[HTML][HTML] How machine learning informs ride-hailing services: A survey
In recent years, online ride-hailing services have emerged as an important component of
urban transportation system, which not only provide significant ease for residents' travel …
urban transportation system, which not only provide significant ease for residents' travel …
[HTML][HTML] Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights
Accurate traffic state (ie, flow, speed, density, etc.) on an urban road network is important
information for urban traffic control and management strategies. However, due to the …
information for urban traffic control and management strategies. However, due to the …
DeepPF: A deep learning based architecture for metro passenger flow prediction
This study aims to combine the modeling skills of deep learning and the domain knowledge
in transportation into prediction of metro passenger flow. We present an end-to-end deep …
in transportation into prediction of metro passenger flow. We present an end-to-end deep …
A novel generative adversarial network for estimation of trip travel time distribution with trajectory data
K Zhang, N Jia, L Zheng, Z Liu - Transportation Research Part C: Emerging …, 2019 - Elsevier
Abstract Knowledge of trip travel times serves an important role in transportation
management and control. Existing travel time estimation approaches generally cover …
management and control. Existing travel time estimation approaches generally cover …
A machine learning based personalized system for driving state recognition
Reliable driving state recognition (eg normal, drowsy, and aggressive) plays a significant
role in improving road safety, driving experience and fuel efficiency. It lays the foundation for …
role in improving road safety, driving experience and fuel efficiency. It lays the foundation for …
Region-aware hierarchical graph contrastive learning for ride-hailing driver profiling
Driver profiling, which is the process of extracting driver preferences and behavioral patterns
from collected driving data, can be performed on a microscopic or macroscopic scale …
from collected driving data, can be performed on a microscopic or macroscopic scale …
Vehicle trajectory data mining for artificial intelligence and real-time traffic information extraction
P Zhang, J Zheng, H Lin, C Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
It aims to improve the efficiency of information collection and extraction in the current
intelligent transportation system, and accurately mine the vehicle trajectory data By using …
intelligent transportation system, and accurately mine the vehicle trajectory data By using …
High-dimensional data analytics in civil engineering: A review on matrix and tensor decomposition
Recent developments in sensing and monitoring techniques have led to the generation of
high-dimensional data in the field of civil engineering. High-dimensional data analytics …
high-dimensional data in the field of civil engineering. High-dimensional data analytics …
Automatic feature engineering for bus passenger flow prediction based on modular convolutional neural network
Deep Neural Network (DNN) has been applied in a wide range of fields due to its
exceptional predictive power. In this paper, we explore how to use DNN to solve the large …
exceptional predictive power. In this paper, we explore how to use DNN to solve the large …
Individualized passenger travel pattern multi-clustering based on graph regularized tensor latent dirichlet allocation
Individual passenger travel patterns have significant value in understanding passenger's
behavior, such as learning the hidden clusters of locations, time, and passengers. The …
behavior, such as learning the hidden clusters of locations, time, and passengers. The …