上海市域快轨崇明线票价策略研究

上海市轨道交通崇明线是上海新建市域快速轨道交通线路之一，研究线路票价策略有利于满足客流出行需求、提高运营效率、减轻政府财政支出负担。首先，本文分析了上海轨道交通票价现状与崇明线特征，认为该线路在功能定位、技术特征和客流出行特征等方面与中心城区轨道交通线路差异较大，不宜选用现行轨道交通票价水平。其次，面向崇明线潜在客流，设计调查问卷，采集并分析乘客出行行为及票价接受度信息。结果表明，客流特征差异化明显，出行需求呈现多样性，以乘客票价可接受程度为导向的合理票价率区间为0.31-0.33元/km。最后，从票价水平、票种方案、优惠策略三个方面提出崇明线定价建议。     

基于公交卡数据的地铁站点客流集聚风险评价研究

人群聚集效应是大都市中最令人担忧的问题之一,特别是在地铁等半封闭空间。在早高峰时期下,地铁系统的一些重要节点时常发生线路故障维修,车站内的乘客很难疏散,从而导致了站内客流聚集的情况。因此,近年来,研究人员将重点放在地铁车站人群风险的风险评估或风险监测上,主要是从设施或乘客行为角度对风险等级进行评级。但是,两者都不能反映客流集聚和疏散能力之间的关系。为了更客观地评价人群风险,本研究基于上海地铁公交卡数据,采用聚类的方法对站内客流聚集风险进行评价。本文以上海地铁1号线为例,对各站点的客流聚集风险进行评估,并根据风险程度,对27个站点的客流集聚风险进行可视化。     

城市轨道交通Y型交路快慢车停站方案优化

在城市轨道交通线路中采取Y型交路与快慢车组合运行的列车开行模式，能够有效扩展线路的运输服务范围，并有效缓解长运距乘客出行时间过长等问题，该模式的组合效益与各类型列车的开行对数和快车停靠站方案等因素密不可分。本文首先对Y型交路快慢车运行模式的特点进行了分析，然后根据客流起讫点对Y型交路客流区段进行划分，建立了乘客广义出行费用函数。在此基础上，构建了Y型交路快慢车停站方案的非线性整数规划模型，最后使用遗传算法进行模型求解。结果表明，相对于Y型交路单一直通运营方式而言，Y型交路结合快慢车的组织模式可以有效优化乘客的出行体验以及降低企业的运营成本。     

苏州轨道交通客流分析预测系统框架设计研究

苏州轨道交通目前仍未有整合地铁、有轨电车、城际铁路的客流分析预测系统。在对现有轨道数据分析的基础上,确定系统功能的定位和设计原则,将客流分析预测系统分为基于大数据及出行链的乘客出行特征分析、多场景需求预测及灵敏度分析、突发事件下影响多方式轨道交通客流分析、多方式轨道交通客流状态实时推演、客流分析与预测展示等5个子系统,初步形成了苏州轨道交通客流分析预测的系统框架。该框架为城市交通管理及控制提供了更好的辅助决策,为提升苏州市交通服务水平给予参考。     

地铁车站建筑设计中贯彻以人为本理念的探讨

结合地铁车站建筑设计实践,坚持以人为本的设计理念,对地铁车站公共区平面布置、影响乘客行进的站内垂直交通、站内环境设计,以及地铁与其它交通方式的接驳等方面进行了分析和探讨,力求使地铁车站建筑设计在满足交通功能的前提下,实现＂让乘客出行更安全、更便捷、更舒适＂的目标。     

公交惯常出行与随机出行的时空分布差异

差异化和精细化的公交服务要求城市管理者和公交运营者加深对公交出行变异性的认识。本文将公交惯常出行定义为乘客个体多次在同一时段或空间乘坐公交的行为，随机出行的定义与之相反。利用厦门市30天连续的常规公交数据，使用DBSCAN算法对出行天数在10天以上的高频乘客进行划分，用核心点来表征惯常出行，用非核心点来表征随机出行。结果表明有78.70%的出行在时间上具有稳定性，有72.32%的出行在空间上具有稳定性。研究结论凸显了公交高频乘客也具有随机出行，且随机出行的统计特征和时空分布与惯常出行有较大差异。研究结论可以应用于个体时空模式挖掘、乘客人群划分、定制公交线路制定、公交差异化服务设置等方面。     

