# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 3 Methods and Technologies for Smart Mobility
### Big Data Framework
The architecture is based on the Hadoop ecosystem. Furthermore, the authors of [49] focused on the big data architecture of ITS. This architecture exploits Apache Hadoop and Apache Spark. It stores traffic management and traffic analysis data for dynamic toll charging. The authors of [50] discussed an architecture capable of built-in storage and analysis. The proposed architecture comprises four modules: a big data collection and preprocessing unit, a big data processing unit, a big data analysis unit, and a visualization unit. This architecture was designed to collect, store, analyze, and visually present continuously produced ITS data to the users. In [51], the authors proposed, developed, and applied a microservice-oriented big data architecture (MOBDA) to meet the functional and non-functional service performance requirements of ITS. This architecture was proposed to combine stream processing and batch processing of big data for smart computation of microservice-oriented transportation metrics that can serve the various needs of users. The authors of [52] proposed a framework for acquiring data and predicting the latency of buses using Bayesian networks after preprocessing. In addition, the authors of [53] suggested a fog-computing-based ITS data analysis architecture. The proposed method exploits both fog and cloud computing. Thus, this architecture compensates for its drawback by providing faster data processing than existing cloud computing frameworks. A large-scale data analysis architecture was suggested using a wireless sensor network, big data, data mining, and other methods in [54]. The proposed architecture can ensure the compatibility of terminals, the usability of functions, real-time data, the universality of regions, and the accuracy of information, providing personalized traffic information service for users. The authors of [55] used MapReduce to preprocess the driving data method. The authors investigated five preprocessing methods: range filtering, meaningless value exclusion, variable filter comparison, statistical technique application, and driving pattern search. Big data technologies such as Spark on Hadoop and MongoDB can process real-time and historical data [56]. This study also highlights the influence of Spark cluster execution. The efficiency of Spark clusters in standalone mode can be observed by comparing the test results. A distributed cloud computing framework based on a big data approach was proposed to scale storage and computing resources to collect and process traffic from large networks in a reasonable amount of time [57]. In addition, a new framework that focuses on real-time anomaly detection based on big data technology was proposed in [58]. This framework consists of BroIDS, Flume, Kafka, Spark Streaming, SparkMLlib, Matplot, and HBase, which were evaluated to demonstrate their efficacy, especially in terms of accuracy, memory consumption, and execution time. Table 1 summarizes the architectures used for big data frameworks. Many subjects are considered, such as storage of geospatial data, traffic control, optimization of vehicle pacing, path optimization, and security. Nevertheless, none of the listed architectures deals with user safety or environmental problems.