Analysis of passenger boarding time difference between adults and seniors based on smart card data

As an essential component of bus dwelling time, passenger boarding time has a significant impact on bus running reliability and service quality. In order to understand the passengers’ boarding process and mitigate passenger boarding time, a regression analysis framework is proposed to capture the difference and influential factors of boarding time for adult and elderly passengers based on smart card data from Changzhou. Boarding gap, the time difference between two consecutive smart card tapping records, is calculated to approximate passenger boarding time. Analysis of variance is applied to identify whether the difference in boarding time between adults and seniors is statistically significant. The multivariate regression modeling approach is implemented to analyze the influences of passenger types, marginal effects of each additional boarding passenger and bus floor types on the total boarding time at each stop. Results show that a constant difference exists in boarding time between adults and seniors even without considering the specific bus characteristics. The average passenger boarding time decreases when the number of passenger increases. The existence of two entrance steps delays the boarding process, especially for elderly passengers.

A high-precision heuristic model to detect home and work locations from smart card data

Smart card-automated fare collection systems now routinely record large volumes of data comprising the origins and destinations of travelers.Processing and analyzing these data open new opportunities in urban modeling and travel behavior research.This study seeks to develop an accurate framework for the study of urban mobility from smart card data by developing a heuristic primary location model to identify the home and work locations.The model uses journey counts as an indicator of usage regularity,visit-frequency to identify activity locations for regular commuters,and stay-time for the classification of work and home locations and activities.London is taken as a case study,and the model results were validated against survey data from the London Travel Demand Survey and volunteer survey.Results demonstrate that the proposed model is able to detect meaningful home and work places with high precision.This study offers a new and cost-effective approach to travel behavior and demand research.

ActivityNET:Neural networks to predict public transport trip purposes from individual smart card data and POIs

Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility.Here,we propose a framework,ActivityNET,using Machine Learning(ML)algorithms to predict passengers’trip purpose from Smart Card(SC)data and Points-of-Interest(POIs)data.The feasibility of the framework is demonstrated in two phases.Phase I focuses on extracting activities from individuals’daily travel patterns from smart card data and combining them with POIs using the proposed“activity-POIs consolidation algorithm”.Phase II feeds the extracted features into an Artificial Neural Network(ANN)with multiple scenarios and predicts trip purpose under primary activities(home and work)and secondary activities(entertainment,eating,shopping,child drop-offs/pick-ups and part-time work)with high accuracy.As a case study,the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose.The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.

Machine identification method of subway service quality based on smart card data

A new automatic evaluationmethod of subway service quality based onmetro smart card data is proposed suitable for three different levels:station pair,railway line and subway network,which has merits of overcoming the previous lagging and subjective evaluation in the system of‘questionnaire survey plus evaluationmethod’.First,passengers’travel time distribution for different operating periods in station OD pairs are introduced initially for service evaluation purposes and are classified into different groups in order to infer the station’s operating characteristics at the different periods.Second,the classification is verified by K-means cluster analysis and K-S tests.Third,the service quality weight indicator is proposed to identify the service quality of the entire metro network from the dual perspectives of passengers and companies.Finally,the feasibility and rationality of the proposed method are verified by Shenzhen metro smart card data as an example.The new automated evaluation method of subway service quality is suitable for online and offline application.

Exploring the Evolution of Passenger Flow and Travel Time Reliability with the Expanding Process of Metro System Using Smartcard Data

Metro system has experienced the global rapid rise over the past decades. However,few studies have paid attention to the evolution in system usage with the network expanding. The paper's main objectives are to analyze passenger flow characteristics and evaluate travel time reliability for the Nanjing Metro network by visualizing the smart card data of April 2014,April 2015 and April 2016. We performed visualization techniques and comparative analyses to examine the changes in system usage between before and after the system expansion. Specifically,workdays,holidays and weekends were specially segmented for analysis.Results showed that workdays had obvious morning and evening peak hours due to daily commuting,while no obvious peak hours existed in weekends and holidays and the daily traffic was evenly distributed. Besides,some metro stations had a serious directional imbalance,especially during the morning and evening peak hours of workdays. Serious unreliability occurred in morning peaks on workdays and the reliability of new lines was relatively low,meanwhile,new stations had negative effects on exiting stations in terms of reliability. Monitoring the evolution of system usage over years enables the identification of system performance and can serve as an input for improving the metro system quality.

