Ride-hailing origin-destination demand prediction with spatiotemporal information fusion  

作　　者：Ning Wang Liang Zheng Huitao Shen Shukai Li 

机构地区：[1]School of Traffic and Transportation Engineering,Central South University,Changsha 410075,China [2]State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,China

出　　处：《Transportation Safety and Environment》2024年第2期63-74,共12页交通安全与环境（英文）

基　　金：supported by the National Natural Science Foundation of China(Grant No.72371251);the National Science Foundation for Distinguished Young Scholars of Hunan Province(Grant No.2024JJ2080);the Excellent Youth Foundation of Hunan Education Department(Grant No.21B0015);the State Key Lab-oratory of Rail Traffic Control and Safety of Beijing Jiaotong Uni-v ersity,China(Gr ant No.RCS2022K004).

摘　　要：Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand,and improving the service level of ride-hailing platforms.In contrast to previous studies,which have primarily focused on the inflow or outflow demands of each zone,this study proposes a conditional generative adversarial network with a Wasserstein divergence objective(CWGAN-div)to predict ride-hailing origin-destination(OD)demand matrices.Residual blocks and refined loss functions help to enhance the stability of model training.Interpretable conditional information is employed to capture external spatiotemporal dependencies and guide the model towards generating more precise results.Empirical analysis using ride-hailing data from Manhattan,New York City,demon-strates that our proposed CWGAN-div model can effectively predict the network-wide OD matrix and exhibits strong convergence performance.Comparative experiments also show that the CWGAN-div outperforms other benchmarking methods.Consequently,the proposed model displays potential for network-wide ride-hailing OD demand prediction.

关 键 词：intelligent transport system ride-hailing generative adversarial networks spatiotemporal dependencies origin-destination(OD)demand prediction 

分 类 号：U463.6[机械工程—车辆工程]