Advances in Connected and Autonomous Mobility – IEEE ITSC 2020

The 2020 annual flagship conference of the IEEE Intelligent Transportation Systems Society will be held in Rhodes, Greece in September. LEVITATE will contribute to this conference with a special session: Advances in Connected and Autonomous Mobility: From Data to Models, Impacts and Enablers for Adoption.

Recent breakthroughs in technology, digital infrastructure, dynamic mapping and big data computing will transform the way we will plan, undertake, interact, make decisions and use our built environment and transportation infrastructure for the movement of people and goods. Connected and Autonomous Mobility (CAM), enabled by vehicle connectivity and automation, cloud computing, artificial intelligence and Internet of Things (IoT), allows unprecedented capability to collect, exchange and analyze large volumes of data to formulate models and tools for optimal decision making at individual, local and city levels and will, therefore, increasingly be revolutionized our economy and society over the next decade. However, to what extent they will disrupt mobility and transport operations is still a subject of research. CAM services are expected to emerge in various forms affecting different user groups and imposing network-level changes on various urban scales. To this end, understanding public acceptance and the levels of adoption (and respective timing) of emerging technologies, devising novel approaches and modeling tools to replicate mixed and CAV (Connected and Autonomous Vehicles) traffic in large-scale urban networks for impact assessment and identifying large-scale procedures and policies for CAV traffic management are the key factors for successful deployment of CAM services.

This special session aims at:

  1. providing the audience with information about the deployment of state-of-the-art vehicular and transportation technologies to CAM in a smart city context;
  2. discussing the latest advancements, existing data, conceptual and modeling hurdles and challenges in both research and practice (particularly on the selected topics below)
  3. identifying potential research gaps and collaboration opportunities between industry and academia.

A taxonomy of potential impacts of connected and automated vehicles

LEVITATE is currently building tools to help European cities, regions and national governments prepare for a future with increasing levels of automated vehicles in passenger cars, urban transport services and urban logistics. The project is preparing a new impact assessment framework to enable policy makers to maximise the benefits of connected and automated transport systems (CATS) and utilise the technologies to achieve societal objectives.

Deliverable 3.1: A taxonomy of potential impacts of connected and automated vehicles

Recently several reports have been published within the LEVITATE project. Deliverable 3.1, A taxonomy of potential impacts of connected and automated vehicles at different levels of implementation focuses on the identification of potential impacts of CATs and indicators that can be used to measure these impacts.

D3.1 provides an inventory and classification of impacts of CATS at different levels of implementation and on different topics such as road safety, mobility and efficiency, environment, economy and society. A distinction is made between direct, systemic and wider impacts. Direct impacts are changes that are noticed by each road user on each trip; Systemic impacts are system-wide impacts within the transport system; and wider impacts are changes occurring outside the transport system, such as changes in land use and employment.  Furthermore, a distinction is made between primary impacts and secondary impacts.

The report highlights that the actual impacts of connected and automated transport systems are unknown and will remain so for a long time. However, potential impacts can be identified, and preliminary estimates can be developed. Most analysts believe that a wide implementation of CATS will improve road safety and possibly the efficiency of traffic operations. However, the reliability of automation technology is currently unknown, and there will most likely be unforeseen and rare events that we cannot be taken into account. With regards to policy making, the report points out that highly uncertain estimates of impacts may serve as the basis for identifying policy interventions to increase the likelihood that impacts will be in the desired direction (e.g., policies that can help prevent the urban sprawl that CATs are expected to increase).

The findings of this deliverable will be key in the further development of the use cases and provide the foundation for subsequent work to look at short-, medium- and long-term impacts.

You can access all the publications and learn more about the project here.