Augmenting Ridership Data with Social Media Data to Analyse the Long-term Effect of COVID-19 on Public Transport

COVID-19 significantly influenced travel behaviours and public attitudes towards public transport. Various studies have illustrated complicated factors related to long-term travel behaviour, indicating difficulty in understanding and predicting post-pandemic long-term travel behaviour via traditional methods. In these complex circumstances,...

Effectiveness of trip planner data in predicting short-term bus ridership

Predictions on public transport ridership are beneficial as they allow for sufficient and cost-efficient deployment of vehicles. At an operational level, this relates to short-term predictions with lead times of less than an hour. Where conventional data sources on ridership, such as Automatic Fare Collection (AFC) data, may have longer lag...

Spatial-temporal analysis of road raveling and its correlation with traffic flow characteristics

Understanding the relationship between pavement raveling and traffic characteristics is important to pavement management and maintenance planning. In this work, we propose a framework to empirically quantify this relationship. It consists of an alignment method to tackle the inconsistent spatial-temporal scales of the raveling and traffic...

Rail network robustness: The role of rapid development and a polycentric structure in withstanding random and targeted attacks

In large-scale urban agglomerations, heavy rail in the form of metro and commuter train serves as the backbone of the metropolitan public transport network. Transport systems are subject to recurrent disruptions that may result in severe consequences for network performance and society at large. The objective of this paper is to compare the...

Exploring demand patterns of a ride-sourcing service using spatial and temporal clustering

On-demand transport has become a common mode of transport with ride-sourcing companies like Uber, Lyft and Didi transforming the mobility market. Recurrent patterns in prevailing demand patterns can be used by service providers to better anticipate future demand distribution and thus support demand-Anticipatory fleet management strategies. To...

Understanding Network Traffic States using Transfer Learning

Large-scale network traffic analysis is crucial for many transport applications, ranging from estimation and prediction to control and planning. One of the key issues is how to integrate spatial and temporal analyses efficiently. Deep Learning is gaining momentum as a go-to approach for artificial vision, and transfer learning approaches allow...