# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 3 Methods and Technologies for Smart Mobility
### Big Data Framework
Then, it recognizes each character instead of recognizing the characters as a sentence or word and merges the last recognized characters. Through this process, it is possible to analyze big data efficiently. The authors of another study [41] proposed a comprehensive and flexible architecture based on a distributed computing platform for real-time traffic control. Apache Kafka was used for the data pipeline and stream processing, and data were stored in HDFS. In addition, SUMO was used to simulate the opening and closing control of the highway shoulder to prove the feasibility of the proposed method. The authors of [42] proposed an efficient model using resources from autonomous vehicles. This architecture consists of a distributed data storage mechanism for real-time analysis and an in-vehicle cloud server tool for batch processing of offline data. In addition, a workflow model for big data architecture was designed to inspect streaming data in real time. Furthermore, the authors of [43] exploited a vehicle network without installing any additional infrastructure or hardware. An optimally distributed data-hopping mechanism was proposed that enables delay-tolerant data routing through a network of connected vehicles. The authors formulated a Markov decision process to solve the next-hop decision optimization and computational complexity problems, proposing a heuristic algorithm and demonstrating performance improvement through extensive simulations. In [44], an intelligent smart transportation system with big graph processing using the Hadoop ecosystem was proposed using Apache Spark rather than Apache Hadoop to increase system efficiency in big data processing. Metro graph processing was performed using Giraph through Apache Hadoop. The proposed architecture exhibited efficiency in terms of scalability and real-time data processing. The authors of another study [45] proposed a decision support system (DSS). In this architecture, public transport is promoted for those who use their cars by proposing a DSS to help public transport planners make decisions related to sustainable mobility development. The authors of [46] proposed an architecture that calculates the quality of a public transportation system using the MapReduce method after preprocessing the GPS data. By applying the MapReduce paradigm implemented in the Apache Hadoop framework, input data not useful for statistical calculation are filtered out, and useful records are sorted and classified. In addition, based on the ideas presented in related literature, the authors proposed a method for accurately predicting travel destinations based on smart card data. This estimate allows one to calculate demand and OD matrices important for transportation planning that are difficult to obtain using traditional methods. The authors of another study [47] presented a method to build a smart transportation system using big data analytics. Billions of connected devices generate terabytes of vehicle network data. These big data can make smart cities a reality by analyzing many aspects of transportation. This architecture has four layers. The first layer is the input layer, including the collection of various types of data, such as from security surveillance cameras, smart traffic signals, GPS, radio-frequency identification (RFID), and the Internet. The second layer is the storage layer, where data collected from various sources are stored for analysis. The third layer is the analysis layer, which takes data from the storage layer and analyzes them using various data analysis tools in a parallel or grid computing environment. Finally, in the communication layer, analysis results are provided to users in real time using other communication media, such as mobile, radio, or television networks or the Internet. The authors of [48] suggested processing the big data architecture of autonomous and connected vehicles driving sensor data. The proposed architecture is an improved model of the existing Cooperation Intelligence Transport System (C-ITS).