Deep learning for trajectory data management and mining: A survey and beyond
Trajectory computing is a pivotal domain encompassing trajectory data management and
mining, garnering widespread attention due to its crucial role in various practical …
mining, garnering widespread attention due to its crucial role in various practical …
Cross-city few-shot traffic forecasting via traffic pattern bank
Traffic forecasting is a critical service in Intelligent Transportation Systems (ITS). Utilizing
deep models to tackle this task relies heavily on data from traffic sensors or vehicle devices …
deep models to tackle this task relies heavily on data from traffic sensors or vehicle devices …
Openbox: A Python toolkit for generalized black-box optimization
Black-box optimization (BBO) has a broad range of applications, including automatic
machine learning, experimental design, and database knob tuning. However, users still face …
machine learning, experimental design, and database knob tuning. However, users still face …
FDTI: Fine-Grained Deep Traffic Inference with Roadnet-Enriched Graph
This paper proposes the fine-grained traffic prediction task (eg interval between data points
is 1 min), which is essential to traffic-related downstream applications. Under this setting …
is 1 min), which is essential to traffic-related downstream applications. Under this setting …
Frequency Enhanced Pre-training for Cross-city Few-shot Traffic Forecasting
The field of Intelligent Transportation Systems (ITS) relies on accurate traffic forecasting to
enable various downstream applications. However, developing cities often face challenges …
enable various downstream applications. However, developing cities often face challenges …
Multi-scale Traffic Pattern Bank for Cross-city Few-shot Traffic Forecasting
Traffic forecasting is crucial for intelligent transportation systems (ITS), aiding in efficient
resource allocation and effective traffic control. However, its effectiveness often relies heavily …
resource allocation and effective traffic control. However, its effectiveness often relies heavily …
guided deep reinforcement learning for coordinated ramp metering and perimeter control in large scale networks
Z Hu, W Ma - Transportation research part C: emerging technologies, 2024 - Elsevier
Effective traffic control methods have great potential in alleviating network congestion.
Particularly, in an urban network consisting of heterogeneous roads (eg, freeways and …
Particularly, in an urban network consisting of heterogeneous roads (eg, freeways and …
Cost-effective mitigation of urban congestion with adaptive traffic signal control
Urban congestion is a widespread issue with detrimental effects on urban efficiency, energy
consumption, and pollution levels. Traditional approaches to mitigating congestion, such as …
consumption, and pollution levels. Traditional approaches to mitigating congestion, such as …