HISTORY OF TRANSPORTATION ANALYTICS

 

HISTORY OF GROWTH OF TRANSPORTATION ANALYTICS

Introduction:

Transportation analytics takes a variety of data ecosystems, helping industry leaders to use advanced analytical techniques such as machine learning, Big Data, and geospatial data to optimize business strategies in the sector.

What are Transportation analytics?

Transportation data analytics offer an up-to-date and easy-to-use data source for improving, calibrating, and validating models. Transportation data analytics offer highly granular datasets suitable for complex modeling, including route information, trip speed, length, duration, travel mode.

The history of transportation analytics can be traced back to the early days of transportation systems when basic data collection and analysis methods were employed. However, significant advancements in technology and computing power have revolutionized transportation analytics over the years.

Here’s the growth of transportation analytics has been discussed below:

1.       1960s-1970s: The early stages of transportation analytics were characterized by the development of traffic simulation models. Researchers used computer-based models to simulate traffic flow and evaluate the impacts of various factors on transportation systems. These models were primarily focused on understanding traffic congestion and optimizing traffic signal timings.

2.       1980s-1990s: The emergence of Geographic Information Systems (GIS) brought a significant advancement to transportation analytics. GIS technology enabled the integration of spatial data with transportation networks, allowing for more detailed analysis and visualization. This integration facilitated the understanding of transportation patterns, land use, and infrastructure planning.

3.       1990s-2000s: The introduction of Intelligent Transportation Systems (ITS) marked a major milestone in transportation analytics. ITS incorporated advanced technologies like sensors, cameras, and communication networks to collect real-time data on traffic conditions. This data, combined with GIS capabilities, enabled better monitoring, management, and control of transportation systems.

4.       Early 2000s: With the increasing availability of GPS devices, transportation analytics began incorporating GPS data for tracking and analyzing travel behavior. This development allowed for more accurate measurement of travel times, speeds, and route choices. GPS data also provided valuable insights into individual travel patterns and helped improve transportation planning and operations.

5.       Mid-2000s: The growth of mobile devices and smartphones provided new opportunities for transportation analytics. Mobile apps and location-based services started generating large amounts of data related to transportation, such as trip origins and destinations, travel times, and route preferences. This data became instrumental in understanding user behavior and developing personalized transportation solutions.

6.       2010s: The advent of big data and machine learning revolutionized transportation analytics. The proliferation of sensors, social media, and connected devices generated vast amounts of data related to transportation systems. Advanced analytics techniques, including machine learning algorithms, were applied to this data to extract valuable insights, predict travel patterns, optimize routing, and support real-time decision-making.

7.       Recent years: Transportation analytics has continued to evolve with the rise of connected vehicles, the Internet of Things (IoT), and smart cities. Connected vehicles generate real-time data on traffic conditions, vehicle performance, and driver behavior, enabling more accurate and dynamic analysis of transportation systems. IoT devices, such as smart traffic lights and sensors, provide additional data for monitoring and managing transportation networks.

 

Conclusion

Transportation analytics plays a crucial role in urban planning, traffic management, infrastructure development, and policymaking. It continues to evolve with advancements in technology, offering new opportunities for optimizing transportation systems, improving efficiency, reducing congestion, and enhancing the overall mobility experience.

 

References

·       https://core.ac.uk/download/pdf/33894957.pdf

·       https://www.its.dot.gov/history/pdf/HistoryofITS_book.pdf

·       https://www.google.com/search?q=transportation+analytics&oq=transportation+analytics&aqs=chrome..69i57j0i512l4j0i22i30j0i15i22i30l2j0i22i30l2.1669519037j0j15&sourceid=chrome&ie=UTF-8

 

 

 

Narsima Ahmed

@INTERNATIONAL SCHOOL OF MANAGEMENT EXCELLENCE

Intern @Hunnarvi Technologies under guidance of Nanobi data and analytics pvt ltd.

Views are personal.

#analytics #transportationanalytics #history #nanobi #hunnarvi #ISME

 

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