Related papers: A low dimensional model for bike sharing demand fo…
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…
In order to study the formation mechanism of residents' low-carbon awareness and provide an important basis for traffic managers to guide urban residents to choose low-carbon travel mode, this paper proposes a low-carbon energy-saving…
Understanding human mobility patterns is important in applications as diverse as urban planning, public health, and political organizing. One rich source of data on human mobility is taxi ride data. Using the city of Chicago as a case…
Car-hailing services have become a prominent data source for urban traffic studies. Extracting useful information from car-hailing trace data is essential for effective traffic management, while discrepancies between car-hailing vehicles…
These last years with the growing population in the smart city demands an efficient transportation sharing (bike sharing) system for developing the smart city. The Bike sharing as we know is affordable, easily accessible and reliable mode…
This paper models the availability of bikes at San Francisco Bay Area Bike Share stations using machine learning algorithms. Random Forest (RF) and Least-Squares Boosting (LSBoost) were used as univariate regression algorithms, and Partial…
Mixture model-based clustering, usually applied to multidimensional data, has become a popular approach in many data analysis problems, both for its good statistical properties and for the simplicity of implementation of the…
Bike-sharing is becoming increasingly popular as an urban traffic mode while increasing the affordability, flexibility, and reliability of interconnected public transportation systems (i.e., interconnected light rail, buses, micro-mobility,…
Although shared rides have the potential to increase vehicle utilization and reduce congestion and emissions, these benefits depend heavily on ridesharing platforms' ability to match riders effectively. As such, shared rides have seen…
Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available supply, historical transport data typically represents a…
The potential of an efficient ride-sharing scheme to significantly reduce traffic congestion, lower emission level, as well as facilitating the introduction of smart cities has been widely demonstrated. This positive thrust however is faced…
Dynamic ride-sharing services, including ride-pooling offered by ride-hailing platforms and demand-responsive buses, have become an essential part of urban mobility systems. These services cater to personalized and on-demand mobility…
Bike-sharing transportation systems have been well studied from a top-down viewpoint, either for an optimal conception of the system, or for a better statistical understanding of their working mechanisms in the aim of the optimization of…
The expansion of urban centers necessitates enhanced efficiency and sustainability in their transportation infrastructure and mobility systems. The big data obtainable from various transportation modes potentially offers critical insights…
Discovering human mobility patterns with geo-location data collected from smartphone users has been a hot research topic in recent years. In this paper, we attempt to discover daily mobile patterns based on GPS data. We view this problem…
Electric vehicles (EVs) play a pivotal role in sustainable ride-hailing services primarily due to their potential in reducing carbon emissions and enhancing environmental protection. Despite their significance, current research in the realm…
This study aims to propose an approach for spatiotemporal integration of bus transit, which enables users to change bus lines by paying a single fare. This could increase bus transit efficiency and, consequently, help to make this mode of…
Classical demand modeling analyzes travel behavior using only low-dimensional numeric data (i.e. sociodemographics and travel attributes) but not high-dimensional urban imagery. However, travel behavior depends on the factors represented by…
Following the growth of dock-based bike sharing systems as an eco-friendly solution for transportation in urban areas, Dockless systems are revolutionizing the market for the increased flexibility they offer to users. Bike redistribution is…
Rapid urbanization places increasing stress on already burdened transportation systems, resulting in delays and poor levels of service. Billions of spatiotemporal call detail records (CDRs) collected from mobile devices create new…