[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 …
[HTML][HTML] DeepTSP: Deep traffic state prediction model based on large-scale empirical data
Real-time traffic state (eg, speed) prediction is an essential component for traffic control and
management in an urban road network. How to build an effective large-scale traffic state …
management in an urban road network. How to build an effective large-scale traffic state …
[HTML][HTML] A graph neural network (GNN)-based approach for real-time estimation of traffic speed in sustainable smart cities
Planning effective routes and monitoring vehicle traffic are essential for creating sustainable
smart cities. Accurate speed prediction is a key component of these efforts, as it aids in …
smart cities. Accurate speed prediction is a key component of these efforts, as it aids in …
FTPG: A fine-grained traffic prediction method with graph attention network using big trace data
Short-term traffic prediction is of great importance to the management of traffic congestion, a
pervasive and difficult-to-solve problem in many metropolises all over the world. However …
pervasive and difficult-to-solve problem in many metropolises all over the world. However …
[HTML][HTML] Big data for traffic estimation and prediction: a survey of data and tools
Big data have been used widely in many areas, including the transportation industry. Using
various data sources, traffic states can be well estimated and further predicted to improve the …
various data sources, traffic states can be well estimated and further predicted to improve the …
Estimating traffic flow states with smart phone sensor data
This study proposes a framework to classify traffic flow states. The framework is capable of
processing massive, high-density, and noise-contaminated data sets generated from …
processing massive, high-density, and noise-contaminated data sets generated from …
Spatio-temporal dual graph neural networks for travel time estimation
Travel time estimation is one of the core tasks for the development of intelligent
transportation systems. Most previous works model the road segments or intersections …
transportation systems. Most previous works model the road segments or intersections …
Cycle-based estimation on lane-level queue length at isolated signalized intersection using license plate recognition data
Q Chen, M Li, C Wang, X Liu, J Tang - Journal of Transportation …, 2023 - ascelibrary.org
Queue length is an important parameter to evaluate the congestion level of urban
intersections. Abundant methods based on different data sources, such as loop detector …
intersections. Abundant methods based on different data sources, such as loop detector …
Real-time estimation of multi-class path travel times using multi-source traffic data
In practice, most of the intelligent transportation systems provide average travel times of all
vehicles on selected paths in real time on a regular basis. However, path travel times of …
vehicles on selected paths in real time on a regular basis. However, path travel times of …
Network-level signal predictive control with real-time routing information
This paper presents a framework for signalized road network predictive optimization using
real-time routing information from connected vehicles (CVs). An important feature of the real …
real-time routing information from connected vehicles (CVs). An important feature of the real …