CNN-LSTM and clustering-based spatial–temporal demand forecasting for on-demand ride services

Ay M., KULLUK S., ÖZBAKIR L., GÜLMEZ B., Öztürk G., Özer S.

Neural Computing and Applications, vol.34, no.24, pp.22071-22086, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 24
  • Publication Date: 2022
  • Doi Number: 10.1007/s00521-022-07681-9
  • Journal Name: Neural Computing and Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.22071-22086
  • Keywords: Demand forecasting for on-demand ride services, Clustering, CNN-LSTM, ConvLSTM2D, PREDICTION, NETWORKS
  • Kayseri University Affiliated: No


© 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.Passenger demand forecasting is of great importance to the on-demand ride systems. With the accurate forecasting of demand, it can be determined from which regions and when the passengers demand a vehicle. In this way, passenger and vehicle waiting times, fuel costs of vehicles can be reduced. In the literature, various models such as time series, long short-term memory (LSTM), convolutional neural network (CNN), and hybrid of CNN-LSTM are used for demand forecasting in on-demand ride service systems. These models forecast demands by considering temporal and spatial data separately or together. In models that use spatial and spatial–temporal data, generally, the city is divided into zones in the form of a grid. This partitioning method has some disadvantages, such as misleading the forecasting accuracy by considering regions without demand and ignoring the geographical conditions. In this study, two new models, ConvLSTM2D-clustering and CNN-LSTM-clustering are proposed to overcome these disadvantages and make more accurate and robust forecasts. The proposed models use clustering instead of grid partitioning in dividing the city into zones and take time-of-day, time-of-week variables into account in forecasting as well as passenger demand. The presented models have been used in the passenger demand forecasting of Turkcell Technology Company, which provides on-demand ride services for its employees in Istanbul, Turkey. Experimental results, validated on real-world data provided by Turkcell, show that the proposed models partition the city more effectively and achieve 14–55% better short- and long-term forecasting performances than the compared models in terms of mean squared error.