Recent technologies for recording and storing data, as well as advancements in data processing techniques, have opened up novel possibilities for urban planners to design a more optimal public transport network. This study aims to initially develop a robust framework for making an insightful understanding of already recorded and available data sets using machine learning approaches. This will give transportation planners a powerful framework to use great recorded datasets to understand the network better and make datasets more meaningful for transport planners. And then introduces an approach to use Machine Learning algorithms and extract hidden patterns for predicting financial loss during any crisis, which is a novel perspective and application. To do this, seven alternative machine learning algorithms were developed to predict ridership: Multiple Linear Regression, Decision Tree, Random Forest, Bayesian Ridge Regression, Neural Networks, Support Vector Regression, and k-Nearest Neighbors. The developed framework was applied to the available 10 years of historical recorded data from the blue bus line number 4 in Stockholm, Sweden. The best model, kNN, with an average R-squared of 0.65 in 10-fold cross-validation, was accepted as the best model. This model is then used to estimate the financial loss of the network during the pandemic in 2020 and 2021. Results reveal a decline of 49% in 2020 and 82% in 2021 in the studied line. Finally, the results were validated with a similar study that analyzed the ticket validations and passenger counts during the spring of 2020.

Road incidents, including accidents, greatly impact public safety, traffic flow, and overall transportation system functioning. Detecting and predicting incidents is crucial for effective incident management. Accurate algorithms rely on high-quality incident data sets. However, uncertainties exist due to the collection and recording process. To address this, cross-validating incident data with other datasets helps resolve inaccuracies. Additionally, enriching incident data with additional sources enables a more precise analysis of societal costs for planning purposes. In this study, we utilize traffic congestion data to examine and quantify the consequences of incidents on the Dutch highway network. First, we map match recorded incidents with related traffic patterns. Then, we label incidents as 'congestion' if significant congestion patterns were identified during or after the incidents or as 'no-congestion' if no significant congestion pattern occurred. For incidents labeled as congestion, we calculate and associate records with the congestion's duration, location, and Vehicle Loss Hours (VLH). The developed methodology has been implemented on five months of recorded data for the six most significant motorways in the Netherlands. This enriched dataset can be utilized for incident detection algorithms, analysis and management, and policy and decision-making.