Related papers: Unveiling the influence of behavioural, built envi…
Predicting future bus trip chains for an existing user is of great significance for operators of public transit systems. Existing methods always treat this task as a time-series prediction problem, but the 1-dimensional time series…
Understanding why travel behavior differs between residents of urban centers and suburbs is key to sustainable urban planning. Especially in light of rapid urban growth, identifying housing locations that minimize travel demand and induced…
This paper investigates the influencing factors in passengers' multimodal traffic choice behaviors and provides a decision-making basis and improvement strategies. By collecting large individual-level data through a comprehensive field…
Data-driven research is becoming a new paradigm in transportation, but the natural lack of individual socio-economic attributes in transportation data makes research such as activity purpose inference and mobility pattern identification…
Individual-level human mobility prediction has emerged as a significant topic of research with applications in infectious disease monitoring, child, and elderly care. Existing studies predominantly focus on the microscopic aspects of human…
Fair access to healthcare facilities is fundamental to achieving social equity. Traditional travel time-based accessibility measures often overlook the dynamic nature of travel times resulting from different departure times, which…
We study spatiotemporal correlations and temporal diversities of handset-based service usages by analyzing a dataset that includes detailed information about locations and service usages of 124 users over 16 months. By constructing the…
The statistical properties of human mobility have been studied in the framework of complex systems physics. Taking advantage from the new datasets made available by the information and communication technologies, the distributions of…
Vehicle mobility has a significant impact on wireless communication between vehicles (buses) in Public Transportation Systems (PTS). Nevertheless, the transportation literature does not provide satisfactory models for bus movements because…
The role of spatial data in tackling city-related tasks has been growing in recent years. To use them in machine learning models, it is often necessary to transform them into a vector representation, which has led to the development in the…
The objective of this paper is to study the effect of travel time information on day-to-day driver route choice behavior. A real-world experimental study is designed to have participants repeatedly choose between two alternative routes for…
School-escorted trips are a significant contributor to traffic congestion. Existing studies mainly compare road traffic during student pick-up/drop-off hours with off-peak times, often overlooking the fact that school-run traffic congestion…
Suppose $A$ and $B$ are two stations within the mass rapid transit network of a city. Both stations see approximately the same average daily number of passengers entering and exiting their gates. However, passengers are evenly distributed…
Human mobility plays a critical role in urban planning and policy-making. However, at certain spatial and temporal resolutions, it is very challenging to track, for example, job and housing mobility. In this study, we explore the usage of a…
Recent mobility scaling research, using new data sources, often relies on aggregated data alone. Hence, these studies face difficulties characterizing the influence of factors such as transportation mode on mobility patterns. This paper…
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and…
Vehicle trajectory data collected via GPS-enabled devices have played increasingly important roles in estimating network-wide traffic, given their broad spatial-temporal coverage and representativeness of traffic dynamics. This paper…
Studies using massive, passively data collected from communication technologies have revealed many ubiquitous aspects of social networks, helping us understand and model social media, information diffusion, and organizational dynamics. More…
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…
Bikesharing has gradually become one adopted sustainable transportation mode recent years to bring us many social, environmental, economic, and health-related benefits and rewards. There is increased research toward better understanding of…