Big data is now vastly used for predicting traffic and avoiding accidents

Road traffic accidents continue to be a major concern as more than 1,25 million people across the globe lose their lives every year. It also remains a leading cause of death amongst people between the ages of 15 and 29, according to a report from World Health Organization.

The WHO has committed to a mighty initiative to reduce the number of those fatalities and injuries from road traffic crashes by 2022. This target seems possible with massive investment and advancement in big data and automotive technology. Currently, one of the major use cases of big data and advanced analytics is to use data to improve the safety of roads and vehicles. Let's see how.

1. Improving Vehicle Performance

Data obtained from a driver's behaviour can be used to change and improve vehicle parameters like power, speed, and torque and create safe driving conditions. We already can see that in vehicles that control the speed of the engine by adjusting fuel injection to the engines. The same effect can be achieved through digital means/ For example, big data can help send an ongoing feedback loop that maintains the vehicle's performance which can prevent drivers from speeding and avert rash behaviour.

• Autonomous vehicles

Apart from improving the safety of normal vehicles, big data can also be used for making autonomous vehicles safer. For example, renowned car manufacturer Tesla has been using machine learning and big data solutions for creating a sense-plan-act program. This program uses great amounts of data for predicting outcomes for specific actions that the autonomous vehicle takes in different scenarios. This enables autonomous vehicles to make the safest and smartest driving decisions even without human involvement.

2. Predictive Analysis and Crash Maps

Fatal accidents can be avoided if the risk hotspots are located and addressed effectively. Predictive analytics and advanced big data systems can be used to collect critical insights on car accidents, such as where, when, and why they happen. According to LegalExpert.co.uk, this information is necessary to create predictive crash maps that analyse historical and recent data to determine the high-risk areas.

Predictive crash maps make it possible to release warnings to motorists to be extra cautious in these areas. In the meantime, local authorities can take the requisite measure to skyrocket road safety. For example, in Tennessee, local authorities have launched an algorithm that predicts areas of fatal accidents by analysing data from traffic citations and crash reports.

The program resulted in a 33% drop in crash response by the highway patrols. The response time decreased by 25 minutes from 37 minutes, and fatal accidents dropped by 3%.

Another scenario where predictive analysis can significantly reduce the accident rates is in the waste management and recycling industries. These industries can improve the behaviour of their employees by reducing the number of accidents and, in turn, lowering the insurance rates. This solution enables industries to instruct their motorists better on reducing infringements and improving their driving skills.

3. Developing Safer Roadway Infrastructure

Smart cities, V2V communications, and connected infrastructure. These are the words that are used right now when we discuss road safety and big data. Improving our roadway infrastructure can significantly impact the safety of drivers and pedestrians alike.

For instance, programmed stop lights allow for a better flow of traffic, reducing the risks of accident – and programming those lights would not be possible without a combination of video data, mapping, and telematics.

The integration of big data to create "smart city intersection" technology is currently being tested within a small Ohio town. The point is that having smart software to dictate traffic in massively crowded intersections can drastically reduce fatalities.

Tracking each automobile's location, direction, speed, and history of accidents can provide detailed data surrounding traffic flow and conditions, common accident points and how drivers respond to varying conditions.

Such insights enable decision-makers in the government infrastructure to decide on major roadway improvements, improving overall driving conditions and reducing human errors.

4. Coaching Driver Behaviour is Now a Thing

One of the best ways big data can contribute to making our roads safer is by fundamentally changing our driving behaviour. For example, new teenage drivers are three times more likely to get involved in car accidents than adults. But telematics solutions powered by big data can significantly change that by reporting on a driver's performance, subsequently giving drivers and parents a chance to identify problematic patterns and where habits can be improved.

Common causes of harsh braking, for instance, may indicate speeding or distraction, which, once identified, can help novice motorists improve their habits and driving skills and basically improve their chances of stay safe behind the wheel.

The same goes for professional drivers and vehicle fleets, a particularly responsive market for data-powered telematic solutions.

Conclusion

With the accident rate skyrocketing on major roadways, there is an urgent need to address this problem, and big data is emerging as a powerful solution. Along with a scaling number of fatalities, the financial losses of accidents are also very high.

Car accidents significantly impact the U.S economy, reaching more than $871 billion per year. Substantial results have already been achieved by using historic driver behaviours and accident insights for the effective functioning of infrastructure and automobiles.