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Initial Planning for Urban Transit Systems
... Robustness refers to the reliable capacity of recovery to the expected normal scenario from the daily disturbances that occur in real-life operations. In order to create a feasible timetable that can cope with these disturbances as adequately as possible, the time supplements, defined as buffer time (in railway context of Kroon et al. 2008) or slack time (in bus one of Wirasinghe 2003), are added into the planned timetable. Kroon et al. (2008) assumed that the periodic order of the trains is fixed. ...
... Only in this way could the buffer times be reallocated based on the same structure of the initial timetable, such that the optimally adjusted timetable is more robust than the original one, which involved a micro-macro approach. Wirasinghe (2003) gave a delay penalty adjusted by a slack time, and Zhao, Dessouky, and Bukkapatnam (2006) developed a method of optimizing slack time with the goal of minimizing passengers' expected waiting time by means of a D/G/c queue model. Furth and Muller (2009) optimized the number of slack time by holding at the time point in order to offer reliable dispatch at the terminal. ...
Urban rail transit (URT) demand often exhibits tidal or centripetal shapes during peak/off-peak hours. Inefficiency occurs when the supply of service is not adjusted to the fluctuating demand. This optimization can be realized by redesigning the URT timetables and/or through operational strategies. Certainly, implementation of the optimal/best-adjusted timetables must be feasible in order to accommodate efficient operations planning. This work develops a new methodology to derive an optimal timetabling design procedure for multiphase demands on a bi-directional URT line. It consists of three operational policies: marshaling, skip-stop operation, and robustness. The order of service is determined by the departure times. Following these considerations, this work develops a hybrid model formulated as a mixed-integer programming framework. The case study demonstrates the ability of the modeling framework to assess integrated effects, guarantee its robustness and to bring about 13% saving of the total cost associated with passengers, operations and reliability.
... Exclusive right of way and station construction costs are large, and vehicle purchase and maintenance costs are significantly higher than for buses. When considering the logistics of dispatching trains, optimal headways on public transit routes have been modeled and optimized (Newell, 1979;Wirasinghe, 2003), while platform and train length have been largely ignored as a factor in determining operating policy. Platform and train length are inherently linked, as train length must be less than or equal to platform length. ...
... There are a number of papers which approach the problem of optimization of urban public transit from the total cost perspective. In this method, relevant costs to passengers and the transit operator are combined and weighted to obtain a total system cost (Byrne and Vuchic, 1972;Hurdle, 1973;Newell, 1979;Strathman and Hopper, 1993;Wirasinghe, 2003). This combination often results in competing terms which can be analyzed to produce an "optimal result". ...
With light rail transit (LRT) and other similar rail-based commuter transit systems, train and associated station platform length provides an added dimension of flexibility not available to buses. Train and platform lengths are important factors in the planning and expansion phases of a network. Existing cost models that determine optimal headway by combining passenger and operational costs provide headways that are small and close to a logistical minimum (2–3 min); this type of standard waiting cost model is not sensitive to train and platform length. In this paper, on-board crowding is used as a cost factor and a cost-of-crowding model is developed from supporting psychological research. Two models are proposed and optimized with respect to train length to determine the optimal train and platform length for a many-to-one peak period commuter LRT system. Data from the C-Train network in Calgary, Alberta is used for numerical analysis of the model. The model demonstrated that crowding has an effect on optimal train length. The model produced feasible results when applied to a real-world scenario.
... The first group, idealized transit systems, was investigated by, for example, Newell (1971), De Palma and Lindsey (2001), and Wirasinghe (2003). Newell (1971) assumed a given passenger-arrival rate as a smooth function of time, with the objective of minimizing total passenger waiting time. ...
... De Palma and Lindsey (2001) develop a method for designing an optimal timetable for a single line with only two stations. Wirasinghe (2003) considered the average value of a unit waiting time per passenger (C 1 ) and the cost of dispatching a vehicle (C 2 ) to show that the passenger-arrival rate in Newell's square root formula is multiplied by (C 1 /2C 2 ). ...
