Work

An adaptive scaled network for public transport route optimisation

Year: 2019

Type: article

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.

Source: Public Transport

DOI: 10.1007/s12469-019-00208-x

Authors: Philipp Heyken Soares, Christine L. Mumford, Kwabena Amponsah, Yong Mao

Institutions: University of Nottingham, Cardiff University

Language: English

Topic: Transportation Planning and Optimization

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $2,690