Bus travel time prediction in mixed traffic: a multisource big data approach with multiple modelling strategies

This study examines the prediction of bus travel times within urban corridors, using an extensive dataset from transit management databases and on-street sensors. The analysis focuses on a range of variables, including corridor configuration, general traffic conditions, and intrinsic bus transit factors. Employing Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest (RF), bus travel times are modelled across four distinct urban corridors in A Coruña, Spain, considering dynamic variables like traffic flow rate, general travel time, and average stream patronage per bus stop, with patronage showing the strongest influence on bus travel time in three of the four corridors, similar to the sum of all general traffic variables. Furthermore, these models are applied to a joint dataset encompassing all corridors, incorporating static variables such as bus stops per kilometre, and, for the first time in the field, the percentage of corridors with adjacent parking and percentage with one lane, both considered as an interaction. This allows predictions of bus travel time changes due to corridor modifications and travel times for new bus routes in unserved areas. Findings reveal that, for our dataset, none of the three ML approaches has consistently proven to be preferable in bus travel time predictions, while MLR provides competitive results, balancing accuracy and interpretability despite its flexibility constraints. The study underscores the importance of selecting models based on data range and transit context, advocating for simplicity in constrained scenarios. This developed methodology provides a valuable planning tool for transport agencies, adaptable to other urban contexts, and highlights the benefits of optimising corridor configurations to enhance bus travel time performance.