The public-transport (transit) operation planning process commonly includes four basic activities, usually performed in sequence: network design, timetable development, vehicle scheduling, and crew scheduling. This work addresses two activities: timetable development and vehicle-scheduling with different vehicles types. Alternative timetables are constructed with either even headways, but not necessarily even passenger loads or even average passenger loads, but not even headways. A method to construct timetables with the combination of both even-headway and even-load concepts is developed for multi-vehicle sizes. The vehicle-scheduling problem is based on given sets of trips and vehicle types arranged in decreasing order of vehicle cost. This problem can be formulated as a cost-flow network problem with an NP-hard complexity level. Thus, a heuristic algorithm is developed. A few examples are used as an expository device to illustrate the procedures developed. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Organizing Committee.
... La programación de tareas es una actividad ampliamente requerida por la industria en general. Este problema ha sido trabajado para el desarrollo de proyectos con restricciones de recursos (Brucker et al., 1999;Neumann et al., 2003); asignación de fuerza laboral (Castillo-Salazar et al., 2016;Hung & Emmons, 1993); agenda de citas en sistemas de salud por su impacto en el desempeño de las instalaciones médicas (LaGanga & Lawrence, 2012;Zacharias & Pinedo, 2014), asignación de horarios en instituciones de educación (Vermuyten et al., 2016) y programación de transporte (de Palma & Lindsey, 2001;Wirasinghe, 2002), específicamente control de trenes (Scheepmaker et al., 2017;Wang & Goverde, 2019), control aéreo (Barnhart et al., 1998) y control marítimo. ...
La programación de horarios se clasifica como un problema combinatorio para el que existen múltiples alternativas de solución incluyendo entre ellas la programación entera. Sin embargo, el modelado de reglas operativas y el tamaño de problemas reales hace que su uso no sea común comparado con otras técnicas. El presente artículo propone un modelo de programación entera (IP), que aborda el problema de programación de horarios conformado por variables asociadas con franjas horarias, asignatura y docente asignado. También se incluyen parámetros como número de franjas horarias mínimas y máximas a impartir por el docente, tiempo de franja horaria, disponibilidad de docente, salones disponibles y costo estimado de insatisfacción generado por el horario asignado. En el modelo se integran siete restricciones duras y diecisiete blandas que proporcionan mayor calidad a la solución final de horarios. Se valida el modelo IP con una función objetivo global, en el que se reportan experimentos y resultados obtenidos en instancias reales de la Universidad de la Salle (ULS). El nuevo enfoque de solución ofrece mejoras en los horarios finales, así como la interacción con los usuarios durante su construcción. Finalmente, en las conclusiones del trabajo se discute el diseño y desarrollo de un sistema que brinda soporte a las decisiones, referenciando sugerencias para futuros desarrollos.
... In order to find an optimal route configuration state, we must develop a cost function to optimize. Following previous work by Byrne andVuchic (1972), Hurdle (1973), Newell (1979), Strathman andHopper (1993), Liu (1995), Wirasinghe and Liu (1995b), Wirasinghe and Liu (1995a), and Wirasinghe (2003) we develop a total cost function that accounts for operational and passenger costs of various sorts and attempts to minimize that cost. We will follow most closely the work done by Wirasinghe and Liu (1995a) in our consideration of individual cost terms. ...
Transit agencies struggle daily with the randomness experienced by vehicles as they traverse their routes. Since reliability is one of the most important factors in determining the quality of a transit system, it is important that agencies use effective and data-informed strategies to combat this randomness as much as possible. One commonly used strategy is holding control, where early buses are held at specifically chosen stops known as time points. This strategy presents a trade-off between improved reliability and longer overall travel time, and leads to the central research question: for a route with varied travel times between stops and fluctuating passenger demand, what is the optimal number and location of time points along a route? This thesis develops a mathematical Markov chain model of bus behaviour on an urban bus route which can capture the effects of time point placement and is sensitive to individual inter-stop travel times. After formulating a cost function to account for the various impacts of unreliability on passengers and operators, two algorithms are presented to optimally locate time points, with an improvement on existing configurations used by Calgary Transit in Calgary, Canada. Finally, a simulation model shows that holding control drastically reduces the occurrence of the phenomenon of bus bunching.
... This minimization process produces a so-called an "optimal" result. This approach has been used numerous times to model varied aspects of transportation systems, especially bus routes [8,9,10,11,12], but in principal it can be used whenever a large volume of passengers must balance with the heavy costs that operators experience. There is a difficulty in determining specific values of the parameters by which each of these costs are weighted, but by adjusting these values priority can be shifted between these competing interests. ...
