热敏计数统计技术在上海轨交换乘客流统计中的应用

换乘客流统计作为轨交客流的清分和换乘站的运营组织的重要依据，对其精确度和实时度的要求很高。采用热敏计数统计技术可为换乘客流的统计提供一种新的解决方法，进而实现辅助自动售检票系统清分客流，提供客流疏导参照的目的。     

广州地铁APM线单程票流失的分析及应对措施

单程票是城市轨道交通售检票系统的重要组成部分,由于其制作成本高、使用量大、流动性强,且具有在使用过程中流失率较高等特点,因此,国内外轨道交通运营公司都十分重视对单程票流失的管控.本文通过分析广州地铁APM线单程票使用情况及管理现状,分析存在的问题及原因,最后针对性地提出了应对措施.     

云计算背景下的城市轨道交通网络安全问题浅析

云计算作为新兴技术手段,高效、节约、便捷是其技术的主要特点,通过与飞速发展的城市轨道交通相结合,在提高城市轨道交通发展智能化、智慧化、便捷化、安全化的同时,也面临一系列安全问题,通过浅析发展过程中的安全问题,对于今后城市轨道交通建设、运营、管理、维护等方面具有重要意义。     

泰山索道公司启用"条形码式自动售票检票系统"

索道安全管理是索道的生命线,索道票务管理维系着索道的经济命脉,两者相辅相成.缺一不可.山东泰山索道公司加强科学管理,实现了索道安全运营18周年,在即将进入新世纪之初,公司管理者在加强安全管理的同时,审时度势,不断追求安全经营管理的完美性.2001年下半年公司再次作出具有里程碑意义的决策,摒弃旧的票务管理制度,以启用"条形码式自动售票检票系统"为契机,建立新的票务管理机制. 此套管理系统是由制票机、检票机、后台计算机三部分组成,通过电脑网络联结起来. 售票处:售票员通过售票机将索道票即时打印出来.票上印有明示的文字信息和用于机器识读的防伪条形码信息.信息中包含有时间、乘车线路、售票员编号、人数、票种、单价、总价、序列号等内容.这些信息基本上都是由计算机自动生成,售票时只需选择票种、人数,占击确认,便可打印出票来.同时电脑上还会显示出应收票款数额,售票员可据此收款,财务结算也非常方便. 检票处:游客将索道票插入检票机,检票机对票进行真假识别后,给予记录、打孔检票,开启门禁,并且使绿灯发光表示可以放行,这一系列的工作只是在一瞬间完成的,条码票只能使用一次. 出站口:在出站口设置了单流向旋转门,对人员流向进行只能出不能进的控制,不设专人职守,该处的管理由下车区的服务员负责. 通过几个月的运作,尤其是经过"十·一国庆黄金周"的检验证明:"条形码式自动售检票系统"的开通带来的不仅是泰山索道公司服务硬件设施的完善和增加经济效益,也必将促进整个公司服务质量的进一步提高. (王瑞国)     

《国内外城市轨道交通事故案例评析》书评

城市轨道交通是为人民群众基本出行提供普遍服务的重要基础设施，也是具有显著公益特性的民生工程。其具有运量大、快速、准时、安全、环保的突出拖量和比较优势，日益成为城市综合交通运输系统的重要支撑。大力发展城市轨道交通，加强城市轨道交通运营管理，是落实国家公交优先发展战略，     

新技术新工艺

城市轨道交通运营安全预警技术成功构建 1月21日,交通运输部建设科技项目“城市轨道交通运营监测预警和应急处置关键技术研究”通过验收,项目系统构建了城市轨道交通运营监测预警技术体系,有利于提升城市轨道交通运营安全。     

城市轨道交通跨线运营模式研究

为探究轨道交通跨线运营在优化城市交通功能,解决城市轨道交通拥堵问题等方面的作用,对城市轨道交通跨线运营模式的应用优势、应用难点及应用方法进行分析,并对信息化背景下的轨道交通跨线运营技术效用进行研究。认为跨线运营是城市轨道交通的发展趋势,且在多元化技术的“加持”下,城市轨道交通跨线运营模式将取得重大突破。     

基于运营调度日志数据的城市轨道交通运营安全风险识别

随着我国城市轨道交通快速发展,运营规模不断扩大,客流量与日剧增,对城市轨道交通安全保障等提出了更高的要求。本文总结阐述城市轨道交通运营系统安全风险特性,提出了系统识别城市轨道交通运营安全风险的相关定性、定量方法与技术流程,并基于运营调度日志数据运用Apriori关联规则算法实现了关键运营安全风险因素的定量化识别与分析。     

