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Title: Application of a Cost-Allocation Model to Swiss Bus and Train Lines
Accession Number: 01662672
Record Type: Component
Record URL: Availability: Find a library where document is available Abstract: Public transport costs are an important decision factor when making system design choices and network or timetable planning. Although many cost models of varying degrees of sophistication have been proposed in literature, practitioners often use relatively simple models such as purely vehicle-kilometer-based cost estimations. This paper applies a cost-allocation model to Swiss bus and train lines. The proposed model is based on operating statistics including ‘productive hours’, vehicle-kilometers, vehicle number and size. The cost model is then applied to a case study for a generic public transport line with a typical demand distribution. The case study calculates the demand level at which train service becomes more effective than bus service for various combinations of minimal frequency, average speed and maximum number of coupled Electric Multiple Units.
Supplemental Notes: The authors confirm contribution to the paper as follows: study conception and design: Marc Sinner, Ulrich Weidmann; data collection: Marc Sinner, Ulrich Weidmann; analysis and interpretation of results: Marc Sinner, Ulrich Weidmann; draft manuscript preparation: Marc Sinner, Andrew Nash. All authors reviewed the results and approved the final version of the manuscript. © National Academy of Sciences: Transportation Research Board 2018.
Report/Paper Numbers: 18-00903
Language: English
Authors: Sinner, MarcWeidmann, UlrichNash, AndrewPagination: pp 431-442
Publication Date: 2018-12
Serial:
Transportation Research Record: Journal of the Transportation Research Board
Volume: 2672 Media Type: Web
Features: Figures
(3)
; Photos; References
(33)
; Tables
(6)
TRT Terms: Geographic Terms: Subject Areas: Finance; Operations and Traffic Management; Planning and Forecasting; Public Transportation; Railroads
Files: TRIS, TRB, ATRI
Created Date: Jan 8 2018 10:13AM
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