Departure lounges are one of the few customizable areas in an airport that do not directly generate revenue. With high terminal construction costs, flexibility in lounge configuration means finding an appropriate seat configuration is in the interest of airport designers. There is also potential for shared used of lounges among gates. Lounge configurations are examined for various terminal layouts, and a mathematical model is presented to determine an optimal seat number, and the space for seats as well as standees, for various given passenger arrival curves. Analytic and numerical analysis of the model investigates possible efficiencies with shared departure lounges.
... For short frequency routes, passengers are more likely arrive at the stops randomly, which means the average waiting time can be expected half the vehicle interval. Based on reexamination of Newell's dispatching policy, Wirasinghe (2003) considered "the average value of a unit waiting time per passenger vw and the cost of dispatching a vehicle f to show that the passenger-arrival rate in Newell's square root formula is multiplied by vw/2f". The estimated total cost combining costs of running numerous bus routes i to j and remuneration from the grid are given as the objective cost function as below: ...
To date, the majority of the work undertaken in the transport literature on electric vehicles has focused on the private car market. However, it is widely recognised that there is enormous scope for the electrification of vehicle fleets, such as delivery vehicles and buses. One of the benefits of electric vehicle fleets, especially in the context of a deregulated power system, is that they offer scope to create flexibility in charging demands which expands the potential for incorporating large scale integration of renewable resources into generation. This benefit however depends on the close integration of fleet charging activity with the operation of the power system, especially with respect to the scheduling of charging events. In the case of electric buses fleets, these scheduling decisions are becoming highly complex, involving various trade-offs between the performance of the bus services experienced passengers, the costs and revenues experienced by operators and the electricity grid balancing costs experienced by the electric utility. The existing approaches in the literature for analysing this problem are limited and simplistic. In this paper we present a novel bus scheduling mechanism that enables electric bus fleets to participate actively and flexibly in electricity markets. In our model, the bus headway, fare and recharging duration are treated as decision variables in a nonlinear optimisation problem that balances the benefits of optimised flexibility to the bus operator and the grid. The multi-interval dynamic programming problem is subject to constraints from regulator, electric networks and elastic passenger utility functions to guarantee reliable services. In this scheme, electric buses can, for example, be dispatched based on both the commuting demand of passengers and the desirability to shift charging demand to bus stations with less congested electric distribution networks offering with lower electricity prices, thus achieved reduced cost of recharging. The contribution of this work is to provide a new tool to enable the quantification and evaluation of the potential operation change of exploiting this flexibility in EV bus fleets in a highly interactive transport network with dynamic electricity pricing schemes. This is illustrated with a simple numerical example.
... Regrettably, most studies on continuum models of transit system design are focused on single-mode networks (Newell, 1973;1979;Holryod, 1967;Vuchic and Newell, 1968;Byrne, 1975;Wirasinghe and Ghoneim, 1981;Chien and Scholfeld, 1997;Wirasinghe, 2003;Daganzo 2010a, b;Estrada et al., 2011;Badia et al., 2014;Ouyang et al., 2014). Some works examined local-and-express systems (Gu et al., 2016;Fan et al., 2017) or other systems with differentiated transit services (Chang and Schonfeld, 1993;Li and Quadrifoglio, 2011;Freyss et al., 2013;Gu et al., 2016). ...
The urban transit system in a real city usually has two major components: a sparse express (e.g. rail) network and a dense local (e.g. bus) network. The two networks intersect and interweave with each other throughout the city to furnish various route options for serving transit patrons with distinct ODs. The optimal design problem of this bimodal transit system, however, has not been well explored in the literature, partly due to the difficulty of modeling the patrons’ complex route choice behavior in the bimodal networks. In light of this, we formulate parsimonious continuum models for minimizing the total cost of the patrons and the transit agency for an intersecting bimodal transit network in a grid city, where the perpendicular local and express lines intersect at transfer stops. Seven distinct route types are identified in this network, which represent realistic intra- and inter-modal route options. A lower-level assignment problem between these routes is embedded in the upper-level network design optimization problem. We develop an efficient method to find near-optimal designs of the intersecting network. Numerical results unveil a number of insightful findings, e.g., that sizable cost savings are observed for the intersecting bimodal design as compared to the single-mode designs for moderate to high demand levels, and that only moderate benefits are observed as compared to the trunk-feeder designs under certain operating conditions. We also show that the conventional practice of designing the local and express networks separately would greatly undermine the benefit of the bimodal system.
