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.
Highlights
The efficiency of public transport (PuT) is of vital importance for urban areas worldwide to decrease car dependency and the accompanying pollution and congestion
It centres on a genetic algorithm (GA) optimising route sets generated by a heuristic initialisation procedure
The following sections present the results of the optimisation procedure described in Sect. 3 and applied to the instance generated in Sect
Summary
The efficiency of public transport (PuT) is of vital importance for urban areas worldwide to decrease car dependency and the accompanying pollution and congestion. The combined task has a very high complexity and researchers typically work with simplifications. One such simplification is the Urban Transit Routing Problem (UTRP). Trip distribution models and mode choice models require a zonal set-up as a common base of trips with all modes. Such models are an integral part of many more complex transport modelling processes, e.g. the standard four-step model (McNally 2000; Rich 2015; Schlaich et al 2013). Surveying more than a hundred publications on the UTRP using journey time calculation in their evaluation revealed that more than 80% of them use node-based travel demand.
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