Delivering in Urban Areas: A Probabilistic-Behavioral Approach for Forecasting the Use of Electric Micromobility

Abstract

Urban delivering is facing some significant changes that are heading towards unsustainable scenarios. At the same time, local administrations as well as city planners are involved in promoting new solutions that can help to improve city sustainability and livability. In this context, electric micromobility could offer a valuable contribution. In fact, electric micromobility systems such as e-bikes and e-scooters, both at an individual level or as a shared service, could represent sustainable mobility options for city logistics, especially for specific classes of parcel delivery, users’ characteristics and travelled distances. Considering both the growth of e-commerce and the spreading of new options for delivering parcels (e.g., crowdshipping), electric micromobility (e-bikes and e-scooters) could support the penetration and acceptability of such new options, limiting the impacts of delivery operations. After analysis of the current e-commerce background and a review of the current delivery options to satisfy delivery demand, crowdshipping stands out. Thus, the potential shift from private transport to e-micromobility for crowdshipping is investigated, assuming that potential crowdshippers may, mainly, be commuters. The methodology is based on using probabilistic-behavioral models developed within random utility theory, which allow the potential shift towards e-micromobility for commuting to be forecasted. The models were calibrated in Rome, where more than 200 interviews with commuters were available.

Describing Micro-Mobility First/Last-Mile Routing Behavior in Urban Road Networks through a Novel Modeling Approach

“…The calibration of these models was based on safety evaluations provided by people who live in this city. These reported safety patterns seem to be in line with the literature findings [13,15,[36][37][38][39]. Nevertheless, the re-calibration of these models is recommended in each study case to accurately represent citizens' safety perceptions [53].…”

“…This study assumed that the flexibility of the e-scooter compared to traditional first/last mile modes (e.g., car or walking) is limited in practice by the users' safety perceptions. The unsafety of using a specific transport mode has a cost that can be expressed in monetary values (e.g., EUR) or additional trip distance, as recent studies have highlighted [37][38][39]. The modeling approach that is proposed in this study is based on two parametric models, namely the perceived safety model and first/last routing model.…”

“…Significant deviations between the model outputs and real traffic flow measurements were observed, proving that e-scooter riders tend to detour to avoid certain street sections, which are unsuitable or unsafe. The study of Castiglione et al [37] integrated perceived safety into binomial models that describe the willingness of people to become crowd shippers using an e-bike or e-scooter for their deliveries. Based on their approach, subjective safety relates to the intention of road users to use or not use micro-mobility modes in mixed traffic conditions.…”

A mixed behavioural and data-driven method for assessing the shift potential to electric micromobility: evidence from Rome

“…In particular, the potential for crowdshippers to use micromobility devices has been investigated through probabilistic-behavioural models. The reader can refer to Castiglione et al (2022) for more details. This article, moving from such first results, proposes further models that allow us to consider further types of micromobility devices calibrated using different techniques.…”

Estimation of Postal Service Delivery Time and Energy Cost with E-Scooter by Machine Learning Algorithms

“…Input parameters of studies were generally determined to analyse electric mobility vehicle usage for studies for particular purposes. One study used probabilistic behavioural models developed within random utility theory, examining factors such as drivers' demographics, drivers' households, type of use, and road conditions to analyse the effects that led to the use of e-micro mobility for commuting [35]. Another study examined changes in physical activity, personal air pollution exposure, and risk of fatal traffic accidents to measure the health effects of using electric micro-mobility vehicles for adults residing in Barcelona [36].…”