Short-term traffic flow prediction in heterogeneous traffic conditions using Gaussian process regression

In recent decades, there has been substantial population growth, leading to a higher volume of vehicles on the roadways. This has contributed to traffic congestion issues, affecting not just major metropolitan areas but also medium-sized and small cities worldwide. The management of roadway traffic is enhanced by accurate short-term traffic flow forecasts, which makes it a crucial part of intelligent transportation systems. This study utilizes Gaussian process regression (GPR) to predict the road traffic flow under heterogeneous conditions for 5 min in the future using past data. GPR model represents the relationship between data points as a probability distribution over functions, rather than a single deterministic function as in traditional linear regression. This allows GPR to capture both the mean and uncertainty of predictions. All of the comparable models were trained and tested on actual data sets that were gathered through field research. Results of the GPR model were compared with other traditional models like autoregressive moving average model, multi-layer perceptron and cascade forward backpropagation. The performance analysis was done and the GPR model was found to be quite effective followed by other traditional neural networks. Study results confirm that the GPR model can be successfully applied for short-term traffic flow prediction under heterogeneous traffic flow conditions.