[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 …
Deep learning support for intelligent transportation systems
J Guerrero‐Ibañez… - Transactions on …, 2021 - Wiley Online Library
Abstract Intelligent Transportation Systems (ITS) help improve the ever‐increasing vehicular
flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation …
flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation …
Urban traffic prediction from mobility data using deep learning
Traffic information is of great importance for urban cities, and accurate prediction of urban
traffics has been pursued for many years. Urban traffic prediction aims to exploit …
traffics has been pursued for many years. Urban traffic prediction aims to exploit …
Context-aware taxi dispatching at city-scale using deep reinforcement learning
Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among
different locations in a city. Recent advances primarily rely on deep reinforcement learning …
different locations in a city. Recent advances primarily rely on deep reinforcement learning …
AARGNN: An attentive attributed recurrent graph neural network for traffic flow prediction considering multiple dynamic factors
Traffic flow prediction is a fundamental part of ITS (Intelligent Transportation System). Since
the correlations of traffic data are complicated and are affected by various factors, traffic flow …
the correlations of traffic data are complicated and are affected by various factors, traffic flow …
Context-aware road travel time estimation by coupled tensor decomposition based on trajectory data
Urban road travel time estimation and prediction on a citywide scale is a necessary and
important task for recommending optimal travel paths. However, this problem has not yet …
important task for recommending optimal travel paths. However, this problem has not yet …
BuildSenSys: Reusing Building Sensing Data for Traffic Prediction With Cross-Domain Learning
With the rapid development of smart cities, smart buildings are generating a massive amount
of building sensing data by the equipped sensors. Indeed, building sensing data provides a …
of building sensing data by the equipped sensors. Indeed, building sensing data provides a …
Large-scale vehicle trajectory reconstruction with camera sensing network
Vehicle trajectories provide essential information to understand the urban mobility and
benefit a wide range of urban applications. State-of-the-art solutions for vehicle sensing may …
benefit a wide range of urban applications. State-of-the-art solutions for vehicle sensing may …
Traffic flow prediction on urban road network based on license plate recognition data: combining attention-LSTM with genetic algorithm
Exploring traffic flow characteristics and predicting its variation patterns are the basis of
Intelligent Transportation Systems. The intermittent characteristics and intense fluctuation on …
Intelligent Transportation Systems. The intermittent characteristics and intense fluctuation on …
Compressive sensing-based IoT applications: A review
The Internet of Things (IoT) holds great promises to provide an edge cutting technology that
enables numerous innovative services related to healthcare, manufacturing, smart cities and …
enables numerous innovative services related to healthcare, manufacturing, smart cities and …