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Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets.

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Summary

This study analyzes online car-hailing travel patterns using diverse data. It introduces a new traffic prediction model that improves accuracy by considering multiple variables, outperforming traditional methods.

Keywords:
hybrid modelmultivariate variables time seriesonline car-hailingtraffic prediction modelingtravel characteristics analysis

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Area of Science:

  • Smart urban systems
  • Transportation engineering
  • Data science

Background:

  • Online car-hailing is a major part of smart transportation, yet travel characteristic studies often overlook it.
  • Traditional traffic prediction models like ARIMA are limited as they only use univariate time series data.
  • Existing models fail to incorporate crucial external factors influencing travel.

Purpose of the Study:

  • To analyze online car-hailing travel characteristics from multiple dimensions.
  • To develop an advanced traffic prediction model for smart transportation systems.
  • To address the limitations of univariate models in capturing complex traffic dynamics.

Main Methods:

  • Analysis of online car-hailing operational datasets.
  • Utilizing Maximal Information Coefficient (MIC) for feature selection.
  • Developing a hybrid model fusing Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) and Long Short-Term Memory (LSTM) for multivariate time series modeling.

Main Results:

  • Identified key online car-hailing travel characteristics across various dimensions.
  • Proposed a novel multivariate hybrid time series traffic prediction model.
  • Demonstrated the model's effectiveness using real-world online car-hailing data.

Conclusions:

  • The developed multivariate model offers a significant advancement for smart transportation.
  • Accurate traffic prediction is crucial for efficient smart urban mobility.
  • This research fills a gap in understanding online car-hailing travel behavior and prediction.