Abstract
Basic Safety Messages that are frequently generated from multiple connected vehicles can play a primordial role in providing transport data see credible and reliable information they contain. Otherwise, when considering the way Basic Safety Messages (BSMs) are treated, multiple deficiencies prevent the latter to be capable of constituting a precious data source. As we know, data become more useful the more widely are used, which is the exact opposite of what happens with the BSMs that exist only temporarily, used locally, considered disposable, and are never stored. In this paper, we introduce a data reuse model that retains collected BSMs, stores, and processes them inside the vehicle constituting a continuous data source holding retained snapshots along the roadway. Our model provided a primary data source available on a large scale, considered to be a worthy dataset for machine learning tasks, capable of visualizing different traffic-related indicators to enhance analytics and support decisions-making. In the study case, we set up an in-vehicle data platform, where we achieved an 80% of BSMs size reduction and provided a rich set of APIs to serve applications. We also adopted the Artificial Neural Networks (ANN) as an information processing paradigm for performing traffic volume prediction, where the obtained results have reached over 99% of accuracy.
Highlights
The increasing volume of traffic that cities currently face is associated with many unpleasant phenomena, such as accidents, time delays, emergencies, as well as high pollution and degradation of life quality
To take our model a step further, we suggest applying a machine learning (ML) method on our database to perceive whether preserved Basic Safety Messages (BSMs) can help the road traffic volume prediction (TV)
Bearing in mind that our primary goal one hand and considering the good value of the coefficient of correlation and very small errors, on the other hand, it can be argued that our developed Artificial Neural Networks (ANN) has successfully reused stored BSM as past data during training, cross-validation, and testing stage to accurately predict future traffic volume despite for a short time
Summary
The increasing volume of traffic that cities currently face is associated with many unpleasant phenomena, such as accidents, time delays, emergencies, as well as high pollution and degradation of life quality. With the digital age constantly moving forward, a revolution in the transportation network is being spurred by advancements in communication technologies. Governments, academia, and industrials making tremendous efforts have made advancement to reinforce the evolution of the commonly named intelligent transportation system (ITS). A contemporary car comprises over 20,000 components, about 40 microprocessors and dozens of sensors. An eclectic selection of technologies strive to offer different vehicular communications models known as vehicle-to-everything (V2X). These progresses in sensing technologies are inaugurating new possibilities, such as connected vehicles (CV). As one of the most heavily researched automotive technologies, CV technology aims at introducing improvements concerning safety and efficiency of the transportation system and roads.
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