Radial bus networks with multi‐period and many‐to‐many demand

Chang, S. K. Jason

doi:10.1002/atr.5670250207

Cited by 3 publications

(1 citation statement)

References 20 publications

(20 reference statements)

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“…In the BP algorithm, we design a heuristic method to generate a good initial solution and use heuristic decisions in the label-setting algorithm to solve the pricing problem. Chang [6] developed a mathematical model for the optimization of radial bus networks with time-dependent supply and demand characteristics and obtained closed-form solutions for optimal route angles, frequency intervals at different periods, and station spacing at different locations to minimize the total cost. Due to the limited driving range and long charging time of electric buses, such models are not suitable for electric buses.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Bus Scheduling Optimization Considering Exhaust Emission Based on Genetic Algorithm: Taking a Route in Nanjing City as an Example

Wang,

Guo,

Zhang

et al. 2024

Applied Sciences

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In order to enhance passenger willingness to choose buses for commuting and to reduce the operating costs and tailpipe emissions of bus companies, a bus scheduling model is established. The model aims to minimize the sum of the operating costs of the bus company, the costs associated with the loss of passengers’ interest, and the cost of tailpipe emissions. It considers constraints such as maintaining an average load factor of the buses above 60%, ensuring a departure interval of greater than 5 min during non-peak hours and less than 30 min during peak hours, and limiting the maximum number of buses allocated to a route. The passenger flow is divided into peak hours and nonpeak hours according to the survey of passenger flow during each period of a bus operation on a route in Nanjing City, China. A genetic algorithm is employed to solve the proposed bus scheduling model and determine the total costs during peak and non-peak hours. After designing the parameters of the genetic algorithm, optimal departure intervals and bus numbers for a day’s operation cycle on a given route are calculated using a weighting method.

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Section: Literature Reviewmentioning

confidence: 99%

Research on Bus Scheduling Optimization Considering Exhaust Emission Based on Genetic Algorithm: Taking a Route in Nanjing City as an Example

Wang,

Guo,

Zhang

et al. 2024

Applied Sciences

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Design of public transit network in urban area with elastic demand

Soehodo

Koshi

1999

J of Advced Transportation

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This research focuses on an efficient design of transit network in urban areas. The system developed is used to create, analyze and optimize routes and frequencies of transit system in the network level. The analysis is based on elastic demand, so the shift of demand between modes in network due to different service level is of prime consideration. The developed system creates all feasible routes connecting all pairs of terminals in the network. Out of this vast pool of routes, a set of optimal routes is generated for a certain predetermined number that maintains connectivity of significant demand. Based on these generated routes, the system fulfils transportation demand by assigning demand that considers path and route choices for non-transit users and transit users. Together with the assignment of demand, transit frequencies are optimized and the related fleet-size is calculated. Having an optimal setting of solution, the system is continued by reconnecting the routes to find some other better solutions in the periphery of the optimal setting. A set of mathematical programming modules is developed. Real data from Sioux Falls city network is used to evaluate the performance of the model and compare with other heuristic methods.

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Optimization of grid bus transit systems with elastic demand

Chien

Spasovic

2002

J of Advced Transportation

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Current analytic models for optimizing urban bus transit systems tend to sacrifice geographic realism and detail in order to obtain their solutions. The models presented here shows how an optimization approach can be successful without oversimplifying spatial characteristics and demand patterns of urban areas and how a grid bus transit system in a heterogeneous urban environment with elastic demand is optimized. The demand distribution overthe service region is discrete, which can realistically represent geographic variation. Optimal network characteristics (route and station spacings), operating headways and fare are found, which maximize the total operator profit and social welfare. Irregular service regions, many-to-many demand patterns, and vehicle capacity constraints are considered in a sequential optimization process. The numerical results show that at the optima the operator profit and social welfare functions are rather flat with respect to route spacing and headway, thus facilitating the tailoring of design variables to the actual street network and particular operating schedule without a substantial decrease in profit. The sensitivities of the design variables to some important exogenous factors are also presented.

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Radial bus networks with multi‐period and many‐to‐many demand

Cited by 3 publications

References 20 publications

Research on Bus Scheduling Optimization Considering Exhaust Emission Based on Genetic Algorithm: Taking a Route in Nanjing City as an Example

Research on Bus Scheduling Optimization Considering Exhaust Emission Based on Genetic Algorithm: Taking a Route in Nanjing City as an Example

Design of public transit network in urban area with elastic demand

Optimization of grid bus transit systems with elastic demand

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