本科毕业设计外文文献及译文1

本科毕业设计外文文献及译文文献、资料题目：Transit Route Network Design Problem:Review文献、资料来源：网络文献、资料发表（出版）日期：2007.1院（部）：xxx专业：xxx班级：xxx姓名：xxx学号：xxx指导教师：xxx翻译日期：xxx外文文献：Transit Route Network Design Problem: Review Abstract: Efficient design of public transportation networks has attracted much interest in the transport literature and practice, with manymodels and approaches for formulating the associated transit route network design problem _TRNDP_ having been developed. The presentpaper systematically presents and reviews research on the TRNDP based on the three distinctive parts of the TRNDP setup: designobjectives, operating environment parameters and solution approach.IntroductionPublic transportation is largely considered as a viable option for sustainable transportation in urban areas, offering advantages such as mobility enhancement, traffic congestion and air pollution reduction, and energy conservation while still preserving social equity considerations. Nevertheless, in the past decades, factors such as socioeconomic growth, the need for personalized mobility, the increase in private vehicle ownership and urban sprawl have led to a shift towards private vehicles and a decrease in public transportation’s share in daily commuting (Sinha 2003; TRB 2001; EMTA 2004; ECMT 2002; Pucher et al. 2007). Efforts for encouraging public transportation use focuses on improving provided services such as line capacity, service frequency, coverage, reliability, comfort and service quality which are among the most important parameters for an efficient public transportation system (Sinha 2003; Vuchic 2004.) In this context, planning and designing a cost and service efficientpublic transportation network is necessary for improving its competitiveness and market share. The problem that formally describes the design of such a public transportation network is referred to as the transit route network design problem (TRNDP);it focuses on the optimization of a number of objectives representing the efficiency of public transportation networks under operational and resource constraints such as the number and length of public transportation routes, allowable service frequencies, and number of available buses (Chakroborty 2003; Fan and Machemehl 2006a,b).The practical importance of designing public transportation networks has attractedconsiderable interest in the research community which has developed a variety of approaches and modelsfor the TRNDP including different levels of design detail and complexity as well as interesting algorithmic innovations. In thispaper we offer a structured review of approaches for the TRNDP; researchers will obtain a basis for evaluating existing research and identifying future research paths for further improving TRNDP models. Moreover, practitioners will acquire a detailed presentation of both the process and potential tools for automating the design of public transportation networks, their characteristics, capabilities, and strengths.Design of Public Transportation NetworksNetwork design is an important part of the public transportation operational planning process _Ceder 2001_. It includes the design of route layouts and the determination of associated operational characteristics such as frequencies, rolling stock types, and so on As noted by Ceder and Wilson _1986_, network design elements are part of the overall operational planning process for public transportation networks; the process includes five steps: _1_ design of routes; _2_ setting frequencies; _3_ developing timetables; _4_ scheduling buses; and _5_ scheduling drivers. Route layout design is guided by passenger flows: routes are established to provide direct or indirect connection between locations and areas that generate and attract demand for transit travel, such as residential and activity related centers _Levinson 1992_. For example, passenger flows between a central business district _CBD_ and suburbs dictate the design of radial routes while demand for trips between different neighborhoods may lead to the selection of a circular route connecting them. Anticipated service coverage, transfers, desirable route shapes, and available resources usually determine the structure of the route network. Route shapes areusually constrained by their length and directness _route directness implies that route shapes are as straight as possible between connected points_, the usage of given roads, and the overlapping with other transit routes. The desirable outcome is a set of routesconnecting locations within a service area, conforming to given design criteria. For each route, frequencies and bus types are the operational characteristics typically determined through design. Calculations are based on expected passenger volumes along routes that are estimated empirically or by applying