Traffic Prediction in Smart Cities Based on Hybrid Feature Space

Abstract

In smart cities of the future, data will be generated, integrated, processed and utilized from heterogeneous sources and at varying levels of complexity. For urban traffic planning in smart cities, one of the biggest challenges is traffic congestion prediction and its avoidance. Traffic congestion is a complex phenomenon and it is a manifestation of various contributing factors. In addition to vehicular mobility, properties of road network, weather, holidays and peak hours play a significant role in traffic congestion especially on arterial roads within a city. In this paper, we proposed a hybrid GRU-LSTM based deep learning model and applied it on city-wide novel traffic data integrated from heterogeneous sources. We have devised our indigenous data pipeline that is composed of a set of algorithms dealing with map matching, sparsity handling, outlier removal, zero speed adjustments, Open Street Map (OSM) and segment mapping etc. Extensive experimentations have been carried out to demonstrate the improved performance of the proposed method. The comparative analysis reveals that our methodology yields 95 % accuracy that outperforms other deep neural network models.