Forecast of Bus Passenger Traffic Based on Exponential Smoothing and Trend Moving Average Method

Author:

Ge Sheng Yang1,Zheng Chang Jiang1,Hou Mao Mao1

Affiliation:

1. Hohai University

Abstract

Two prediction models, exponential smoothing and trend moving average method, are selected for bus passenger traffic prediction. An analysis is made from aspects of basic idea, basic theory and so on. With the bus passenger traffic of Anqing, underlying index is used to compare the prediction accuracy of two models. The results demonstrate that trend moving average method has better effect on bus passenger traffic prediction. Therefore, the trend moving average method is rational and effective on the bus passenger traffic prediction.

Publisher

Trans Tech Publications, Ltd.

Reference5 articles.

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1. Short-term Subway Passenger Flow Prediction based on GCN-LSTM-MHA;2023 4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE);2023-08-25

2. A comparison between ARIMA, LSTM, ARIMA-LSTM and SSA for cross-border rail freight traffic forecasting: the case of Alpine-Western Balkan Rail Freight Corridor;Transportation Planning and Technology;2023-08-09

3. Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia;Transportation Research Record: Journal of the Transportation Research Board;2023-02-28

4. LSTM Network Integrated with Particle Filter for Predicting the Bus Passenger Traffic;Journal of Signal Processing Systems;2023-01-12

5. Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network;Sensors;2022-10-04

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