Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges

• Published in: International journal of communication systems Vol. 34; no. 9
• Main Authors: Nama, Mahima, Nath, Ankita, Bechra, Nancy, Bhatia, Jitendra, Tanwar, Sudeep, Chaturvedi, Manish, Sadoun, Balqies
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01.06.2021
• Subjects: Algorithms; Data collection; Human resource management; intelligent transportation system (ITS); Intelligent transportation systems; Machine learning; Optimization; Police; reallocation; Resource management; Scheduling; Taxonomy; Traffic congestion; Traffic control; Traffic flow; Traffic information; Traffic police; Traffic safety; Traffic volume
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.4814

Summary The rise in traffic congestion has become a significant concern in the urban city environment. The conventional traffic control systems with inefficient human resources management fail to control the traffic discipline, leading to increased traffic density and road offenses. However, the intelligent transportation system (ITS) can provide safety, efficiency, and sustainability for large‐scale vehicular traffic dilemmas. ITS unites machine learning with the available traffic control force and performs real‐time police scheduling to ensure the smooth flow of traffic. Many researchers have demonstrated notable work in intelligent traffic police scheduling and deployment using various optimization algorithms. However, the compilation of such praiseworthy work as a whole is still missing. Motivated by these facts, we provide a comprehensive review of the machine learning‐based state‐of‐the‐art technologies that can be used to form a three‐tier solution taxonomy. The first tier describes various tools and technologies that can be utilized to collect traffic data. The second tier highlights the machine learning algorithms and their accuracy, which forms a pattern in the collected data and then yields some crucial information about traffic flow, congestion levels, and so forth. In the third tier, the most vital taxonomy layer, various traffic police scheduling strategies are discussed. The proposed survey also presents the use of cases of traffic police scheduling that elaborates this review's applicability in various domains. Finally, some of the key challenges in the subject being reviewed are discussed, which initiates a further scope of improvement. We have explored the traffic police scheduling architecture along with components involved in it. We have reviewed the various scheduling and reallocation of traffic police mechanisms based on the real time traffic densities. Finally, we have highlighted various issues and challenges in traffic police scheduling along with the need of technological advancements for the traffic management.