Smart City Gnosys
Smart city article details
| Title | Vehicular Traffic Flow Prediction Using Deployed Traffic Counters In A City |
|---|---|
| ID_Doc | 61010 |
| Authors | Almeida A.; Brás S.; Oliveira I.; Sargento S. |
| Year | 2022 |
| Published | Future Generation Computer Systems, 128 |
| DOI | http://dx.doi.org/10.1016/j.future.2021.10.022 |
| Abstract | The sustainable growth of cities created the need for better informed decisions based on information and communication technologies to sense the city and quantify its pulse. An important part of this concept of “smart cities” is the characterization of vehicular traffic flows and the prediction of urban mobility. Although there are several sensors that are able to infer the traffic flows in the city, road-mounted traffic counters can measure the number of vehicles in different parts of the roads. However, they are not usually used in traffic city prediction; therefore, we can provide a first step for the usefulness of these sensors in the city management. In this paper we study both statistical and deep learning methods to describe, understand and predict the city traffic profile. Although traffic presents seasonal patterns, in occasional situations these may not be verified. Considering the proposed approaches, statistical algorithms, such as SARIMA, and neural network algorithms, such as FFNN, LSTM, CNN and hybrid LSTM-CNN, we found that statistical models are significantly good to predict the traffic counters data in the short-term, even when anomalous traffic conditions are observed. For long-term predictions, CNNs have shown to be efficient and robust. Long-term and short-term forecasting, in the context of traffic flow prediction, may be a strategy to accomplish different goals. Long-term forecasting can be chosen for traffic flow description, and short-term forecasting can be used to identify and mitigate anomalies. © 2021 Elsevier B.V. |
| Author Keywords | Deep learning; Forecasting; Smart urban mobility; Traffic flow |
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