Abstract
The aim of this work is to investigate the simplifica-tion of public transport networks (PTNs) for megacities and the optimisation of route planning based on the de-mand density of complex networks. A node deletion rule for network centre areas and a node merging rule for net-work border areas in the PTN are designed using the de-mand density of complex networks. A transit route plan-ning (TRP) model is established, which considers the demands of direct passengers, transfer passengers at the same stop and transfer passengers at different stops, and aims at maximising the transit demand density of a PTN. An optimisation process for TRP is developed based on the ant colony optimisation (ACO). The proposed method was validated through a sample application in Handan City in China. The results indicate that urban PTNs can be simplified while retaining their local attributes to a great extent. The hierarchical structure of the network is more obvious, and the layer-by-layer planning of routes can be effectively used in TRP. Moreover, the operating efficiency and service level of urban PTNs can be en-hanced effectively.
Highlights
The planning level of public transport networks (PTNs) will directly affect the operating efficiency and service level of the network [1]
As the main part of PTN planning, transit route planning (TRP) has been extensively studied [2], where the method of “one-by-one wiring, optimised for network” method adopted by Zhou K. et al in [3] has been widely used because of its simple operation
To test the validation of the proposed TRP, methods for minimising the travel time method and maximising direct transit demand are introduced for comparison and solved by ant colony optimisation (ACO)
Summary
The planning level of public transport networks (PTNs) will directly affect the operating efficiency and service level of the network [1]. As the main part of PTN planning, transit route planning (TRP) has been extensively studied [2], where the method of “one-by-one wiring, optimised for network” method adopted by Zhou K. et al in [3] has been widely used because of its simple operation. The overall planning of a PTN is highly inefficient because of the complexity and time-consuming nature of the route searching phase, especially for megacities with huge public transport systems. With the maturity of the algorithm, it becomes difficult to continuously improve planning efficiency, especially megacity PTN planning. It is feasible to reduce the number of nodes and edges to simplify the network (reduce network size) by studying the network topology
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