transit assignmenttechniques, under frequency requirement constraints _minimum and maximum allowedfrequencies guaranteeing safety and tolerable waiting times, respectively_, desired load factors, fleet size, and availability. These steps as well as the overall design.process have been largely based upon practical guidelines, the expert judgment of transit planners, and operators experience _Baaj and Mahmassani 1991_. Two handbooks by Black _1995_ and Vuchic _2004_ outline frameworks to be followed by planners when designing a public transportation network that include: _1_ establishing the objectives for the network; _2_ defining the operational environment of the network _road structure, demand patterns, and characteristics_; _3_ developing; and _4_ evaluating alternative public transportation networks.Despite the extensive use of practical guidelines and experience for designing transit networks, researchers have argued that empirical rules may not be sufficient for designing an efficient transit network and improvements may lead to better quality and more efficient services. For example, Fan and Machemehl _2004_ noted that researchers and practitioners have been realizing that systematic and integrated approaches are essential for designing economically and operationally efficient transit networks. A systematic design process implies clear and consistent steps and associated techniques for designing a public transportation network, which is the scope of the TRNDP.TRNDP: OverviewResearch has extensively examined the TRNDP since the late 1960s. In 1979, Newell discussed previous research on the optimal design of bus routes and Hasselström _1981_ analyzed relevant studies and identified the major features of the TRNDP as demand characteristics, objective functions, constraints, passengerbehavior, solution techniques, and computational time for solving the problem. An extensive review of existing work on transit network design was provided by Chua _1984_ who reported five types of transit system planning: _1_ manual; _2_ marketanalysis; _3_ systems analysis; _4_ systems analysis with interactive graphics; and _5_ mathematical optimization approach. Axhausemm and Smith _1984_ analyzed existing heuristic algorithms for formulating the TRNDP in Europe, tested them, anddiscussed their potential implementation in the United States. Ceder and Wilson _1986_ reportedprior work on the TRNDP and distinguished studies into those that deal with idealized networks and to those that focus on actual routes, suggesting that the main features of the TRNDP include demand characteristics, objectivesand constraints, and solution methods.At the same period, Van Nes et al. _1988_ grouped TRNDP models into six categories: _1_ analytical models for relating parameters of the public transportation system; _2_ models determining the links to be used for public transportation route construction; _3_ models determining routes only; _4_ models assigning frequencies to a set of routes; _5_ two-stage models for constructing routes and then assigning frequencies; and _6_ models for simultaneously determining routes and frequencies. Spacovic et al. _1994_ and Spacovic and Schonfeld _1994_ proposed a matrix organization and classified each study according to design parameters examined, objectives anticipated, network geometry, and demand characteristics. Ceder and Israeli _1997_ suggested broad categorizations for TRNDP models into passenger flow simulation and mathematical programming models. Russo _1998_ adopted the same categorization and noted that mathematical programming models guarantee optimal transit network design but sacrifice the level of detail in passenger representation and design parameters, while simulation models address passenger behavior but use heuristic procedures obtaining a TRNDP solution. Ceder _2001_ enhanced his earlier categorization by classifying TRNDP models into simulation, ideal network, and mathematical programming models. Finally, in a recent series of studies, Fan and Machemehl _2004, 2006a,b_ divided TRNDP approaches into practical approaches, analytical optimization models for idealized conditions, and metaheuristic procedures for practical problems.The TRNDP is an optimization problem where objectives are defined, its constraints are determined, and a methodology is selected and validated for obtaining an optimal solution. The TRNDP is described by the objectives of the public transportation network service to be achieved, the operational characteristics and environment under which the network will operate, and the methodological approach for obtaining the optimal network design. Based on this description of the TRNDP, we propose a three-layer structure for organizing TRNDP approaches _Objectives, Parameters, and Methodology_. Each