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 …
A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing
Accurate and timely predicting citywide traffic crowd flows precisely is crucial for public
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks
Traffic flow prediction is crucial for public safety and traffic management, and remains a big
challenge because of many complicated factors, eg, multiple spatio-temporal dependencies …
challenge because of many complicated factors, eg, multiple spatio-temporal dependencies …
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 …
Co-prediction of multiple transportation demands based on deep spatio-temporal neural network
Taxi and sharing bike bring great convenience to urban transportation. A lot of efforts have
been made to improve the efficiency of taxi service or bike sharing system by predicting the …
been made to improve the efficiency of taxi service or bike sharing system by predicting the …
Predicting multi-step citywide passenger demands using attention-based neural networks
Predicting passenger pickup/dropoff demands based on historical mobility trips has been of
great importance towards better vehicle distribution for the emerging mobility-on-demand …
great importance towards better vehicle distribution for the emerging mobility-on-demand …
[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 …
MLRNN: Taxi demand prediction based on multi-level deep learning and regional heterogeneity analysis
Taxi demand prediction is valuable for the decision-making of online taxi-hailing platforms.
Data-driven deep learning approaches have been widely utilized in this area, and many …
Data-driven deep learning approaches have been widely utilized in this area, and many …
Taxi demand prediction using parallel multi-task learning model
Accurate and real-time taxi demand prediction can help managers pre-allocate taxi
resources in cities, which assists drivers quickly finding passengers and reduce passengers' …
resources in cities, which assists drivers quickly finding passengers and reduce passengers' …
Development and application of an energy use and CO2 emissions reduction evaluation model for China's online car hailing services
Emerging online car hailing services have caused many unintended consequences in urban
centers such as more congested traffic and increased vehicle travels. Those unintended …
centers such as more congested traffic and increased vehicle travels. Those unintended …