# A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction
## 5 Conclusions and Future Work  
Over the past few years, the emergence of many sensor-based applications in urban environments progressively favors the emergence of the concept of smart cities. Among a wide range of novel services offered to urban residents and decision-makers, transportation on demand has radically changed the way operations are distributed amongst potential passengers and delivery companies. When considering and modelling taxi demand and supplies, many factors impact the waiting times of taxi passengers, from the efficiency of the urban network infrastructure to the functional organization of the city, to the optimization of taxi resources and passenger pickup locations, to traffic flows to mention a few examples [40]. The research developed in this paper introduced an experimental neural network model whose objective is to predict and optimize taxi allocation times to passenger demands in an urban environment. The approach is based on a close integration of multi-dimensional data, from historical taxi trajectories, to a spatio-temporal distribution of human behaviors in the city according to different functional, temporal constraints and weathers. The applicability, accuracy and effectiveness of the proposed model are primarily compared with actual historical taxi trajectory data and a spatio-temporal behavioral model of urban residents. Lastly, the whole neural network approach is compared to a few alternative modelling approaches. We conducted experiments with 70 days of taxi trajectory data in Wuhan. The experimental results show that under the weather at any time and place, the MAE value of the prediction result of this model is less than 73.27, and the RSME value is less than 7.33. The results also show that our modelling approach performs relatively well in terms of accuracy and efficiency. The advantage of this modelling approach is twofold: it can help taxi drivers to reduce the no-load rate while reducing waste of energy resources, and it can improve the balance  
Figure 13: (**a**) Application used in front of the university at 12 noon; (**b**) application used in front of the community at 12 midnight; (**c**) application used before the small at 3 pm. between the supply and demand of taxis and passengers to some extent this being a key issue in taxi demand allocation tasks [40]. For passengers, predicting the waiting time in advance can also improve the success rate of taking a taxi and help passengers arrange their journey appropriately. Although the research has made some preliminary progress, additional issues need to be further developed. Subsequent work will continue around the following three points: first by further optimizing the time-space modeling, such as different holidays and area without function category identification. Secondly, based on predicted taxi waiting times, the optimization of allocation pickup points might be still explored with further algorithmic approaches. Third, the generalization performance will be in consideration. Conceptualization, [PERSON] and [PERSON]; Methodology, [PERSON], [PERSON] and [PERSON]; Software, [PERSON], [PERSON] and [PERSON]; Writing--original draft, [PERSON] and [PERSON]; Writing--review and editing, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. This research is funded by Key Project of Science and Technology Research Program of Hubei Provincial Education Department (grant number D20201006), Hubei Province Natural Science Foundation Item (grant number 2019 CFB757), the National Natural Science Foundation of China (61803149), General Project of the National Natural Science Foundation of China (grant number 61977021), and Hubei Provincial Technology Innovation Projects (grant number 2019 ACA144, 2020 AEA008). The data presented in this study are available on request from the corresponding author. The data are not publicly available due to Data provider's requirements.