last mile transport

2025 International Urban Freight Conference

The International Urban Freight Conference (I-NUF) is the premier biennial conference that addresses all aspects of city logistics and goods movement in the world’s metropolitan areas. It is a showcase for cutting-edge research and dynamic information-sharing and provides a forum for researchers, policy makers, and practitioners to reimagine and guide the future of the industry.

Assessing Bike-Transit Accessibility

Research Product Type
Associated Publication
This paper presents a methodology for assessing bicycle first-last mile trips from one area to many possible areas using three visualizations on accessibility, travel times, and transit mode(s) utilized.

Assessing Sustainability of E-Commerce Goods Distribution

Research Product Type
Dissertation / Thesis
As e-retailers compete with increasingly consumer-focused service, urban freight witnesses a significant increase in associated distribution costs and negative externalities including greenhouse gas emissions advancing global climate change, as well as criteria pollutant emissions worsening local air quality and thus affecting those living close to logistics clusters. Thus, the author considers the potential of e-commerce to render economically viable, environmentally efficient, and socially equitable urban goods flow, which is pertinent to understand the opportunities and challenges associated with urban freight in light of the increasingly consumer-focused e-commerce distribution.

Coping with the Rise of E-commerce Generated Home Deliveries through Innovative Last-mile Technologies and Strategies

Research Product Type
Research Report
This study investigates the opportunities and challenges associated with alternate last-mile distribution strategies for an e-retailer offering expedited service with rush delivery within strict timeframes. The authors formulated a last-mile network design (LMND) problem as a dynamic-stochastic two-echelon capacitated location routing problem with time-windows (DS-2E-C-LRP-TW) addressed with an adaptive large neighborhood search (ALNS) metaheuristic.