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A comprehensive data analysis system is implemented for the extraction of information and comparison of North American public transport systems. The system is based on network representations of the transport systems and makes use of a span…
Nowadays many cities around the world have introduced electric buses to optimize urban traffic and reduce local carbon emissions. In order to cut carbon emissions and maximize the utility of electric buses, it is important to choose…
We investigate the behavior of extended urban traffic networks within the framework of percolation theory by using real and synthetic traffic data. Our main focus shifts from the statistical properties of the cluster size distribution…
Even though clustering trajectory data attracted considerable attention in the last few years, most of prior work assumed that moving objects can move freely in an euclidean space and did not consider the eventual presence of an underlying…
In this paper, a new methodology, journey-based equity analysis, is presented for measuring the equity of transit convenience between income groups. Two data sources are combined in the proposed transit equity analysis: on-board ridership…
Providing real time information about the arrival time of the transit buses has become inevitable in urban areas to make the system more user-friendly and advantageous over various other transportation modes. However, accurate prediction of…
The recent outbreak of a novel coronavirus and its rapid spread underlines the importance of understanding human mobility. Enclosed spaces, such as public transport vehicles (e.g. buses and trains), offer a suitable environment for…
Navigation route recommendation is one of the important functions of intelligent transportation. However, users frequently deviate from recommended routes for various reasons, with personalization being a key problem in the field of…
Nowadays, human movement in urban spaces can be traced digitally in many cases. It can be observed that movement patterns are not constant, but vary across time and space. In this work,we characterize such spatio-temporal patterns with an…
Recent years have witnessed an increased focus on interpretability and the use of machine learning to inform policy analysis and decision making. This paper applies machine learning to examine travel behavior and, in particular, on modeling…
Although a lot of attentions have been paid to human mobility, the relationship between travel pattern with city structure is still unclear. Here we probe into this relationship by analyzing the metro passenger trip data.There are two…
In the context of rail transit operations, real-time passenger flow prediction is essential; however, most models primarily focus on normal conditions, with limited research addressing incident situations. There are several intrinsic…
Urban traffic congestion remains a persistent issue for cities worldwide. Recent macroscopic models have adopted a mathematically well-defined relation between network flow and density to characterize traffic states over an urban region.…
Activity generation plays an important role in activity-based demand modelling systems. While machine learning, especially deep learning, has been increasingly used for mode choice and traffic flow prediction, much less research exploiting…
Both evaluating the service quality of a public transport system and understanding how passengers choose between modes or routes is imperative for public transport operators, providers of competing mobility services and policy makers.…
Accessibility, defined as travel impedance between spatially dispersed opportunities for activity, is one of the main determinants of public transport use. In-depth understanding of its properties is crucial for optimal public transport…
This paper presents a modeling approach to infer scheduling and routing patterns from digital freight transport activity data for different freight markets. We provide a complete modeling framework including a new discrete-continuous…
An important aspect of the quality of a public transport service is its reliability, which is defined as the invariability of the service attributes. Preventive measures taken during planning can reduce risks of unreliability throughout…
Human mobility regularity is crucial for understanding urban dynamics and informing decision-making processes. This study first quantifies the periodicity in complex human mobility data as a sparse identification of dominant positive…
In this paper, a new practice-ready method for the real-time estimation of traffic conditions and travel times on highways is introduced. First, after a principal component analysis, observation days of a historical dataset are clustered.…