... However, compared to numerous studies on departing passengers in multi-airport regions, little is known about the specific needs of transfer passengers, regardless of their importance to many airlines and hub airports. Because transfer signifies the moving and waiting at airports, minimization of walking distances was widely discussed while assessing different terminal configurations (Bandara and Wirasinghe 1992;Wirasinghe 2003), and was considered essential in order to maximize operational efficiency and to minimize connection times. As an example study, Barros et al. (2007) sought to examine the perceptions of airport transfer passengers on facilities and services related to transfer by utilizing regression analysis in the context of Bandaranaike International Airport in Sri Lanka. ...
Due mainly to the privatization and commercialization of airline companies and deregulation of the aviation rules, the demand for air transport has continuously been increasing. Airport authorities state that transfer passengers, who contribute to the large portion of the airports’ profits, are gaining much more importance, particularly in the Northeast Asia region where the air transport industry is very vital. Therefore, this study aims to investigate the competitiveness of IIA (Incheon International Airport) with other major airports located in Northeast Asia in passenger transfers made between Southeast Asia and China to North America using Conjoint Analysis. Results have indicated that airport brand is the most important attribute for the competitiveness of airport, followed by cost, connectivity and duty free shops. In further analysis focusing on brand value of the three airports measured by the use of transfer passengers, it was revealed that IIA needs more effort in developing their brand identity to become the leading transfer hub airport. Based on the results, recommendations for increasing the brand value have also been suggested.
... Banks [8] presented a model for setting headways in a transit-route system. Wirasinghe [9] examined the validity of a traditional method, formulated by Newell, for determining frequencies. ...
Due to relatively low passenger demand, headways of suburban bus route are usually longer than those of urban bus route. Actually it is also difficult to balance the benefits between passengers and operators, subject to the service standards from the government. Hence the headway of a suburban bus route is usually determined on the empirical experience of transport planners. To cope with this problem, this paper proposes an optimization model for designing the headways of suburban bus routes by minimizing the operating and user costs. The user costs take into account both the waiting time cost and the crowding cost. The feasibility and validity of the proposed model are shown by applying it to the Route 206 in Jiangning district, Nanjing city of China. Weightages of passengers’ cost and operating cost are further discussed, considering different passenger flows. It is found that the headway and objective function are affected by the weightages largely.
... The transit scheduling problem has received much attention in the past years [1][2][3]. LeBlanc introduced a model for determining frequencies using a modal-split assignment programming model with distinct transit routes to capture the effects of increases or decreases on individual transit line frequencies [4]. Lee et al. revealed that the total fleet size required for the operation of a bus network with mixedsize vehicles is smaller than the fleet size necessary when all fleet vehicles have the same regular size [5]. ...
This paper focuses on an urban transit line which connects several residential areas and a workplace during the morning rush hours. The congestion is represented by some passengers who must wait for an extended duration and board the next or the third departure vehicles. This paper divides the time horizon equally into several small periods to measure the dynamic passenger demands. Under period-dependent demand conditions, a biobjective optimization model is developed to determine the departure times of transit vehicles at the start station with strict capacity constraints, in which a heuristic algorithm based on intelligent search and local improvement is designed to solve the model. The developed model can address the case in which more than two passengers arrive at a station simultaneously during one same period and calculate the number of boarded passengers. Finally, the model and algorithm have been successfully verified by a numerical example.
... More complex models plan both routes and frequencies at a strategic level [28]; however, they are hardly employed, since they cannot incorporate practical and operational considerations in the optimization analysis [5]. In addition, PTCs prefer adjusting frequencies rather than passively implementing drastic changes from the MBA blackboxes [29]. Moreover, owing to their complexity, these models can be solved by heuristic methods only. ...
Knowledge of ridership data on bus routes is pivotal for the quality and the efficient operational planning of public transport companies. Automatic Passenger Counting can represent a powerful resource for supporting this activity, because it can provide a databank of accurate counts.
However, relevant challenges, such as the matching of data to the bus stop, data validation, tackling anomalies and building intelligible performance reports, must be faced in order to make Automatic Passenger Counting data a mainstream source of information.