Comparative Analysis of Metro Passengers’Mobility Patterns and Jobs-housing Balance of Metropolitan

The advent of the big data era has provided many types of transportation datasets,such as metro smart card data,for studying residents’mobility and understanding how their mobility has been shaped and is shaping the urban space.In this paper,we use metro smart card data from two Chinese metropolises,Shanghai and Shenzhen.Five metro mobility indicators are introduced,and association rules are established to explore the mobility patterns.The proportion of people entering and exiting the station is used to measure the jobs-housing balance.It is found that the average travel distance and duration of Shanghai passengers are higher than those of Shenzhen,and the proportion of metro commuters in Shanghai is higher than that of Shenzhen.The jobs-housing spatial relationship in Shenzhen based on metro travel is more balanced than that in Shanghai.The fundamental reason for the differences between the two cities is the difference in urban morphology.Compared with the monocentric structure of Shanghai,the polycentric structure of Shenzhen results in more scattered travel hotspots and more diverse travel routes,which helps Shenzhen to have a better jobs-housing balance.This paper fills a gap in comparative research among Chinese cities based on transportation big data analysis.The results provide support for planning metro routes,adjusting urban structure and land use to form a more reasonable metro network,and balancing the jobs-housing spatial relationship.

基于深度学习算法的智能网卡数据流卸载模型

为实现智能网卡数据流卸载优化的问题,在完全信息场景和不完全信息场景下,分别采用了不同的基于深度学习算法的智能网卡数据流卸载策略。结果表明,基于深度学习算法的完全信息场景下智能网卡数据流卸载策略中,运用Stackelberg博弈模型进行流量卸载策略研究,总流量负载、博弈方法总流量负载、对比方法总流量负载随着时段的变化规律基本相同,且在相同的时段下,流量从大至小顺序为总流量负载、对比方法总流量负载、博弈方法总流量负载。在不完全信息场景下,将Gradient Bandit算法用于在数据流分类和卸载中,总流量负载、强化学习方法总流量负载、对比方法总流量负载随着时段的变化规律基本相同。

基于客流特征的城市轨道交通站点分类——以青岛市为例

为提高城市轨道交通与常规公交衔接服务能力,提升城市公共交通系统整体吸引力,结合服务需求对轨道交通站点进行了分类,并针对不同类型的轨道交通站点提出常规公交接驳服务建议。基于青岛市轨道交通刷卡数据,提取乘客出行特征和车站客流分布特征,应用K-Means算法对轨道交通站点进行分类,并结合轨道交通站点客流特征将轨道交通站点识别为就业导向型、职住混合型、居住导向型、娱乐购物型等4类。在此基础上,提出不同类型站点常规公交出行接驳服务建议:对于就业导向型站点,建议实施以乘客总换乘时间最短为目标的接驳公交运营时刻表,减少乘客等车时间;对于职住混合型站点,建议采用定制公交模式提供个性化接驳出行服务;对于居住导向型站点,建议提供“区域巡游定制公交”服务,在保障公共服务的基础上,提升接驳公交车运营效益;对于娱乐购物型站点,建议在周末或旅游旺季提供休闲微循环线路,以满足该区域乘客游玩购物的出行需求。

居民出行异质性与城市活动结构

城市活动结构在居民出行过程中会产生动态变化,发现城市活动结构已经成为城市地理学、交通地理学研究的热点问题,对于城市规划与管理、公交线路调配、流量估计等具有重要意义。传统的城市结构发现方法是从计算机技术发展而来的,通常只考虑区域之间的交互,忽视了区域的属性信息,并且在顾及居民出行异质性方面研究不足。本文基于自编码模型,提出一种顾及居民出行异质性探测城市活动结构的表示学习方法,实现静态属性信息与动态出行信息的融合,并引入多元高斯分布模型,旨在完成从节点表示到社区表示的完整过程,实现更为准确的表示结果;借助地理探测器思想,顾及不同社区之间的居民出行异质性特征。试验发现,本文方法能够较为准确地发现城市活动结构以及居民的出行规律,通过与传统方法的比较,证明该方法在居民出行行为的挖掘上具有独特优势,便于从居民出行的角度重新认识城市活动结构。

