With increased digitization comes the need for infrastructural stability and safety. That’s where TensorFlow can provide maximum returns, especially in the road safety domain. On-road traffic accidents can be minimized with greater efficiencies in overall traffic management. There is also greater control and oversight when dealing with multiple vehicles across various roads.
Introduction: What is TensorFlow?
TensorFlow is a Python-friendly open source library that can be used for complex computation, making Machine Learning more efficient. With a convenient front-end API, it allows developers to execute complex tasks using existing libraries in neural mapping, deep learning, etc. The technology can be used to train and run complex neural networks for a variety of tasks.
Since it is an AI library, it can be used to design robust models involving complex dataflow graphs. Each node within the dataflow graph shows a mathematical operation, with each connection being a multidimensional array (tensor).
Development teams can create neural networks of large scales, that have multiple layers interacting with one another. It can then help in managing complex structures, like road safety management, through data provided from smart cameras, sensors and radar detectors.
The challenges of road safety management
Road safety is a critical area that every economy is tackling head-on. According to estimates by WHO, close to 1.25 million people enter into a fatal accident each year, with an additional 20-50 million injured globally. Accidents also cost countries upwards of $518 Billion each year, reflecting around 1-2% of their individual annual GDPs.
Road safety has become one of the most pressing matters in today’s day and age. With the rise of driverless vehicles and on-road traffic mapping, countries are facing even more layers of complexities. They are using modern solutions to understand the challenge from a technological level, by employing AI and Deep Learning.
How TensorFlow is enhancing road safety
TensorFlow is allowing increased visibility and clarity in the flow of traffic, among other areas. This is giving local and state departments increased information about how traffic is being managed from end to end. They can then visualize the data better and come up with better conclusions that can work at scale. Other areas like congestion analysis, critical junctions and peak-hours analysis are also being extracted from the core framework.
1. Analyzing rush-hour (peak) traffic
Using information derived from sensors, radars, and cameras, TensorFlow can analyze data holistically. It can provide key time-slots, areas, roads, and traffic stops, where the likelihood of congestion is higher. Drivers may become anxious and enter into an accident more frequently, which is what the framework can capture mathematically.
It can take into account the speed limits, road surface and weather conditions to measure the flow of traffic over time. This can give significant insight to road-safety departments that are looking for ways to ease congestion during these hours.
2. Predicting car accident risk
The risk of a car accident involves multiple factors. These factors may also be localized to certain neighborhoods or even times of the day. That’s why a platform such as TensorFlow is so successful. It can take in massive amounts of real-time information to give you a more dynamic prediction model for better on-road safety.
A marquee example of this was in the state of Iowa, USA. The TensorFlow framework was used to provide insights into traffic behavior. Officials at the Iowa Department of Transportation were able to predict the risk of a car crash, especially during extreme weather conditions. This helped mobilize rescue teams and auto-repair solutions to come to the scene of a crash quicker.
3. Greater knowledge of on-road conditions
TensorFlow provides greater insights into on-road conditions at any time. In places where the weather changes significantly, within the hour, it’s important to have the right data at your fingertips. TensorFlow can create a real-time map of on-road conditions, giving greater control to departments within the public sector.
The technology is also allowing greater integration of on-road data with other public systems. This can aid ambulances to arrive on time, and public transport systems to run seamlessly. Everything from a pot-hole to a crash site can be captured by a robust TensorFlow application.
4. Driving behavior over specific roads
Driving behavior can be mapped over specific roads and avenues, to understand the best ways to prevent accidents. TensorFlow can use Flow/Density models to calculate relative speeds. These relative speeds can help in judging driver performance and susceptible hot-spots within the geography.
Cameras can also analyze driver behavior over time by using time series neural networks to create detailed models. Predictive speeds can be calculated over certain roads, giving more authority to traffic departments to take preventive action.
5. The effectiveness of safety measures
When traffic departments install safety compliance measures like signs, billboards, key messaging and patrol vehicles, the effectiveness can be tested using TensorFlow. Using historical datasets and predictive mapping, the framework can set goals for the measures over time. The results of the safety measures and campaigns can then be tested and enhanced upon at scale.
Teams can also set out to create hypotheses based on the data presented. Areas such as road expansion, highway development, and increased stop signs can be tested using the model developed. This makes the utility of technology more robust and comprehensive.
The potential of TensorFlow for better Road Safety
In the near future, TensorFlow development can be used to predict accidents in real-time. With greater access to real-time information, it can analyze car speeds, dimensions, and road-conditions to give you a standardized dashboard of critical results. Officials can take quicker action based on the insights provided and predict accidents before they even occur.
As the training data becomes more sophisticated, the deep learning model will become expansive. Through this, state departments can run simulations over the network to estimate the impact of critical changes. Stoplight speeds, traffic diversion, and parking zones can be tested from a single screen to give more control over road-safety. The future of road safety management is in good hands, with TensorFlow playing a critical role.