How Analytics is Transforming the Transport Industry

The transportation industry ensures the efficient and safe transportation of people and goods from one place to another. The growth of technology, such as traffic sensors, electronic access, mobility management, and monitoring display systems in transportation is powering analytics in this industry. According to Markets and Markets, Transportation Analytics is set for fast and steady growth and is expected to reach $27.4 billion globally by2024.

In recent times, many cities have adapted multi-modal transportation, which ensures flawless travel between different transportation modes, including buses, cycles, trains, airports, and even personal vehicles.

 

A multi-modal transportation system requires systematic data collection, as technological advancements lead to many different data sources like camera, GPS, and geo-location. Analysis of the transportation industry will have to consider this diverse data ecosystem. This unmatched amount of data can help players in the transportation industry use advanced analytical techniques like predictive analytics to enhance functioning, reduce costs, and better serve travellers.

 

Role of Predictive Analytics in the Transportation Industry

Transport agencies worldwide believe that obtaining insights from live data can help them find the best substitute to get from one destination to the other. With predictive analytics, agencies can answer the question of “What’s the best possible result?” instead of using prior history information.

 

·       Transport agencies can also get insights into how metro line closures, unprecedented events such as a labour strike or transit maintenance projects can affect public transport.

·       Transport agencies can spot and foresee the occurrences of traffic, accidents, or vehicle breakdown and suggest efficient responses.

·       Predictive analytics can figure out the impact of various development projects and help identify an alternative project without obstructing mobility.

·        

Implementation of Multimodal Transport Segment-wise Analysis

With the growth of multi-modal transport, the need for segment-wise analysis is essential. The primary modes of transportation include roadways, railways, waterways, and airways.

Let us explore the implementation of analysis in each segment:

 

 

Roadways

Using analytics for one of the most used modes of transportation, roadways, has several benefits:

 

Advanced data can be used to analyze where, why, and when accidents happen. With this data, they can create Prognostication Crash Maps (shown in the image) that analyze data to shortlist high-risk areas. These maps can help issue warnings to be extra careful at these locations and help authorities take precautionary measurements.

Road Traffic Management

Keeping a record of automobiles moving patterns, velocity, and lane changing behaviour can help us understand how different road designs can influence driving. The insights are useful for smarter traffic control and identifying congestion in the road layout when planning future infrastructural developments. The graph depicts the same.

 

Railways

Rail Traffic Management

how data analytics is transforming the transportation industry 3

There is a whole range of possibilities that railways can explore in big data analytics. Applications in the railway industry include booking, improving security, automatic scheduling and planning, network enhancement and ticket management. The existing data from the passenger operating control, reservation system, CCTV, and maintenance depots can be used to our advantage to yield business benefits in the above areas. Real-time train information system (RTIS), the Nation Train Enquiry System (NTES), and the control office application (COA) are some examples where data analytics is used.

 

Airways

Air Traffic Management

Long queues are a top annoyance of air travellers. However, by accessing data of those travellers coming through the facility, advanced analytics can help airport workers easily visualize the busiest periods for their security checkpoints. Over time, machine learning-powered by AI can generate predictive models that can allow the airport to strategize better and allocate resources.

 

 

 

Waterways

Ship Monitoring and Route Optimization

Ship monitoring is one of the most critical factors for seamless planning and execution. Various tools such as vessel’s sensors, weather station reports, and satellite reports will increase ships’ efficiency. The entire data array can be processed through machine learning, and the following questions can be answered using the same:

• When does the hull need cleaning to save fuel?

• When should the ship equipment be changed?

• Which is the best route in terms of weather, safety, and which route is fuel sustainable?

 

Conclusion:

it is evident that the transportation industry is undergoing significant changes due to technological advancements and the adoption of multi-modal transportation systems. The integration of various modes of transportation, along with the use of advanced analytics, is aimed at improving efficiency, safety, and cost-effectiveness in the transportation sector. Transportation analytics, driven by technologies such as traffic sensors, electronic access systems, and monitoring displays, is poised for rapid and steady growth. The projected market size of $27.4 billion globally by 2024 indicates the increasing importance and investment in analytics solutions for the transportation industry. The implementation of multi-modal transportation systems requires systematic data collection from diverse sources such as cameras, GPS, and geo-location devices. This wealth of data presents opportunities for the transportation industry to leverage advanced analytical techniques, such as predictive analytics, to optimize operations, reduce expenses, and provide better services to travellers.  the transportation industry is embracing technology and data-driven approaches to enhance its functionality and meet the evolving needs of commuters. The combination of multi-modal transportation systems and analytics holds the potential to revolutionize the way people and goods are transported, leading to improved efficiency and a more seamless travel experience.

 

Reference: https://www.latentview.com/blog/how-data-analytics-is-transforming-the-transportation-industry/

ISME Student Doing internship with Hunnarvi Technologies Pvt Ltd under guidance of Nanobi data and analytics. Views are personal

#TransportationAnalytics #MultiModalTransport #DataDrivenTransport #SmartTransportation #PredictiveAnalytics #TransportTech #EfficientTransport #FutureofTransport #TransportationIndustry #AnalyticsInTransportation#SustainableTransport #DigitalTransportation #InnovationInTransport #InternationalSchoolofManagementExcellence #NanobiDataandAnalytics #hunnarvi

Comments

Post a Comment

Popular posts from this blog

Koala: A Dialogue Model for Academic Research

Image
  Koala: A Dialogue Model for Academic Research Introduction The accidental leak of the weights associated with Meta AI’s LLM LLaMA has sparked tremendous innovation in the open-source LLM space. Since the furious leak, we have seen models like Alpaca, Vicuna, ChatLlama, and several others expand on the foundations of LLaMA to build innovative conversational agents that match the capabilities of ChatGPT. One of the latest additions to the list is Koala (yes, I know, another animal-named model), a chatbot created by Berkeley AI Research (BAIR) that fine-tunes LLaMA on conversations gathered from the internet. The core goal of Koala is to highlight the balance between mega-large closed-source LLMs and smaller, open-source LLMs. BAIR’s thesis is that smaller models can achieve performance that matches mega models like ChatGPT with a fraction of the cost while also improving in areas such as fine-tuning, transparency, and many others. Koala Koala is a version of LLaMA fine-tu...
Read more
  What is FLAN-T5? Artificial Intelligence (AI) has been making strides in recent times, with advancements in Large Language Models (LLMs) and their applications taking much of the credit. One of the most prominent models in this domain is GPT-3, developed by OpenAI. However, it is not the only model making waves. FLAN-T5, developed by Google Research, has been getting a lot of eyes on it as a potential alternative to GPT-3. ·        FLAN stands for “Fine-tuned Language Net”         ·        T-5 stands for “Text-To-Text Transfer Transformer” Back in 2019, Google's first published a paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer", introducing a novel where they introduced the original T5 architecture. The pretrained encoder-decoder model worked well on multiple tasks, and particlarly well suited for translation and summarization tasks. In 2022 G...
Read more