防疫检查对地铁站站内客流通行效率的影响——以深圳茶光地铁站为例

在后疫情时代，使用地铁出行需要接受普遍的防疫检查，对乘客的出行时间造成影响，也给地铁客流管理带来了挑战。本文量化分析防疫检查对乘客出行的影响，并提出优化建议。借助社会力模型，对地铁站的进出站客流行为进行模拟，进而评估防疫检查增加前后乘客出行时间的变化，。择深圳茶光地铁站为案例，实地调查并分析了地铁站内设施的布局和参数，建立了仿真模型，对未加防疫检查、防疫检查、优化防疫检查三种情况进行仿真模拟。结果表明防疫检查会使乘客的进站时间增加13.43%，优化防疫检查后可以使增加时间降低为7.45%。建议地铁站工作人员优化防疫检查设施的布局，提高防疫检查服务的质量。     

综合客运枢纽信息设置影响行人行为的调查分析

基于综合客运枢纽信息内涵及作用,根据不同的分类标准对综合客运枢纽信息进行了分类。枢纽内行人对信息有需求,枢纽信息的设置才会对行人有影响;因此,分别从不熟悉枢纽环境的进站行人、出站行人、换乘行人3个方面详细分析了枢纽内不同走行路径的行人对信息的不同需求。最后,通过对上海虹桥火车站实地调查,并对调查数据进行分析,得出在换乘过程中设置快速路径指示标识和实时信息都将影响枢纽内换乘行人的行为,并建议在地铁换乘高铁的A/B方向通道上,规范信息设置,凸显指路信息。     

基于广义费用的城市轨道交通客流分配

为了更加客观全面地反映城市轨道交通出行者决策行为，提升城市轨道交通客流预测准确性，缓解线路运营面临的高峰时段客流拥挤现象，从出行广义费用角度出发，开展城市轨道交通客流分配研究。综合考虑票价、出行时间、舒适度、准时性等因素，构建了城市轨道交通乘客出行广义费用计算模型；以北京地铁5号线为例，将广义费用作为出行阻抗引入客流分配之中，构建了基于广义费用的随机用户平衡条件下的城市轨道交通客流分配模型，并提出了嵌套Logit模型的迭代加权法用以求解模型；基于分配模型及算法，对不同票价和列车容量条件下的城市轨道交通客流分配结果进行了对比分析。结果表明，基于广义费用的城市轨道交通客流分配模型能够实现对轨道交通流量的有效预测，可对各类缓解高峰时段拥挤措施的实施效果进行评估，辅助城市轨道交通领域相关决策。     

国铁与城市轨道交通过轨运营客流适用性研究

国铁与城市轨道交通线路间的过轨运营可减少乘客换乘,降低换乘站运营组织压力,改善轨道交通运营服务质量和安全性;但将导致土建投资成本增加。本文针对以上特性,提出了国铁与城市轨道交通过轨运营下社会总效用的计算方法。案例结果表明：过轨运营下,乘客出行成本减少的幅度大于投资成本增加的幅度,社会总效用为＋19.7亿元,说明适合采用过轨运营。根据过轨运营效果的影响大小排序,分别为乘客平均换乘时间、客流增长率和换乘客流比例。同时,换乘客流比例越大、客流年增长率越大,乘客平均换乘时间越长时,过轨运营越合适。     

A multiobjective planning model for intercity train seat allocation

This paper presents a multiobjective planning model for generating optimal train seat allocation plans on an intercity rail line serving passengers with many‐to‐many origin‐destination pairs. Two planning objectives of the model are to maximise the operator's total passenger revenue and to minimise the passenger's total discomfort level. For a given set of travel demand, train capacity, and train stop‐schedules, the model is solved by fuzzy mathematical programming to generate a best‐compromise train seat allocation plan. The plan determines how many reserved and non‐reserved seats are to be allocated at each origin station for all subsequent destination stations on each train run operated within a specified operating period. An empirical study on the to‐be‐built Taiwan's high‐speed rail system is conducted to demonstrate the effectiveness of the model. The model can be used for any setting of travel demand and stop‐schedules with various train seating capacities.     

