Big data in Transportation analytics:
Introduction
Big data is already
changing the world around us. The retail, healthcare, financial services, and
entertainment industries are all using big data to improve their quality of
service and optimize efficiency. However, big data’s usefulness is not limited
to changing the world of business — it also has the potentially to completely
change transportation as we know it.
What is big
data?
Big
data is exactly what the name suggests, a “big” amount of data. Big Data means
a data set that is large in terms of volume and is more complex. Because of the
large volume and higher complexity of Big Data, traditional data processing
software cannot handle it. Big Data simply means datasets containing a large
amount of diverse data, both structured as well as unstructured.
Big
Data allows companies to address issues they are facing in their business, and
solve these problems effectively using Big Data Analytics. Companies try to
identify patterns and draw insights from this sea of data so that it can be
acted upon to solve the problem(s) at hand.
Big data in
Transportation analytics:
Here
are some key aspects of big data in transportation analytics:
Increasing Efficiency
Congestion, lack of
parking, and long commutes are all urban transportation problems that big data innovators are aiming to solve in the coming years.
Data analytics can help pinpoint causes of congestion and where drivers spend
the most time looking for parking, then provide alternative routes tailored to
specific individual needs, all pulled from the massive data pools that big data
provides. Big data can also provide insights into implementing infrastructure
for pedestrians and cyclists, making everything move more quickly for everyone.
Big data is also set
to increase efficiency for the private sector on
the road as well. As industries that rely on shipping adopt IoT-driven
intelligence and incorporate machine learning models, shipping vehicles will be
able to communicate with one another to reduce travel time and accidents on the
road. Ultimately, we will see a network of automated shipping vehicles that
operate efficiently without any of the pitfalls that come with human drivers.
Identifying Danger
As big data becomes more
integrated into transportation models and vehicles, safety will naturally increase. Right hook accidents — where a vehicle
initiates a right-hand turn while there is a cyclist or vehicle to their right
in a blind spot — frequently cause dangerous accidents. These and other common
vehicular accidents will slowly fade as vehicles become more intelligent and
interconnected.
Big data helps car
companies develop automated vehicles and vehicles with driver-assist
technology, much of which is designed specifically with both driver and
pedestrian safety in mind. By gathering data on car crashes big data can produce predictive crash maps,
giving city officials insight into how they can improve infrastructure with
safety as a priority. AI driven by big data can also monitor drivers by
predicting driver risk from gathered acceleration and braking habits, as well
as seat belt usage and instances of speeding.
Driverless Cars Are
Already In Demand
Big data has been instrumental in the rise of the autonomous vehicle in
recent years. Self-driving vehicles rely on data gathered from GPS, radar and
sensor technology, as well as cameras on the vehicles themselves, and big data
has provided car manufacturers the tools that they need to develop technology
that can make efficient use of all of this data.
Vehicles with
driver-assisting technology are already widely available on the market today,
with some base models even coming with these safety measures standard. This is
a major benefit to consumers seeking out a new vehicle rather than a used one.
The future of driving is automated, and consumers are already pining for self-driving
technology today. Lane departure warnings, adaptive cruise control, automatic
emergency braking, and self-parking technologies are already in high demand
from consumers, showing the auto industry that the public is more ready than
ever for fully autonomous vehicles. Driverless cars result in fewer accidents,
reduced emissions, as well as less traffic and better commute times. This makes
automated driverless vehicles good for not just the individual driving, but the
public at large as well.
While we are still a ways
off from highways full of driverless vehicles effortlessly and safely changing
lanes around one another, we are well on our way thanks to big data. Whether it
is increasing efficiency, ensuring safety, or simply making parking just a bit
easier, big data is helping our transportation dreams become a reality.
Public Transportation Improvement:
Big data analytics can
improve public transportation services by analyzing data from ticketing
systems, smart cards, and mobile apps. It helps authorities optimize routes,
adjust schedules, and enhance service quality based on passenger demand,
resulting in improved reliability and customer satisfaction.
Fleet Management and Maintenance: Big data analytics can optimize fleet management by
analyzing data from vehicles, including fuel consumption, maintenance records,
and sensor data. It helps transportation companies schedule preventive
maintenance, optimize vehicle routing, and monitor driver behavior for improved
efficiency and reduced costs.
Intelligent Transportation Systems (ITS): Big data analytics plays a crucial role in
intelligent transportation systems, which leverage data from various sources to
improve transportation operations. ITS applications include dynamic traffic management,
adaptive signal control systems, incident detection and management, and
integrated multimodal transportation systems.
Environmental Sustainability: Big data analytics can support environmental
sustainability in transportation by analyzing data on fuel consumption,
emissions, and traffic patterns. It helps identify opportunities for promoting
sustainable transportation modes, optimizing logistics to reduce environmental
impact, and assessing the effectiveness of green transportation initiatives.
Supply Chain Optimization: Big data analytics can optimize the logistics and
supply chain operations within the transportation industry. By analyzing data
on inventory levels, transportation routes, demand patterns, and supplier
performance, companies can streamline operations, reduce costs, and improve
supply chain efficiency.
Conclusion:
By delivering useful
insights, boosting operational efficiency, and improving the entire travel
experience, big data analytics has the potential to revolutionize the transportation
sector. The use of big data in transportation analytics will develop greater
advancements in efficiency, safety, and sustainability as technology progresses
and data sources increase.
References:
2. https://www.analyticsvidhya.com/blog/2021/05/what-is-big-data-introduction-uses-and-applications/
Aniket Shukla
ISME Student Doing an internship with Hunnarvi under the guidance of nanobi
data and analytics. Views are personal.
# Big data in Transportation analytics# analytics #nanobi #hunnarvi
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