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.
Highlights
It is believed that urbanization has generated several economic, environmental, and social issues that must be addressed when planning for traffic management
We propose that Land Use Change Ontology (LUCO) be linked to a temporal traffic prediction model based on a collaborative learning technique using the deep learning Recurrent Neural Network
The presented research sought to answer these questions: What was the land use change ontology, and how was this change applied through using different deep learning and data mining methods to represent traffic prediction? To address these questions, we developed LUCO and Recurrent Neural Network (RNN) models
Summary
It is believed that urbanization has generated several economic, environmental, and social issues that must be addressed when planning for traffic management. The current technological innovation era has led urban planners and decision-makers to consider data analysis and data management when addressing traffic prediction. Over the past three decades, Calgary’s steep economic and urban progress has resulted in it becoming a metropolis of 1.3 million residents. Researchers have identified a relation between land use change and traffic prediction. There are various models currently employed to model land use changes and traffic prediction [2,3]. GIS and remote sensing techniques make it possible to collect vast amounts of data about the Earth’s surface at a finer resolution. Detection of land use change is one of the key research areas of GIS. Several techniques [2,4,5]
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