| Abstract |
As a viable alternative to traditional and electric cars and vans, e-cargo bikes have the potential to improve the sustainability of urban logistics operations, particularly for last-mile deliveries. In this study, e-cargo bike trips are modelled from a small business pilot rental scheme, and the effects of identified variables of: a) trip length and b) rainfall conditions on the attractiveness of e-cargo bikes as a mode of goods delivery are assessed. For the study, an intelligent modelling framework consisting of a) Data Acquisition System, b) Intelligent Learning Unit, and c) Output Unit is built. The effectiveness of the learning system is evaluated through its application as a case study in Dublin, Ireland. It is discovered that small businesses prefer e-cargo bikes for goods delivery over longer distances in warmer and drier weather conditions. There is a strong interaction effect between weather and distance. A drop in temperature exacerbates the deterring effect of the wet weather, making e-cargo bikes less appealing as a mode of goods delivery for small businesses. Following weather conditions, the critical variable influencing trip length is trip hour, a spatial variable used in the study as a lurking variable representing the traffic flow peak. The study concludes a strong joint effect of wet weather and temperature that affects the attractiveness of e-cargo as a mode of small business goods delivery. The study demonstrates the benefit of using a hybrid modelling framework in trip and mode choice modelling for sustainable logistics modes. |
