time series ARIMA neural networks pooling method

Background:

Taiwan is considered as a small island comparing to her population. Besides, with high population density in the city area such as in Taipei city, New Taipei city, it is quite difficult to find an available parking space in those places. Therefore, we want to propose a mobile application that can provide parking lot information to customer with value-added services such as the availability of the parking lot and information about how long the customer needs to wait if the parking lot is full. With our proposed mobile application, the customer can plan their schedule in advance which can increase their satisfaction in using the facility. Moreover, with the forecasting approximate waiting time, the customer will be prepared for waiting which can help lower their frustration. Additionally, the parking lot company can better manage their parking lots via the user log in the mobile application.

Solution:

To begin with, we explored the data and chose six parking lots based on their use. In addition, basically, the time series of those parking lots has no trend but seasonality. Then we have tried many forecasting models in order to obtain the most suitable output for our value-added services. We have provided the outcomes with point values and in range of forecasted values. In terms of the performance, our models can capture the pattern of each different type of parking lots and provide a useful forecasting. For more information, please refer to the detailed report below.

Project site: [link]

PPT: [link]

Report: [link]