Urban population growth, the rise of e-commerce, and the increased need for economically and environmentally sustainable solutions represent urban freight distribution’s biggest challenges. Traffic and city logistics are often two sides of the same coin, as congestion affects city freight movements and vice versa.
For this reason, it is important to develop comprehensive mobility and traffic management solutions that consider both systems. During the last decade, technology improvements in wireless communication, computational and sensing technologies, have paved the way to a series of mobility and transportation options (e.g., crowdshipping and driverless vehicles) that could transform the landscape of last-mile delivery. The main contribution of this dissertation consists of modeling urban freight impacts on traffic and investigating the potential implications of innovative last-mile solutions.
The first part of this dissertation focuses on the feedback between freight movements and traffic, taking into consideration the impact of passenger vehicles on commercial vehicles, and vice versa. In order to achieve this goal, it is necessary to model trucks’ movements and loading/unloading operations with ad-hoc traffic simulations. Most of existing research has focused on analytical, static, or microscopic models, that either lack accuracy or scalability. Hence, this dissertation creates algorithms that couple existing macroscopic traffic flow models with the microscopic behavior of delivery vehicles. This issue is investigated both at single-link and network levels, by means of a suitable simulation framework. In both cases, applications of the modeling approach for freight traffic and freight demand management are shown.
In the second part of this dissertation the potential impacts of last-mile delivery solutions are evaluated using the developed simulation framework. First, the impacts of alternative City Logistics solutions, such as off-peak deliveries and access restrictions are investigated. Then, the developed modeling framework is extended to investigate a crowdsourced service for parcel deliveries. The effects on traffic and emissions are investigated for the adoption of crowdshipping by carriers delivering parcels in the city center of Rome, Italy. The externalities associated with several strategic (chosen mode) and operational (detour length, parking behavior, and traffic conditions) aspects of this service are analyzed by means of simulation in realistic settings.
Some results allow preliminary considerations about the effects of last-mile delivery solution that can been confirmed in other studies. Other findings, instead, are in line with studies from previous literature that adopted different approaches. The practice of off-peak deliveries, consisting in shifting part of the trips and operations to less congested hours of the day (typically evening and night) has proved to be an effective solution to freight-related congestion in urban settings. Restricting from deliveries specific links or sets of links, instead, could be beneficial only in some situations. Alternative crowdshipping implementation features, such as the transportation mode choice, but also operational aspects (such as availability of parking, optimization of existing trips, and implementation during off-peak hours) can also considerably influence the final traffic and emissions impacts of this service.Civil, Architectural, and Environmental Engineerin
