城市智能公交调度管理系统（二）

海信公交智能化调度系统HSAPTS包括三级调度管理系统、车载终端系统、电子站牌系统、行车计划与配车排班编制系统、信息发布系统(包括公交网站和手机查询服务系统)和车辆技术信息管理系统，各个子系统的功能如下：     

智能公交动态监控调度系统研究

为提高公交系统运营效率,提高公交的智能化水平,针对当前公交监控调度系统中存在问题,提出建立具有实时处理能力的公交动态监控调度系统,能够根据实时变化的道路环境、车辆运行状态、客流情况及其它异常事件,对公交车辆的行车计划进行调整,使整个公交运营系统的运营成本达到最优。     

河北廊坊首批20辆智能公交车运营

9月23日，廊坊市首批20辆崭新智能化公交车运抵公交公司停车场，车上装有目前国内最先进的3G智能调度系统。首批公交分为3种颜色，是为了区分不同线路而定制的。即将投入运营的这100辆车，全部为10米－12米长的空调智能车，     

动动手指打公交

如果每天上下班,有一辆从小区门口直达公司门口的班车,帮你免除等车、挤车、换车的疲惫,是不是很棒呢?除了有些公司的班车可以实现一站式上班,目前还有很多地区公交公司开设定制公交,方便上班族往返. 定制公交早有历史 2013年8月,青岛开设首条定制公交.共设两条路线:1号线沿金水路一路东行至滨海公路,经松岭路绕行香港东路,抵达香港中路下车点,全程28公里;2号线沿黑龙江路一路南下,经海尔路绕行至香港中路抵达各下车点,全程22公里.解决了部分市民上班要转车、等车的困境.     

单线路半自动公交编组与调度模型

基于半自动公交能够实现多辆公交单元协同行驶的特点，本文提出半自动公交编组与调度模型。考虑编组公交动态运行特性、乘客特性等因素，以公交公司运营成本与乘客出行时间成本为优化目标，优化发车间隔和编组规模，并采用改进的遗传算法进行求解。最后基于成都市116路公交线路对本文提出的模型进行评价，结果表明：与非编组模式相比本策略能降低35.9%的乘客出行成本与20.0%的运营成本，证明了本文提出的半自动公交编组模型的有效性。     

城市公交区域运营模式及其关键技术

<正>通过整合公交资源,分阶段逐步推进公交区域运营,实现公交运营体制由线路运营模式向区域运营模式的转变是公共交通发展的必然趋势。公交区域运营模式尚未在我国施行,调度技术方面的相对落后是其中的重要原因。随着公交优先战略的施行,现实中对公交区域调度理论与方法的需求已十分强烈,在国外先进方法的基础上开展对公交区域调度关键技术的研究,建立适用于我国     

三线城市公交“高攀”得起新能源吗? 一位运营者这样说

<正>三线城市的公交公司"高攀"得起新能源公交车吗?相信很多人都难以给出肯定答案。众所周知,新能源公交车价格动辄上百万元,混合动力客车普遍售价七八十万元,纯电动客车高达200万也不稀奇。对于这么高售价的新能源公交客车,钱少、运营难的三线公交公司确实很难"高攀"得起。不过,对于湖南湘潭而言,答案却是"YES"!湖南湘潭公交,不但于去年大胆地一次性批量引进新能源公交车,且在使用半年后,还定下了"今后放弃传统车,只买新能源车"的采购策略。     

城市交通

海口新能源大巴试运行，公交车实现智能管理 9月2日，7辆4路新能源公交车在海口市区正式试用运行。市民仍只需花1元即可乘坐宽敞舒适的豪华公交车出行。智能公交调度系统在海口正式投入运行，海口公共汽车公司计划今年年底将27条线路、500多辆车全部纳入智能化管理，市民将逐渐告别“久等不来，一来好几辆”的尴尬局面。海口市公共汽车公司与中国电信合作，运用电话网、移动网、互联网融合优势，率先在海口启动智能调度示范运行系统。     

基于熵权模糊物元模型的港区温室气体减排潜力综合评价

针对港区温室气体减排的定量化评价,本本基于熵权和模糊物元模型,通过将熵权法确定评价指标的权重系数,物元分析和所计算熵权通过欧式贴近度得到有机的结合,建立了港区温室气体减排潜力熵权模糊物元评价模型,完成了对港区不同排放源的综合评价,为科学有效的港口区域温室气体减排提供技术支持。     

公交专用道线网规划方案评价方法

公交专用道线网规划方案评估是一项重要工作。以武汉市主城区公交专用道线网的布局规划方案为例，研究公交专用道线网规划方案评价方法和指标。采用复杂网络理论和动态仿真模型相结合的方法，构建公交专用道线网规划方案评价体系，从复杂网络结构特性角度构建了公交专用道线网评价指标，利用VISSIM对部分路段的公交专用道运行效果进行仿真实验。实验结果表明，设置公交专用道并规划公交专用道线网使公交车行程延误降低了约59%，行程时间减少约44%，平均车速上升，对优先发展公共交通有较好的促进作用，为公交专用道线网规划方案的评价提供理论参考。     