基于复杂网络的快速公交-轨道交通站点分级研究

为研究大运量交通站点精细化管理问题,构建复杂网络,分析站点分级。通过构建Space-L有向加权复杂网络模型,并基于站点客流提取节点强度、加权介数中心性、加权接近中心性以及加权PageRank4项指标,运用系统聚类方法进行站点分级。以厦门快速公交(BRT)-轨道双模式网络系统为研究对象,从复杂网络视角分析站点网络特征和层级关系。结果表明:1)在复杂网络模型下,网络站点可分为核心枢纽型、局部联系型、网络中转型、强网络传导型、弱网络传导型和网络尽端型6类。2)各类换乘站点呈现不同特点,单模式换乘站具有调节网络客流的能力,双模式换乘站具有吸引网络客流的能力,单-双模式结合的换乘站具有辐射网络客流的能力。3)在BRT-轨道交通网络的单模式站点中,岛内的BRT站点发挥效果优于轨道站点。文章提出的站点分级指标和方法能很好地揭示站点网络特性,有助于提高城市BRT-轨道网络系统运营效率。

基于聚类算法的公共交通乘客出行特征分析——以北京市为例

为掌握乘客的精细化出行需求,进而提升公共交通系统的服务品质,以北京市为例,利用智能卡数据对乘客的出行特征进行分析。首先通过数据预处理获取乘客的完整出行链,然后从出行强度、出行时间和出行空间三个维度提取乘客出行特征指标,并利用主成分分析法(Principal Component Analysis, PCA)对其进行降维处理,最后基于降维后的特征利用不同的聚类算法对乘客进行分类。研究结果显示:K-means++算法的聚类效果最佳,聚类结果包含5类具有不同出行特征的乘客群体;其中类型一乘客的出行强度较大且出行时空特征稳定性较高,具有较明显的通勤出行特征,该类型乘客人数仅占总乘客数的18.4%,但其出行量占比超过55%,由于其公共交通依赖度较高,高峰期间应作为重点保障对象;类型二主要为生活类出行乘客,其出行时空稳定性较低,应深入挖掘此类乘客的个性化生活出行需求;类型三~类型五主要为低频或偶然出行乘客。根据乘客多天的出行链,进一步挖掘类型一乘客的居住地和工作地,结果显示乘客居住地主要分布在回龙观、天通苑以及黄村等区域,工作地主要分布在国贸、中关村和望京等区域,与北京市现状相符。

基于智能刷卡数据的乘客上车站点估计研究

针对城市公交自动收费系统中缺少乘客上车站点的问题,本文首先设计两阶段、改进K近邻和改进模糊C均值聚类这3种估计算法,并将估计结果与传统的时间窗算法进行对比;其次,采用熵率方法确定不同算法估计乘客上车站点的准确率;最后,以珠海市18路公交的智能刷卡数据为例,验证所提出算法的有效性。研究结果表明,3种算法均能实现所有乘客上车站点的全部匹配,与传统的时间窗算法相比,匹配率高约36.3%。就3个维度样本数据的平均熵率而言,乘客上车站点估计的准确率从高到低分别为两阶段算法,改进K近邻算法,改进模糊C均值聚类算法;两阶段算法与改进K近邻算法准确率相差不大,选择熵率最小的算法确定乘客最终的上车站点。本文研究方法可以应用于城市公交系统。

智慧校园一卡通建设探索与实现

基于“人工智能+物联网”的安全性和可操作性,建设智慧校园一卡通系统,实现信息高度共享和融合。坚持统筹规划、分步实施、整体推进、突出重点的总体思路和原则,将数据作为学校的核心资源加以利用,切实提高师生的应用体验。融合虚拟卡、移动互联、人脸识别、物联网、聚合支付等技术,实现了一账通多介质、多支付渠道聚合、多入口适应、管理智能化建设目标,为师生提供便利多样化服务,为学校管理决策提供支撑。

一卡通在智慧校园财务管理中的应用

阐述一卡通对智慧校园财务管理的意义,探讨一卡通技术在智慧校园财务管理中的应用对策,包括完善一卡通的各项功能、促进智慧校园信息共享、保障一卡通的数据安全。

一种针对非接触智能卡的抗干扰测试方法

智能卡业界领域很少有专门针对非接触智能卡的抗干扰测试方法,但非接触智能卡同样存在数据健壮性问题,需要进行抗干扰测试,本文专门介绍一种非接触智能卡的抗干扰测试方法。

