OTSS: Oulu traffic simulation system

Abstract. This thesis presents the design and the implementation of Oulu Traffic Simulation System (OTSS), a traffic simulation system for the City of Oulu, Finland. Following agent-based approach, the simulation generates artificial agents that represent the population synthesis of the City of Oulu. Data from several sources, including official statistics, government-organized open data and crowdsourced information were collected and used as input for the simulation. Two traffic demand models are presented in this thesis: (1) the random model which generates traffic trips as random, discrete events; and (2) the activity-based model which defines traffic trips as sequential events in the agents’ day plan. The software development of the system follows the spiral model of software development and enhancement. During the implementation, several development cycles were conducted before the UML software design. The system was executed on two computation systems to test its real-time performance. To evaluate the traffic models, data extracted from the simulation was compared with aggregated survey data from Finnish Transport Agency and traffic count stations around the city. The results showed that a typical server is capable of running the simulation, and even though there were differences in the duration and distance of individual trips, the simulation reflects real-life traffic count significantly well

A State-of-the-art Integrated Transportation Simulation Platform

Nowadays, universities and companies have a huge need for simulation and modelling methodologies. In the particular case of traffic and transportation, making physical modifications to the real traffic networks could be highly expensive, dependent on political decisions and could be highly disruptive to the environment. However, while studying a specific domain or problem, analysing a problem through simulation may not be trivial and may need several simulation tools, hence raising interoperability issues. To overcome these problems, we propose an agent-directed transportation simulation platform, through the cloud, by means of services. We intend to use the IEEE standard HLA (High Level Architecture) for simulators interoperability and agents for controlling and coordination. Our motivations are to allow multiresolution analysis of complex domains, to allow experts to collaborate on the analysis of a common problem and to allow co-simulation and synergy of different application domains. This paper will start by presenting some preliminary background concepts to help better understand the scope of this work. After that, the results of a literature review is shown. Finally, the general architecture of a transportation simulation platform is proposed

Learning Realistic Traffic Agents in Closed-loop

Realistic traffic simulation is crucial for developing self-driving software in a safe and scalable manner prior to real-world deployment. Typically, imitation learning (IL) is used to learn human-like traffic agents directly from real-world observations collected offline, but without explicit specification of traffic rules, agents trained from IL alone frequently display unrealistic infractions like collisions and driving off the road. This problem is exacerbated in out-of-distribution and long-tail scenarios. On the other hand, reinforcement learning (RL) can train traffic agents to avoid infractions, but using RL alone results in unhuman-like driving behaviors. We propose Reinforcing Traffic Rules (RTR), a holistic closed-loop learning objective to match expert demonstrations under a traffic compliance constraint, which naturally gives rise to a joint IL + RL approach, obtaining the best of both worlds. Our method learns in closed-loop simulations of both nominal scenarios from real-world datasets as well as procedurally generated long-tail scenarios. Our experiments show that RTR learns more realistic and generalizable traffic simulation policies, achieving significantly better tradeoffs between human-like driving and traffic compliance in both nominal and long-tail scenarios. Moreover, when used as a data generation tool for training prediction models, our learned traffic policy leads to considerably improved downstream prediction metrics compared to baseline traffic agents. For more information, visit the project website: https://waabi.ai/rtrComment: CORL 202

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in terms of adapting to dynamic traffic environment and coordinating thousands of agents including vehicles and pedestrians. A key factor in the success of modern reinforcement learning relies on a good simulator to generate a large number of data samples for learning. The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios. This motivates us to create a new traffic simulator CityFlow with fundamentally optimized data structures and efficient algorithms. CityFlow can support flexible definitions for road network and traffic flow based on synthetic and real-world data. It also provides user-friendly interface for reinforcement learning. Most importantly, CityFlow is more than twenty times faster than SUMO and is capable of supporting city-wide traffic simulation with an interactive render for monitoring. Besides traffic signal control, CityFlow could serve as the base for other transportation studies and can create new possibilities to test machine learning methods in the intelligent transportation domain.Comment: WWW 2019 Demo Pape

A multi-agent simulation platform applied to the study of urban traffic lights

Proceedings of 6th International Conference on Software and Data Technologies, ICSOFT 2011The Multi-Agent system paradigm allows the development of complex software platforms to be used in a wide range of real-world scenarios. One of the most successful areas these technologies have been applied are in the simulation and optimization of complex systems. Traffic simulation/optimization problems are a specially suitable target for such a platform. This paper proposes a new Multi-Agent simulation platform, where agents are based on a Swarm model (lightweight agents with very low autonomy or proactivity). Using this framework, simulation designers are free to configure road networks of arbitrary complexity, by customizing road width, geometry and intersection with other roads. To simulate different traffic flow scenarios, vehicle trajectories can be defined by choosing start and end locations and providing traffic generation functions for each one trajectory defined. Finally, how many vehicles are generated at each time step can be determined by a time series function. The domain of traffic simulation has been selected to investigate the effect of traffic light configuration on the flow of vehicles in a road network. The experimental results from this platform show a strong correlation between traffic light behavior and the flow of traffic through the network that affects the congestion of the road.This work has been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2010-19872 and by Jobssy.com

Realistic Speed Control of Agents in Traffic Simulation

Agents in multi-agent traffic simulation tend to be more dependent on the rules and existing instructions to move mechanically and unnaturally imitating human behaviors. The agents will not accelerate or decelerate as humans do. Humans have an irregular pattern of acceleration and deceleration when it comes to real-time driving. This includes hitting breaks when not necessary and sometimes even driving above the speed limit to catch up. In prior works, other factors such as drag and simulation-specific parameters were not considered in the models. Additionally, the models were not tested on the traffic simulation frameworks like SUMO. Instead, they utilized simple numerical models to simulate the environment and evaluate the performance of the models. Therefore, there is a need to further investigate and incorporate these additional factors, as well as validate the models on the SUMO platform, to enhance the realism and applicability of the research. It is also difficult to calibrate SUMO to a given traffic scenario as traffic engineers might need to specify manually the vehicle specifications while designing the experiments. It would be easier for engineers to populate the road network with pre-trained agents that require minimal tuning which includes specifying maximum acceleration, deceleration, and minimum and maximum speed of the vehicles to be simulated. We propose a unified system for agents to decide when to accelerate and decelerate with the help of deep reinforcement learning aided by a combination of factors such as instantaneous speed, time, and other important metrics. The proposed system will aid the agents to behave more like humans by acting based on the surrounding agents in complex situations. This in turn can help create a diverse traffic flow that can mimic real-life traffic scenarios