This paper presents the first results of a Delphi survey aimed at eliciting experts' opinions on possible scenarios of integration between passenger and freight transport enabled by a Mobility as a Service (MaaS) platform. Combining freight shipments with passenger trips is a promising addition to the MaaS business model that could help to reduce the number of freight vehicles and contribute to a more efficient use of passenger transport services and modes. The research objective of this paper is therefore to explore the feasibility of an extended version of MaaS including freight, called “MaaS for Passenger and Freight” (MaaS4PaF). Passenger and freight transport experts were asked to express their opinions with respect to the potential market penetration, business ecosystem, and implementation of this concept. Results allow to elaborate on opportunities and barriers, especially related to uncertain business models and the multitude of actors involved, and propose a research agenda to further investigate the feasibility and potential of MaaS for passenger and freight transport integration.

This paper presents an agent-based model (ABM) to simulate and compare two different operation strategies of a public transport service in small-sized cities, namely a fixed-route transit (FRT) and a demand-responsive transport (DRT) service, under varying demand rates and supply configurations. The ABM builds upon a previous work by the Authors, where flexible and feeder services of a Mass Rapid Transit system were simulated. In this paper, instead of a many-to-one pattern typical of a feeder service, we considered a many-to-many one. The objective is to investigate the conditions that make a DRT more attractive than a FRT in small-sized cities and guide its design considering the demand fluctuation, land-use pattern, service constraints and passenger preferences. A dispatching algorithm for the DRT allows to assign each new trip request to a vehicle, and a couple of origin and destination stops, updating the vehicle schedule in real time. The service includes fixed and virtual stops, allowing request consolidation and balancing operator-related (cost of the service) and user-related (quality of service) needs. The model is applied to Vittoria (Italy), a small city with 60,000 residents in Southern Italy where most trips are made by car, also due to the absence of an urban public transport service. First results highlight the benefits of providing a flexible service compared to a fixed one to minimize detours, waiting times and walking distances experienced by passengers while allowing for a higher shareability and efficiency of the service.

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.