GPS data in urban online ride-hailing: The technical potential analysis of demand prediction model

Wenxiao Jiang, Haoran Zhang, Yin Long, Jinyu Chen, Yi Sui, Xuan Song, Ryosuke Shibasaki, Qing Yu

Research output: Contribution to journalArticle

Abstract

Online ride-hailing as an innovative travel mode becomes increasingly popular in cities around the world. To improve the efficiency of dispatching system of ride-hailing, many ride-hailing demand prediction models based on deep learning architecture have been proposed to reduce the gross travel distance between drivers and passengers. However, in most of these prediction models, only the error metrics are used for performance evaluation. There is scarce evidence on how much the demand prediction model reduces the unnecessary travel distance in reality. In this study, a multi-scenario-based method is proposed to evaluate technical potentials of the ride-hailing demand prediction model in ride-hailing dispatching system simulation. The ride-hailing dispatching is simulated in three scenarios: traditional dispatching system, prediction model-based dispatching system, and perfect prediction model-based dispatching system. One-month data of Didi Express service provided by Didi Chuxing GAIA Initiative in Chengdu is employed to support the simulation. Two terms, empty distance and relative performance, are introduced as the criteria of prediction model performance measurement. Simulation results reveal that the total empty distance reduced 1,164 km per day by using the prediction model compared with the traditional dispatching system. The relative performance is only 58.9% compared with the perfect prediction model-based dispatching system.

Original languageEnglish
Article number123706
JournalJournal of Cleaner Production
Volume279
DOIs
Publication statusPublished - 10 Jan 2021

Keywords

  • Convolutional long short-term memory
  • Data mining
  • Dispatching system simulation
  • Online ride-hailing
  • Technical potential analysis

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