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  • 1.
    Ait Ali, Abderrahman
    et al.
    KTH, Transportplanering, ekonomi och teknik.
    Warg, Jennifer
    KTH, Transportplanering, ekonomi och teknik.
    Eliasson, Jonas
    KTH, Transportplanering, ekonomi och teknik.
    Measuring the Socio-economic Benefits of Train Timetables: Application to Commuter Train Services in Stockholm2017In: 20th EURO Working Group on Transportation Meeting, EWGT 2017, 4-6 September 2017, Budapest, Hungary, Elsevier , 2017, p. 849-856Conference paper (Refereed)
    Abstract [en]

    On highly used railway lines with heterogeneous traffic, timetabling is challenging. In particular, the limited existing capacity means that to guarantee an acceptable level of quality, the infrastructure manager must cancel some train services on the expense of others. In this article, we study the conflict between commercial long-distance trains and subsidized commuter trains with a socio-economic perspective (i.e. travelers and train operators). The study attempts to answer the following question: What is the socio-economic effect of modifying the timetable of a commuter service?The case study treats the commuter train services in Stockholm. Trip data was collected from the local commuter train operator. An entropy maximization-based model was implemented to estimate the dynamic network Origin-Destination (OD) matrix. This dynamic matrix, of one full working day, was then used to estimate the number of travelers per train, and further converted for use in the microscopic simulation tool RailSys. Travel and waiting time are estimated for each OD pair and with that the generalized costs for the travelers and operators. The effect of crowding in the trains is included in the estimation. The article can be considered as an initiation to a novel method to calculate effects of changes in commuter train timetables. This novel approach enables to price commercial train slots in the capacity allocation process such as in an auction. It provides a new way to estimate the local train operator´s valuation of the different parameters (i.e. waiting, travel time and interchanges). Using RailSys for the estimation of times makes it possible to include capacity aspects that normally are difficult to reveal.

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