Customer Prediction using Parking Logs with Recurrent Neural Networks
Jeju National University, Jeju Island, South Korea. ycb@jejunu.ac.kr & mudassar192@hotmail.com
- DOI
- 10.2991/ijndc.2018.6.3.2How to use a DOI?
- Keywords
- Neural Network; Convolutional; Recurrent; Prediction; Traffic Patterns; Long Short-Term Memory; Vanilla
- Abstract
Neural Networks have been performing state of the art for almost a decade now; when it comes to classification and prediction domains. Within last few years, neural networks have been improved tremendously and their performance is even better than humans in some domains, e.g. AlphaGo vs Lee Sedol and Image Net Challenge-2009. It’s a beneficial factor for any parking lot to know that what would be a parking position at any given point in time. If we are able to know in advance that are we going to get parking tomorrow afternoon in a busy super store parking lot, its very beneficial to plan accordingly. In this paper, we predict customer influx in a specific departmental store by analyzing the data of its parking lot. We use this parking data to predict the customer influx and outflux for that parking lot as this parking influx is directly proportional to the customer influx in the store. We use Recurrent Neural Network on the top of two years of historical data. We generate promising results using this dataset by predicting the traffic flow for each hour for next 7 days. We further improve our performance on this dataset by incorporating three more environmental factors along with the parking logs.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
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TY - JOUR AU - Liaq Mudassar AU - Yung-Cheol Byun PY - 2018 DA - 2018/07/31 TI - Customer Prediction using Parking Logs with Recurrent Neural Networks JO - International Journal of Networked and Distributed Computing SP - 133 EP - 142 VL - 6 IS - 3 SN - 2211-7946 UR - https://doi.org/10.2991/ijndc.2018.6.3.2 DO - 10.2991/ijndc.2018.6.3.2 ID - Mudassar2018 ER -