A Neural Embedding-based Recommender System to Get the Most out of EV Recharge Times

Starace, Luigi Libero Lucio; Bianco, Luca; Martino, Sergio Di

doi:10.1109/esars-itec57127.2023.10114855

Cited by 1 publication

(2 citation statements)

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“…Li et al in [34] investigated air pollution due to trafc emissions in the city of Beijing in China by analysing the trajectories of more than 12K taxis. Historic data on taxi trajectories have also been used to improve the charging efciency of electric vehicles charging networks [3,35]. In [36], Zhong et al proposed an approach that leveraged gyroscope and accelerometer data from driver smartphones to detect and monitor potholes and road pavement degradation during vehicle trips.…”

Section: Related Workmentioning

confidence: 99%

“…On top of VCS-collected information, several novel and compelling applications can be developed. Examples of these use cases include mobility recommender solutions [3], better surveillance of urban scenarios [4], more accurate mobility estimates [5], or air quality monitoring [6]. Recently, a study conducted by the McKinsey & Company consulting frm [7] reported that properly exploiting the knowledge that could be extracted from vehicle-collected data "could deliver $250 billion to $400 billion in annual incremental value for players across the ecosystem in 2030," acknowledging the potential of VCS.…”

Section: Introductionmentioning

confidence: 99%

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Vehicular Crowdsensing with High-Mileage Vehicles: Investigating Spatiotemporal Coverage Dynamics in Historical Cities with Complex Urban Road Networks

Starace

Torrepadula

Martino

et al. 2023

Journal of Advanced Transportation

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Background. Vehicular crowdsensing (VCS) can be a cost-effective solution to gather data in urban environments, leveraging the onboard sensors of modern vehicles moving around the city. Many experimental studies have proven that high-mileage vehicles, such as taxis, can be effectively used for VCS. However, these studies have been mostly carried out in cities with regular, grid-based, road networks. Conversely, little work has been conducted to assess the suitability of VCS in cities with more complex urban road networks, such as historical ones. Goal. As a step towards filling this gap, the present study investigates the feasibility of using different-sized fleets of taxis to crowdsense information in the urban areas of the historical cities of Porto (Portugal) and Rome (Italy), whose road networks evolved over the centuries and feature a complex topology. Data and Methodology. This work leverages massive real-world datasets of taxi trajectories collected over three contiguous weeks in the cities of Porto and Rome to estimate the spatiotemporal coverage achievable by different-sized fleets of taxis if they were used for VCS. Indeed, using these trajectories, several simulations were conducted, considering four sizes of taxi fleets, ranging from 50 to 400 vehicles, for both cities. The achievable spatiotemporal road network coverage metrics were computed at a fine-grained scale of single road segments. Results. Results show that the achievable coverage in both historical cities exhibits very similar trends, with as few as 50 vehicles being capable of visiting a relevant part of the road network at least once in the considered time frame. As expected, increasing the number of involved vehicles improves spatial and temporal coverage. Still, time gaps between subsequent visits can be possibly inadequate for some VCS use cases. As a consequence, recruiting more vehicles and/or devising specialized routing/incentivization mechanisms might be necessary to achieve more comprehensive coverage of the urban road network.

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