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Increasing the modal share of bicycle traffic to reduce carbon emissions, reduce urban car traffic, and to improve the health of citizens, requires a shift away from car-centric city planning. For this, traffic planners often rely on…
Increasing the modal share of bicycle traffic to reduce carbon emissions, reduce urban car traffic, and to improve the health of citizens, requires a shift away from car-centric city planning. For this, traffic planners often rely on…
Urban traffic simulation is vital in planning, modeling, and analyzing road networks. However, the realism of a simulation depends extensively on the quality of input data. This paper presents an intersection traffic simulation tool that…
Current autonomous vehicle (AV) simulators are built to provide large-scale testing required to prove capabilities under varied conditions in controlled, repeatable fashion. However, they have certain failings including the need for user…
This paper presents a systematic benchmarking of the model-based microscopic traffic simulator SUMO against state-of-the-art data-driven traffic simulators using large-scale real-world datasets. Using the Waymo Open Motion Dataset (WOMD)…
Traffic microsimulation is a crucial tool that uses microscopic traffic models, such as car-following and lane-change models, to simulate the trajectories of individual agents. This digital platform allows for the assessment of the impact…
Traffic simulation is an efficient and cost-effective way to test Autonomous Vehicles (AVs) in a complex and dynamic environment. Numerous studies have been conducted for AV evaluation using traffic simulation over the past decades.…
Traffic simulators are important tools for tasks such as urban planning and transportation management. Microscopic simulators allow per-vehicle movement simulation, but require longer simulation time. The simulation overhead is exacerbated…
Simulation is a valuable tool for traffic management experts to assist them in refining and improving transportation systems and anticipating the impact of possible changes in the infrastructure network before their actual implementation.…
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…
This paper presents a step-by-step guide to generating and simulating a traffic scenario using the open-source simulation tool SUMO. It introduces the common pipeline used to generate a synthetic traffic model for SUMO, how to import…
With growing urbanization worldwide, efficient management of traffic infrastructure is critical for transportation agencies and city planners. It is essential to have tools that help analyze large volumes of stored traffic data and make…
Traffic simulation is an essential tool for transportation infrastructure planning, intelligent traffic control policy learning, and traffic flow analysis. Its effectiveness relies heavily on the realism of the simulators used. Traditional…
Traffic simulation tools, such as SUMO, are essential for urban mobility research. However, such tools remain challenging for users due to complex manual workflows involving network download, demand generation, simulation setup, and result…
Urban traffic anomalies, such as collisions and disruptions, threaten the safety, efficiency, and sustainability of transportation systems. In this paper, we present a simulation-based framework for modeling, detecting, and predicting such…
Smart traffic control and management become an emerging application for Deep Reinforcement Learning (DRL) to solve traffic congestion problems in urban networks. Different traffic control and management policies can be tested on the traffic…
With the rapid growth of urban transportation and the continuous progress in autonomous driving, a demand for robust benchmarking autonomous driving algorithms has emerged, calling for accurate modeling of large-scale urban traffic…
Traffic microsimulation software such as SUMO generate rich spatio-temporal data describing individual vehicle movements, interactions, and support the development of control strategies. While numerical outputs and 2D visualisations are…
Traffic congestion and collisions represent significant economic, environmental, and social challenges worldwide. Traditional traffic management approaches have shown limited success in addressing these complex, dynamic problems. To address…
Urban traffic regulation policies are increasingly used to address congestion, emissions, and accessibility in cities, yet their impacts are difficult to assess due to the socio-technical complexity of urban mobility systems. Recent…