Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks

Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30 min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts.     

基于Windows95的内燃机机油耗微机测量系统的研究

机油消耗率不仅直接关系到内燃机的经济性,动力性和可靠性,而且与内燃机颗粒排放之间有着密切的联系。为此,文章介绍了在高性能价格比的研华ＰＣＬ－１８００型数据采集卡资源基础上,进行的基于Ｗｉｎｄｏｗｓ９５的内燃机机油耗散微机测量系统的研究。     

广珠城际铁路票制选择的思考

从广珠城际铁路的票制现状引发对广珠城际铁路储值票问题的思考。分析非接触式IC卡的应用情况、主要类型及发展趋势。探讨广珠城际铁路票制选择的方向。     

城际铁路票制的选择

城际铁路速度高,又兼具城市轨道交通运量大、开行密度大、公交化等特点,建议为城际铁路移植一卡通票制。以既有的湖北城际铁路线为例,考虑客票实名制及便携性,提出采用居民第二代身份证作为一卡通票制技术实现的平台,并详细地论证了其可行性;借鉴广珠城际铁路的实践经验,给出在湖北城际铁路运营中推行一卡通票制的具体方案。对远期全部施行一卡通票制的效益进行了估算。一卡通票制可适用于湖北城际铁路的运营实践,可兼为铁路投融资平台建设提供新途径。     

浅谈动车组列控车载数据分析方法

通过举例介绍了动车组列控车载数据的分析方法,对于车载、地面设备的故障查找处理提供了重要依据。     

金融IC卡在轨道交通领域的应用研究

介绍了非接触式金融IC卡在城市轨道交通中的应用前景,分析了该应用方案的系统总体结构、交易流程、资金清算及安全体系等。分析表明,经过各相关单位相关应用系统的协调建设,可以实现非接触式金融IC卡在城市轨道交通中的应用。并根据具体案例,介绍了城市轨道交通应用金融IC卡时相关系统的主要改造内容。     

基于IC卡刷卡数据的深圳市轨道交通站点活力研究

基于深圳市IC卡刷卡数据,分析深圳市轨道交通网络客流分布的特征规律,探讨轨道交通的客流模式,并提出一种基于客流强度和客流时间持续性的综合评估指标,定量分析深圳轨道交通站点的客流活力分布特征,而后对轨道交通站点进行分类分析。研究结果表明：深圳轨道交通网络的客流分布极不均衡,少数站点占据了大量的轨道交通客流量,前10的站点客流比重为19.64%;不同类型的站点客流模式存在显著差异,工作日和非工作日客流模式也有所不同。深圳轨道交通网络活力相对匮乏的站点比重较大,超过60%,非工作日轨道交通网络的客流活力明显优于工作日。     

Design of New Long-Distance Intelligent Watt-Hour Meter

The design scheme for a new kind of long-distance intelligent watt-hour meter is presented. In the system, the control computer communicates with the bank＇s host computer by the X. 25 network; the built-in embeded secure access module （ESAM） can verify data by commutation with the intelligent nodes that enhance the security of the data. IC TSS721 is adopted to collect data remotely, and the CPU IC card is used to ensure the the bargaining security and reliability. In addition, the software of the system is introduced.     

公交持卡乘客下车站点推导方法研究及验证

本文通过分析公交持卡乘客的出行数据,挖掘乘客的出行规律,改进基于出行链理论的下车站点推导方法,并建立、检索站点邻域集以提升下车站点的推导效率。借助攀枝花市特殊的自动收费系统,利用分段计费线路的下车数据验证了方法的有效性,结果表明下车站点的推导结果具有较高的精度。同时探究了最大步行距离阈值对推导结果的影响,给出不同场景下的应用建议。此外研究分析了不同线路、不同群体的推导效果,对其内在原因做出解释,提供了模型方法的改进方向。研究结果有助于分析乘客的公交出行行为,支撑辅助型公交的需求分析和布设,提升公交服务水平。     

Inferring patterns in the multi-week activity sequences of public transport users

The public transport networks of dense cities such as London serve passengers with widely different travel patterns. In line with the diverse lives of urban dwellers, activities and journeys are combined within days and across days in diverse sequences. From personalized customer information, to improved travel demand models, understanding this type of heterogeneity among transit users is relevant to a number of applications core to public transport agencies’ function. In this study, passenger heterogeneity is investigated based on a longitudinal representation of each user’s multi-week activity sequence derived from smart card data. We propose a methodology leveraging this representation to identify clusters of users with similar activity sequence structure. The methodology is applied to a large sample (n = 33,026) from London’s public transport network, in which each passenger is represented by a continuous 4-week activity sequence. The application reveals 11 clusters, each characterized by a distinct sequence structure. Socio-demographic information available for a small sample of users (n = 1973) is combined to smart card transactions to analyze associations between the identified patterns and demographic attributes including passenger age, occupation, household composition and income, and vehicle ownership. The analysis reveals that significant connections exist between the demographic attributes of users and activity patterns identified exclusively from fare transactions.