Smart transportation planning: Data, models, and algorithms

Abstract By developing cities and increasing population, smart transportation becomes an essential component of modern societies. Extensive research activities using machine learning techniques and several industrial needs have paved the way for the emerging field of smart transportation. This paper presents data, methods, and models that are essential for intelligent planning of transportation. In particular, the current data sources for gathering information to control or forecast traffic are described, connected Vehicles (CVs) that bring smart and green transportation to modern life is also discussed. Clustering Analysis as an effective unsupervised machine learning method in trip distribution and generation and traffic zone division is discussed in the paper. Various machine learning techniques and models that use time series prediction are introduced in this paper including ARIMA, Kalman filtering, Holt winters'Exponential smoothing, Random walk, KNN Algorithm, and Deep Learning. Finally, a discussion on the main advantages and drawbacks of these models, as well as the business adoption of the forecasting models are presented.