# InTAS -- The Ingolstadt Traffic Scenario for SUMO

arXiv: https://arxiv.org/abs/2011.11995 · HTML: https://mlsanity.com/paper/2011.11995/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 11 of 11)

- [DuaLight: Enhancing Traffic Signal Control by Leveraging Scenario-Specific and Scenario-Shared Knowledge](https://mlsanity.com/paper/2312.14532/) (2023)
- [URB -- Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles](https://mlsanity.com/paper/2505.17734/) (2025)
- [Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach](https://mlsanity.com/paper/2312.07795/) (2023)
- [Diverse Policy Optimization for Structured Action Space](https://mlsanity.com/paper/2302.11917/) (2023)
- [Deep Reinforcement Learning for the Joint Control of Traffic Light Signaling and Vehicle Speed Advice](https://mlsanity.com/paper/2309.09881/) (2023)
- [Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing](https://mlsanity.com/paper/2502.13188/) (2025)
- [Purpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?](https://mlsanity.com/paper/2311.08429/) (2023)
- [Robustness of Reinforcement Learning-Based Traffic Signal Control under Incidents: A Comparative Study](https://mlsanity.com/paper/2506.13836/) (2025)
- [Generalising Travel Time Prediction To Varying Route Choices In Urban Networks](https://mlsanity.com/paper/2605.06918/) (2026)
- [SPADE: SPaT Attack Detection from the Connected Vehicle's Perspective](https://mlsanity.com/paper/2609.02741/) (2026)
- [Collective Autonomous Vehicle Routing Needs Social Awareness for Coexistence with Human Drivers](https://mlsanity.com/paper/2610.13439/) (2026)

## Abstract (arXiv)

Vehicular Ad Hoc Networks (VANETs) are expected to be the next big step towards safer road transport, supporting applications to exchange information between vehicles. To develop novel applications for this area a high number of tests, considering all traffic situations, are demanded. However, it is unfeasible to reproduce these tests in real life, by the fact that any failure on the applications would cause severe impacts on transport system safety and could risk human lives. Thus, this paper presents the concept, model, and validation for InTAS, a realistic traffic scenario for Ingolstadt. InTAS road topology accurately represents Ingolstadt real road map. Elements such as buildings, bus stops, and traffic lights were added to the map. Twenty traffic lights systems were simulated according to the real program deployed on the traffic lights. Traffic demand was modeled based on the activitygen method, considering demographic data and real-traffic information. The city public transport system was also simulated accordingly to bus time-tables and their routes. The simulation step was implemented considering the best value for device.rerouting.probability, which was defined by evaluating InTAS output and real traffic data. The scenario was validated by comparing real-traffic data from 24 measurement points with InTAS simulation results.