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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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A Unified Spatio-Temporal Inference Network for Car-Sharing Serial Prediction.

Nihad Brahimi¹, Huaping Zhang¹, Syed Danial Asghar Zaidi¹

¹School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)

|February 24, 2024

Summary

Accurate car-sharing demand prediction is crucial for efficient operations. The novel Unified Spatio-Temporal Inference Prediction Network (USTIN) effectively models complex spatio-temporal factors, outperforming existing methods.

Keywords:

prediction spatial feature spatio-temporal feature spatio-temporal inference temporal features uncertainty analysis

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Area of Science:

Artificial Intelligence
Machine Learning
Transportation Systems

Background:

Car-sharing systems necessitate precise demand prediction for optimal resource allocation and scheduling.
Accurate vehicle demand forecasting is hindered by complex spatio-temporal interdependencies.

Purpose of the Study:

To introduce USTIN (Unified Spatio-Temporal Inference Prediction Network), a novel neural network for enhanced car-sharing demand prediction.
To develop a model capable of capturing intricate temporal, spatial, and spatio-temporal relationships in car-sharing demand.

Main Methods:

Developed USTIN, a neural network with distinct temporal, spatial, and spatio-temporal feature units.
Temporal unit processes historical data across hourly, daily, weekly, and monthly scales.
Spatial unit integrates points of interest data; spatio-temporal unit incorporates weather data.

Main Results:

USTIN effectively learned complex spatio-temporal demand patterns from real-world car-sharing data.
The proposed USTIN model significantly outperformed existing state-of-the-art prediction approaches.
Negative binomial regression identified key factors influencing car usage patterns.

Conclusions:

USTIN offers a robust solution for accurate car-sharing demand prediction.
The model's ability to integrate diverse data sources enhances its predictive power.
Understanding influential factors can further optimize car-sharing system management.