Abbas Sidra, Sampedro Gabriel Avelino, Almadhor Ahmad, Abisado Mideth, Marzougui Mehrez, Kim Tai-Hoon, Alasiry Areej
Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan.
Faculty of Information and Communication Studies, University of the Philippines Open University, Los Baños, Philippines.
PeerJ Comput Sci. 2024 May 13;10:e2050. doi: 10.7717/peerj-cs.2050. eCollection 2024.
The statewide consumer transportation demand model analyzes consumers' transportation needs and preferences within a particular state. It involves collecting and analyzing data on travel behavior, such as trip purpose, mode choice, and travel patterns, and using this information to create models that predict future travel demand. Naturalistic research, crash databases, and driving simulations have all contributed to our knowledge of how modifications to vehicle design affect road safety. This study proposes an approach named PODE that utilizes federated learning (FL) to train the deep neural network to predict the truck destination state, and in the context of origin-destination (OD) estimation, sensitive individual location information is preserved as the model is trained locally on each device. FL allows the training of our DL model across decentralized devices or servers without exchanging raw data. The primary components of this study are a customized deep neural network based on federated learning, with two clients and a server, and the key preprocessing procedures. We reduce the number of target labels from 51 to 11 for efficient learning. The proposed methodology employs two clients and one-server architecture, where the two clients train their local models using their respective data and send the model updates to the server. The server aggregates the updates and returns the global model to the clients. This architecture helps reduce the server's computational burden and allows for distributed training. Results reveal that the PODE achieves an accuracy of 93.20% on the server side.
全州消费者交通需求模型分析了特定州内消费者的交通需求和偏好。它涉及收集和分析出行行为数据，如出行目的、出行方式选择和出行模式，并利用这些信息创建预测未来出行需求的模型。自然主义研究、碰撞数据库和驾驶模拟都为我们了解车辆设计的改变如何影响道路安全做出了贡献。本研究提出了一种名为PODE的方法，该方法利用联邦学习（FL）来训练深度神经网络以预测卡车的目的地状态，并且在起讫点（OD）估计的背景下，在每个设备上本地训练模型时保留敏感的个人位置信息。联邦学习允许在不交换原始数据的情况下跨分散的设备或服务器训练我们的深度学习模型。本研究的主要组成部分是基于联邦学习的定制深度神经网络、两个客户端和一个服务器以及关键的预处理程序。为了高效学习，我们将目标标签的数量从51个减少到11个。所提出的方法采用两个客户端和一个服务器的架构，其中两个客户端使用各自的数据训练其本地模型，并将模型更新发送到服务器。服务器汇总更新并将全局模型返回给客户端。这种架构有助于减轻服务器的计算负担，并允许进行分布式训练。结果表明，PODE在服务器端的准确率达到了93.20%。
