A cost-minimization model for bus fleet allocation featuring the tactical generation of short-turning and interlining options. (January 2019)

• Record Type: Journal Article
• Authors: Gkiotsalitis, K.; Wu, Z.; Cats, O.
• Is Part Of: Transportation research. Volume 98(2019)
• Page Start: 14
• Page End: 36
• Publication Date: 2019-01
• Subjects: Tactical planning -- Vehicle allocation -- Interlining -- Bus operations -- Route design -- Short-turning; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2018.11.007
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Tackling asymmetric and unbalanced demand patterns result with inefficiency. Tactical fleet allocation model featuring short-turning and interlining services. Rule-based generation of a set of candidate services. Minimize passenger and fleet operations costs without enlarging the fleet size. Few additional service patterns reduce passenger and operator costs by 5–15%. Abstract: Urban public transport operations in peak periods are characterized by highly uneven demand distributions and scarcity of resources. In this work, we propose a rule-based method for systematically generating and integrating alternative lining options, such as short-turning and interlining lines, into the frequency and resource allocation problem by considering the dual objective of (a) reducing passenger waiting times at stops and (b) reducing operational costs. The bus allocation problem for existing and short-turning/interlining lines is modeled as a combinatorial, constrained and multi-objective optimization problem that has an exponential computational complexity and a large set of decision variables due to the additional set of short-turning/interlining options. This constrained optimization problem is approximated with an unconstrained one with the use of exterior point penalties and is solved with a Genetic Algorithm (GA) meta-heuristic. The modeling approach is applied to the bus network of The Hague with the use of General Transit Feed Specification (GTFS) data and Automated Fare Collection (AFC) data from 24 weekdays. Sensitivity analysis results demonstrate a significant reduction potential in passenger waiting time and operational costs with the addition of only a few short-turning and interlining options.