scholarly journals An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-20 ◽  
Author(s):  
Shushan Chai ◽  
Qinghuai Liang
Keyword(s):  
Network Design ◽  
Travel Time ◽  
Policy Formulation ◽  
Nsga Ii ◽  
Transit Network ◽  
Generation Mode ◽  
Np Hard Problem ◽  
Transit Network Design ◽  
Population Genetic Algorithm ◽  
Transit Routes

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.

Transit network design based on travel time reliability

2014 ◽  
Vol 43 ◽  
pp. 233-248 ◽  
Author(s):  
Baozhen Yao ◽  
Ping Hu ◽  
Xiaohong Lu ◽  
Junjie Gao ◽  
Mingheng Zhang
Keyword(s):  
Network Design ◽  
Travel Time ◽  
Travel Time Reliability ◽  
Transit Network ◽  
Transit Network Design

Importance of Objectives in Urban Transit-Network Design

2000 ◽  
Vol 1735 (1) ◽  
pp. 25-34 ◽  
Author(s):  
R. van Nes ◽  
P.H.L. Bovy
Keyword(s):  
Network Design ◽  
Travel Time ◽  
Analytical Models ◽  
Urban Transit ◽  
Transit Network ◽  
Transit Networks ◽  
Design Variables ◽  
Line Spacing ◽  
Transit Network Design ◽  
Stop Spacing

Stop spacing and line spacing are key design variables in urban transit-network design. They determine both the travel time and the operational costs. It is therefore essential to know what the main relationships are for these design variables. The question is, What are the optimal values for stop spacing and for line spacing in urban transit networks, given traveler preferences and supply-budget constraints? Possible objectives are discussed and analyzed using analytical models. The results of these analytical models for two typical city types are analyzed by comparing performance characteristics (i.e., travel time, operator costs, and patronage). Modeling outcomes are compared with actual data for urban transit networks in Europe. A supplemental analysis is made of the impact of considering different traveler groups. It was found that although many objectives may be formulated, only a few objectives are suitable for transit-network design. Currently applied stop spacings prove to be too short. Focusing the design to specific traveler groups might lead to a variation of stop spacing and line spacing ranging from −12 percent to +13 percent at most. Recommendations for urban transit-network design and for further research are given.

scholarly journals Application of Modified NSGA-II to the Transit Network Design Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Jie Yang ◽  
Yangsheng Jiang
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Pareto Front ◽  
Network Design Problem ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Transit Network ◽  
Set Size ◽  
Transit Network Design ◽  
Initial Route

The transit network design problem involves determining a certain number of routes to operate in an urban area to balance the costs of the passengers and the operator. In this paper, we simultaneously determine the route structure of each route and the number of routes in the final solution. A novel initial route set generation algorithm and a route set size alternating heuristic are embedded into a nondominated sorting genetic algorithm-II- (NSGA-II-) based solution framework to produce the approximate Pareto front. The initial route set generation algorithm aims to generate high-quality initial solutions for succeeding optimization procedures. To explore the solution space and to have solutions with a different number of routes, a route set size alternating heuristic is developed to change the number of routes in a solution by adding or deleting one route. Experiments were performed on Mandl’s network and four larger Mumford’s networks. Compared with a fixed route set size approach, the proposed NSGA-II-based solution method can produce an approximate Pareto front with much higher solution quality as well as improved computation efficiency.

Sign in / Sign up

Export Citation Format

Share Document