Artificial Intelligence for Urban Low-Emission Autonomous Traffic (AIForLEssAuto) investigates how autonomous electrified traffic should be organized and managed in order to maximise the reduction of CO2 emissions in cities.
Main info

Project duration 1.1.2022 – 31.12.2024 

Fudning: This project has received funding from the European Union – NextGenerationEU instrument and is funded by the Research Council of Finland for 2022-2024.


  • Prof. Laura Ruotsalainen, University of Helsinki, Helsus
  • Assoc. Prof. Ville Kyrki, Aalto University
  • Assoc. Prof. Claudio Roncoli, Aalto University
  • Prof. Leena Järvi, INAR, Helsus

Collaborators: Traficom, Finnish Climate Fund, Sensible 4, HSL, HSY 

Project goals
  • developing a plan how autonomous electrified traffic should be organized and managed in order to maximise the reduction of CO2 emissions in cities
  • developing a framework of computational modelling tools to evaluate the CO2 emissions originating from the introduction of electrified automated vehicles in the urban road context; 
  • developing AI-based control strategies for from vehicle-level to city-center wide traffic control in which CO2 emissions are minimized. 
Research questions
  1.  How much do the different driving routines and connected driving of AVs (for example accelerations, change of lanes, traffic light communication) impact the electricity consumption of electrified vehicles and furthermore CO2 emissions?
  2. What kind of traffc organization, i.e. electric vehicles, public transport, proportion of autonomy, positioning of electric charging devices, would create least CO2 emissions?
  3. Are there significant impacts of introducing autonomous electrified vehicles to traffic scenarios to city-level CO2 emissions?
Work packages


Principal investigator


1. Stakeholder interaction

Laura Ruotsalainen

This WP aims to define traffic scenarios with key stakeholders during joint workshops and to form recommendations on future traffic implementations based on the results of the project.

2. AI-based minimization of vehicle level emissions

Ville Kyrki

This WP aims to answer the question, what level of emission reduction can be gained if control of autonomous vehicles is optimized based on minimizing emissions on the level of single vehicles and local fleets.

3. Novel online traffic decisions

Claudio Roncoli

This WP aims at developing traffic management strategies to reduce the emissions at local levels (i.e., links and junctions) and at network level (group of intersections and routing decisions within a network).

4. RL traffic level modelling

Laura Ruotsalainen

This WP aims at simulating the traffic with different vehicle types and traffic planning decisions, and optimize the system to reduce the resulting CO2 emissions.

5. Calculation of CO2 emissions

Leena Järvi

This WP aims at estimating the total CO2 emissions of mixed road traffic in different vehicle, local and city level scenarios at different temporal scales.





Klavdiia Bochenina, Anton Taleiko, Laura Ruotsalainen. Simulation-based origin-destination matrix reduction: a case study of Helsinki city area // SUMO User Conference 2023.


Shaya Vosough, Claudio Roncoli. Drivers’ social routing behavior: Evidence from stated and revealed preferences experiments in two European cities // Transportation Research Board 102nd Annual Meeting.

Roozbeh Mohammadi, Shaya Vosough, Claudio Roncoli. User-based transit signal priority in a connected vehicle environment accounting for schedule delays and environmental impacts // Transportation Research Board 102nd Annual Meeting.

Olena Vol, Shaya Vosough, Claudio Roncoli. Drivers’ compliance

with social route recommendations: Stated intentions vs actual behavior // 8th International Conference on Models and Technologies for Intelligent Transportation Systems.

Khashayar Khavarian, Shaya Vosough, Claudio Roncoli. How do electric bikes affect the route choice of cyclists? A case study of Greater Helsinki // 11th Symposium of the European Association for Research in Transportation.


Simon Hu et al. High time-resolution queue profile estimation at signalized intersections based on Extended Kalman Filtering // IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 21274-21290, Nov. 2022.

Pak Lun Fung, Omar Al-Jaghbeer, Liisa Pirjola, Hermanni Aaltonen, Leena Järvi. Exploring the discrepancy between top-down and bottom-up approaches of fine spatio-temporal vehicular CO2 emission in an urban road network // Science of the Total Environment.

Gokhan Alcan, Fares J. Abu-Dakka, Ville Kyrki. Trajectory Optimization on Matrix Lie Groups with Differential Dynamic Programming and Nonlinear Constraints.

Jinxiong Lu, Gokhan Alcan, Ville Kyrki. Integrating Expert Guidance for Efficient Learning of Safe Overtaking in Autonomous Driving Using Deep Reinforcement Learning.



Coordinator: Laura Ruotsalainen,