How Do Metro Station Crowd Flows Influence the Taxi Demand Based on Deep Spatial-Temporal Network?

Bao, Yu; Sun, Yue; Bu, Xiaofei; Du, Yang; Wu, Xiaocan; Huang, He; Luo, Yonglong; Huang, Liusheng

doi:10.1109/msn.2018.00031

Cited by 3 publications

(4 citation statements)

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“…Previous studies on taxi demand prediction are generally based on historical taxi trajectory data. Previous studies have shown the feasibility of obtaining predictions from historical taxi trajectory data [1,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Methods of traffic demand prediction can be classified into three types: linear system theory (such as the autoregressive moving average model [24], Kalman filtering model, and time series model), nonlinear system theory (such as the neural network model, gray prediction model, and random forest model (RFM)), and combination forecasting model (CFM).…”

Section: Introductionmentioning

confidence: 99%

“…Yao et al proposed a deep multiview spatiotemporal network framework to simulate spatiotemporal relationships based on traffic prediction models [32]. Bao et al considered the interaction between subways and taxis based on univariate traffic prediction and applied the residual neural network to predict the taxi demand in different regions [6]. Ishiguro et al proposed a taxi demand prediction algorithm using realtime demographic data generated by cellular networks and used a stacked denoising autoencoder to assess the impact of real-time demographic data on taxi demand prediction accuracy [12].…”

Section: Introductionmentioning

confidence: 99%

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Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots

Liu

Chen

et al. 2020

Journal of Advanced Transportation

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Accurate taxi demand prediction can solve the congestion problem caused by the supply-demand imbalance. However, most taxi demand studies are based on historical taxi trajectory data. In this study, we detected hotspots and proposed three methods to predict the taxi demand in hotspots. Next, we compared the predictive effect of the random forest model (RFM), ridge regression model (RRM), and combination forecasting model (CFM). Thereafter, we considered environmental and meteorological factors to predict the taxi demand in hotspots. Finally, the importance of indicators was analyzed, and the essential elements were the time, temperature, and weather factors. The results indicate that the prediction effect of CFM is better than those of RFM and RRM. The experiment obtains the relationship between taxi demand and environment and is helpful for taxi dispatching by considering additional factors, such as temperature and weather.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots

Liu

Chen

et al. 2020

Journal of Advanced Transportation

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“…D. Zhang improved the hidden Markov chain model and proposed a D-model to forecast the taxi demand [10]. For exploring the relationship between taxi and subway, Y. Bao et al took the interaction between taxi demand and subway demand into account to explore the impacts of the interaction on the accuracy of taxi demand and proposed a taxi demand prediction method based on a neural network model [11]. N. Davis explored the impacts of tessellation on-demand prediction effects and proposed a combination algorithm of different tessellation strategies to predict taxi demand [12].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, three accuracy measures are applied to evaluate the performance of online taxi-hailing prediction. The measures are root-mean-square error (RMSE), mean absolute percentage error (MAPE) and goodness of fit (R 2 ), which are calculated as Equations ( 9)- (11).…”

Section: Evaluation Criteriamentioning

confidence: 99%

Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method

et al. 2020

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The development of the intelligent transport system has created conditions for solving the supply–demand imbalance of public transportation services. For example, forecasting the demand for online taxi-hailing could help to rebalance the resource of taxis. In this research, we introduced a method to forecast real-time online taxi-hailing demand. First, we analyze the relation between taxi demand and online taxi-hailing demand. Next, we propose six models containing different information based on backpropagation neural network (BPNN) and extreme gradient boosting (XGB) to forecast online taxi-hailing demand. Finally, we present a real-time online taxi-hailing demand forecasting model considering the projected taxi demand (“PTX”). The results indicate that including more information leads to better prediction performance, and the results show that including the information of projected taxi demand leads to a reduction of MAPE from 0.190 to 0.183 and an RMSE reduction from 23.921 to 21.050, and it increases R2 from 0.845 to 0.853. The analysis indicates the demand regularity of online taxi-hailing and taxi, and the experiment realizes real-time prediction of online taxi-hailing by considering the projected taxi demand. The proposed method can help to schedule online taxi-hailing resources in advance.

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