Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada

Abstract

Land use and transportation planning have a significant impact on the performance of cities’ traffic conditions and the quality of people’s lives. The changing characteristics of land use will affect and challenge how a city is able to manage, organize, and plan for new developments and transportation. These challenges can be better addressed with effective methods of monitoring and predicting, which can enable optimal efficiency in how a growing city like Calgary, Canada, can perform. Using ontology in land use planning is a new initiative currently being researched and explored. In this regard, ontology incorporates relationships between the various entities of land use. The aim of this study is to present Land Use Change Ontology (LUCO) with a deep neural network for traffic prediction. We present a Land Use Change Ontology (LUCO) approach, using expressions of how the semantics of land use changes relate to the integration of temporal land use information. This study examines the City of Calgary’s land use data from the years 2001, 2010, and 2015. In applying the LUCO approach to test data, experimental outcomes indicated that from 2001 to 2015 residential land use increased by 30% and open space decreased by 40%. Forecasting traffic is increasingly essential for successful traffic modelling, operations, and management. However, traditional means for predicting traffic flow have largely assumed restrictive model architectures that have not controlled for the amounts of land use change. Inspired by deep learning methods and effective data mining computing capabilities, this paper introduces the deep learning Recurrent Neural Network (RNN) to predict traffic while considering the impact of land use change. The RNN was successful in learning the features of traffic flow under various land use change situations. Experimental results indicated that, with the consideration of LUCO, the deep learning predictors had better accuracy when compared with other existing models. Success of our modeling approach indicates that cities could apply this modeling approach to make land use transportation planning more efficient.

Forecasting Traffic land Demand in Guangdong-Hong Kong-Macao Greater Bay Area Based on Gray-BP Neural Network Model

“…Set the scale of urban land use, urbanization rate, total population, regional GDP, general budget income of local finance, total fixed asset investment of the whole society, consumption expenditure per capita, total industrial output value, and the number of employees in secondary and tertiary industries as independent variable groups. Set 7 sets of comparison sequences, as in (2).…”

“…The expansion of traffic land has a stimulating effect on regional social and economic development. At the same time, the development of traffic land will also bring conflicts between different types of land [2].The Greater Bay Area is one of the economic development regions with greatest openness in China. The Greater Bay Area include Guangzhou, Shenzhen, Zhuhai, Foshan, Huizhou, Dongguan, Zhongshan, Jiangmen, Zhaoqing, Hong Kong and Macau.…”

“…Reasonable expansion will also promote the rapid interflow of people and logistics, and promote the rapid growth of regional production capacity. Therefore, The realization of urban planning, land use, and regional land resource conservation and sustainable development requires scientific methods to reasonably predict the demand for traffic land and determine the scale of land use [2].…”

Sentinel-2 data classification for land use land cover mapping in northern Algeria

“…Different deep-learning techniques have been proposed for LULC in remote sensing (RS) imagery. These techniques include convolutional neural networks (CNN) [21], deep belief networks (DBNs) [22], recurrent neural networks (RNN), and auto-encoder (AE) [23].…”

Estimating Land-Use Change Using Machine Learning: A Case Study on Five Central Coastal Provinces of Vietnam

“…Three major approaches are commonly applied in land-use change prediction, which are spatial pattern, statistical analysis, and artificial intelligence [3][4][5][6][7][8][9][10][11][12][13]. Models based on the simulation of the spatial pattern of land-use change processes, such as the Markov model, are deployed to know and interpret regional land changes and trends of regional land-use in effective ways [14].…”