# Traffic Prediction and Management Techniques

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/traffic-prediction-and-management-techniques/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the application of deep learning, neural networks, and spatio-temporal data analysis for traffic flow prediction and forecasting in urban environments. The research covers topics such as short-term forecasting, graph convolutional networks, time series analysis, and the integration of intelligent transportation systems. |
| Domain | Physical Sciences |
| Field | Engineering |
| OpenAlex ID | t11344 |
| Works | 95 |

## Topic papers all

Showing 15 of 95.

- [Graph neural network for traffic forecasting: A survey](https://scholariq.org/papers/graph-neural-network-for-traffic-forecasting-a-survey/)
- [Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting](https://scholariq.org/papers/spatiotemporal-multi-graph-convolution-network-for-ride-hailing-demand/)
- [A survey on machine learning for data fusion](https://scholariq.org/papers/a-survey-on-machine-learning-for-data-fusion/)
- [Multi-Agent Tensor Fusion for Contextual Trajectory Prediction](https://scholariq.org/papers/multi-agent-tensor-fusion-for-contextual-trajectory-prediction/)
- [Automatic congestion detection system for underground platforms](https://scholariq.org/papers/automatic-congestion-detection-system-for-underground-platforms/)
- [LC-RNN: A Deep Learning Model for Traffic Speed Prediction](https://scholariq.org/papers/lc-rnn-a-deep-learning-model-for-traffic-speed-prediction/)
- [Big Data Analytics and IoT in logistics: a case study](https://scholariq.org/papers/big-data-analytics-and-iot-in-logistics-a-case-study/)
- [Detection of flood disaster system based on IoT, big data and convolutional deep neural network](https://scholariq.org/papers/detection-of-flood-disaster-system-based-on-iot-big-data-and-convolutional-deep/)
- [A deep learning model for air quality prediction in smart cities](https://scholariq.org/papers/a-deep-learning-model-for-air-quality-prediction-in-smart-cities/)
- [Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers?](https://scholariq.org/papers/towards-disaster-resilient-smart-cities-can-internet-of-things-and-big-data/)
- [Prediction of home energy consumption based on gradient boosting regression tree](https://scholariq.org/papers/prediction-of-home-energy-consumption-based-on-gradient-boosting-regression-tree/)
- [Learning Driver Behavior Models from Traffic Observations for Decision Making and Planning](https://scholariq.org/papers/learning-driver-behavior-models-from-traffic-observations-for-decision-making/)
- [Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities](https://scholariq.org/papers/design-and-implementation-of-an-ml-and-iot-based-adaptive-traffic-management/)
- [Graph Neural Network for Traffic Forecasting: The Research Progress](https://scholariq.org/papers/graph-neural-network-for-traffic-forecasting-the-research-progress/)
- [Geospatial data to images: A deep-learning framework for traffic forecasting](https://scholariq.org/papers/geospatial-data-to-images-a-deep-learning-framework-for-traffic-forecasting/)

## Topic primary papers

Showing 15 of 46.

- [Graph neural network for traffic forecasting: A survey](https://scholariq.org/papers/graph-neural-network-for-traffic-forecasting-a-survey/)
- [Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting](https://scholariq.org/papers/spatiotemporal-multi-graph-convolution-network-for-ride-hailing-demand/)
- [Automatic congestion detection system for underground platforms](https://scholariq.org/papers/automatic-congestion-detection-system-for-underground-platforms/)
- [LC-RNN: A Deep Learning Model for Traffic Speed Prediction](https://scholariq.org/papers/lc-rnn-a-deep-learning-model-for-traffic-speed-prediction/)
- [Detection of flood disaster system based on IoT, big data and convolutional deep neural network](https://scholariq.org/papers/detection-of-flood-disaster-system-based-on-iot-big-data-and-convolutional-deep/)
- [Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities](https://scholariq.org/papers/design-and-implementation-of-an-ml-and-iot-based-adaptive-traffic-management/)
- [Graph Neural Network for Traffic Forecasting: The Research Progress](https://scholariq.org/papers/graph-neural-network-for-traffic-forecasting-the-research-progress/)
- [Geospatial data to images: A deep-learning framework for traffic forecasting](https://scholariq.org/papers/geospatial-data-to-images-a-deep-learning-framework-for-traffic-forecasting/)
- [Modeling Trajectories with Recurrent Neural Networks](https://scholariq.org/papers/modeling-trajectories-with-recurrent-neural-networks/)
- [TrajVAE: A Variational AutoEncoder model for trajectory generation](https://scholariq.org/papers/trajvae-a-variational-autoencoder-model-for-trajectory-generation/)
- [Data fusion for ITS: A systematic literature review](https://scholariq.org/papers/data-fusion-for-its-a-systematic-literature-review/)
- [Exploring Data Validity in Transportation Systems for Smart Cities](https://scholariq.org/papers/exploring-data-validity-in-transportation-systems-for-smart-cities/)
- [A Graph-Based Temporal Attention Framework for Multi-Sensor Traffic Flow Forecasting](https://scholariq.org/papers/a-graph-based-temporal-attention-framework-for-multi-sensor-traffic-flow/)
- [A Review on Machine Learning Strategies for Real-World Engineering Applications](https://scholariq.org/papers/a-review-on-machine-learning-strategies-for-real-world-engineering-applications/)
- [A comparative assessment of multi-sensor data fusion techniques for freeway traffic speed estimation using microsimulation modeling](https://scholariq.org/papers/a-comparative-assessment-of-multi-sensor-data-fusion-techniques-for-freeway/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.