov chain based Bayesian decision tree algorithm is developed in this study.Using the time invariance property of the Markov chain,the algorithm is further optimized and simplified to have a linear computational complexity.This algorithm is verified with transit vehicles equipped with global positioning system(GPS) data loggers.Our verification results demonstrated that the proposed algorithm is effective in extracting transit passengers' origin information from SC transactions with a relatively high accuracy.Such transit origin data are highly valuable for transit system planning and route optimization.展开更多
The smart card-based automated fare collection （AFC） system has become the main method for collecting urban bus and rail transit fares in many cities worldwide. Such smart card technologies provide new opportunities...The smart card-based automated fare collection （AFC） system has become the main method for collecting urban bus and rail transit fares in many cities worldwide. Such smart card technologies provide new opportunities for transportation data collection since the transaction data obtained through AFC system contains a significant amount of archived information which can be gathered and leveraged to help estimate public transit origin–destination matrices. Boarding location detection is an important step particularly when there is no automatic vehicle location （AVL） system or GPS information in the database in some cases. With the analysis of raw data without AVL information in this paper, an algorithm for trip direction detection is built and the directions for any bus in operation can be confirmed. The transaction interval between each adjacent record will also be analyzed to detect the boarding clusters for all trips in sequence. Boarding stops will then be distributed with the help of route information and operation schedules. Finally, the feasibility and practicality of the methodology are tested using the bus transit smart card data collected in Guangzhou, China.展开更多
尽管基于城市轨道交通自动售检票(automatic fare collection,AFC)系统采集的智能卡数据(smart card data,SCD)能够精准记录人们的出行时间和地点,但无法直接反映出行目的或活动类型.本研究提出一种方法,将约束种子K-means算法的站点聚...尽管基于城市轨道交通自动售检票(automatic fare collection,AFC)系统采集的智能卡数据(smart card data,SCD)能够精准记录人们的出行时间和地点,但无法直接反映出行目的或活动类型.本研究提出一种方法,将约束种子K-means算法的站点聚类与隐含狄利克雷分布(latent Dirichlet allocation,LDA)模型的客流出行目的挖掘相结合,以揭示城市轨道交通客流出行数据中的潜在活动模式.首先,基于车站周边的人口特征、客流特征及兴趣点(points of interest,POI)分布,使用约束种子K-means算法将站点划分为8类:就业集聚型、居住集聚型、职住复合型、商业中心型、旅游景点型、综合枢纽型、对外枢纽型以及客流培育型.其次,基于出站时间、活动时长、起点车站类型以及终点车站类型构建了LDA模型.该模型成功识别出5类主要活动,分别为购物消费、工作、回家、休闲旅游及其他.此外,这些模式进一步细分为若干子主题,每个子主题在时间和空间特征上具有显著差异,为深入理解节假日城市轨道交通客流出行行为提供了理论支持.展开更多
基金The Key Technology R& D Program of Jiangsu Scienceand Technology Department(No.BE2006010)the Key Technology R& DProgram of Nanjing Science and Technology Bureau(No.200601001)Sci-ence and Technology Research Projects of Nanjing Metro Headquarters(No.8550143007).
文摘The fund budget of multipurpose transit smart card systems is studied by stochastic programming to assign limited funds to different applications reasonably. Under the constraints of a gross fund, models of chance-constrained and dependentchance for the fund budget of multipurpose transit smart card systems are established with application scale and social demand as random variables, respectively aiming to maximize earnings and satisfy the service requirements the furthest; and the genetic algorithm based on stochastic simulation is adopted for model solution. The calculation results show that the fund budget differs greatly with different system objectives which can cause the systems to have distinct expansibilities, and the application scales of some applications may not satisfy user demands with limited funds. The analysis results indicate that the forecast of application scales and application future demands should be done first, and then the system objective is determined according to the system mission, which can help reduce the risks of fund budgets.
基金Project supported by the National Natural Science Foundation of China (No. 51138003)the Beijing Transportation Research Center (BTRC),China
文摘The automated fare collection(AFC) system,also known as the transit smart card(SC) system,has gained more and more popularity among transit agencies worldwide.Compared with the conventional manual fare collection system,an AFC system has its inherent advantages in low labor cost and high efficiency for fare collection and transaction data archival.Although it is possible to collect highly valuable data from transit SC transactions,substantial efforts and methodologies are needed for extracting such data because most AFC systems are not initially designed for data collection.This is true especially for the Beijing AFC system,where a passenger's boarding stop(origin) on a flat-rate bus is not recorded on the check-in scan.To extract passengers' origin data from recorded SC transaction information,a Markov chain based Bayesian decision tree algorithm is developed in this study.Using the time invariance property of the Markov chain,the algorithm is further optimized and simplified to have a linear computational complexity.This algorithm is verified with transit vehicles equipped with global positioning system(GPS) data loggers.Our verification results demonstrated that the proposed algorithm is effective in extracting transit passengers' origin information from SC transactions with a relatively high accuracy.Such transit origin data are highly valuable for transit system planning and route optimization.
基金The United States Department of Transportation, University Transportation Center through the Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE) at The University of North Carolina at Charlotte (Grant Number: 69A3551747133) for sponsoring this research project entitled ‘estimation of origin–destination matrix and identification of user activities using public transit smart card data’
文摘The smart card-based automated fare collection （AFC） system has become the main method for collecting urban bus and rail transit fares in many cities worldwide. Such smart card technologies provide new opportunities for transportation data collection since the transaction data obtained through AFC system contains a significant amount of archived information which can be gathered and leveraged to help estimate public transit origin–destination matrices. Boarding location detection is an important step particularly when there is no automatic vehicle location （AVL） system or GPS information in the database in some cases. With the analysis of raw data without AVL information in this paper, an algorithm for trip direction detection is built and the directions for any bus in operation can be confirmed. The transaction interval between each adjacent record will also be analyzed to detect the boarding clusters for all trips in sequence. Boarding stops will then be distributed with the help of route information and operation schedules. Finally, the feasibility and practicality of the methodology are tested using the bus transit smart card data collected in Guangzhou, China.
文摘尽管基于城市轨道交通自动售检票(automatic fare collection,AFC)系统采集的智能卡数据(smart card data,SCD)能够精准记录人们的出行时间和地点,但无法直接反映出行目的或活动类型.本研究提出一种方法,将约束种子K-means算法的站点聚类与隐含狄利克雷分布(latent Dirichlet allocation,LDA)模型的客流出行目的挖掘相结合,以揭示城市轨道交通客流出行数据中的潜在活动模式.首先,基于车站周边的人口特征、客流特征及兴趣点(points of interest,POI)分布,使用约束种子K-means算法将站点划分为8类:就业集聚型、居住集聚型、职住复合型、商业中心型、旅游景点型、综合枢纽型、对外枢纽型以及客流培育型.其次,基于出站时间、活动时长、起点车站类型以及终点车站类型构建了LDA模型.该模型成功识别出5类主要活动,分别为购物消费、工作、回家、休闲旅游及其他.此外,这些模式进一步细分为若干子主题,每个子主题在时间和空间特征上具有显著差异,为深入理解节假日城市轨道交通客流出行行为提供了理论支持.
