Deep learning

Physics

Business

Demand forecasting

Service (business)

Passenger transport

Operations research

Smart card

Machine learning

Engineering

Marketing

Artificial intelligence

Power (physics)

Transport engineering

Computer science

Quantum mechanics

Computer security

Reliability (semiconductor)

Artificial neural network

Public transport

Journal of Urban Management

Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. The comparative evaluation results showed that BiLSTM models outperformed other models tested and was able to predict passenger demands with over 90% accuracy.

AI-based neural network models for bus passenger demand forecasting using smart card data