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Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

PUBLISHED
18.02.2022
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Copyright (c) 2024 Mingbao Pang, Xing Wang, Lixia Ma

Transit Route Planning for Megacities Based on Demand Density of Complex Networks

Authors:

Mingbao Pang

Xing Wang
School of Civil and Transportation, Hebei University of Technology, Tianjin, China

Lixia Ma
Planning and Design Institute of Handan City

Keywords:urban public transport, network simplification, transit route planning, demand density of complex network, ant colony optimisation

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.

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How to Cite
Pang, M. (et al.) 2022. Transit Route Planning for Megacities Based on Demand Density of Complex Networks. Traffic&Transportation Journal. 34, 1 (Feb. 2022), 13-23. DOI: https://doi.org/10.7307/ptt.v34i1.3752.

SPECIAL ISSUE IS OUT

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Marko Matulin, PhD, Dario Babić, PhD, Marko Ševrović, PhD


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