# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 3 Methods and Technologies for Smart Mobility
### Machine Learning
#### 3.1.2 Traffic Flow Prediction  
Congestion prediction is one of the most common subjects in smart mobility research because, with the advent of connected vehicles (CV), the importance of research has increased as vehicles share their coordinates. This section includes an introduction to research on congestion prediction, optimal route selection, and arrival time prediction. The authors of [7] focused on traffic delay factors. The multilayer perceptron (MLP), convolution neural network (CNN), long short-term memory recurrent neural network (LSTM RNN), gated recurrent unit recurrent neural network (GRU RNN), and autoregressive integrated moving average (ARIMA) methods were used for smart mobility data analysis. In [8], the authors predicted traffic flow, exploiting the stacked autoencoder (SAE) model. The proposed method was compared with backpropagation, random walk, support vector machine (SVM), and radial basis function (RBF) methods. In addition, in [9], the authors proposed prediction of vehicle speed by exploiting route types, route curvature, driver behavior, weather, and traffic condition data. The model uses big data analytics and an adaptive neuro-fuzzy inference system (ANFIS). In [10], the authors proposed a system to reduce travel time and CO\({}_{2}\) emissions using connected vehicles to detect traffic congestion and allow users to change the paths to faster routes. A Cassandra-based big data cluster was developed, and simulation of urbanmobility (SUMO) and OMNet++ discreet event network simulators were used to evaluate the proposed system. Moreover, in [11], the authors proposed a fuzzy theory and an SVM-based real-time highway traffic congestion prediction model. The model exploits Apache Storm's spout and bolt using road density, traffic condition, and rainfall data. The authors of [12] investigated the feasibility of an active real-time traffic monitoring strategy that simultaneously evaluates operation and safety. A real-time collision prediction model was implemented using data mining algorithms, such as random forest and Bayesian inference techniques. Furthermore, the authors of [13] proposed strong reliance on results from big data prediction systems that predict accidents, estimated arrival time, and custom clearance times. Accident analysis and research revealed that driver mistakes are the main cause of car accidents. Fully autonomous vehicles eliminate human error and reduce accidents. The authors proposed a very light and simple algorithm for identification and calculation of optimal trajectories. A framework for calculation of safety indices and travel time was proposed in [14], exploiting real-time data collected from connected vehicles. The results revealed that the proposed algorithm can provide different paths when considering the safety index and travel time. Moreover, in [15], the authors implemented an artificial neural network (ANN) composed of three layers to predict traffic flow. The ANN was trained to exploit forward propagation and backpropagation learning according to the date, time, traveler's vehicle, commercial vehicles, number of vehicles occupying the road segment, and vehicle speed. A CNN, Region-CNN (R-CNN), Fast R-CNN, and Faster R-CNN were used in [16] to recognize vehicle models or identify vehicle classifications and locations. These methods focus on building a current road traffic situation recognition system. A graph was generated using traffic data observations (TDOs) to determine the historical average of the traffic flow in [17]. Finally, regression models, such as linear regression, sequential minimal optimization regression, and M5-based regression trees, were used to determine traffic data and annual average daily traffic (AADT). A multilevel strategy was introduced in [18] to process raw big data into a compressed time series. This strategy employed Granger causality and Lasso regression, which are suitable for causal and regression analysis, respectively, to better prepare the data for traffic modeling.