The growth in e-commerce that our society has faced in recent years is changing the view companies have on last-mile logistics, due to its increasing impact on the whole supply chain. New technologies are raising users’ expectations with the need to develop customized delivery experiences; moreover, increasing pressure on supply chains has also created additional challenges for suppliers. At the same time, this phenomenon generates an increase in the impact on the liveability of our cities, due to traffic congestion, the occupation of public spaces, and the environmental and acoustic pollution linked to urban logistics. In this context, the optimization of last-mile deliveries is an imperative not only for companies with parcels that need to be delivered in the urban areas, but also for public administrations that want to guarantee a good quality of life for citizens. In recent years, many scholars have focused on the study of logistics optimization techniques and, in particular, the last mile. In addition to traditional optimization techniques, linked to the disciplines of operations research, the recent advances in the use of sensors and IoT, and the consequent large amount of data that derives from it, are pushing towards a greater use of big data and analytics techniques—such as machine learning and artificial intelligence—which are also in this sector. Based on this premise, the aim of this work is to provide an overview of the most recent literature advances related to last-mile delivery optimization techniques; this is to be used as a baseline for scholars who intend to explore new approaches and techniques in the study of last-mile logistics optimization. A bibliometric analysis and a critical review were conducted in order to highlight the main studied problems, the algorithms used, and the case studies. The results from the analysis allow the studies to be clustered into traditional optimization models, machine learning approaches, and mixed methods. The main research gaps and limitations of the current literature are assessed in order to identify unaddressed challenges and provide research suggestions for future approaches.

Feeder transport services are fundamental as first and last-mile connectors of mass rapid transit (MRT). They are especially beneficial in low-demand areas where private transport is usually the main transport mode. Besides, the rapid spread of new technologies such as vehicle automation and the shared mobility paradigm gave rise to new mobility-on-demand modes that can dynamically match demand with service supply. In this context, the new generation of real-time demand-responsive transport services can act as on-demand feeders of MRT, but their performance needs to be compared with conventional fixed-route fixed-schedule feeders. This article aims at presenting an agent-based model able to simulate different feeder services and explore the conditions that make a demand-responsive feeder (DRF) service more or less attractive than a fixed-route fixed-schedule feeder (FRF). The parametric simulation environment creates realistic constraints and parameters that are usually not included in analytical models because of high computational complexity. First, we identified the critical demand density representing a switching point between the two services. Once the demand density is fixed, exploratory scenarios are tested by changing the demand spatial distribution and patterns, service area, and service configurations. Main results suggest that the DRF is to be preferred when the demand is spatially concentrated close to the MRT station (e.g., in a TOD-like land-use area) or when station spacing is quite high (e.g., a regional railway service), whereas the FRF performs better when the demand is mainly originated at the MRT station to any other destinations in the service area (e.g., during peak hours). Besides, automated vehicles could play a role in reducing the operator cost if the service is performed with many small vehicles rather than higher-capacity vehicles, even if this would not imply a major benefit gain for the users.

Transport decision-making problems are typically spatially based and involve a set of feasible alternatives with multiple evaluation criteria. Besides, transport decisions affect citizens’ quality of life, as well as specific interests of general stakeholders (e.g., transport companies), thus needing a participatory approach to decision-making. Geographic Information Systems (GIS) have the ability to visualize spatial data and represent the impact of location based transport alternatives, thus helping experts to conduct robust assessments. Moreover, with the recent diffusion of Volunteered Geographic Information (VGI) and development of Public Participatory GIS (PPGIS) platforms, the process can be enhanced thanks to the collection of a large amount of updated spatial data and the achievement of an active community participation. In this study, we provide an overview based on a structured literature review of the use of VGI and PPGIS in transport studies, exploring the fields of application, role played by GIS, level of public involvement and decision stage at which they are applied. From the overview’s results, we propose a general framework for the evaluation of transport alternatives using GIS from a multiple stakeholder point of view; the main conclusion is the usefulness of the integration between Public Participation, GIS and quantitative evaluation methods, in particular Multi Criteria Decision Analysis, in order to foster technically sound and shared decisions.

Micromobility has a high potential to change mobility habits towards the use of sustainable transport modes. The shared mobility paradigm encourages the development of new mobility services, such as bike and e-scooter sharing, potentially reducing the need of car ownership, enlarging the accessibility of public transport and enriching the transport options needed to exploit Mobility as a Service solutions. While bike-sharing services have been used in urban areas for many years, shared e-scooter services (and private e-scooters) have been spreading only in the last few years. Due to the novelty of this mode, few attempts have been made for proper micromobility network planning. This paper proposes a multicriteria GIS-based analysis aimed at planning priority networks for e-scooters, focusing on safety, transport and land use characteristics. The case study is Catania, a medium-sized city in southern Italy, which suffers from a lack of adequate infrastructures for such sustainable modes of transport. By applying the methodology, it is possible to prioritise the road network elements that better fit the needs of e-scooters, thus paving the way for suitable infrastructures and network planning.

Increasing the Quality of Service (QoS) of Public Transport (PT), in order to attract more users, is one of the goals of transport companies and urban policy-makers. A continuous monitoring of data on users’ satisfaction is desirable, but most of the time such process is costly. Finding correlations between PT accessibility, user satisfaction and PT ridership might be the key to prioritize where and how service quality improvements have to be put in place. The aim of this paper is to investigate the correlation among PT use, user satisfaction and PT accessibility using a spatial and statistical approach to find useful and simple indicators for sustainable mobility planning. The case study is Catania, a medium-sized city located in southern Italy, with a focus on the mobility of University students. In this respect, students experienced fare-free PT from 2018 to 2020 as one of the main results of collaboration between the University and the two urban PT operators, providing free access to bus and metro transport services in the city. Student satisfaction and relevant data regarding their mobility behavior were constantly monitored via a web survey: the analysis conducted in this study is based on a database of about 4000 responses collected between 2018 and 2019. Spatial and statistical correlations between user satisfaction, transit ridership and accessibility will provide useful information for a correct planning and management of PT networks by transport companies, highlighting diverse insights for different PT options.