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Modelling the attraction of travel to shopping destinations in large-scale modelling

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  • Kristoffersson, Ida
  • Daly, Andrew
  • Algers, Staffan

Abstract

Development of major shopping centres continues even though online shopping is increasing. This has implications for mode and destination choice for shopping travel and therefore also for sustainability, which need to be considered in planning policy. In this paper, we estimate models for shopping travel using an unusually rich data set of shopping attractions. We find that shopping travel is best represented in three separate models: consumables in short and long activity segments and durables. In all of these models, we show that representing nearby attractions outside the destination zone adds to the measured attraction. For long activity consumables and for durables, the addition of secondary attractions within 2 km of the main destination gives the best models. For short activity consumables, both 2 km and 5 km add to the model, but 5 km is slightly better. Furthermore, we find significant within-zone correlation in the consumables models but are unable to find significant between-zone correlation, indicating that zone boundaries have some behavioural meaning for shopping travellers, but larger areas are not viewed in this way. Shopping attractions with a specifically Swedish impact, Systembolaget (official alcohol outlet in Sweden) and IKEA, proved to be important in all the models. These attractors work better as part of the size than as part of the utility, indicating that they appear to be separate attractors of trips, rather than as adding to the utility of other attractors. The models are also applied in two policy scenario analyses in which the impacts of new IKEA establishments and availability of Systembolaget in all zones on destination and mode choice are assessed.

Suggested Citation

  • Kristoffersson, Ida & Daly, Andrew & Algers, Staffan, 2018. "Modelling the attraction of travel to shopping destinations in large-scale modelling," Transport Policy, Elsevier, vol. 68(C), pages 52-62.
    • Handle: RePEc:eee:trapol:v:68:y:2018:i:c:p:52-62
    DOI: 10.1016/j.tranpol.2018.04.013
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    Cited by:

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    2. Liu, Chengxi & Tapani, Andreas & Kristoffersson, Ida & Rydergren, Clas & Jonsson, Daniel, 2020. "Development of a large-scale transport model with focus on cycling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 134(C), pages 164-183.
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    4. Méndez-Vogel, Gonzalo & Marianov, Vladimir & Lüer-Villagra, Armin & Eiselt, H.A., 2023. "Store location with multipurpose shopping trips and a new random utility customers’ choice model," European Journal of Operational Research, Elsevier, vol. 305(2), pages 708-721.
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    More about this item

    Keywords

    Shopping travel behaviour; Large-scale modelling; Travel attractions; Planning policy; R12; R14; R15; R41; R42; R48;
    All these keywords.

    JEL classification:

    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy

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