The rapid emergence and evolution of new technologies in the transportation industry will significantly impact how the industry operates in the future.
Despite the adverse effects of the coronavirus pandemic two years ago, the transportation industry has been a lifeline for many people. For many, the transportation system was a vital part of their lives, as it delivered essential items and healthcare services.
The transportation industry is constantly evolving and adapting to the needs of its customers. Due to this, in the transportation industry, companies are continually looking for new ways to improve their efficiency and effectiveness. This has resulted in a boom in organizations implementing emerging technology.
Here are the six technologies that embody the newest transportation revolution.
The Internet Of Things
The Internet of Things is a concept that suggests that you can connect all objects and people to networks. This could potentially affect our daily driving.
- Route plan – In cars, sensors collect data about the vehicle’s current location and use this information to create a route map. This information is displayed on a head-up display.
- Accident avoidance – Collision prevention systems are also designed to alert drivers to other vehicles on the road. These systems can then control the vehicle’s settings to avoid an accident.
- Safety – In addition, specific vehicle safety systems, such as those that monitor the physiological condition of the driver, can prevent the vehicle from starting if the driver is intoxicated or tired.
Electric Vehicles: Green & Clean Transportation
Despite the decline in mobility, transit emissions in the U.S. continued to increase in 2020. About 27% of GHG emissions are from the transportation sector, contributing to the major portion. Despite the increasing popularity of electric vehicles, Americans still want more sustainable public transit options.
Cities, transportation agencies, and electric vehicle companies must work together to make sure that everyone can ride an E.V. This includes the establishment of charging stations and the redesign of cities to support the increasing number of electric vehicle fleets. The federal government will significantly boost the electric vehicle industry sale upto 50% by investing in the infrastructure bill.
Autonomous Tech is gaining pace toward reality.
The development of self-driving cars, such as the Google car and Telsa’s vehicle, has made the concept of this technology a reality. Several states in the U.S. have begun regulating the technology and encouraging its development. Despite the positive effects of this technology, the public still has many questions about its safety.
In 2016, a series of accidents involving multiple vehicles highlighted the issue of safety concerns for autonomous cars. However, in 2020, distracted driving was a reported factor in 8.1% of fatal motor vehicle crashes. (NHTSA)
Unlike Electric Vehicles, the evolution of self-driving cars has led to consumer adoption and is now being tested in various public transportation systems, such as buses and ride-hail. In 2021, Ford launched a pilot program in the U.S. that will test the capabilities of self-driving cars on multiple routes. The NC Department of Transportation also launched autonomous shuttle services to improve the efficiency and safety of its transportation system.
The goal of autonomous vehicles is to improve the safety and mobility of our communities. In 2022, we will see more innovations in the technologies and platforms that will help make these cars more successful.
Simulation Tools for pre-deployment testing
Cities and transportation agencies must have the necessary plans to allow the use of new technology in their operations. Unfortunately, the increasing number of riders and the complexity of their transportation needs have prevented the transportation industry from developing new solutions. One of the factors that can be taken into account when implementing new technology is simulation software.
The use of simulation software was highlighted during the pandemic as it allowed transit operators to be more agile and prepared for any situation. In addition, the digital twin tool improves the planning process for transportation.
The use of simulation software can be used to implement new technology in the transportation industry beyond 2022. However, aside from analyzing the data collected by the software, tech providers must also make it simpler for users to access and visualize it.
The use of new technology can help overcome the challenges of the Covid-19 era and build a more resilient and sustainable transportation system. However, it is up to the city leaders and transit authorities to make this happen. Collaboration with industry thought leaders are also needed to ensure that the transportation industry is equipped with the necessary tools and resources.
Hyperloop
One of the most ambitious technologies for transportation is SpaceX’s proposed hyperloop system, which would let cars travel at speeds of up to 300 mph. The first phase of the project would connect San Francisco and Los Angeles. It would allow passengers to travel a distance of 350 miles in just over an hour. Presently, its top speed is about 750 miles an hour.
The viability of the proposed high-speed transportation system known as the Hyperloop is still in its early stages, and it’s still not clear if it will be able to replace long-distance travel in the U.S. The project’s initial cost is around $6 billion, and private funding will limit the government’s involvement.
New technologies are forcing people to rethink the way they think about transportation. While some of the significant changes in the past few years are technological changes, introducing new transportation systems such as the hyperloop could lead to a new era of innovation.