layer includes one or more items that characterize each study.The ―Objectives‖ layer incorporates the goals set when designing a public transportation system such as the minimization of the costs of the system or the maximization of the quality of services provided. The ―Parameters‖ layer describes the operating environment and includes both the design variables expected to be derived for the transit network _route layouts, frequencies_ as well as environmental and operational parameters affecting and constraining that network _for example, allowable frequencies, desired load factors, fleet availability, demand characteristics and patterns, and so on_. Finally, the ―Methodology‖ layer covers the logical–mathematical framework and algorithmic tools necessary to formulate and solve the TRNDP. The proposed structure follows the basic concepts toward setting up a TRNDP: deciding upon the objectives, selecting the transit network items and characteristics to be designed, setting the necessary constraints for the operating environment, and formulating and solving the problem.TRNDP: ObjectivesPublic transportation serves a very important social role while attempting to do this at the lowest possible operating cost. Objectives for designing daily operations of a public transportation system should encompass both angles. The literature suggests that most studies actually focus on both the service and economic efficiency when designing such a system. Practical goals for the TRNDP can be briefly summarized as follows _Fielding 1987; van Oudheudsen et al. 1987; Black 1995_: _1_ user benefit maximization; _2_ operator cost minimization; _3_ total welfare maximization; _4_ capacity maximization; _5_ energy conservation— protection of the environment; and _6_ individual parameter optimization.Mandl _1980_ indicated that public transportation systems have different objectives to meet. He commented, ―even a single objective problem is difficult to attack‖ _p. 401_. Often, these objectives are controversial since cutbacks in operating costs may require reductions in the quality of services. Van Nes and Bovy _2000_ pointed out that selected objectives influence the attractiveness and performance of a public transportation network. According to Ceder and Wilson _1986_, minimization of generalized cost or time or maximization of consumer surplus were the most common objectives selected when developing transit network design models. Berechman _1993_ agreed that maximization of total welfare is the most suitable objective for designing a public transportation system while Van Nes and Bovy _2000_ argued that the minimization of total user and system costs seem the most suit able and less complicatedobjective _compared to total welfare_, while profit maximization leads to nonattractive public transportation networks.As can be seen in Table 1, most studies seek to optimize total welfare, which incorporates benefits to the user and to the system. User benefits may include travel, access and waiting cost minimization, minimization of transfers, and maximization of coverage, while benefits for the system are maximum utilization and quality of service, minimization of operating costs, maximization of profits, and minimization of the fleet size used. Most commonly, total welfare is represented by the minimization of user and system costs. Some studies address specific objectives from the user, theoperator, or the environmental perspective. Passenger convenience, the number of transfers, profit and capacity maximization, travel time minimization, and fuel consumption minimization are such objectives. These studies either attempt to simplify the complex objective functions needed to setup the TRNDP _Newell 1979; Baaj and Mahmassani 1991; Chakroborty and Dwivedi 2002_, or investigate specific aspects of the problem, such as objectives _Delle Site and Fillipi 2001_, and the solution methodology _Zhao and Zeng 2006; Yu and Yang 2006_.Total welfare is, in a sense, a compromise between objectives. Moreover, as reported by some researchers such as Baaj and Mahmassani _1991_, Bielli et al. _2002_, Chackroborty and Dwivedi _2002_, and Chakroborty _2003_, transit network design is inherently a multiobjective problem. Multiobjective models for solving the TRNDP have been based on the calculation of indicators representing different objectives for the problem at hand, both from the user and operator perspectives, such as travel and waiting times _user_, and capacity and operating costs _operator_. In their multiobjective model for the TRNDP, Baaj and Majmassani_1991_ relied on the planner’s judgment and experience for selecting the optimal public transportation network, based on a set of indicators. In contrast, Bielli et al. _2002_ and Chakroborty and Dwivedi _2002_, combined indicators