A Short-term Traffic Supply-Demand Gap Prediction Model with Integrated GCN-LSTM Method for Online Car-hailing Services

Song, Chaofei; Chang, Runfeng; Zhang, Zibo; Liu, Anying; Li, Ruowen; Zhou, Shenghan

doi:10.1109/dsit55514.2022.9943914

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As LSTM has the advantage in preserving a long dependency relationship, combining the characteristics of temporal data, and considering the influence brought by the changes in the demand before and after.It can better fit the tidal model of the demand for online car-hailing [13] . Therefore, in this paper, LSTM is chosen as the theme model, combining GRU model to reduce the iterative training time.…”

Section: Modelmentioning

confidence: 99%

Online Ride-hailing Demand Prediction Model Based on GRU & LSTM

Wang,

Zhao,

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

While online car-hailing provides fast and convenient services, it has the same problems in the process of ‘seeking customers’ as traditional taxis. With the research goal of the demand for online ride-hailing, an online ride-hailing demand forecast model based on G & LSTM is proposed. Firstly, grey relational degree analysis and other methods are used to analyze which and how the relevant factors affect the demand for online car-hailing. Secondly, the demand prediction model of online car-hailing based on GRU&LSTM is trained an adjusted. Through the adjustment and comparison experiment, the values of each parameter in the model are properly adjusted. Finally, based on the running data of Chengdu, the model proposed is verified and evaluated. The experimental results show that the GRU&LSTM prediction model has good prediction effect. By comparing the prediction effect of weighting data and unweighting data, it can be seen that weighted data has better prediction effect.

show abstract

Section: Modelmentioning

confidence: 99%