The communication technology revolution in this era has increased the use of smartphones in the world of transportation. In this paper, we propose to leverage IoT device data, capturing passengers' smartphones' Wi-Fi data in conjunction with weather conditions to predict the expected number of passengers waiting at a bus stop at a specific time using deep learning models. Our study collected data from the transit bus system at James Madison University (JMU) in Virginia, USA. This paper studies the correlation between the number of passengers waiting at bus stops and weather conditions. Empirically, an experiment with several bus stops in JMU, was utilized to confirm a high precision level. We compared our Deep Neural Network (DNN) model against two baseline models: Linear Regression (LR) and a Wide Neural Network (WNN). The gap between the baseline models and DNN was 35% and 14% better Mean Squared Error (MSE) scores for predictions in favor of the DNN compared to LR and WNN, respectively.
翻译:这个时代的通信技术革命增加了交通界对智能手机的使用。 在本文中,我们提议利用IOT设备数据,结合天气条件收集乘客的智能手机无线网络数据,以利用深层学习模型预测特定时间在公共汽车站等候的乘客的预期人数。我们的研究从美国弗吉尼亚州詹姆斯·麦迪逊大学(JMU)的中转公共汽车系统中收集了数据。本文研究了在公共汽车站等候的乘客人数与天气条件之间的相互关系。在JMU的几个公共汽车站进行的一项实验很生动地证实了一个高精确度。我们比较了深神经网络(DNN)模型与两个基线模型:线性回归(LR)和宽线性神经网络(WNN),基线模型与DNN(DN)之间的差距分别为35%和14%,用于预测DNN与L和WNN(MS)的偏差值。