This paper proposes an off-line framework for addressing these challenges. In order to illustrate a possible application of the framework, its use for setting bus frequencies is investigated. The results are represented by easy-to-read control dashboards made up with tables and graphs. The methodology is tested experimentally with data records provided by the bus operator CTM in Cagliari (Italy). Finally, we discuss the implications on service rearrangement.
Measuring the value of research results is very important to justify the efficiency and effectiveness of research programs. However, it has received very little attention by transportation agencies and researchers because of the lack of knowledge in benefit measures, scarcity of data required, and absence of consistent quantification methods. The purpose of this paper is to develop a mapping table that links research categories and subcategories, benefit categories and subcategories, and benefit measures by taking the following steps: data collection, data analysis, and development of a mapping table. Because this study pertains to qualitative research, thematic analysis and a clustering-and-coding approach were used for the data analysis, and the research findings were reviewed by experts from state DOTs in the Southeast Transportation Consortium to assure the credibility of the study. The proposed mapping table can assist public agencies in the development of reliable, systematic, research program evaluation procedures.
Timetable design is crucial to the reliability of a metro service. In terms of the delays caused by passengers' boarding and alighting behaviors during rush hours, the planned timetable for a metro line with high-frequency service tends to be difficult to implement. General oversaturation events, rather than accidents or track damage, still have a significant impact on metro systems, so that trains are canceled and delayed. When the activity reality diverges from the real-time or historical information, it is imperative that dispatchers present a good solution during the planning stage in order to minimize the nuisance for passengers and reduce the crowding risk. This paper presents a robust timetabling model (RTM) for a metro line with passenger activity information, which takes into account congestion and buffer time adjustments. The main objective pursued by dispatchers in the model is the enhancement of punctuality while minimizing train delays by adjusting the buffer time. By explicitly taking the passenger activity information into account, a mixed integer nonlinear programming (MINLP) model was developed, and a genetic algorithm (GA) is proposed to solve the model. Finally, numerical experiments based on the Batong line of the Beijing Metro were carried out, the results of which verify the effectiveness and efficiency of our method.
With advances in the intelligent-transportation-system technology of passenger information systems, the importance of even and clock headways in transit timetables is reduced. This allows for the creation of timetables with even average maximum passenger loads on individual vehicles. The maximum load attained is a load standard desired at the maximum-load stop. These timetables minimize overcrowding and produce more reliable and efficient schedules than other timetables from both passenger and operator perspectives. Thus, it will make the transit service more attractive. This paper provides a procedure for the creation of transit timetables to improve the correspondence of vehicle departure times with passenger demand. The algorithm developed yields departure times (a timetable) for vehicles to achieve an even maximum load on each vehicle and a smoothing consideration in the transition between time periods. A case study was carried out using the data of one bus route in Auckland, New Zealand, with modeling and simulation analyses. The results of the even maximum load on individual vehicles, in terms of the elimination of overcrowding and schedule adherence, exhibit significant improvement over timetables with even headways or with headways based on hourly even maximum loads.
Community shuttle plays an important role in public transit microcirculation, and developing an optimal timetable for the community shuttle to make it dovetail with the metro schedule well is an important job. The paper presents a solution for coordinated timetable designing problem with the objective of minimizing the total cost, including user cost and shuttle supplier cost. User cost can be divided into four parts further: user access cost, in-vehicle cost, wait cost and transfer cost, where wait cost and supplier cost are related to the shuttle headway directly, and transfer cost is related to the timetable generated by the headway. So in the process of solving the problem, a heuristic algorithm is developed to determine the minimized transfer cost for a given headway, then a genetic algorithm is used to determine the optimal headway. At last, a case study based on passenger count data of an existing route served for Tian Tongyuan Community in Beijing is presented. The result shows that the optimal timetable obtained from the model saves much cost compared with the existing timetable.
Determination of bus stop locations and bus stop frequencies are important issues in public transportation planning. This study analyzes the relationships among demand, travel time, bus stop locations, frequency, fleet size and passenger capacity parameters and develops models for bus stop locations and bus service frequency using fuzzy linear programming and linear goal programming approaches. The models are microscopic and applied to determine the bus stop locations and bus service frequency in the city of Izmir, Turkey, where 26 bus routes pass through two stops in the center city. The fuzzy optimization model minimizes the passenger access time and in-vehicle travel time. The reduction of the values of the bus service frequency and time parameters derived by the two proposed models are validated by a cost function. Encouraging results are obtained.
