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 destination to another.
Improvement on Driving and Passenger Safety
Machine learning algorithms have the potential to improve the safety of driving as well as that of passengers. The occurrence of accidents on roads is a great safety threat because it results in injuries and damage to vehicles, not the risk of death to involved individuals. According to Greguric et al. (2020), factors such as aggressive lane changing or overtaking are sometimes seen in traffic flows, which can have some negative effects. Some of these effects include a threat to the safety of passengers because of the likelihood of accidents. However, machine learning can facilitate the prediction of the probability of an accident based on real-time and historical data.
According to Dong et al. (2018), administrators can use data obtained from state highway asset management databases and police accident reports to identify the influencing factors and establish a relationship between these factors and traffic accidents. This information can then be used to predict the probability of a traffic accident. Specifically, deep learning techniques that use traffic feature representations and crash counts as inputs and training targets, respectively, can be used in determining the relationship between influencing variables and traffic accidents (Dong et al., 2018). Based on this relationship, it becomes easy to predict the probability of a traffic accident occurrence.
Also, machine learning can be used in identifying the safety profiles of drivers. According to Mantouka et al. (2019), data obtained from drivers' mobile phones can be used to predict their driving behavior and help detect unsafe driving habits such as overspeeding and the use of mobile phones while driving. This data is obtained in real-time, making it easy to provide real-time evidence of carelessness on the road. Variables such as the driver's acceleration, brakes, speeding, and mobile usage while driving can be used to predict drivers' behavior. Drivers with harsh acceleration and brakes, speeding over the limit, and high mobile usage are predicted as unsafe drivers (Mantouka et al., 2019). Drivers can use such information to adjust their driving behaviors and adopt safer driving habits to avoid traffic accidents and increase passenger confidence about their safety.
Efficiency Improvement
It is important to improve traffic efficiency in the transportation system to reduce traffic congestion and accidents. Machine learning can facilitate traffic efficiency through the prediction and monitoring of changes in traffic flow. According to Younes and Boukerche (2020), traffic efficiency can be measured based on traffic speed, volume, and density data collected through machine learning. These variables can then be used to make path recommendations to enhance traffic efficiency. For instance, the use of intelligent traffic lights has been investigated and identified a...