Prediction of Bus Arrival Time Using Real-Time on-Line Bus Locations

Chan Tong Lam, Benjamin Ng, Su Hou Leong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)


Reliable and accurate prediction of bus arrival time is considered as one of the important services to attract people's choice of bus ridership. In this paper, we develop a simple yet accurate real-time bus arrival prediction system for a crowded small tourist city, like Macao, using accurate on-line bus locations provided by the government website. These accurate bus locations are freely available on-line, which are generated by dedicated sensors installed in bus stops and buses. We first proposed a link time model for storing all of the link times between adjacent bus stops on different bus routes in the entire network so that the trip time between any two of the bus stops in the network can be predicted in real-time. Three different simple models based on historical and real-time on-line bus locations, namely Simple Moving Average (SMA), Artificial Neural Network (ANN) and Hybrid Model, are proposed for the bus arrival prediction system, taking into account the real-time weather conditions. It was found that the Hybrid model perform the best among the three models. The average mean absolute percentage error (MAPE) for the Hybrid model is 17% and the average mean absolute error (MAE) and root mean square error (RMSE) is less than 1 minute. For future works, more advanced deep learning models with Kalman filtering can be evaluated, using on-line bus locations from more bus routes.

Original languageEnglish
Title of host publication2019 IEEE 19th International Conference on Communication Technology, ICCT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728105352
Publication statusPublished - Oct 2019
Event19th IEEE International Conference on Communication Technology, ICCT 2019 - Xi'an, China
Duration: 16 Oct 201919 Oct 2019

Publication series

NameInternational Conference on Communication Technology Proceedings, ICCT


Conference19th IEEE International Conference on Communication Technology, ICCT 2019


  • artificial neural network
  • bus arrival time prediction
  • hybrid model
  • real-time bus locations
  • simple moving average


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