|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|93976||160242||2016||10 صفحه PDF||سفارش دهید||دانلود کنید|
• We studied individual distance travelled for woodland leisure trips in Wallonia.
• Variables describing the individual-, residential- and destination-level characteristics were analysed.
• Cross-classified multilevel regression models were built.
• Walking, cycling and car-borne trip distance responded differently to variables at different levels.
• Our study could provide new methodological insights for trip distance estimation in woodland recreation demand models.
Based on an extensive survey of woodland visitors in Wallonia, south Belgium, we examined a wide range of individual-, residential- and destination-level variables for their associations with the distance travelled for woodland leisure on foot, by bicycle and by car. For each transport mode, explanatory bivariate analyses were conducted firstly to identify potential correlates of the distances travelled. Then, cross-classified multilevel analysis was performed to build estimation models for the trip distance. The results showed that, amongst the multilevel variables selected, walking trip distance was only associated with individual trip behaviour, while cycling and car-borne trip distance could also be associated with individual socio-economic profile as well as a large range of residential and destination attributes on land use, land cover and visitor support services. The final estimation model for (i) walking trip distance included trip duration as the only explanatory variable, for (ii) cycling trip distance included variables on trip duration, proportion of woodland area at residence and presence of service facilities at destination, and for (iii) car-borne trip distance included variables on trip duration, visitor's employment status, whether the trip is on weekend or in summer, proportion of woodland area at residence and remoteness of destination from urban area. Despite being simple in form, all multilevel estimation models show a satisfactory explanatory power and a better performance than the ordinary single-level models. Our results add new empirical evidences on the key factors associated with the transport mode-specific travel distance, in particular, for woodland leisure. The cross-classified multilevel analysis used in our study provides new methodological insights for the estimation of individual trip distance, which could benefit future modelling of woodland recreation demand.
Journal: Urban Forestry & Urban Greening - Volume 15, 2016, Pages 123–132