地铁车站票务设备节能优化研究

为解决城市轨道交通车站售票能力冗余导致城市电力资源及地铁公司运营成本闲置的问题,分析普通车站日常客流分布,综合考虑地铁车站售票设备成本和乘客的时间成本因素,建立地铁车站售票设备开启数量优化模型,同时通过排队论和Lingo软件进行计算,获得最优化的地铁车站售票设备开启数量。以郑州地铁农业南路站的日常客流为例,应用地铁车站售票设备开启数量优化模型,计算得到农业南路站售票设备优化方案,通过优化方案进行节能成果分析证明,该方案可有效节约城市电力资源及地铁公司运营成本。     

Assessing the usage and level-of-service of pedestrian facilities in train stations: A Swiss case study

A framework for assessing the usage and level-of-service of rail access facilities is presented. It consists of two parts. A dynamic demand estimator allows to obtain time-dependent pedestrian origin–destination demand within walking facilities. Using that demand, a traffic assignment model describes the propagation of pedestrians through the station, providing an estimate of prevalent traffic conditions in terms of flow, walking times, speed and density. The corresponding level-of-service of the facilities can be directly obtained. The framework is discussed at the example of Lausanne railway station. For this train station, a rich set of data sources including travel surveys, pedestrian counts and trajectories has been collected in collaboration with the Swiss Federal Railways. Results show a good performance of the framework. To underline its practical applicability, a six-step planning guideline is presented that can be used to design and optimize rail access facilities for new or existing train stations. In the long term, the framework may also be used for crowd management, involving real-time monitoring and control of pedestrian flows.     

基于旅客特性的离站静态标识空间布局设计

铁路客运枢纽是城市内外交通衔接的纽带,铁路客运站内部的离站交通标识对旅客在铁路客运站中安全快速流动和转移起到了重要作用。如何使旅客舒适快捷地离站,不仅是离站旅客也是铁路客运枢纽管理者所密切关心的问题。文章从标识的基本概念出发,对静态交通标识进行详细分类,基于旅客特性,对静态标识的摆放位置进行研究,并建立各类静态标识高度设计模型,为大中型铁路客运枢纽中的静态标识空间布局设计提供参考。     

Time of day intervals partition for bus schedule using GPS data

Time of day partition of bus operating hours is a prerequisite of bus schedule design. Reasonable partition plan is essential to improve the punctuality and level of service. In most mega cities, bus vehicles have been equipped with global positioning system (GPS) devices, which is convenient for transit agency to monitor bus operations. In this paper, a new algorithm is developed based on GPS data to partition bus operating hours into time of day intervals. Firstly, the impacts of passenger demand and network traffic state on bus operational performance are analyzed. Then bus dwell time at stops and inter-stop travel time, which can be attained based on GPS data, are selected as partition indexes. For buses clustered in the same time-of-day interval, threshold values of differences in dwell time at stops and inter-stop travel time are determined. The buses in the same time-of-day interval should have adjacent dispatching numbers, which is set as a constraint. Consequently, a partition algorithm with three steps is developed. Finally, a bus route in Suzhou China is taken as an example to validate the algorithm. Three partition schemes are given by setting different threshold values for the two partition indexes. The present scheme in practice is compared with the three proposed schemes. To balance the number of ToD intervals and partition precision, a Benefit Evaluation Index is proposed, for a better time-of-day interval plan.     

Crowding in public transport systems: Effects on users,operation and implications for the estimation of demand

The effects of high passenger density at bus stops, at rail stations, inside buses and trains are diverse. This paper examines the multiple dimensions of passenger crowding related to public transport demand, supply and operations, including effects on operating speed, waiting time, travel time reliability, passengers’ wellbeing, valuation of waiting and in-vehicle time savings, route and bus choice, and optimal levels of frequency, vehicle size and fare. Secondly, crowding externalities are estimated for rail and bus services in Sydney, in order to show the impact of crowding on the estimated value of in-vehicle time savings and demand prediction. Using Multinomial Logit (MNL) and Error Components (EC) models, we show that alternative assumptions concerning the threshold load factor that triggers a crowding externality effect do have an influence on the value of travel time (VTTS) for low occupancy levels (all passengers sitting); however, for high occupancy levels, alternative crowding models estimate similar VTTS. Importantly, if demand for a public transport service is estimated without explicit consideration of crowding as a source of disutility for passengers, demand will be overestimated if the service is designed to have a number of standees beyond a threshold, as analytically shown using a MNL choice model. More research is needed to explore if these findings hold with more complex choice models and in other contexts.     