Demand and supply uncertainties at schedule-based transit network levels strongly impact different passengers’ travel behavior.
In this paper, a new multi-class user reliability-based dynamic transit assignment model is presented. Passengers differ in
their heterogeneous risk-taking attitudes towards the random travel cost. The stochastic characteristics of the main travel
cost components (in-vehicle travel time, waiting time, and early or late penalty) are demonstrated by specifying the demand
and supply uncertainties and their interactions. Passenger route and departure time choice is determined by each passenger’s
respective reliability requirements. Vehicle capacity constraint for random passenger demand is handled by an in-vehicle congestion
parameter. The proposed model is formulated as a fixed-point problem, and solved by a heuristic MSA-type algorithm. The numerical
result shows that the risk-taking attitude will impact greatly on passengers’ travel mode and departure time choices, as well
as their money and time costs. This model is also capable of generating transit service attributes such as the stochastic
vehicle dwelling time and the deviated timetable.
KeywordsReliability-based stochastic user equilibrium-Schedule-based transit assignment-Multi-class-Demand uncertainty-Supply uncertainty-Capacity constraint
JEL ClassificationR410
Passenger transportation in most large cities relies on an efficient mass transit system, whose line configuration has direct impacts on the system operating cost, passenger travel time and line transfers. Unfortunately, the interplay between transit line configuration and passenger line assignment has been largely ignored in the literature. This paper presents a model for simultaneous optimization of transit line configuration and passenger line assignment in a general network. The model is formulated as a linear binary integer program and can be solved by the standard branch and bound method. The model is illustrated with a couple of minimum spanning tree networks and a simplified version of the general Hong Kong mass transit railway network.
Passenger transportation in most large cities relies on an efficient mass transit system, whose line configuration has direct impacts on the system operating cost, passenger travel time and line transfers. Unfortunately, the interplay between transit line configuration and passenger line assignment has been largely ignored in the literature. This paper presents a model for simultaneous optimization of transit line configuration and passenger line assignment in a general network. This model is formulated into a linear binary integer program and can be solved by any integer solver. The standard branch and bound method is used in our model, and one network example is tested.
The two parts of this book treat probability and statistics as mathematical disciplines and with the same degree of rigour as is adopted for other branches of applied mathematics at the level of a British honours degree. They contain the minimum information about these subjects that any honours graduate in mathematics ought to know. They are written primarily for general mathematicians, rather than for statistical specialists or for natural scientists who need to use statistics in their work. No previous knowledge of probability or statistics is assumed, though familiarity with calculus and linear algebra is required. The first volume takes the theory of probability sufficiently far to be able to discuss the simpler random processes, for example, queueing theory and random walks. The second volume deals with statistics, the theory of making valid inferences from experimental data, and includes an account of the methods of least squares and maximum likelihood; it uses the results of the first volume.
Suppose that a given number n of vehicles (buses, trains, etc.) maybe dispatched at any times from some source to serve passengers along a route. The arrival rate of passengers is some given (nonconstant) smooth function of time, typically having one or more peaks. One wishes to choose the dispatch times t j , j = 1, ..., n in such a way as to minimize the total waiting time of all passengers. It is shown that if the capacity of the vehicles is sufficiently large to serve all waiting passengers and n is large, then the optimal flow rate of vehicles (reciprocal of the headways) and the number of passengers served per vehicle, both vary with time approximately as the square root of the arrival rate of passengers. If the vehicles have limited capacity, their dispatch schedule will be distorted so that certain vehicles are dispatched as soon as they are full.
Many optimization problems involve the determination of a large number of (perhaps vector-valued) parameters which collectively can be represented as a set of points in some space of small dimension. For example, one may wish to determine a set of times at which to dispatch buses (a set of points on the real line), or to locate public facilities in a city (a set of points in the plane). In many of these problems, the objective function is more sensitive to spacings between the points than to their precise location. By elementary methods, one can often find an approximate global optimal by simply choosing the spacings (or approximate density of points) so as to give a “local optimal” everywhere in the space.
