An adaptive scaled network for public transport route optimisation
Document Type
Journal Article
Publication Date
2019
Subject Area
mode - bus, place - europe, place - urban, planning - methods, planning - network design, planning - route design, planning - travel demand management, ridership - demand
Keywords
Public transport, Route optimisation, Network design, Benchmark instance, Genetic algorithm
Abstract
We introduce an adaptive network for public transport route optimisation by scaling down the available street network to a level where optimisation methods such as genetic algorithms can be applied. Our scaling is adapted to preserve the characteristics of the street network. The methodology is applied to the urban area of Nottingham, UK, to generate a new benchmark dataset for bus route optimisation studies. All travel time and demand data as well as information of permitted start and end points of routes, are derived from openly available data. The scaled network is tested with the application of a genetic algorithm adapted for restricted route start and end points. The results are compared with the real-world bus routes.
Rights
Permission to publish the abstract has been given by SpringerLink, copyright remains with them.
Recommended Citation
Heyken Soares, P., Mumford, C.L., Amponsah, K. & Mao, Y. (2019). An adaptive scaled network for public transport route optimisation. Public Transport, Vol. 11, pp. 379-412.
