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Training and evaluating deep learning models on road graphs for traffic prediction using SUMO
(2024)
The escalation of traffic volume in urban areas poses multifaceted challenges including increased accident risks, congestion, and prolonged travel times. Traditional approaches of expanding road infrastructure face limitations such as space constraints and the potential exacerbation of traffic issues.
Intelligent Transport Systems (ITS) present an alternative strategy to alleviate traffic problems by leveraging data-driven solutions. Central to ITS is traffic prediction, a process vital for applications like Traffic Management and Navigation Systems.
Recent advancements in traffic prediction have witnessed a surge of interest, particularly in deep learning methods optimized for graph-based data processing, being considered the most promising avenue presently.
These methods typically rely on real-life datasets containing traffic sensor data such as METR-LA and PeMS. However, the finite nature of real-life data prompts exploration into augmenting training and testing datasets with simulated traffic data.
This thesis explores the potential of utilizing traffic simulations, employing the microscopic traffic simulator SUMO, to train and test deep learning models for traffic prediction. A framework integrating PyTorch and SUMO is proposed for this purpose, aiming to elucidate the feasibility and effectiveness of using simulated traffic data for enhancing predictive models in traffic management systems.
The usage of microservices promises a lot of benefits concerning scalability and maintainability, rewriting large monoliths is however not always possible. Especially in scientific projects, pure microservice architectures are therefore not feasible in every project. We propose the utilization of microservice principles for the construction of microsimulations for urban transport. We present a prototypical architecture for the connection of MATSim and AnyLogic, two widely used simulation tools in the context of urban transport simulation. The proposed system combines the two tools into a singular tool supporting civil engineers in decision making on innovative urban transport concepts.
Background: Virtual reality (VR) is increasingly used as simulation technology in emergency medicine education and training, in particular for training nontechnical skills. Experimental studies comparing teaching and learning in VR with traditional training media often demonstrate the equivalence or even superiority regarding particular variables of learning or training effectiveness.
Objective: In the EPICSAVE (Enhanced Paramedic Vocational Training with Serious Games and Virtual Environments) project, a highly immersive room-scaled multi-user 3-dimensional VR simulation environment was developed. In this feasibility study, we wanted to gain initial insights into the training effectiveness and media use factors influencing learning and training in VR.
Methods: The virtual emergency scenario was anaphylaxis grade III with shock, swelling of the upper and lower respiratory tract, as well as skin symptoms in a 5-year-old girl (virtual patient) visiting an indoor family amusement park with her grandfather (virtual agent). A cross-sectional, one-group pretest and posttest design was used to evaluate the training effectiveness and quality of the training execution. The sample included 18 active emergency physicians.
Results: The 18 participants rated the VR simulation training positive in terms of training effectiveness and quality of the training execution. A strong, significant correlation (r=.53, P=.01) between experiencing presence and assessing training effectiveness was observed. Perceived limitations in usability and a relatively high extraneous cognitive load reduced this positive effect.
Conclusions: The training within the virtual simulation environment was rated as an effective educational approach. Specific media use factors appear to modulate training effectiveness (ie, improvement through “experience of presence” or reduction through perceived limitations in usability). These factors should be specific targets in the further development of this VR simulation training.
With an increasing complexity and scale, sufficient evaluation of Information Systems (IS) becomes a challenging and difficult task. Simulation modeling has proven as suitable and efficient methodology for evaluating IS and IS artifacts, presupposed it meets certain quality demands. However, existing research on simulation modeling quality solely focuses on quality in terms of accuracy and credibility, disregarding the role of additional quality aspects. Therefore, this paper proposes two design artifacts in order to ensure a holistic quality view on simulation quality. First, associated literature is reviewed in order to extract relevant quality factors in the context of simulation modeling, which can be used to evaluate the overall quality of a simulated solution before, during or after a given project. Secondly, the deduced quality factors are integrated in a quality assessment framework to provide structural guidance on the quality assessment procedure for simulation. In line with a Design Science Research (DSR) approach, we demonstrate the eligibility of both design artifacts by means of prototyping as well as an example case. Moreover, the assessment framework is evaluated and iteratively adjusted with the help of expert feedback.