An activity-based approach for scheduling multimodal transit services

This paper proposes a new activity-based transit assignment model for investigating the scheduling (or timetabling) problem of transit services in multi-modal transit networks. The proposed model can be used to generate the short-term and long-term timetables of multimodal transit lines for transit operations and service planning purposes. The interaction between transit timetables and passenger activity-travel scheduling behaviors is captured by the proposed model, as the activity and travel choices of transit passengers are considered explicitly in terms of departure time choice, activity/trip chain choices, activity duration choice, transit line and mode choices. A heuristic solution algorithm which combines the Hooke–Jeeves method and an iterative supply–demand equilibrium approach is developed to solve the proposed model. Two numerical examples are presented to illustrate the differences between the activity-based approach and the traditional trip-based method, together with comparison on the effects of optimal timetables with even and uneven headways. It is shown that the passenger travel scheduling pattern derived from the activity-based approach is significantly different from that obtained by the trip-based method, and that a demand-sensitive (with uneven headway) timetable is more efficient than an even-headway timetable.     

Development of computerized vehicle scheduling system for specialized transportation: delaware case

A computer‐aided vehicle scheduling system is developed for Delaware's state‐wide specialized transportation system for elderly and handicapped persons. This paper presents the model, computer program package, system implementation, evaluation and the lessons learned from the project. The project which spanned two and a half years consists of five phases: (1) examine the manual method of vehicle scheduling (prior to the computerization); (2) develop a scheduling model; (3) develop a computer program package for the operation of the model; (4) install and operate the system; and (5) evaluate the system performance. The scheduling model consolidates passengers by block of time, origin and destination zones and along the direction of vehicle travel, first; second, minimizes the empty vehicle travel; and, lastly, assigns drivers to route considering assignment priority among the types of drivers and passengers. The product of the model is a set of driver log‐sheets which specify the sequence of passenger pick‐ups and drop‐offs. The computer program package includes necessary auxiliary data management functions such as registration of trip reservation, operating statistics and parameter value changes, as well as the execution of the model. It also allows the dispatcher to override the computer generated log‐sheet based on his discretion. The system has been operational for more than one year and it has brought about a number of changes in the operation and the role of the dispatcher. A before‐and‐after comparison of the operation and the lessons learned are also shown.     

On the cost of misperceived travel time variability

Because individuals may misperceive travel time distributions, using the implied reduced form of the scheduling model might fall short of capturing all costs of travel time variability. We reformulate a general scheduling model employing rank-dependent utility theory and derive two special cases as econometric specifications to study these uncaptured costs. It is found that reduced-form expected cost functions still have a mean–variance form when misperception is considered, but the value of travel time variability is higher. We estimate these two models with stated-preference data and calculate the empirical cost of misperception. We find that: (i) travelers are mostly pessimistic and thus tend to choose departure times too early to achieve a minimum cost, (ii) scheduling preferences elicited using a stated-choice method can be relatively biased if probability weighting is not considered, and (iii) the extra cost of misperceiving the travel time distribution might be nontrivial when time is valued differently over the time of day and is substantial for some people.     

Modeling activity conflict resolution strategies using scheduling process data

Developments in activity scheduling process data have recently allowed for much improvement and validation of rule-based activity scheduling models. The use of actual scheduling process data allows for a potentially more realistic model in terms of how the travel episodes are actually derived. Since these types of models are simulations of the scheduling process, scheduling conflicts naturally arise in the creation of the final schedule. The handling of these scheduling conflicts is a critical component of all rule-based activity models. Many of these models rely on the concept of priority of the activities to resolve conflicts. However, recent research has shown that this is often not how conflicts are actually resolved. In order to more accurately model actual conflict resolution behavior, scheduling process data was used to estimate conflict resolution rules using decision trees. The use of a conflict resolution model allows the strategy chosen to depend on the attributes of the individual and the conflicting activities, rather than assuming a prior definition of precedence based on the activity types. The conflict resolution decision showed reasonable ability to predict the resolution strategy chosen in actual scheduling process survey data, and represents an important advance in developing a functional computational process model of activity scheduling.     

Scheduling decision styles on leisure and social activities

This paper investigates scheduling decisions associated with different types of leisure and social activities. Correlations among decisions and self-selection biases are explicitly investigated by using a sample selection model with a bivariate probit selection rule. A dataset collected in the first wave of a recent activity-travel scheduling panel survey carried out in Valencia (Spain) was used for empirical investigation. Significant differences are revealed in the empirical models for leisure and social activities in planning decisions, including different effects of temporal, companionship and demographic factors. The findings of the empirical model have important implications to travel behavior and activity-travel scheduling model developments. These results confirm the existence of different mechanisms underlying the activity-travel decision processes when leisure and social activities are of concerns. Results provide significant insights into enhancing the performances of an activity scheduling model by capturing accurate activity-travel scheduling tradeoffs in flexible activity types e.g. leisure and social activities.     