Thirty-five papers are compiled to perceive that public transportation can be provided through a variety of technologies and institutional arrangements. Part I (Historical Development) covers the history of public transportation development in the US. The chapters are in chronological order historically and tie the actual events to causes and effects. Part II (Systems and Technologies) exposes various modes used presently to provide public transportation service and how the modes interrelate. Part II (Comparing Transit Modes) concentrates on the factors to consider in alternative analyses studies of transportation systems. Part IV (Planning Public Transportation Systems) covers the vital planning area, including the historical development of planning; significant current issues regarding the planning process; the newest planning tool, the TSM; planning for small systems as found in rural areas; and how system planning for public transportation differs from transportation planning per se. Part V (Managing and Operating Public Transit Systems) delves into the important field of managing an area of growing concern. The increased development of the industry through expansion and new systems is putting a severe strain on the trained management market. Part VI (Policy Considerations) goes into the specifics of various factors that influence public transit services. Part VII (The Future) looks at the future from several viewpoints.
A decision analytic model for the selection of mass transit technology is suggested. The model considers a transit corridor with known right of way category and rules of operation. The system with technology under evaluation satisfies the users’, operators’ and community requirements roughly equally and has identical level of comfort, convenience and other nonquantifiable attributes of performance measures. Cost attributes comprise of access/egress cost, riding time cost, waiting time cost in users’ side, transit operating cost, station cost, line cost and fleet cost in the operators’ side, and the measurable cost of air pollution on the community's cost side. Given the subjective probabilities of the chance event influencing the decision and possible outcomes of the event, technology, which offers the maximum expected utility, is established. This utility indicator together with other unmodellable factors can form the basis for decision making on technology selection. The problem is extended to include multiple chance events and outcomes of more definitive experiments with updated probabilities. It is shown that transit technology similar to Light Rail Transit could be considered viable in developing countries only when the value of travel time is considerably higher than what it is now.
This paper describes a simulation model of schedule design for a fixed transit route adopting the holding control strategy. The model is capable of determining the locations of time points and the amount of slack time allocated to each time point by minimizing the total cost associated with the schedule. The optimization is carried out through a process, which combines a heuristic search, enumeration, and population ranking and selection techniques. Examples showing applications and potential savings of the proposed model are given. It is shown that the model can serve as a practical tool for designing reliable, economical as well as operational transit schedules.
Urban bus services still play an important role in the movement of people in Britain, although since the 1950's bus patronage has been declining and costs of operation have been increasing. Most of the urban bus networks in Britain (and to a very large extent the Western World) have developed or evolved over the years and it is sometimes said that, despite the changing conditions of bus transport, few of these bus networks in Britain have undergone major re-organisation. A survey was carried out to ascertain this view and to establish the approaches used by British urban bus operators.
Five approaches to the planning of urban bus routes and frequencies have been identified: (1) manual; (2) Market Analysis Project; (3) systems analysis; (4) systems analysis with interactive graphics; and (5) mathematical. Previous research in, and application of, the different approaches are described and examined.
Between 1970 and 1980, 82.4% of those British urban operators who responded to the survey carried out some kind of major bus study. The survey results run counter to the view that there has been little recent change in urban bus networks in Britain, but the alleged conservatism of the bus industry appears when the approaches used for re-planning bus services are examined - 71.4% of the operators used a manual approach and only a meagre 28.6% made use of simple assignment techniques to predict the potential passenger impacts of the alternative networks appraised.
A psychological scaling technique, magnitude estimation, is used to rate time spent on various elements of bus transit trips. Relative values of time are found for in-vehicle portions of trips, walking, waiting and transferring. Because magnitude estimation produces a ratio scale, results can be directly incorporated into modal choice analyses, route planning and evaluation procedures where monetary values of time are not necessarily required.
An analytical model for the determination of the number and locations of time points as well as the amount of slack times in transit schedule design is developed. The model considers a bus route with a special passenger demand pattern in which all boarding passengers coordinate their arrivals at each stop in such a way that they never miss their intended bus, and therefore designing the schedule separately a single run at a time, becomes possible. The model employs the dynamic programming method to deal with the trade-offs among various cost components associated with the schedule quantitatively, and yet is flexible enough to incorporate the existing rules of thumb as well as transit operators' policies. Numerical examples that illustrate the applications of the model are given. The model, although not quite applicable to bus routes with general passenger demand patterns, is useful in the analysis of the contributing factors to the design of an economical, reliable, and operational transit schedule, and is likely to be adaptable for more realistic cases.
Modelling Transportation
- J Ortuzar
- D De
- L G Willumsen
Canadian Transit Handbook
- R M Soberman