into an overall, weighted sum value, which served as the criterion for determining the optimaltransit network.TRNDP: ParametersThere are multiple characteristics and design attributes to consider for a realistic representation of a public transportation network. These form the parameters for the TRNDP.Part of these parameters is the problem set of decision variables that define its layout and operational characteristics _frequencies, vehicle size, etc._. Another set of design parameters represent the operating environment _network structure, demand characters, and patterns _, operational strategies and rules, and available resources for the public transportation network. These form the constraints needed to formulate the TRNDP and are, a-priori fixed, decided upon or assumed.Decision VariablesMost common decision variables for the TRNDP are the routes and frequencies of the public transportation network _Table 1_. Simplified early studies derived optimal route spacing between predetermined parallel or radial routes, along with optimal frequencies per route _Holroyd 1967; Byrne and Vuchic 1972; Byrne 1975, 1976; Kocur and Hendrickson 1982; Vaughan 1986_, while later models dealt with the development of optimal route layouts and frequency determination. Other studies, additionally, considered fares _Kocur and Hendrickson 1982; Morlok and Viton 1984; Chang and Schonfeld 1991; Chien and Spacovic 2001_, zones _Tsao and Schonfeld 1983; Chang and Schonfeld 1993a_, stop locations _Black 1979; Spacovic and Schonfeld 1994; Spacovic et al. 1994; Van Nes 2003; Yu and Yang 2006_ and bus types _Delle Site and Filippi 2001_.Network StructureSome early studies focused on the design of systems in simplified radial _Byrne 1975; Black 1979; Vaughan 1986_, or rectangular grid road networks _Hurdle 1973; Byrne and Vuchic 1972; Tsao and Schonfeld 1984_. However, most approaches since the 1980s were either applied to realistic, irregular grid networks or the network structure was of no importance for the proposed model and therefore not specified at all.Demand PatternsDemand patterns describe the nature of the flows of passengers expected to be accommodated by the public transportation network and therefore dictate its structure. For example, transit trips from a number of origins _for example, stops in a neighborhood_ to a single destination _such as a bus terminal in the CBD of a city_ and vice-versa, are characterized as many-to-one _or one-tomany _ transit demand patterns. These patterns are typically encountered in public transportation systems connecting CBDs with suburbs and imply a structure of radial orparallel routes ending at a single point; models for patterns of that type have been proposed by Byrne and Vuchic _1972_, Salzborn _1972_, Byrne _1975, 1976_, Kocur and Hendrickson _1982_, Morlok and Viton _1984_, Chang and Schonfeld _1991, 1993a_, Spacovic and Schonfeld _1994_, Spacovic et al. _1994_, Van Nes _2003_, and Chien et al. _2003_. On the other hand, many-to-many demand patterns correspond to flows between multiple origins and destinations within an urban area, suggesting that the public transportation network is expected to connect various points in an area.Demand CharacteristicsDemand can be characterized either as ―fixed‖ _or ―inelastic‖_ or―elastic‖; the later meaning that demand is affected by the performance and services provided by the public transportation network. Lee and Vuchic _2005_ distinguished between two types of elastic demand: _1_ demand per mode affected by transportation services, with total demand for travel kept constant; and _2_ total demand for travel varying as a result of the performance of the transportation system and its modes. Fan and Machemehl _2006b_ noted that the complexity of the TRNDP has led researchers intoassuming fixed demand, despite its inherent elastic nature. However, since the early 1980s, studies included aspects of elastic demand in modeling the TRNDP _Hasselstrom 1981; Kocur and Hendrickson 1982_. Van Nes et al. _1988_ applied a simultaneous distribution-modal split model based on transit deterrence for estimatingdemand for public transportation. In a series of studies, Chang and Schonfeld _1991, 1993a,b_ and Spacovic et al. _1994_ estimated demand as a direct function of travel times and fares with respect to their elasticities, while Chien and Spacovic 2001_, followed the same approach assuming that demand is additionally affected by headways, route spacing and fares. Finally, studies by Leblanc _1988_, Imam _1998_, Cipriani et al. _2005_, Lee and Vuchic _2005_; and Fan and Machemehl _2006a_ based demand estimation on mode choice models for estimating transit demand as a function of total demand for travel.中文译文：公交路线网络设计问题：回顾摘要：公共交通网络的有效设计让交通理论与实践成为众人关注的焦点，随之发展出了很多规划相关公交路线网络设计问题（TRNDP）的模型与方法。