Online prediction of network-level public transport demand based on principle component analysis

Author:

Zhong ChengORCID,Wu Peiling,Zhang Qi,Ma Zhenliang

Funder

Kungliga Tekniska Högskolan

Publisher

Elsevier BV

Subject

Transportation,Control and Systems Engineering,Decision Sciences (miscellaneous)

Reference43 articles.

1. Data Needs, Data Collection and Data Quality Requirements of Activity-Based Transport Demand Models;Arentze,2000

2. Public transport trends in Australia during the COVID-19 pandemic: an investigation of the influence of bio-security concerns on trip behaviour;Beck;J. Transport Geogr.,2021

3. Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition;Büyükşahin;Neurocomputing,2019

4. FACE RECOGNITION USING PCA ALGORITHM;Chaudhari,2012

5. Prediction of city-scale dynamic taxi origin-destination flows using a hybrid deep neural network combined with travel time;Duan;IEEE Access,2019

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