How analytics can be used in train delay prediction system

Train delay prediction systems have been used since a long time. These systems are static and are operated with human assistance. Most of these systems do not consider historical train journey data collected from various information sources. As a result, relying on the current running train data is prone to inaccurate predictions. Most of us have already acknowledge this while travelling to different places by train. Moreover, certain setbacks have also been associated with the system, and many researchers and companies are working dynamically to make it error proof and precise.

The Big Data Role

At the time when organizations consider big data analytics as part of their business approach, the very first question arises is, what will be the outcome? What kind of worth business analytics will motivate? All these questions helps to choose the right data analytics tools and solve the business problems. Many businesses have already witnessed radical changes in their processes, empowering them to taste glory like never before with big data analytics.

Incorporating different analytical tools and techniques for predicting train delays-based prediction system looks quite promising. These techniques and algorithms can be used to quickly extract value from a large amount of raw data available and make exact predictions

Problem with Current System

Present-day techniques for train delay prediction are based on line characteristics, train characteristics and general statistics data, targeting at figuring the amount of time necessary to finish a specific segment of its journey and play on it for predictions. However, these attributes fail to consider external influencing factors affecting railway operations, which leads to problematic situations.

Big Data Solutions 

Big data analytics solutions can be certainly used in the above scenario to:

1) Study Train Delays Using Train Movements and Weather Condition Data

The historic data of train travel and climate situations can be recorded, to perceive what happened in the past and try to forecast what will be the chance in future. The common attributes that can be considered as head may include:

● Temperature

● Humidity

● Wind

● Rain Level

● Pressure

● Solar Radiation

2) Study Check Point's Delay Recording

Once the attributes are jotted down, analysts can include previous records and statistics regarding the arrival or departure of a specific train at a specific checkpoint in the analysis.

Steps Involved in Big Data Analysis Implementation

1) Description of Available Data

The attributes collected must be handled with utmost care. These should be labeled with unique identification numbers and must be checked for any outliers.

2) Model Building

After segregation and selection of input data, analytical models using algorithms such as Extreme Learning Machines and Random Forest can be built around the checkpoints and movements. Here, for each checkpoint, the weather data from the nearest station can be collected, and this will be updated with the next checkpoint and station. For instance, if the trip has 10 check points on an average with 100 trains running on the route then 1000 movements would be recorded.

3) Algorithm Testing

The selected algorithms can be tested for actual applications, and further validated and used for future simulation procedures.

4) Simulation Results

The simulation results have the capabilities to trace out the insight of the whole prediction process. The results can be studied and analyzed critically as they form the foundation for future strategic planning. Also, they can be used to pick out the contradiction among the standard versus actual process outputs.

Summary

Big data analytics can absolutely be a pace mover for decision-making in the current scenario.Trains generate a tremendous amount of raw data every time they move. As the technological era is demanding more process insights; the predictions can be fulfilled with a helping hand of big data.

Big data analytics hold a significant potential in most of the fields if effectively utilized. Delay management proves to be a field where data analytics can solve a variety of issues the current rail industry is facing. If used effectively, big data analytics will undoubtedly be a precision boosting tool for train delay prediction system across countries.