Traffic Prediction Based on Land Use Applying Deep Learning: Case Studies in Calgary, Canada

Land uses impact the transportation system in the communities, which reflects changes in vehicular traffic, and transportation capacity. Therefore, proper assessments of the traffic prediction from land use are critical in determining the required improvements in transportation infrastructure. These assessments can also help to provide appropriate policies that serve the urban development goals. Land use and transportation planning are interdependent and are essential factors in forecasting traffic. In recent years, predicting traffic based on land use, along with several other variables, such as demographics, has become a worthwhile area of study as this helps in urban planning. This manuscript-based thesis examines Artificial Neural Network (ANN), Deep Neural Network Regression (DNN-Regression), and Recurrent Neural Network (RNN) methods to predict traffic in Calgary, Canada. These methods used three key variables: land use, demographics, and temporal data. The proposed methods were compared and evaluated with other existing traditional methods, such as Negative Binomial Regression and Auto Regressive Integrated Moving Average (ARIMA). Comparative experiments showed that the proposed methods outperformed the traditional methods. The land use change characteristics also affect and challenge how a city manages, organizes, and plans for new developments and transportation. These challenges can be better tackled with effective monitoring and predicting methods, enabling the best possible efficiency for a growing city like Calgary. Using the concept of ontology in land use change is an initiative currently being researched and explored. Ontology incorporates relationships between various entities of land use. This study also aims to present Land Use Change Ontology (LUCO) with a deep neural network for traffic prediction. This study is inspired by deep learning methods and effective data mining computing capabilities of RNN to predict traffic while considering the impact of land use change. 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. The success of these modelling approaches indicates that cities could apply these traffic prediction modelling approaches to make land use transportation planning more efficient.

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