Travel time reliability is known to be a critical issue in the contexts of both travellers' choices and decisions and freight transportation. The temporal variability of travel time is known as reliability and is affected by numerous factors. Traffic volume, incidents and inclement weather are among the most profound factors, and their effects have been the subject of many studies. The work reported in this article is unique due to the simultaneous implementation of a genetic algorithm (GA) with multiple machine learning (ML) methods. A GA can eliminate overfitting, which is a common problem in ML models. The numerical results showed that the performance of the K-nearest neighbours method was significantly enhanced when a GA was imposed on it. In terms of the stability ratio, a 12% decrease was observed; the mean squared errors for the training set and the testing set decreased, but the reductions were not significant. To further illustrate the advantages of GA implementation, the numbers of predictions with a mean absolute percentage error greater than 0.05 were compared and a notable reduction was found. Sensitivity analysis was carried out to determine how the planning time index responds to fluctuations of independent variables.

This paper explores application of Bayesian inference (BI) to dynamic travel time estimation in the Helsinki region. Accurate travel time prediction is crucial in a wide range of fields, including departure time and routing. Limited real-time data challenge modelling accuracy. To address this, this paper utilises BI, particularly sequential Bayesian inference (SBI) for evolving observed values. Incorporating 2018 real-time data and 2015 information as prior knowledge, travel time distribution will be updated. Validation yields a 4.0% mean absolute error between the updated 2018 distribution and actual travel time. Also, using the 2015 posterior distribution as prior by way of SBI yields a 4.6% mean absolute error. Results highlight that SBI is an effective tool for updating distributions. This paper underscores potential of BI in addressing data scarcity and enhancing accuracy of transportation models. By supplying precise travel time estimates, this approach benefits congestion relief and travel planning. With evolving travel time data, BI promises to advance transportation modelling.