“…Furthermore, the matching degree C m between urban rail transit network and urban trips can be calculated by Equation (13). The value of C m is between 0 and 1, and the closer the index value is to 1, the higher the matching degree between the network and urban trips.…”
Section: Trip Intensitymentioning
confidence: 99%
“…where () P is the land-use intensity calculated by the sum of resident and employed population, D P is the fractal dimension of the land-use intensity. Furthermore, the matching degree m C between urban rail transit network and urban trips can be calculated by Equation (13). The value of m C is between 0 and 1, and the closer the index value is to 1, the higher the matching degree between the network and urban trips.…”
Section: Land-use Intensitymentioning
confidence: 99%
“…For the quantitative methods of rail transit network design, mathematical programming is most commonly used. During the optimization process, the planning objectives determined by construction and service requirements include maximizing the benefits of operators [5][6][7][8], users [9] or both [10][11][12][13]. For research projects, the rail transit design can be classified as the design of a new rail transit network and the expansion of an existing rail transit network [14].…”
In the process of urban rail transit network design, the urban road network, urban trips and land use are the key factors to be considered. At present, the subjective and qualitative methods are usually used in most practices. In this paper, a quantitative model is developed to ensure the matching between the factors and the urban rail transit network. In the model, a basic network, which is used to define the roads that candidate lines will pass through, is firstly constructed based on the locations of large traffic volume and main passenger flow corridors. Two matching indexes are proposed: one indicates the matching degree between the network and the trip demand, which is calculated by the deviation value between two gravity centers of the stations’ importance distribution in network and the traffic zones’ trip intensity; the other one describes the matching degree between the network and the land use, which is calculated by the deviation value between the fractal dimensions of stations’ importance distribution and the traffic zones’ land-use intensity. The model takes the maximum traffic turnover per unit length of network and the minimum average volume of transfer passengers between lines as objectives. To solve the NP-hard problem in which the variables increase exponentially with the increase of network size, a neighborhood search algorithm is developed based on simulated annealing method. A real case study is carried out to show that the model and algorithm are effective.
“…Furthermore, the matching degree C m between urban rail transit network and urban trips can be calculated by Equation (13). The value of C m is between 0 and 1, and the closer the index value is to 1, the higher the matching degree between the network and urban trips.…”
Section: Trip Intensitymentioning
confidence: 99%
“…where () P is the land-use intensity calculated by the sum of resident and employed population, D P is the fractal dimension of the land-use intensity. Furthermore, the matching degree m C between urban rail transit network and urban trips can be calculated by Equation (13). The value of m C is between 0 and 1, and the closer the index value is to 1, the higher the matching degree between the network and urban trips.…”
Section: Land-use Intensitymentioning
confidence: 99%
“…For the quantitative methods of rail transit network design, mathematical programming is most commonly used. During the optimization process, the planning objectives determined by construction and service requirements include maximizing the benefits of operators [5][6][7][8], users [9] or both [10][11][12][13]. For research projects, the rail transit design can be classified as the design of a new rail transit network and the expansion of an existing rail transit network [14].…”
In the process of urban rail transit network design, the urban road network, urban trips and land use are the key factors to be considered. At present, the subjective and qualitative methods are usually used in most practices. In this paper, a quantitative model is developed to ensure the matching between the factors and the urban rail transit network. In the model, a basic network, which is used to define the roads that candidate lines will pass through, is firstly constructed based on the locations of large traffic volume and main passenger flow corridors. Two matching indexes are proposed: one indicates the matching degree between the network and the trip demand, which is calculated by the deviation value between two gravity centers of the stations’ importance distribution in network and the traffic zones’ trip intensity; the other one describes the matching degree between the network and the land use, which is calculated by the deviation value between the fractal dimensions of stations’ importance distribution and the traffic zones’ land-use intensity. The model takes the maximum traffic turnover per unit length of network and the minimum average volume of transfer passengers between lines as objectives. To solve the NP-hard problem in which the variables increase exponentially with the increase of network size, a neighborhood search algorithm is developed based on simulated annealing method. A real case study is carried out to show that the model and algorithm are effective.
“…As can be seen, generally, the problem is formulated by minimizing the travel cost [5][6][7][8], minimizing the construction and operation cost [7,9], and maximizing the passenger attraction or coverage [5,6,9,10] etc. e studies about the problem can be classi ed into the entire network design and the routes design based on existing network [7]. At present, the multi-mode networks integration method [11] and the multi-phase integration method in a network mode [12,13] are engaged.…”
The transit network design and frequency setting problem is related to the generation of transit routes with corresponding frequency schedule. Considering not only the influence of transfers but also the delay caused by congestion on passengers’ travel time, a multi-objective transit network design model is developed. The model aims to minimize the travel time of passengers and minimize the number of vehicles used in the network. To solve the model belongs to a NP-Hard problem and is intractable due to the high complexity and strict constraints. In order to obtain the better network schemes, a multi-population genetic algorithm is proposed based on NSGA-II framework. With the algorithm, network generation, mode choice, demand assignment, and frequency setting are all integrated to be solved. The effectiveness of the algorithm which includes the high global convergence and the applicability for the problem is verified by comparison with previous works and calculation of a real-size case. The model and algorithm can be used to provide candidates for the sustainable policy formulation of urban transit network scheme.
“…An adaptive large neighborhood search metaheuristic is used to tackle the network design and line planning problems simultaneously. López-Ramos et al [28] proposed an optimization-based approach to simultaneously solve the network design and the frequency-setting phase in the context of railway rapid transit networks. A combined lexicographic goal programming technique and a line splitting algorithm is used to solve the model.…”
This paper considers an urban transit network design problem (UTNDP) that deals with construction of an efficient set of transit routes and associated service frequencies on an existing road network. The UTNDP is an NP-hard problem, characterized by a huge search space, multiobjective nature, and multiple constraints in which the evaluation of candidate route sets can be both time consuming and challenging. This paper proposes a hybrid differential evolution with particle swarm optimization (DE-PSO) algorithm to solve the UTNDP, aiming to simultaneously optimize route configuration and service frequency with specific objectives in minimizing both the passengers’ and operators’ costs. Computational experiments are conducted based on the well-known benchmark data of Mandl’s Swiss network and a large dataset of the public transport system of Rivera City, Northern Uruguay. The computational results of the proposed hybrid algorithm improve over the benchmark obtained in most of the previous studies. From the perspective of multiobjective optimization, the proposed hybrid algorithm is able to produce a diverse set of nondominated solutions, given the passengers’ and operators’ costs are conflicting objectives.
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