Full-scale spatio-temporal traffic flow estimation for city-wide networks: A transfer learning based approach
The full-scale spatio-temporal traffic flow estimation/prediction has always been a hot spot in
transportation engineering. The low coverage rate of detectors in transport networks brings …
transportation engineering. The low coverage rate of detectors in transport networks brings …
Multi-models machine learning methods for traffic flow estimation from floating car data
Traffic flow measurement is very important for traffic management systems. However, the
existing traditional measurement approaches are highly time-consuming and expensive to …
existing traditional measurement approaches are highly time-consuming and expensive to …
Network-wide traffic flow estimation with insufficient volume detection and crowdsourcing data
With the rapid development of urbanization and modernization, it is increasingly crucial to
sense network-wide traffic. Network-wide traffic volume information is of great benefit for …
sense network-wide traffic. Network-wide traffic volume information is of great benefit for …
A spatial–temporal-based state space approach for freeway network traffic flow modelling and prediction
C Dong, Z Xiong, C Shao, H Zhang - Transportmetrica A: Transport …, 2015 - Taylor & Francis
Effective traffic flow prediction is an essential component of any proactive traffic control
system and one of the pillars of an advanced traffic management system. Hence, the main …
system and one of the pillars of an advanced traffic management system. Hence, the main …
Incorporating traffic flow model into A deep learning method for traffic state estimation: a hybrid stepwise modeling framework
YA Pan, J Guo, Y Chen, S Li… - Journal of Advanced …, 2022 - Wiley Online Library
Traffic state estimation (TSE), which reconstructs the traffic variables (eg, speed, flow) on
road segments using partially observed data, plays an essential role in intelligent …
road segments using partially observed data, plays an essential role in intelligent …
Hierarchical traffic flow prediction based on spatial-temporal graph convolutional network
In recent years, traffic flow prediction has attracted more and more interest from both
academia and industry since such information can provide effective guidance for traffic …
academia and industry since such information can provide effective guidance for traffic …
Dynamic data-driven local traffic state estimation and prediction
Traffic state prediction is a key problem with considerable implications in modern traffic
management. Traffic flow theory has provided significant resources, including models based …
management. Traffic flow theory has provided significant resources, including models based …
A novel time efficient machine learning-based traffic flow prediction method for large scale road network
How to effectively improve the traffic efficiency of the road network plays a crucial role in
ensuring the regular operation of modern society. This is also a key concern in the field of …
ensuring the regular operation of modern society. This is also a key concern in the field of …
Predicting network flows from speeds using open data and transfer learning
Traffic flow/volume data are commonly used to calibrate and validate traffic simulation
models. However, these data are generally obtained from stationary sensors (eg loop …
models. However, these data are generally obtained from stationary sensors (eg loop …
A tailored machine learning approach for urban transport network flow estimation
This study deals with urban transport network flow estimation based on Cellphone Location
(CL) and License Plate Recognition (LPR) data. We first propose two methods to filter CL …
(CL) and License Plate Recognition (LPR) data. We first propose two methods to filter CL …