Traveler delay costs and value of time with trip chains, flexible activity scheduling and information

The delay costs of traffic disruptions and congestion and the value of travel time reliability are typically evaluated using single trip scheduling models, which treat the trip in isolation of previous and subsequent trips and activities. In practice, however, when activity scheduling to some extent is flexible, the impact of delay on one trip will depend on the actual and predicted travel time on itself as well as other trips, which is important to consider for long-lasting disturbances and when assessing the value of travel information. In this paper we extend the single trip approach into a two trips chain and activity scheduling model. Preferences are represented as marginal activity utility functions that take scheduling flexibility into account. We analytically derive trip timing optimality conditions, the value of travel time and schedule adjustments in response to travel time increases. We show how the single trip models are special cases of the present model and can be generalized to a setting with trip chains and flexible scheduling. We investigate numerically how the delay cost depends on the delay duration and its distribution on different trips during the day, the accuracy of delay prediction and travel information, and the scheduling flexibility of work hours. The extension of the model framework to more complex schedules is discussed.     

A random utility maximization (RUM) based dynamic activity scheduling model: Application in weekend activity scheduling

The paper presents a modeling framework for dynamic activity scheduling. The modeling framework considers random utility maximization (RUM) assumption for its components in order to capture the joint activity type, location and continuous time expenditure choice tradeoffs over the course of the day. The dynamics of activity scheduling process are modeled by considering the history of activity participation as well as changes in time budget availability over the day. For empirical application, the model is estimated for weekend activity scheduling using a dataset (CHASE) collected in Toronto in 2002–2003. The data set classifies activities into nine general categories. For the empirical model of a 24-h weekend activity scheduling, only activity type and time expenditure choices are considered. The estimated empirical model captures many behavioral details and gives a high degree of fit to the observed weekend scheduling patterns. Some examples of such behavioral details are the effects of time of the day on activity type choice for scheduling and on the corresponding time expenditure; the effects of travel time requirements on activity type choice for scheduling and on the corresponding time expenditure, etc. Among many other findings, the empirical model reveals that on the weekend the utility of scheduling Recreational activities for later in the day and over a longer duration of time is high. It also reveals that on the weekend, Social activity scheduling is not affected by travel time requirements, but longer travel time requirements typically lead to longer-duration social activities.     

Integrated optimization on train scheduling and preventive maintenance time slots planning

We address the problem of simultaneously scheduling trains and planning preventive maintenance time slots (PMTSs) on a general railway network. Based on network cumulative flow variables, a novel integrated mixed-integer linear programming (MILP) model is proposed to simultaneously optimize train routes, orders and passing times at each station, as well as work-time of preventive maintenance tasks (PMTSs). In order to provide an easy decomposition mechanism, the limited capacity of complex tracks is modelled as side constraints and a PMTS is modelled as a virtual train. A Lagrangian relaxation solution framework is proposed, in which the difficult track capacity constraints are relaxed, to decompose the original complex integrated train scheduling and PMTSs planning problem into a sequence of single train-based sub-problems. For each sub-problem, a standard label correcting algorithm is employed for finding the time-dependent least cost path on a time-space network. The resulting dual solutions can be transformed to feasible solutions through priority rules. Numerical experiments are conducted on a small artificial network and a real-world network adapted from a Chinese railway network, to evaluate the effectiveness and computational efficiency of the integrated optimization model and the proposed Lagrangian relaxation solution framework. The benefits of simultaneously scheduling trains and planning PMTSs are demonstrated, compared with a commonly-used sequential scheduling method.     

Use of repeated cross-sectional travel surveys for developing meta models of activity-travel scheduling processes

Transportation - The paper presents an investigation of the temporal transferability of activity scheduling process models and a Meta model of activity scheduling processed by using repeated...     

Train routing and timetabling problem for heterogeneous train traffic with switchable scheduling rules

This paper proposes a mathematical model for the train routing and timetabling problem that allows a train to occasionally switch to the opposite track when it is not occupied, which we define it as switchable scheduling rule. The layouts of stations are taken into account in the proposed mathematical model to avoid head-on and rear-end collisions in stations. In this paper, train timetable could be scheduled by three different scheduling rules, i.e., no switchable scheduling rule (No-SSR) which allows trains switching track neither at stations and segments, incomplete switchable scheduling rule (In-SSR) which allows trains switching track at stations but not at segments, and complete switchable scheduling rule (Co-SSR) which allows trains switching track both at stations and segments. Numerical experiments are carried out on a small-scale railway corridor and a large-scale railway corridor based on Beijing–Shanghai high-speed railway (HSR) corridor respectively. The results of case studies indicate that Co-SSR outperforms the other two scheduling rules. It is also found that the proposed model can improve train operational efficiency.