Many applications in intelligent transportation systems are demanding an accurate vehicle Global Position System (GPS) location prediction. In this study, we satisfy this demand by designing an automated GPS location prediction system based on the well known traditional Auto-Regressive Integrated Moving Average (ARIMA). To increase the proposed model accuracy, make it dynamic, and reduce its execution time, the traditional ARIMA model has been modified extensively by using different combinations of design options of the model. To perform GPS location prediction, the proposed model depends the previous recorded vehicle locations, speed, and bearing reading to predict the vehicle future locations. To make the proposed model dynamic, it is designed to regenerate all its parameters periodically. To deal with such dynamic environment, only a specified window of the historical data is used. To reduce the regeneration of the model execution time, the model selection process is enhanced and several model selection approaches are proposed. The proposed model and the different design options are evaluated using a realistic vehicle dataset traces that are recorded using a GPS receiver embedded in a smart phone, as well as, using traces from a previous study called the INFATI Dataset. To deal with any imperfection in the data used in generating the model in this study, we propose a novel anomaly detection and smoothing technique. The results show that the proposed framework can generate ARIMA models that can predict the future GPS locations of a vehicle accurately and with a reasonable execution time. The results also show that the proposed model can predict the vehicle’s location for several future steps with an acceptable accuracy.

智能交通系统中的许多应用都要求精确的车辆全球定位系统（GPS）位置预测。在这项研究中，我们通过基于众所周知的传统自回归综合移动平均值（ARIMA）设计自动GPS位置预测系统来满足这一需求。为了提高建议的模型准确性，使其动态并减少执行时间，对传统的ARIMA模型进行了广泛修改，方法是使用模型设计选项的不同组合。为了执行GPS位置预测，建议的模型取决于先前记录的车辆位置，速度和方位读数来预测车辆的未来位置。为了使建议的模型具有动态性，它被设计为定期重新生成其所有参数。为了应对这种动态环境，仅使用历史数据的指定窗口。为了减少模型执行时间的再生，改进了模型选择过程，并提出了几种模型选择方法。拟议的模型和不同的设计选项使用真实的车辆数据集轨迹进行评估，该轨迹使用智能手机中嵌入的GPS接收器记录下来，并使用之前研究中的轨迹进行记录。INFATI数据集。为了处理本研究中用于生成模型的数据中的任何缺陷，我们提出了一种新颖的异常检测和平滑技术。结果表明，所提出的框架可以生成ARIMA模型，该模型可以准确预测车辆的未来GPS位置并具有合理的执行时间。结果还表明，提出的模型可以以可接受的精度预测未来几个步骤的车辆位置。