The multi-mode integrated railway system, anchored by the high-speed railway, caters to the
diverse travel requirements both within and between cities, offering safe, comfortable …

Predicting urban metro ridership holds significant practical value for optimizing operational
scheduling and guiding individual travel planning. Understanding the complexity of metro …

Urban traffic congestion significantly affects economic productivity, environmental
sustainability, and quality of life. Traditional traffic congestion prediction models, which are …

Contemporary urban transportation systems frequently depend on a variety of modes to
provide residents with travel services. Understanding a multimodal transportation system is …

Data mining in transportation networks (DMTNs) refers to using diverse types of spatio-
temporal data for various transportation tasks, including pattern analysis, traffic prediction …

Predicting multi-type passenger demand, such as for adults, seniors, pensioners, and
students, is essential for improving the operational efficiency, equity, and sustainability of …

The accumulation of big data by intelligent and information systems in transportation has 8
created a basis for the use of various methods of forecasting and operational management …

Bike-sharing services are popular for short-distance trips, and bikes can be rented for a short
period and left at convenient locations for the next user. However, the rapid development of …

The renewal speed of urban roads is far from meeting the growing demand for vehicle travel,
leading to significant urban road congestion. To address this issue, intelligent transportation …