Machine Learning in Managing Traffic Congestion, Improving Safety, and Enhancing Efficiency

Machine Learning Will Drive Future Mobility
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Machine Learning will Drive Future Mobility
The development of machine learning and artificial intelligence has made a significant difference in many fields, such as social media. In transportation, machine learning has been identified as an appropriate application in the development of smart transportation to facilitate route optimization and the detection/prevention of accidents on public roads (Zantalis et al., 2019). Based on the traffic database, machine learning algorithms can provide a more accurate prediction of traffic and human reaction, optimizing mobility in the traffic system to reduce congestion, improve driving and passenger safety, and enhance travel efficiency, albeit under uncertainty.
Congestion Reduction
Modern cities around the world are struggling with traffic congestion that causes delays and is costly. The delays contribute to lower productivity as people spend more time in traffic instead of working, necessitating the calls for traffic congestion reduction. Various solutions have been identified, but most of them are inefficient in addressing the traffic congestion variables in cities. However, machine learning algorithms have been identified as a potential, efficient solution in reducing traffic congestion. According to Akhtar and Moridpour (2021), the use of shallow and deep machine learning models has increased exponentially in predicting traffic congestion.
On the one hand, shallow machine learning models have been identified as efficient in predicting traffic congestion in the short term since they process linear features of traffic flow quite effectively. On the other hand, deep machine learning models efficiently process both linear and non-linear features of traffic flow, thereby increasing the accuracy of the predictions (Akhtar & Moridpour, 2021). Efficiency and accuracy in traffic congestion prediction can mitigate the risk of traffic congestion in cities. As more traffic data continues to be collected, the current machine learning models will have more to rely on, improving the efficiency and accuracy of their predictions.
Further, machine learning is being used in identifying the best alternative routes, thus reducing traffic congestion. According to Jabbarpour et al. (2017), this has enabled drivers to identify and prevent potential bottlenecks on certain routes. Specifically, the machine learning models use various components such as road models, vehicle models, real-time traffic data, and historical traffic data to identify appropriate routes. The use of historical traffic data facilitates accurate prediction of recurring congestion, while real-time data facilitates accurate prediction of non-recurring congestion (Jabbarpour et al., 2017). In addition, machine learning has facilitated the identification of the most suitable path while considering the environmental, path, and driver factors. Path planning algorithms based on deep machine learning can ensure that the right routing methods reduce congestion. Identifying alternative routes using real-time data can help drivers avoid traffic congestion, thus reducing the time taken to travel from one destina...