Public transport planning adaption under the COVID-19 pandemic crisis: literature review of research needs and directions

Figures & data

Table 1. Key performance indicators considered by stop-skipping models that can be applied at a strategic level.

Study	Key performance indicators	Model formulation	Solution Method
Jamili and Aghaee (2015)	Vehicle travel times	Integer non-linear	Decomposition and simulated annealing
Gkiotsalitis (2019)	Passenger waiting and in-vehicle times and vehicle travel times	Integer linear programme	Genetic algorithm
Chen, Liu, Zhu, and Wang (2015)	Passenger waiting and in-vehicle times and vehicle travel times	Integer non-linear programme	Artificial bee colony
Wu, Liu, Jin, and Ma (2019)	Passenger waiting times and vehicle travel times	Integer non-linear	Response surface methodology

Table 2. Overview of selected frequency setting models that consider line variations.

Study	Key performance indicators	Line flexibility	Vehicle capacities
Gkiotsalitis et al. (2019b)	Passenger waiting costs and vehicle running and depreciation costs	Yes: short-turning and interlining of lines are allowed	Not considered in the optimisation process
Verbas and Mahmassani (2015)	Ridership and waiting time savings	Yes: service patterns that only use a subset of the entire stops of a route	Considered only if passenger demand is irresponsive to the pattern structure
Cortés et al. (2011)	Waiting time, in-vehicle time, personnel costs and running costs	Yes: short-turning and deadheading for a single line	Not considered in the optimisation process
Delle Site and Filippi (1998)	Waiting times, running costs and personnel costs	Yes: short-turning	Not considered in the optimisation process

Note: Model formulations that consider the vehicle capacity can be adapted and adopted more easily to the COVID-19 requirements of reduced vehicle capacity due to physical distancing.

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