[HTML][HTML] A survey of traffic prediction: from spatio-temporal data to intelligent transportation
H Yuan, G Li - Data Science and Engineering, 2021 - Springer
Intelligent transportation (eg, intelligent traffic light) makes our travel more convenient and
efficient. With the development of mobile Internet and position technologies, it is reasonable …
efficient. With the development of mobile Internet and position technologies, it is reasonable …
Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction
Traffic prediction has drawn increasing attention in AI research field due to the increasing
availability of large-scale traffic data and its importance in the real world. For example, an …
availability of large-scale traffic data and its importance in the real world. For example, an …
Deep multi-view spatial-temporal network for taxi demand prediction
Taxi demand prediction is an important building block to enabling intelligent transportation
systems in a smart city. An accurate prediction model can help the city pre-allocate …
systems in a smart city. An accurate prediction model can help the city pre-allocate …
Learning from multiple cities: A meta-learning approach for spatial-temporal prediction
Spatial-temporal prediction is a fundamental problem for constructing smart city, which is
useful for tasks such as traffic control, taxi dispatching, and environment policy making. Due …
useful for tasks such as traffic control, taxi dispatching, and environment policy making. Due …
BERT-based deep spatial-temporal network for taxi demand prediction
D Cao, K Zeng, J Wang, PK Sharma… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Taxi demand prediction plays a significant role in assisting the pre-allocation of taxi
resources to avoid mismatches between demand and service, particularly in the era of the …
resources to avoid mismatches between demand and service, particularly in the era of the …
Latent space model for road networks to predict time-varying traffic
Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an
important problem for intelligent transportation systems and sustainability. However, it is …
important problem for intelligent transportation systems and sustainability. However, it is …
[PDF][PDF] Modeling spatial-temporal dynamics for traffic prediction
Spatial-temporal prediction has many applications such as climate forecasting and urban
planning. In particular, traffic prediction has drawn increasing attention in data mining …
planning. In particular, traffic prediction has drawn increasing attention in data mining …
STGNN-TTE: Travel time estimation via spatial–temporal graph neural network
Estimating the travel time of urban trajectories is a basic but challenging task in many
intelligent transportation systems, which is the foundation of route planning and traffic …
intelligent transportation systems, which is the foundation of route planning and traffic …
Stochastic weight completion for road networks using graph convolutional networks
Innovations in transportation, such as mobility-on-demand services and autonomous driving,
call for high-resolution routing that relies on an accurate representation of travel time …
call for high-resolution routing that relies on an accurate representation of travel time …
Learning travel time distributions with deep generative model
Travel time estimation of a given route with respect to real-time traffic condition is extremely
useful for many applications like route planning. We argue that it is even more useful to …
useful for many applications like route planning. We argue that it is even more useful to …