More efficient traffic and fleet management
Athina Tympakianaki
Senior Scientific Researcher
Connected and efficient multimodal traffic and fleet management systems
CONDUCTOR is an EU-funded research project that aims to build on state-of-the-art fleet and traffic management solutions for cooperative connected autonomous mobility (CCAM).
The CONDUCTOR project leverages AI and data fusion to develop next generation simulation models and tools with the aim of enhancing the capabilities of transport authorities and operators, helping them to become true conductors of our future mobility networks.
How can we benefit from CCAM systems?
CCAM offers connectivity and automation possibilities that can improve multimodal traffic and fleet management by providing richer, more reliable information about supply and demand. Exploiting this data aids the development of solutions and tools to improve the network performance, achieve higher levels of road safety and reduce environmental impacts.
Which tools can model and assess CCAM systems?
Reliable and holistic mobility simulation systems are a key tool in facilitating the design, integration and demonstration of advanced traffic and fleet management.
The CONDUCTOR project team is using transport simulation models to virtually assess the impact of traffic and fleet management strategies, combined with new mobility services and connected and automated transport systems. Innovative network-level multimodal traffic management solutions, incident management and optimized last-mile delivery services are integrated into simulation tools and assessed as part of different use cases in the project’s pilot cities of Athens, Almelo, and Madrid.
Depending on the scope of the application, different simulation tools can be used.
Aimsun contributes to the adaptation and upgrading of multi-resolution and multimodal simulation models, ensuring the interoperability between the fleet and traffic management strategies developed with the virtual transport ecosystem. In collaboration with project partners Deusto and Nommon, Aimsun will demonstrate and validate, in a simulation context, the proposed approaches through two use cases for the city of Madrid:
Use case 1: Managing disruptive events
Recovering transport network operations after a disruptive event is a challenging and complex problem for many cities. This use case focuses on improving traffic management by proposing effective intervention strategies considering vehicle connectivity and testing various levels of CCAM penetration.
The study area is the Madrid M-30 ring road, a 32km urban highway that surrounds the central neighborhoods of Madrid. The secondary roads in the adjacent urban road network provide possible detour options.
The use case is based on a simulated environment using Aimsun traffic simulation software. Simulation is a highly efficient tool for testing what-if scenarios. By stipulating KPIs, the simulation allows the CONDUCTOR team to identify and quantify the impacts of planned and unplanned events in the network.
For instance, planned events can be roadworks that last from a few weeks to years and can have severe implications for traffic, causing delays as well as safety risks. On the other hand, unplanned events are unexpected situations, such as road accidents, that can cause congestion and disturbances in the network operations.
The simulation will emulate the communication of vehicles with the environment and receive personalized information, such as optimal departure times and routes, lane selection, and speed adaptation. An integrated optimization module will adjust the schedule and develop new CAV routes that will be transmitted to the simulator.
The expected benefits of this UC are the reduction in: (i) recovery time, (ii) average travel time and travel distance per connected vehicle, (iii) economic losses due to travel delays, and (iv) total vehicle emissions of CO2 and NOx.
Use case 2: Urban distribution of goods
Leveraging the excess capacity of CCAM-enabled Demand Responsive Transport (DRT) vehicles during periods of lower demand for urban parcel delivery, this UC aims to develop coordination and integration strategies for urban last-mile delivery of parcels and DRT services
How can we assess the potential benefits and effectiveness of such strategies before deploying them in real-life? We will adopt machine learning, data fusion techniques, and traffic simulations through the following steps:
- Demand estimation for last-mile deliveries as well as the generation of the DRT service demand
- Integration of DRT-CCAM and delivery demand through optimal demand-supply balancing strategies and routes
- Simulation-based evaluation of the DRT and parcel delivery service, capturing the interactions with the “background” traffic in the network
The goal is to enhance the capabilities of authorities and operators, making them true conductors of future mobility networks.
Stay tuned for the first results, coming out early next year!
