Accepted Industrial Session

The following Industrial session has been accepted and will be scheduled in the conference program. Here are the details and speakers. The program will be published in some weeks, once the peer review process of technical contributions is over and notifications have been issued to the authors. Stay tuned for updates, and do not forget to register to attend this industrial session!

Next-gen mobility planning

Motivation and objectives:

This session will focus on the use of big data and machine learning in transport planning. The session will cover topics such as trustworthy AI, travel behavior shifts, predicting the impacts of (mega)trends on travel patterns, macro simulation, scenario planning, short- & long-term transport policy and strategy. Overall, the session is expected to be a valuable forum for exploring the latest trends, strategies and practices in next-gen mobility planning, with insights from international experts across the transport authority, industry and academia.

  • Topics: Big data, AI, machine learning for transport planning, Trustworthy AI (XAI) for transport planning, Ethical AI for travel demand modelling, Modelling travel behavior shift, Predicting PESTLE impacts of (mega)trends e.g. connected, electric and autonomous vehicles on travel pattern, Scenario planning, Transport policy and strategy for long-term transport planning
  • Format: Half day (4 hours)

Intended audience:

Traffic infrastructure managers, traffic innovation experts, traffic regulators/planners/modelers in city, regional and national levels



Sida Jiang, lead planner transportation at WSP Sweden and cofounder of FellowBot,

Sida originates from Guilin, China and works as lead planner transportation at WSP upon her graduation from Royal Institute of Technology, KTH in 2010. Sida becomes IEEE ITS member since 2019 and as guest editor of smart railway session of ITSC 2020. Sida is responsible for official next-gen traffic prediction in both Sweden (commissioned by Trafikverket, 2021-2022) and Norway (commissioned by Jernbanedirektoratet 2020-2022). Sida has also co-founded FellowBot which is nominated by Nvidia as one of top 50 AI start-ups in Europe, 2018.

Luis Willumsen, visiting professor at University College London, Managing Partner of Nommon Solutions and Technologies.

Dr. Luis Willumsen is an internationally recognised authority in Transport and Traffic modelling. He is co-author of “Modelling Transport” a book that will have its fifth edition published later this year. He taught at Leeds University and University College London and then became a Director of a transport consultancy leaving in 2010 to develop his own practice. He is Managing Partner of Nommon Solutions and Technologies, a company specialised in the use of big data and Artificial Intelligence to deliver useful mobility insights.  He is also Visiting Professor at University College London.

Invited Speakers

Transport authority

  • Jonas Foss Blakstad (Viken County Council, Norway, he has extensive experience in policymaking and execution for next-gen mobility networks and leads a data-driven project to plan for the zero-emissions energy-mix futures for public transport for 2 million inhabitants in the Oslo-area.
  • Jon Robert Dohmen (Norwegian Rail Directorate – Jernbanedirektoratet, Norway – senior advisor and Europe’s Rail Flagship Area 7 manager. He has management experience with big Data premises including open and closed data sources, Machine Learning models (AI) and developing innovative and new systematic methods. His main practices are making policy and strategy in transport planning based upon quantifying qualitative perspectives on forming trends.
  • Tania Gullón(Spanish Ministry of Transport): she leads a project dealing with daily monitoring of mobility in Spain based on mobile network data and other big data sources.


  • Ulises Wensell (Acciona, Spain, he has extensive experience working with mobile network data-based OD matrices in transport concessions; in some research projects we have also explored with him the use of ML models for forecasting demand of shared mobility services.
  • Desmond Wright (WSP, Sweden, Desmond has worked in the transport industry for over 20 years across many countries and so brings some unique perspectives to the discussion on public transportation. In this talk he will focus answering questions such as why traditional sources of data for transport analysis may not be enough to understand trends and behaviour post pandemic.
  • Jordi Casas/Athina Tympakianaki / Rafael Mena Yedra (Aimsun, Spain, European R&D projects looking at how to exploit new big data sources to improve transport and traffic models.
  • Thomas Nikodem (PTV, Germany, will present on using AI to estimate certain zone attributes, needed for a transport model, from readily available (big) proxy data, instead of procuring the original data (which may be onerous, take time or money, scale less well.
  • Ricardo Herranz (Nommon, Spain, has 20 years of experience as an engineer, researcher and entrepreneur. He is co-founder and CEO of Nommon, a company that provides decision support solutions for the planning and management of transport and mobility systems. He will discuss how Nommon combines mobile network data and smart card data with ML algorithms to analyse and forecast mobility patterns and travel behaviour.


  • Stefan Flügel (Institute of Transport Economics – TOI, Norway, overall plan for next-gen modelling in Norway with machine learning (ML) trained on travel survey data to predict activity plans of agents in a synthetic population, and ML trained on MATSim simulation outcomes to predict traffic patterns.
  • Lei Chen (Research Institutes of Sweden – RISE, Sweden, focus the on ICV-safe project which is a bilateral joint effort to identify safety-critical scenarios and to develop risk assessment and mitigation methods for intelligent connected vehicles (ICVs), on the large-scale open test environment in Shanghai, China.
  • Bill Roungas (Institute of Communication & Computer Systems ICCS, representative of EU project “ENENTS”) presents a safe and cost-effective way to generate large amounts of training data. This presentation aims to initiate a discussion on the overarching main challenges to be, the diversity of the produced data and the accuracy of the utilized simulation models.
  • Samitha Samaranayake (Assistant Professor in the School of Civil and Environmental Engineering at Cornell University, This talk will focus on some algorithmic and practical questions related to integrating mass transit operations with agile, demand-responsive services with the goal of enabling more sustainable and equitable personal mobility for all.