# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 3 Methods and Technologies for Smart Mobility
### Machine Learning
#### 3.1.2 Traffic Flow Prediction
In addition, an objective methodology based on engineering judgment considering the traffic accident rate of the road section, the degree of roadside development, and the geometric characteristics of the road section on urban roads was developed in [19]. Furthermore, the authors of [20] discussed an extended smart traffic management platform (STMP) based on unsupervised online incremental machine learning, deep learning, and deep reinforcement learning. STMP integrates heterogeneous big data streams, such as IoT, smart sensors, and social media, to detect conceptual drift; distinguish between repetitive and non-recurring traffic events; and determine impact propagation, traffic flow prediction, commuter sentiment analysis, and optimized traffic control. In [21], the authors aimed to develop tools for accurate and timely prediction of traffic flow information. The authors proposed a traffic flow prediction system with reduced complexity using machine learning, genetic algorithms, soft computing, and deep learning algorithms. In [22], the authors presented a decision support system (DSS) framework that proposes alternative travel strategies for car use by assessing large volumes of traffic system data from different devices. The proposed framework was structured to collect, integrate, aggregate, converge, manage, and disseminate open big data. The framework architecture is based on a centralized database management system (DBMS) aimed at performing geographic data mining analysis. In addition, a regression model based on LSTM to predict 4 h of traffic data was proposed in [23]. The proposed system collects 24 h of traffic data online and labels it to construct an LSTM model. The proposal proves its efficiency by comparing the performance to logistic regression. The authors of [24] exploited a deep neural network (DNN) to classify traffic congestion in non-congested and congested areas. The ANN was composed of three layers, and the results indicated 99% accuracy. Nei-TTE, a neighborhood-based deep learning method for travel time estimation, was discussed in [25]. Nei-TTE divides the entire trajectory into several separate segments and uses historical trajectory data that are perfect at the time level. The proposed model captures the characteristics of each segment and uses the trajectory characteristics of adjacent segments when the road network topology and speed interact. In [26], the authors proposed intelligent transportation based on real-time traffic conditions using graphs. They suggested an architecture that efficiently processes real-time vehicle big data using a parallel processing system and big graph processing technology. In addition, various graph algorithms were used to intelligently respond to user queries. A traffic image analysis system based on computer vision techniques was proposed in [27]. The system core detects and classifies vehicles in traffic images. Thus, two models were implemented. The first model is an MoG and SVM system, and the second is based on Fast R-CNN for object detection in images. Furthermore, a general architecture for fog computing applications and services in a vehicle ad hoc networks (VANET) environment was proposed in [28]. A proof-of-concept system was provided for data analysis in a hybrid VANET/ fog environment. The system was used in two fog applications: one for detection of road traffic anomalies and the other for prediction of bus arrival times. A graph-oriented mechanism for implementation of smart city transportation systems was proposed in [29]. Deploying sensors on roads for traffic data collection generates numerous IoT big data, and an efficient architecture was proposed using Giraph tools and parallel processing servers to generate big graphs. In [30], the authors proposed an optimized prediction algorithm for a radial basis function neural network based on an improved artificial bee colony (ABC) algorithm in a big data environment. Due to the uncertainty of traffic flow, the existing linear model had difficulty eliciting user satisfaction.