基于北京公交刷卡数据和兴趣点的功能区识别

城市在其发展过程中逐渐形成居住区、工业区和商业区等不同的功能区。识别这些功能区并理解其分布特征,对于把握城市结构以及制定和使用科学合理的规划具有重要作用。本研究基于2008年4月北京市连续一周的7797余万条公交IC卡刷卡数据,将其转换为每个公交站台流量的二维时间序列数据,结合居民日常出行行为研究,利用数据挖掘技术,构建了基于公交刷卡数据和兴趣点的城市功能区识别模型,并将识别结果在交通分析小区尺度上汇总。研究结果显示,利用城市功能区识别模型,通过冗余数据的筛除和特征的创建实现对数据的有效降维,并选用期望最大化算法对处理后的数据进行聚类分析,结合居民日常出行相关特征和兴趣点分布数据对聚类结果进行诠释,可以快速有效地识别出与北京市土地利用现状地图具有一定匹配度的北京市各功能区。本研究的方法可以辅助规划人员和公众有效识别和理解复杂的城市空间结构,对城市地理及规划研究具有重要的理论和实践价值。

结合出行链的公交IC卡乘客下车站点判断概率模型

综述了一票制公交IC卡乘客下车站点推断的研究现状.为充分利用乘客出行链和站点上车客流量等信息,分析了公交出行的特征,提出了公交出行若干假设条件.在此基础上,定义了相关集合变量来描述单个乘客的出行链,将乘客个体出行特征融入到站点吸引权重的计算中.然后根据乘客出行链信息的完整程度,综合集计分析和非集计分析方法建立了结合出行链的概率模型,并提出了应用模型判断乘客下车站点的算法,以及模型检验方法.最后,以广州公交448路为例,对比了本文模型和单纯非集计分析方法的下车站点判断结果.结果表明,本文提出的模型适用性更广,在集计分析层面具有更高的可靠性.

城市轨道交通网络脆弱性分析

城市轨道交通网络是城市公交系统的重要组成部分。以北京地铁和深圳地铁为研究对象,建立两个城市的轨道交通网络模型,利用两个城市的地铁乘客刷卡数据,估计城市轨道交通需求OD;基于复杂网络理论分析两个城市轨道交通网络的拓扑结构,提出城市轨道交通网络脆弱性的评价指标;建立城市公共交通双层网络,对城市轨道交通网络的脆弱性进行更深层次的分析。研究结果表明:北京市与深圳市轨道交通网络脆弱边地理分布很相似,深圳市轨道交通网络平均而言更加脆弱;轨道交通网络的脆弱性与其网络复杂度密切相关;城市常规公交网络可以降低城市轨道交通系统的脆弱性。

一种基于乘客出行轨迹的公交断面客流估算方法

为充分利用公交车GPS数据和IC卡数据来估算公交断面客流,提出一种对刷卡乘客分类推断上、下车站点并扩样叠加轨迹的方法。首先通过融合公交车GPS数据和IC卡数据来推断不同类别刷卡乘客的上、下车站点;然后,结合投币乘客的出行特征,用低出行频次刷卡乘客的出行轨迹拟合投币乘客的出行轨迹,并对全部乘客的出行轨迹进行叠加;最后以广州公交为例进行了实验。实验结果表明提出的方法可以免交通调查近似得到单条公交线路的断面客流。

峰前折扣票价下轨道交通乘客出发时间弹性

以北京地铁峰前折扣票价政策为背景,利用自动售检票数据研究折扣票价对乘客出发时间的影响.首先,从"消费行为"视角建立客观的分类指标并对客运市场进行细分;其次,以较长时期内出发时间中位数为指标,基于"事前—事后对比法"对出发时间发生转移的乘客进行辨识;最后,准确测定不同类型乘客出发时间对票价变动的敏感程度.结果显示:乘客出发时间票价弹性随转移时长增加而急剧下降,30 min几乎是乘客所能接受的最大转移时长;通勤类乘客弹性最低,低频及生活类乘客相对富有弹性;限制当前折扣票价政策效果的主要因素在于折扣截止时间过早.该研究能够为差别定价方案编制及优化提供关键参数.