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Related Concept Videos

Bus Impedance Matrix01:24

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Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
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Friction is an essential force that influences the motion of objects in daily life. Depending on the situation, it can be either beneficial or problematic. Consider a bus with a mass of three megagrams and its center of mass at a specific point, moving along a banked road at a constant speed. The coefficient of static friction between the tires and the road is 0.5. Find the maximum angle of the banked road at which the bus would not slip or tip.
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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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A Peak Traffic Congestion Prediction Method Based on Bus Driving Time.

Zhao Huang1,2, Jizhe Xia1, Fan Li1,3

  • 1Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

This study introduces a novel traffic congestion prediction method using bus driving times and long short-term memory (LSTM) technology. The approach accurately forecasts congestion levels, offering optimized routes to minimize travel time.

Keywords:
GPS trajectoryLSTMdriving timeintelligent transportation systemsroad congestion prediction

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Area of Science:

  • Intelligent Transportation Systems (ITS)
  • Traffic Engineering
  • Data Science

Background:

  • Road traffic congestion significantly impacts travel efficiency.
  • Existing traffic prediction methods often rely on floating car data, leading to unstable accuracy due to speed fluctuations.
  • Accurate traffic congestion prediction is a critical area within ITS.

Purpose of the Study:

  • To propose and evaluate a novel traffic congestion prediction method based on bus driving times (TCP-DT) utilizing long short-term memory (LSTM) technology.
  • To address the limitations of existing methods, particularly those dependent on fluctuating floating car data.
  • To provide a reliable system for predicting traffic congestion status and optimizing driving routes.

Main Methods:

  • Collected 66,228 bus driving records from 50 buses over 66 working days in Guangzhou, China.
  • Calculated congestion time as the difference between actual and standard bus driving times derived from GPS trajectories and station data.
  • Employed a time-series prediction model based on LSTM (T-LSTM) for forecasting future bus congestion times.
  • Utilized a congestion index and classification (CI-C) model to categorize congestion levels into five tiers.

Main Results:

  • The T-LSTM model demonstrated effective prediction of bus congestion times across various road sections and time periods.
  • Achieved an average Mean Absolute Percentage Error (MAPE) of 11.25% and Root Mean Square Error (RMSE) of 14.91 during morning peak hours.
  • Reported an average MAPE of 12.3% and RMSE of 14.57 during evening peak hours.
  • The TCP-DT method successfully predicted traffic congestion status.

Conclusions:

  • The proposed TCP-DT method, leveraging LSTM technology, offers a robust and accurate approach to traffic congestion prediction.
  • The system can effectively classify congestion levels and provide optimized driving routes with minimal congestion time.
  • This research contributes to the advancement of intelligent transportation systems by improving traffic flow management and reducing travel delays.