Public transport service reliability is crucial for all stakeholders, including users, operators,
and society. Bus bunching, where two or more buses on the same route travel closely …

Network-based systems are at the core of our everyday life. Whether it is electronic
networking, electricity grids or transportation, users expect the networks to function properly …

Regarding tools and systems from artificial intelligence (AI), chat-based ones from the area
of generative AI have become a major focus regarding media coverage. ChatGPT and …

The notorious phenomenon of bus bunching prevailing in uncontrolled bus systems
produces irregular headways and downgrades the level of service by increasing …

Traffic signal control (TSC) is still one of the most significant and challenging research
problems in the transportation field. Reinforcement learning (RL) has achieved great …

This paper presents TrajGen, an approach to generate artificial datasets of mobility
trajectories based on an original trajectory dataset while retaining the utility of the original …

With advanced artificial intelligence and deep learning techniques, a growing number of
data sources are playing more and more critical roles in planning and operating …

Different from the existing train delay studies that had strived to explore sophisticated
algorithms, this paper focuses on finding the bound of improvements on predicting multi …

The ability to accurately predict public transit ridership demand benefits passengers and
transit agencies. Agencies will be able to reallocate buses to handle under or over-utilized …

Deep neural networks are being increasingly used for short-term traffic flow prediction,
which can be generally categorized as convolutional (CNNs) or graph neural networks …