南通首座“陆地空港”开门迎客

立体无缝换乘、自动售取票、陆地航空式服务……1月8日，南通交通格局迎来崭新变革，南通汽车客运东站正式投入试运营，让百姓出行更便捷更舒适。     

基于大数据的城市轨道交通运维信息化技术应用研究

阐述了城市轨道交通运维信息化技术发展状况,说明了大数据环境中城市轨道交通运维信息化技术运用价值,论述了城市轨道交通运维信息化技术相关内容,同时分析了大数据背景下城市轨道交通运维信息化技术的应用情况,进而增强城市轨道交通运维信息化技术应用的实际成效。     

Calibrating a transit assignment model using smart card data in a large-scale multi-modal transit network

This paper describes a practical automated procedure to calibrate and validate a transit assignment model. An optimization method based on particle swarm algorithm is adopted to minimize a defined error term. This error term which is based on the percentage of root mean square error and the mean absolute percent error encompasses deviation of model outputs from observations considering both segment level as well as the mode level and can be applied to a large scale network. This study is based on the frequency-based assignment model using the concept of optimal strategy while any transit assignment model can be used in the proposed methodological framework. Lastly, the model is validated using another weekday data. The proposed methodology uses automatic fare collection (AFC) data to estimate the origin–destination matrix. This study combines data from three sources: the general transit feed specification, AFC, and a strategic transport model from a large-scale multimodal public transport network. The South-East Queensland (SEQ) network in Australia is used as a case study. The AFC system in SEQ has voluminous and high quality data on passenger boardings and alightings across bus, rail and ferry modes. The results indicate that the proposed procedure can successfully develop a multi-modal transit assignment model at a large scale. Higher dispersions are seen for the bus mode, in contrast to rail and ferry modes. Furthermore, a comparison is made between the strategies used by passengers and the generated strategies by the model between each origin and destination to get more insights about the detailed behaviour of the model. Overall, the analysis indicates that the AFC data is a valuable and rich source in calibrating and validating a transit assignment model.

     

Transit passenger response: Short and long term elasticities using time series analysis

This paper investigates the effects of price and service changes on transit ridership. The concept of elasticity is introduced and the traditional methods for estimating elasticities are discussed. In this paper an extra dimension is added by investigating short and long term elasticities. Time series analysis, developed by Box and Jenkins is chosen for the analysis. The Box and Jenkins methodology is applied to a monthly time series of average weekday ridership on the Chicago Transit Authority (CTA) rail system. Four categories of explanatory variables are investigated: fare on the CTA rail system, service provided on the CTA rail system, cost of car trips and weather effects. The effects of gas prices and rail service were found to be significant; however the results indicate a twelve month delay before service changes influence ridership. The effect of transit fares was found to be insignificant, indicating that both the short and long term fare elasticities are zero.     

Automatic fare collection in transit: a synthesis of current practice

Many industries are keeping pace with changing technology in an effort to improve operating efficiency and attract consumers with innovations. The transit industry is no exception, one example being the installation and operation of automatic fare‐collection (AFC) systems. There are many issues to consider in implementing an AFC system, including the type of equipment required and the expected quality of system performance.

This article presents a synthesis of several sources of information pertaining to AFC operations. It includes a discussion of system requirements, and the advantages and disadvantages of system operation. Performance measures of reliability and maintainability are also defined, and AFC systems are evaluated using these measures. Several conclusions are reached concerning current AFC practice.     

The effect of slow zones on ridership: An analysis of the Chicago Transit Authority “El” Blue Line

Transit agencies frequently upgrade rail tracks to bring the system to a state of good repair (SGR) and to improve the speed and reliability of urban rail transit service. For safety during construction, agencies establish slow zones in which trains must reduce speed. Slow zones create delays and schedule disruptions that result in customer dissatisfaction and discontinued use of transit, either temporarily or permanently. While transit agencies are understandably concerned about the possible negative effects of slow zones, empirical research has not specifically examined the relationship between slow zones and ridership. This paper partially fills that gap. Using data collected from the Chicago Transit Authority (CTA) Customer Experience Survey, CTA Slow Zone Maps, and, the Automatic Fare Collection System (AFC), it examines whether recurring service delays due to slow zones affect transit rider behavior and if the transit loyalty programs, such as smart card systems, increase or decrease rider defections. Findings suggest that slow zones increase headway deviation which reduces ridership. Smart card customers are more sensitive to slow zones as they are more likely to stop using transit as a result of delay. The findings of this paper have two major policy implications for transit agencies: (1) loyalty card users, often the most reliable source of revenue, are most at risk for defection during construction and (2) it is critical to minimize construction disruptions and delays in the long run by maintaining state of good repair. The results of this paper can likely be used as the basis for supporting immediate funding requests to bring the system to an acceptable state of good repair as well as stimulating ideas about funding reform for transit.     

