Zone-based public transport route optimisation in an urban network
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Zone‑based public transport route optimisation in an urban network Philipp Heyken Soares1 Accepted: 7 July 2020 © The Author(s) 2020
Abstract The majority of academic studies on the optimisation of public transport routes consider passenger trips to be fixed between pairs of stop points. This can lead to barriers in the use of the developed algorithms in real-world planning processes, as these usually utilise a zone-based trip representation. This study demonstrates the adaptation of a node-based optimisation procedure to work with zone-to-zone trips. A core element of this process is a hybrid approach to calculate zone-to-zone journey times through the use of node-based concepts. The resulting algorithm is applied to an input dataset generated from real-world data, with results showing significant improvements over the existing route network. The dataset is made publicly available to serve as a potential benchmark dataset for future research. Keywords Public transport · Route optimisation · Network design · Benchmark instance · Genetic algorithm
1 Introduction 1.1 Opening The efficiency of public transport (PuT) is of vital importance for urban areas worldwide to decrease car dependency and the accompanying pollution and congestion. In general, the task to design efficient PuT networks can be described as five interconnected phases: (1) route design, (2) Vehicle frequency setting, (3) timetable development, (4) vehicle scheduling, and (5) crew scheduling (Ceder and Wilson 1986). Due to the interconnections, the combined task has a very high complexity and researchers typically work with simplifications. One such simplification is the Urban Transit Routing Problem (UTRP). It focuses on optimising the layout of routes while assuming a fixed time penalty for all transfers (instead * Philipp Heyken Soares [email protected] 1
Laboratory of Urban Complexity and Sustainability, University of Nottingham, Nottingham, UK
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of varying transfer times resulting from different frequencies and starting times). The work presented in this paper is based on this approach. Researchers have been working for many decades on automated procedures with which to solve the UTRP. Thus far, however, no results of this research have found widespread real-world application, and most planning processes are still based on experience and published guidelines (Nielsen et al. 2005; Walter 2010). The reasons for this gap have not yet been researched in detail (Walter 2010). However, one possible explanation is that the concepts used in many studies are based on instances (i.e. sets of required input data) which are far removed from real-world planning processes (Kepaptsoglou and Karlaftis 2009). This study is part of an incremental approach for better applicable UTRP research. The previous publication (Heyken Soares et al. 2019) focused on the generation of more realistic instances. The present paper builds on this work by adapting and extending the concepts used in Heyken Soares e
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