traffic prediction

DataFunTalk

Dec 18, 2021 · Artificial Intelligence

Adaptive Mutual Supervision Multi‑Task Graph Neural Network for Fine‑Grained Urban Traffic Demand Prediction

This work proposes an adaptive mutual‑supervision multi‑task graph neural network that captures spatio‑temporal dynamics and heterogeneous group behaviors to predict fine‑grained urban travel demand, demonstrating over 10% performance gains on real‑world Beijing and Shanghai datasets compared with classic baselines.

adaptive supervisiondeep learninggraph neural network

0 likes · 24 min read

Adaptive Mutual Supervision Multi‑Task Graph Neural Network for Fine‑Grained Urban Traffic Demand Prediction

Amap Tech

Mar 5, 2021 · Artificial Intelligence

AI Applications in Mobility: Route Planning, ETA Prediction, Dynamic Event Mining, and Global Scheduling

The article surveys Amap’s AI‑driven mobility solutions—from personalized, multi‑objective route planning using Cell‑Based Routing and bias‑aware sorting, through spatio‑temporal ETA prediction and lightweight BERT‑based traffic‑event mining, to rapid POI freshness updates and a future global scheduling system that coordinates vehicles and signals via multi‑agent reinforcement learning.

AIMulti-Agent Reinforcement LearningRoute Planning

0 likes · 14 min read

AI Applications in Mobility: Route Planning, ETA Prediction, Dynamic Event Mining, and Global Scheduling

DataFunTalk

Oct 11, 2020 · Artificial Intelligence

Spatio‑Temporal Graph Convolution Networks for Traffic Forecasting: Gaode's HSTGCN Approach

The presentation by Gaode senior algorithm expert Ji Chenguang details a hybrid spatio‑temporal graph convolution network (HSTGCN) that predicts future traffic conditions from massive navigation data, dramatically improving congestion forecasting accuracy and enabling proactive traffic dispatch.

AIETA estimationHSTGCN

0 likes · 10 min read

Spatio‑Temporal Graph Convolution Networks for Traffic Forecasting: Gaode's HSTGCN Approach

Amap Tech

Sep 24, 2020 · Artificial Intelligence

Hybrid Spatio‑Temporal Graph Convolutional Network for Precise Traffic Prediction

At the 2020 Yunqi Conference, Amap’s senior algorithm expert presented the Hybrid Spatio‑Temporal Graph Convolutional Network, which leverages massive real‑time navigation data to estimate future traffic flow, transform it into travel‑time features, and outperform prior models, enabling proactive congestion avoidance and dynamic traffic‑scheduling for millions of users.

AIHSTGCNgraph convolutional network

0 likes · 11 min read

Hybrid Spatio‑Temporal Graph Convolutional Network for Precise Traffic Prediction

58 Tech

Dec 16, 2019 · Artificial Intelligence

Data Intelligence in the Used‑Car Business: User Traffic Prediction and Identification (Part 1)

This article details how the 58 Group applied data‑driven methods—user segmentation, interest description, clustering, and predictive modeling—to forecast and identify traffic in the used‑car scenario, illustrating the end‑to‑end pipeline, experimental results, and practical impact on downstream business processes.

data intelligencemachine learningrecommendation

0 likes · 19 min read

Data Intelligence in the Used‑Car Business: User Traffic Prediction and Identification (Part 1)

Didi Tech

Dec 2, 2019 · Operations

Capacity Estimation Methodology for Growing Services

The article presents a systematic capacity‑estimation methodology that links service traffic to order volume, uses CPU‑Idle as a primary metric, predicts traffic growth and upper‑bound limits, validates predictions with load‑testing, and provides scaling recommendations while noting limitations of the CPU‑Idle baseline.

Performance MonitoringScalingcapacity planning

0 likes · 9 min read