相关论文: Mixed Data and Classification of Transit Stops

Cluster number is typically a parameter selected at the outset in clustering problems, and while impactful, the choice can often be difficult to justify. Inspired by bioinformatics, this study examines how the nature of clusters varies with…

We apply a simple clustering algorithm to a large dataset of cellular telecommunication records, reducing the complexity of mobile phone users' full trajectories and allowing for simple statistics to characterize their properties. For the…

A major source of delays in public transportation is the clustering instability, which causes late buses to become progressively later while the buses trailing it become progressively earlier. In this paper, we study this instability and…

Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public…

The analysis of longitudinal travel data enables investigating how mobility patterns vary across the population and identify the spatial properties thereof. The objective of this study is to identify the extent to which users explore…

Understanding how people move in the urban area is important for solving urbanization issues, such as traffic management, urban planning, epidemic control, and communication network improvement. Leveraging recent availability of large…

We introduce a modified model of random walk, and then develop two novel clustering algorithms based on it. In the algorithms, each data point in a dataset is considered as a particle which can move at random in space according to the…

This paper proposes a general equilibrium model for multi-passenger ridesharing systems, in which interactions between ridesharing drivers, passengers, platforms, and transportation networks are endogenously captured. Stable matching is…

Pedestrian routing choices play a crucial role in shaping collective crowd dynamics, yet the influence of interactions among unfamiliar individuals remains poorly understood. In this study, we analyze real-world pedestrian behavior at a…

This study models and examines commuter's preferences for short-distance transportation modes, namely: walking, taking a bus or riding a metro. It is used a unique dataset from a large-scale field experiment in Singapore that provides rich…

In this research, we propose a series of methodologies to mine transit riders travel pattern and behavioral preferences, and then we use these knowledges to adjust and optimize the transit systems. Contributions are: 1) To increase the data…

This research project's objective was to investigate the impacts of transit stop location (near-side versus far-side) on pedestrian safety and traffic operations. Three different video-based behavioral observation data collections at…

In an effort to improve user satisfaction and transit image, transit service providers worldwide offer delay compensations. Smart card data enables the estimation of passenger delays throughout the network and aid in monitoring service…

There is great interest in finding meaningful subgroups of attributed network data. There are many available methods for clustering complete network. Unfortunately, much network data is collected through sampling, and therefore incomplete.…

This paper provides an analysis of human mobility data in an urban area using the amount of available bikes in the stations of the community bicycle program Bicing in Barcelona. The data was obtained by periodic mining of a KML-file…

With the emergence of e-hailing taxi services, a growing number of scholars have attempted to analyze the taxi trips data to gain insights from drivers' and passengers' flow patterns and understand different dynamics of urban public…

In urban settings, bus transit stands as a significant mode of public transportation, yet faces hurdles in delivering accurate and reliable arrival times. This discrepancy often culminates in delays and a decline in ridership, particularly…

Clustering is a technique for the analysis of datasets obtained by empirical studies in several disciplines with a major application for biomedical research. Essentially, clustering algorithms are executed by machines aiming at finding…

We propose here a methodology to help to understand the shortcomings of public transportation in a city via the mining of complex networks representing the supply and demand of public transport. We show how to build these networks based…

Novel forms of data analysis methods have emerged as a significant research direction in the transportation domain. These methods can potentially help to improve our understanding of the dynamic flows of vehicles, people, and goods.…