Instituto Federal Catarinense Campus Araquari, Araquari 89245-000, Brazil.
Departamento de Electrónica Telecomunicações e Informática e Instituto de Telecomunicações, Universidade de Aveiro, 3810-193 Aveiro, Portugal.
Sensors (Basel). 2022 Jun 14;22(12):4485. doi: 10.3390/s22124485.
Forecasting road flow has strong importance for both allowing authorities to guarantee safety conditions and traffic efficiency, as well as for road users to be able to plan their trips according to space and road occupation. In a summer resort, such as beaches near cities, traffic depends directly on weather conditions, variables that should be of great impact on the quality of forecasts. Will the use of a dataset with information on transit flows enhanced with meteorological information allow the construction of a precise traffic flow forecasting model, allowing predictions to be made in advance of the traffic flow in suitable time? The present work evaluates different machine learning methods, namely long short-term memory, autoregressive LSTM, and a convolutional neural network, and data attributes to predict traffic flows based on radar and meteorological sensor information. The models trained to predict the traffic flow have shown that weather conditions were essential for this forecast, and thus, these variables were employed in the evaluated deep-learning models. The results pointed out that it is possible to forecast the traffic flow at a reasonable error level for one-hour periods, and the CNN model presented the lowest prediction error values and consumed the least time to build its predictions.
预测道路流量对于保障安全条件和交通效率非常重要，也能让道路使用者根据空间和道路占用情况规划行程。在度假胜地，如城市附近的海滩，交通直接取决于天气条件，这些变量对预测质量应该有很大影响。使用增强了气象信息的过境流量数据集是否可以构建精确的交通流量预测模型，以便提前预测交通流量？本工作评估了不同的机器学习方法，即长短期记忆、自回归 LSTM 和卷积神经网络，以及基于雷达和气象传感器信息预测交通流量的数据属性。训练用于预测交通流量的模型表明，天气条件对该预测至关重要，因此，这些变量被应用于评估的深度学习模型中。结果表明，能够以合理的误差水平预测一小时时间段的交通流量，并且 CNN 模型呈现出最低的预测误差值，并花费最少的时间来构建预测。
