Pisa bike sharing system

CC BY Daniele Napolitano


The workshop program consists of 4 paper presentation and a keynote speech (date and time: 15.30 CEST, June 7th, 2021)

  • C. Cicconetti, L. Lossi, E. Mingozzi, A. Passarella - A Preliminary Evaluation of QUIC for Mobile Serverless Edge Applications (see teaser video)
  • F. Martelli, M.E. Renda - Comparison of Trip Matching Algorithms for Mobility Sharing Applications
  • M.D. Hoffmann, P. Kryszkiewicz- Similarity Measures for Location-Dependent MMIMO, 5G Base Stations On/Off Switching Using Radio Environment Map
  • E. Gindullina, S. Mortag, M. Dudin, L. Badia - Multi-Agent Navigation of a Multi-Storey Parking Garage via Game Theory

Keynote speech

Prof. Amelia Regan (University of California, Irvine) - Traffic data analysis for dynamic anticipatory road pricing(date and time: 17.00 CEST, June 7th, 2021)

This talk will discuss prior and ongoing work with Reza Asadi (PhD UCI, Computer Science 2020), Daniel Siddiqi (UCI MBA expected 2022) and Boyuan Jiang (UCI MS Civil Engineering 2020, UCI MS Computer Science expected 2021). We have recently published a number of papers which apply deep learning models for clustering, missing data imputation and finally short term traffic flow prediction. Using the often employed Caltrans PeMS data we examined the performance of this system relative to other commonly used models, and found that it has very good predictive ability. In the next decade or two, as we transition to automated vehicles, use-based and congestion-based pricing will be both necessary and feasible to implement on a wide scale. Aguma and Regan have recently begun to examine anticipatory pricing methods for real time freeway management. Such pricing methods rely on good short term traffic flow estimates and allow for accurate calculation of marginal pricing tolls. This talk will discuss the recent work and provide a roadmap for our new project which combines traffic data analysis and machine learning models to cluster and forecast traffic data and to use the forecasts to inform marginal cost based anticipatory tolls.

Call for papers

Mobility is a key aspect in the management of modern cities, as growing traffic and environmental concerns place an ever greater emphasis on public transportation and micro mobility. The dual challenge posed by climate change, which requires a shift away from private driving and towards sustainable solutions, and pandemic risks, which are mitigated by avoiding crowded spaces and agglomerations of people, will have an important role in the cities of the future.

In this context, ICT and data science can help in many ways, both as a basic enabler through mobile technologies to track people and manage multiple modes of transportation and as a tool of analysis: graph theory methods have been applied to the analysis of flows of people and transportation networks, and machine learning techniques can uncover hidden patterns that can improve the efficiency of existing services. Furthermore, there is the possibility to exploit the synergy between transport and ICT systems to improve the latter, not just the former: the abundance of mobility data and other transport information from the Smart City infrastructure and sensors can help manage mobile networks, improving coverage and Quality of Service for network users as well as providing a better transit experience.

The workshop aims at bringing together experts from the industry and academia across the Smart City and urban transportation communities, and will accept original research results related to the following topics, as well as others relevant to the call:

  • Integrated network solutions for smart mobility
  • Crowd-sensing and public transportation
  • Joint optimization of mobility services using Smart City data
  • Internet-based services for the optimization of transportation and sharing systems
  • ICT-based integration of mass transit and bike or scooter sharing
  • Social distancing- or tracing-friendly solutions for commuting
  • Platforms and IoT solutions for the management of mobility data
  • Optimization of mobile networks using live urban mobility data
  • Integration of information from vehicular networks and Smart City sensors
  • Learning-based solutions to analyze mobility data

Accepted papers will be published on the IEEEXplore website as part of the IEEE WoWMoM 2021 Workshops Proceedings.

Submit a paper through EDAS

Author information

Submissions must follow the standard two-column IEEE conference style with 10 point size font. The maximum length is 6 pages for initial submissions. One additional page can be added to the camera-ready version for accepted papers conditional on the payment of overlength charges, for a total maximum length of 7 pages. The template for submissions may be found on the IEEE templates page.

Important dates

  • 1st March 2021 15th March 2021: paper submission deadline (EXTENDED)
  • 1st April 202123rd April 2021: paper selection
  • 19th April 20215th May 2021: camera ready submission deadline

Workshop organizers

Federico Chiariotti

Aalborg University

Federico Librino

Istituto di Informatica e Telematica, CNR

Andrea Zanella

University of Padova

Technical Program Committee

  • Israel Leyva-Mayorga (Aalborg University)
  • Marco Giordani (University of Padova)
  • Michele Polese (Northeastern University)
  • Miguel Sepulcre (Miguel Hern├índez University of Elche)

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