Secrets of success: assessing the large increases in transit ridership achieved by Houston and San Diego transit providers

This paper summarizes and updates the findings from an earlier study by the same authors of transit systems in Houston (all bus) and San Diego (bus and light rail). Both systems achieved unusually large increases in transit ridership during a period in which most transit systems in other metropolitan areas were experiencing large losses. Based on ridership models estimated using cross section and time series data, the paper quantifies the relative contributions of policy variables and factors beyond the control of transit operators on ridership growth. It is found that large ridership increases in both areas are caused principally by large service increases and fare reductions, as well as metropolitan employment and population growth. In addition, the paper provides careful estimates of total and operating costs per passenger boarding and per passenger mile for Houston's bus operator and San Diego's bus and light rail operators. These estimates suggest that the bus systems are more cost-effective than the light rail system on the basis of total costs. Finally, the paper carries out a series of policy simulations to analyze the effects of transit funding levels and metropolitan development patterns on transit ridership and farebox recovery ratio.     

Evaluating security of rail transit systems: A metric system approach

Crime and fear of crime is a major problem plaguing U.S. transit systems, particularly those serving large urban areas. This paper presents a normative framework for assessing rail transit security following a system-wide metric approach. The security metric can also be used to assess the marginal improvement in security as a result of improving or adopting alternative policing and monitoring strategies. The model consists of five tasks: surveying rail transit security systems, developing a rail transit security metric, assigning efficiency ratings to rail security functions, developing a composite index for the efficiency of the overall security system, and applying a probability matrix to temper the results. Efficiency ratings can be translated into probability of occurrence figures that can be used in a decision tree context to improve rail transit security.     

Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations

Transportation - Smart card data derived from automatic fare collection (AFC) systems of public transit enable us to study resident movement from a macro perspective. The rhythms of traffic...     

Fare deregulation of transit services: Winners and losers in a competitive market

Currently, the Hong Kong government imposes fare control on buses and taxi while the rail services are immune to such a control. This study examined four scenarios of fare deregulation on transit services by considering three related parties of a transit system – service providers, travelers, and society in general, with their respective objectives represented as – revenue, travel utility, and congestion. Analyzing the resultant impacts on these three parties, we found that a different regulatory environment would favor or hurt a different set of parties. There is no clear win‐win situation for all parties. Deciding a socially acceptable regulatory environment is likely to involve difficult tradeoffs among these parties.     

Estimating the influence of common disruptions on urban rail transit networks

With the continuous expansion of urban rapid transit networks, disruptive incidents—such as station closures, train delays, and mechanical problems—have become more common, causing such problems as threats to passenger safety, delays in service, and so on. More importantly, these disruptions often have ripple effects that spread to other stations and lines. In order to provide better management and plan for emergencies, it has become important to identify such disruptions and evaluate their influence on travel times and delays. This paper proposes a novel approach to achieve these goals. It employs the tap-in and tap-out data on the distribution of passengers from smart cards collected by automated fare collection (AFC) facilities as well as past disruptions within networks. Three characteristic types of abnormal passenger flow are divided and analyzed, comprising (1) “missed” passengers who have left the system, (2) passengers who took detours, and (3) passengers who were delayed but continued their journeys. In addition, the suggested computing method, serving to estimate total delay times, was used to manage these disruptions. Finally, a real-world case study based on the Beijing metro network with the real tap-in and tap-out passenger data is presented.     

An integrated framework for assessing service efficiency and stability of rail transit systems

A well-designed service plan efficiently utilizes its infrastructure and ensures an acceptable level of service stability with consideration of potential incidents that disturb or disrupt the rail transit services. To perform service evaluation, an integrated process combining capacity, resource usage, and system reliability is required to quantify service efficiency and stability in a consistent way. This study adopts capacity-based indices, “capacity utilization” and “expected recovery time”, as the attributes for service efficiency and stability, and develops a comprehensive evaluation framework with three corresponding modules to incorporate capacity, service plan, and system reliability and maintainability simultaneously. The capacity analysis module computes the rail transit capacities under normal and degraded operations. The reliability module classifies and fits the proper reliability and maintainability distributions to the historical interruption data. The service efficiency and stability module analyzes the results of the previous two modules and evaluates the service efficiency and stability of rail transit service plans. Empirical results show that the established evaluation framework can not only evaluate the service efficiency and stability but also identify critical sections and time slots. This tool can help rail transit operators rapidly assess their operational changes and investment strategies related to efficiency and stability so as to provide efficient and stable services to their customers.