Abstract Within the Intelligent Public Transportation Systems (IPTS) field, the prediction of
public transportation demand is a key point for enhancing the quality of the services. These …

The increased development of urban areas results in a larger number of vehicles on the
road network, leading to traffic congestion, which often leads to potentially dangerous …

现有轨道交通车站高峰客流预测方法简化了车站高峰形成过程, 基于默认假设,
即车站高峰小时与所属线路高峰小时一致进行预测, 忽略了车站与线路间存在的高峰偏差现象 …

Timely and efficient analysis of big data collected from various gateways installed in a smart
city is an intractable problem and requires immediate priority. Given the stochastic and …

Tensor-based models emerged only recently in modeling and analysis of the spatiotemporal
road traffic data. They outperform other data models regarding the property of …

Subway station-level peak hour ridership (SPR) is a crucial input parameter for multiple
applications, including the planning, design, construction, and operation of stations …

Existing techniques for estimation of subway station-level long-term peak-hour ridership
(PHR) may produce underestimated PHR values that may result in stations being designed …

In recent years, a growing number of large, densely populated cities have emerged, which
need urban traffic planning and therefore knowledge of mobility patterns. Knowledge of …

Shared mobility services (such as electric scooters and ride shares) and the data they
generate give us a unique opportunity to understand human mobility by combining trips with …

High-dimensional data analysis has recently raised dramatically increasing research
interests due to its broad compatibility with different data types. In this dissertation, the …