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Bus Operations Scheduling Subject to Resource Constraints Using Evolutionary Optimization

NEC Laboratories Europe, 69115 Heidelberg, Germany
Author to whom correspondence should be addressed.
Current address: Kurfuersten Anlage 36, 69115 Heidelberg, Germany.
These authors contributed equally to this work.
Informatics 2018, 5(1), 9;
Received: 19 December 2017 / Revised: 28 January 2018 / Accepted: 2 February 2018 / Published: 6 February 2018
PDF [976 KB, uploaded 7 February 2018]


In public transport operations, vehicles tend to bunch together due to the instability of passenger demand and traffic conditions. Fluctuation of the expected waiting times of passengers at bus stops due to bus bunching is perceived as service unreliability and degrades the overall quality of service. For assessing the performance of high-frequency bus services, transportation authorities monitor the daily operations via Transit Management Systems (TMS) that collect vehicle positioning information in near real-time. This work explores the potential of using Automated Vehicle Location (AVL) data from the running vehicles for generating bus schedules that improve the service reliability and conform to various regulatory constraints. The computer-aided generation of optimal bus schedules is a tedious task due to the nonlinear and multi-variable nature of the bus scheduling problem. For this reason, this work develops a two-level approach where (i) the regulatory constraints are satisfied and (ii) the waiting times of passengers are optimized with the introduction of an evolutionary algorithm. This work also discusses the experimental results from the implementation of such an approach in a bi-directional bus line operated by a major bus operator in northern Europe. View Full-Text
Keywords: bus bunching; evolutionary optimization; constrained optimization; service reliability bus bunching; evolutionary optimization; constrained optimization; service reliability

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Gkiotsalitis, K.; Kumar, R. Bus Operations Scheduling Subject to Resource Constraints Using Evolutionary Optimization. Informatics 2018, 5, 9.

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