Network-level short-term traffic state prediction incorporating critical nodes: A knowledge-based deep fusion approach
The critical nodes (CNs) in urban transportation networks, defined as road entities (such as
road segments or detectors in a road network) that present highly volatile traffic states, can …
road segments or detectors in a road network) that present highly volatile traffic states, can …
GATC and DeepCut: Deep spatiotemporal feature extraction and clustering for large-scale transportation network partition
The network partition is an important method for many key transport problems, eg, transport
network zoning, parallel computing of traffic assignment problem, and analysis of the …
network zoning, parallel computing of traffic assignment problem, and analysis of the …
Traffic congestion assessment tool for urban roads based on traffic and geometric characteristics: a case of Hyderabad, India
This study developed a travel time congestion index (TTCI) keyed to the differences in actual
versus desired travel times for various types of roadways, and found that the TTCI is …
versus desired travel times for various types of roadways, and found that the TTCI is …
Vehicular traffic flow reconstruction analysis to mitigate scenarios with large city changes
Drastic changes into city road traffic may impact in large portions of the city, then
hypothetical scenarios have to be analyzed to identify the best solutions to maintain high …
hypothetical scenarios have to be analyzed to identify the best solutions to maintain high …
Freeway traffic speed prediction under the intelligent driving environment: a deep learning approach
The intelligent transportation system (ITS) has been proven capable of effectively
addressing traffic congestion issues. For vehicles to perform effectively and improve mobility …
addressing traffic congestion issues. For vehicles to perform effectively and improve mobility …
Exploring the design of reward functions in deep reinforcement learning-based vehicle velocity control algorithms
The application of deep reinforcement learning (DRL) techniques in intelligent transportation
systems garners significant attention. In this field, reward function design is a crucial factor …
systems garners significant attention. In this field, reward function design is a crucial factor …
DRL-based adaptive signal control for bus priority service under connected vehicle environment
Transit Signal Priority (TSP) strategy gives public transit vehicles privileges to pass through
the intersection without stopping. Most previous studies have adopted the compulsory TSP …
the intersection without stopping. Most previous studies have adopted the compulsory TSP …
Identifying, Analyzing, and forecasting commuting patterns in urban public Transportation: A review
With the continuous evolution and refinement of urban functional spaces, the escalating
reliance of commuters on public transportation for work-related travel has surged with time …
reliance of commuters on public transportation for work-related travel has surged with time …
GMHANN: A Novel Traffic Flow Prediction Method for Transportation Management Based on Spatial-Temporal Graph Modeling
Q Wang, W Liu, X Wang, X Chen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Traffic flow prediction significantly affects the intelligent transportation for digitized urban
transportation management and urban traffic control. Considering the complexity and strong …
transportation management and urban traffic control. Considering the complexity and strong …
A traffic state recognition model based on feature map and deep learning
C Wang, W Zhang, C Wu, H Hu, H Ding… - Physica A: Statistical …, 2022 - Elsevier
Real-time and accurate traffic state identification can provide reference for urban traffic
control and guidance. Due to the randomness and complexity of traffic flow, it is difficult to …
control and guidance. Due to the randomness and complexity of traffic flow, it is difficult to …