Abstract

A better understanding of the traffic flow in a city helps to smooth transport resulting in a better

street environment, affecting not only road users and people in proximity. Good predictions of

the flow of traffic helps to control and further develop the road network in order to avoid

congestion and unneccessary time spent while traveling. This study investigates three different

machine learning models with the purpose of predicting traffic flow on different road types in

urban Stockholm using loop sensor data between 2013 and 2023. The models used was Long

short term memory (LSTM), Temporal convolutional network (TCN) and a hybrid model of LSTM

and TCN. The results from the hybrid model indicates a slightly better mean absolute error than

TCN suggesting that a hybrid model might be advantagous when predicting traffic flow using

loop sensor data. LSTM struggled to capture the complexity of the data and was unable to

provide a proper prediction as a result. TCN produced a mean absolute error slightly bigger

than the hybrid model and was to an extent able to capture the trends of the traffic flow, but

struggled with capturing the scale of the traffic flow suggesting the need for further data

preprocessing. Furthermore, this study suggests that the loop sensor data was able to act as a

foundation for predicting the traffic flow using machine learning methods. However, it suggest

that improvements to the data itself such as incorporating more related parameters might be

advantageous to further improve traffic flow prediction.

Teknisk-naturvetenskapliga fakulteten, Uppsala universitet. Utgivningsort U ppsal a. H andl edare: Beatrice Fritz, Äm nesgranskar e: Justin Pearson, Exami nator: El ísabet André