Industrial Revolution 4.0
The 4th Industrial Revolution was brought about by the emergence of new technologies that are connected, data-driven, and integrated. Some of these include smart transport systems used to improve the efficiency of various transportation activities. These include the use of smart containers and temperature-controlled cameras on buses. In addition, these technologies are being repurposed to meet crisis response needs.
Due to the economic recovery, private vehicle use is expected to increase due to sanitary concerns. This will require smart traffic lights and other advanced technologies to ensure the safety and efficiency of both pedestrians and vehicles. In addition, smart delivery systems and smart parking systems can help minimize freight movement.
In the long run, smart buses and other transportation innovations will help revive the public transport industry and establish a sense of safety.
Wrapping it up
The rapid emergence and evolution of new technology has created a unique opportunity for transportation to transform its operations and deliver a more sustainable and inclusive transportation system. May it be the vision of mobility as a service or the integration of Artificial Intelligence transportation systems, the need for safer and more reliable transportation systems remains crucial. In the future, it will not only benefit the industry but will also become a necessity.
For more insights on emerging technology solutions and implementation, feel free to contact us at contact@aicorespot.io
Top 6 Emerging Technology Trends in Transportation
As the owner or manager of an e-commerce business, you likely already know how important technology is in today’s market. The brick-and-mortar stores of yesterday are quickly becoming a thing of the past and more people are shopping online than ever before. On top of that, digital technology can also help your business run smoother and with fewer errors.
How to integrate digital technology into your e-commerce business
As the owner or manager of an e-commerce business, you likely already know how important technology is in today’s market. The brick-and-mortar stores of yesterday are quickly becoming a thing of the past and more people are shopping online than ever before. On top of that, digital technology can also help your business run smoother and with fewer errors.
Use Early Stopping to halt the training of neural networks at the correct time
A problem with training neural networks is in the selection of the number of training epochs to use. A lot of epochs can cause overfitting of the training dataset, while too few might have the outcome of an underfit problem. Early stopping is a strategy that facilitates you to mention an arbitrary large number of training epochs and stop training after the model performance ceases improving on a hold out validation dataset.
Training-validation-test split and cross-validation performed right
One critical step within machine learning is the selection of model. An apt model with relevant hyperparameter is the foundation to a good forecasting outcome. When we are encountered with a selection between models, how should the decision be made?
An intro to recurrent neural networks and the math that drives it
With regards to sequential or time series data, conventional feedforward networks can’t be leveraged for learning and forecasting/prediction. A mechanism is needed that can retain historical data to predict the future values. Recurrent neural networks or RNNs in short are a variety of the traditional feedforward artificial neural networks that can handle sequential data and can be trained to retain the know-how, from a historical perspective.
How to code the GAN Training Algorithm and Loss Functions
The Generative Adversarial Network, or GAN for short, is an architecture for training of a generative model. The architecture is consisted of dual models. The generator that we are concerned with, and a discriminator model that is leveraged to help in the training of the generator. To start with, both of the generator and discriminator models were implemented as Multilayer Perceptrons (MLP), even though more lately, the models are implemented as deep convolutional neural networks.
How to implement Wasserstein Loss for Generative Adversarial Networks
The Wasserstein Generative Adversarial Network, or Wasserstein GAN is an extension to the generative adversarial network (GAN) that both enhances the stability during training of the model and furnishes a loss function that corresponds with the quality of produced imagery.
Deep learning frameworks for human activity identification
Human activity recognition, or HAR in short, is a difficult time series classification activity. It consists of forecasting the movement of an individual on the basis of sensor information and conventionally consists of deep domain expertise and strategies that range from the raw data in order to go about fitting a machine learning model. Lately, deep learning strategies like convolutional neural networks and recurrent neural networks have demonstrated potent and even accomplish cutting-edge outcomes by automatically learning features from the
Vulnerability Assessment vs. Penetration Test
There are several perspectives on what the difference is between a vulnerability assessment versus a penetration test. The primary distinction, appears to be that many hold the belief a comprehensive penetration test consists of identification of as many vulnerabilities as feasible, while others hold the belief that Penetration Tests are objective-oriented and primarily don’t concern themselves with other vulnerabilities might exist.
Loading and exploring household electricity utilization data
With the rapid proliferation of smart electricity meters and the widespread adoption of electricity generation tech such as solar panels, there is a literal treasure trove of electricity usage data available at our disposal today. This data signifies a multivariate time series of power-related variables, which in turn could be leveraged to model and even predict future electricity consumption. In this guide, you will learn about a household power consumption dataset for multi-step time series predictions and how to better comprehend the