Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation最新文献

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Data-driven Microscopic Traffic Modelling and Simulation using Dynamic LSTM 基于动态LSTM的数据驱动微观交通建模与仿真

Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2021-05-21 DOI: 10.1145/3437959.3459258

Htet Naing, Wentong Cai, Nan Hu, Tiantian Wu, Liang Yu

{"title":"Data-driven Microscopic Traffic Modelling and Simulation using Dynamic LSTM","authors":"Htet Naing, Wentong Cai, Nan Hu, Tiantian Wu, Liang Yu","doi":"10.1145/3437959.3459258","DOIUrl":"https://doi.org/10.1145/3437959.3459258","url":null,"abstract":"With the increasing popularity of Digital Twin, there is an opportunity to employ deep learning models in symbiotic simulation system. Symbiotic simulation can replicate multiple what-if simulation instances from its real-time reference simulation (base simulation) for short-term forecasting. Hence, it is a useful tool for just-in-time decision making process. Recent trends on symbiotic simulation studies emphasize on its combination with machine learning. Despite its success and usefulness, very few works focus on application of such a hybrid system in microscopic traffic simulation. Existing application of machine (deep) learning models in microscopic traffic simulation is confined to either predictive analysis or offline simulation-based prescriptive analysis. Thus, there is also lack of work on updating parameters of a deep learning model dynamically for real-time traffic simulation. This is necessary if the learning-based model is to be used as part of the base simulation so that \"Just-in-time (JIT)\" what-if simulation initialized from the model can make better short-term forecasts. This paper proposes a data-driven modelling and simulation framework to dynamically update parameters of Long Short-term Memory (LSTM) for JIT microscopic traffic simulation. Extensive experiments were carried out to demonstrate its effectiveness in terms of more accurate short-term forecasting than other baseline models.","PeriodicalId":169025,"journal":{"name":"Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128645936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Comparing Implementations of Cellular Automata as Images: A Novel Approach to Verification by Combining Image Processing and Machine Learning 比较元胞自动机作为图像的实现:一种结合图像处理和机器学习的验证新方法

Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2021-05-21 DOI: 10.1145/3437959.3459256

M. Wozniak, P. Giabbanelli

{"title":"Comparing Implementations of Cellular Automata as Images: A Novel Approach to Verification by Combining Image Processing and Machine Learning","authors":"M. Wozniak, P. Giabbanelli","doi":"10.1145/3437959.3459256","DOIUrl":"https://doi.org/10.1145/3437959.3459256","url":null,"abstract":"Discrete models such as cellular automata may be ported from one platform or language onto another to improve performances, for instance by rewriting legacy Matlab code into C++ or adding optimizations into a Python implementation. Although such transformations can offer benefits such as scalability or maintainability, they also have the risk of introducing bugs. While standard verification techniques can always be applied, this situation presents a unique opportunity since the two implementations can be directly compared based on their simulation runs. Although comparing average results across runs of a same configuration is a common practice, our paper shows that many bugs would not be detected at this aggregate level. We thus propose comparing implementations of cellular automata by analyzing their outputs as images. In this paper, we examine the detection of several implementation errors using five different techniques (supervised/unsupervised image processing, decision trees, random forests, or deep learning) across three different cellular automata models (forest fire, tumor, HIV). We show that in some models, random forests can detect 4 out of 5 erroneous runs, although the accuracy depends both on the model and on the nature of the errors.","PeriodicalId":169025,"journal":{"name":"Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116079625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Differentiable Agent-Based Simulation for Gradient-Guided Simulation-Based Optimization 基于可微智能体的梯度导向仿真优化

Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2021-03-23 DOI: 10.1145/3437959.3459261

Philipp Andelfinger

{"title":"Differentiable Agent-Based Simulation for Gradient-Guided Simulation-Based Optimization","authors":"Philipp Andelfinger","doi":"10.1145/3437959.3459261","DOIUrl":"https://doi.org/10.1145/3437959.3459261","url":null,"abstract":"Simulation-based optimization using agent-based models is typically carried out under the assumption that the gradient describing the sensitivity of the simulation output to the input cannot be evaluated directly. To still apply gradient-based optimization methods, which efficiently steer the optimization towards a local optimum, gradient estimation methods can be employed. However, many simulation runs are needed to obtain accurate estimates if the input dimension is large. Automatic differentiation (AD) is a family of techniques to compute gradients of general programs directly. Here, we explore the use of AD in the context of time-driven agent-based simulations. By substituting common discrete model elements such as conditional branching with smooth approximations, we obtain gradient information across discontinuities in the model logic. On the example of microscopic traffic models and an epidemics model, we study the fidelity and overhead of the differentiable models, as well as the convergence speed and solution quality achieved by gradient-based optimization compared to gradient-free methods. In traffic signal timing optimization problems with high input dimension, the gradient-based methods exhibit substantially superior performance. Finally, we demonstrate that the approach enables gradient-based training of neural network-controlled simulation entities embedded in the model logic.","PeriodicalId":169025,"journal":{"name":"Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"158 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134588607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

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