Development of efficient stop planning optimization process for high‐speed rail systems

The Taiwan High Speed Rail (THSR) has recently added three additional stations to its original network. Although the three additional stations can improve accessibility to the system, these new stations can present difficulties in the transportation planning process, particularly for planning of train stops. The additional stations may benefit some passengers, but may also lengthen the travel time for the other passengers. Therefore, the main challenge faced by THSR is finding an efficient way to design appropriate stopping patterns. Past studies on stop planning usually adopted meta‐heuristics or decomposition methods to solve this complex problem. Although these solution techniques can improve solution efficiency, none of them can guarantee the optimality of the solution and capture the transfer movement of different stopping patterns. In this research, we proposed an innovative network structure to address complex stop planning problems for high‐speed rail systems. Given its special network structure, two binary integer programming models were developed to simultaneously form and determine the optimal stopping patterns for real‐world THSR stop planning problems. An optimization process was also developed to accurately estimate the station transfer time corresponding to the variation in stopping patterns and passenger flow. Results of the case studies suggest that the proposed binary integer programming models exhibit superior solution quality and efficiency over existing exact optimization models. Consequently, using this stop planning optimization process can help high‐speed rail system planners in designing optimal stopping patterns that correspond to passenger demand. Copyright © 2017 John Wiley & Sons, Ltd.     

An application-oriented model of passenger waiting time based on bus departure time intervals

This paper develops an application-oriented model to estimate waiting times as a function of bus departure time intervals. Bus stops are classified into Type A and B depending on whether they are connected with urban rail transit systems. Distributions of passenger arrival rates are analyzed based on field data for Beijing. The results indicate that the best fits for the distribution of passenger arrival rates for Type A and B bus stops are the lognormal distribution and gamma distribution, respectively. By analyzing relationships between passenger arrival rates and bus departure time intervals, it is demonstrated that parameters of the passenger arrival rate distribution can be expressed by the average and coefficient of variation of bus departure time intervals in functional relationships. The validation shows that the model provides a reliable estimation of the average passenger waiting time based on readily available bus departure time intervals.     

A bi-level model for single-line rail timetable design with consideration of demand and capacity

This paper proposes a bi-level model to solve the timetable design problem for an urban rail line. The upper level model aims at determining the headways between trains to minimize total passenger cost, which includes not only the usual perceived travel time cost, but also penalties during travel. With the headways given by the upper level model, passengers’ arrival times at their origin stops are determined by the lower level model, in which the cost-minimizing behavior of each passenger is taken into account. To make the model more realistic, explicit capacity constraints of individual trains are considered. With these constraints, passengers cannot board a full train, but wait in queues for the next coming train. A two-stage genetic algorithm incorporating the method of successive averages is introduced to solve the bi-level model. Two hypothetical examples and a real world case are employed to evaluate the effectiveness of the proposed bi-level model and algorithm. Results show that the bi-level model performs well in reducing total passenger cost, especially in reducing waiting time cost and penalties. And the section loading-rates of trains in the optimized timetable are more balanced than the even-headway timetable. The sensitivity analyses show that passenger’s desired arrival time interval at destination and crowding penalty factor have a high influence on the optimal solution. And with the dispersing of passengers' desired arrival time intervals or the increase of crowding penalty factor, the section loading-rates of trains become more balanced.     

What determines rail transit passenger volume? Implications for transit oriented development planning

Transit oriented development (TOD) has been an important topic for urban transportation planning research and practice. This paper is aimed at empirically examining the effect of rail transit station-based TOD on daily station passenger volume. Using integrated circuit (IC) card data on metro passenger volumes and cellular signaling data on the spatial distribution of human activities in Shanghai, the research identifies variations in ridership among rail transit stations. Then, regression analysis is performed using passenger volume in each station as the dependent variable. Explanatory variables include station area employment and population, residents’ commuting distances, metro network accessibility, status as interchange station, and coupling with commercial activity centers. The main findings are: (1) Passenger volume is positively associated with employment density and residents’ commuting distance around station; (2) stations with earlier opening dates and serving as transfer nodes tend to have positive association with passenger volumes; (3) metro stations better integrated with nearby commercial development tend to have larger passenger volumes. Several implications are drawn for TOD planning: (1) TOD planning should be integrated with rail transit network planning; (2) location of metro stations should be coupled with commercial development; (3) high employment densities should be especially encouraged as a key TOD feature; and (4) interchange stations should be more strategically positioned in the planning for rail transit network.