【文章內(nèi)容簡(jiǎn)介】 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 mented, ―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 _2021_ pointed out that selected objectives influence the attractiveness and performance of a public transportation work. According to Ceder and Wilson _1986_, minimization of generalized cost or time or maximization of consumer surplus were the most mon objectives selected when developing transit work 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 _2021_ argued that the minimization of total user and system costs seem the most suit able and less plicated 畢業(yè)設(shè)計(jì)外文文獻(xiàn)及譯文 6 objective _pared to total welfare_, while profit maximization leads to nonattractive public transportation works. 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 monly, total welfare is represented by the minimization of user and system costs. Some studies address specific objectives from the user, the operator, 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 plex objective functions needed to setup the TRNDP _Newell 1979。 Baaj and Mahmassani 1991。 Chakroborty and Dwivedi 2021_, or investigate specific aspects of the problem, such as objectives _Delle Site and Fillipi 2021_, and the solution methodology _Zhao and Zeng 2021。 Yu and Yang 2021_. Total welfare is, in a sense, a promise between objectives. Moreover, as reported by some researchers such as Baaj and Mahmassani _1991_, Bielli et al. _2021_, Chackroborty and Dwivedi _2021_, and Chakroborty _2021_, transit work 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 work, based on a set of indicators. In contrast, Bielli et al. _2021_ and Chakroborty and Dwivedi _2021_, bined indicators into an overall, weighted sum value, which served as the criterion for determining the optimal transit work. TRNDP: Parameters There are multiple characteristics and design attributes to consider for a realistic representation of a public transportation work. These form the parameters for the TRNDP. 畢業(yè)設(shè)計(jì)外文文獻(xiàn)及譯文 7 Part of these parameters is the problem set of decision variables that define its layout and operational characteristics _frequencies, vehicle size, . Another set of design parameters represent the operating environment _work structure, demand characters, and patterns _, operational strategies and rules, and available resources for the public transportation work. These form the constraints needed to formulate the TRNDP and are, apriori fixed, decided upon or assumed. Decision Variables Most mon decision variables for the TRNDP are the routes and frequencies of the public transportation work _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 2021_, zones _Tsao and Schonfeld 1983。 Chang and Schonfeld 1993a_, stop locations _Black 1979。 Spacovic and Schonfeld 1994。 Spacovic et al. 1994。 Van Nes 2021。 Yu and Yang 2021_ and bus types _Delle Site and Filippi 2021_. Network Structure Some early studies focused on the design of systems in simplified radial _Byrne 1975。 Black 1979。 Vaughan 1986_, or rectangular grid road works _Hurdle 1973。 Byrne and Vuchic 1972。 Tsao and Schonfeld 1984_. However, most approaches since the 1980s were either applied to realistic, irregular grid works or the work structure was of no importance for the proposed model and therefore not specified at all. Demand Patterns Demand patterns describe the nature of the flows of passengers expected to be acmodated by the public transportation work 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 viceversa, are characterized as manytoone _or onetomany _ transit demand patterns. These patterns are typically encountered in public transpo