Home Data Engineering Data News How Big Data & Advanced Analytics Will Change Transportation

How Big Data & Advanced Analytics Will Change Transportation

How Big Data & Advanced Analytics Will Change Transportation

The transportation industry has been experiencing unprecedented amounts of data captured from different sources such as on-board sensors and data collection points introduced by passenger counting systems, vehicle location systems, ticketing and fare collection systems, and scheduling and asset management systems.

Existing data warehouses contain unprecedented potential for deriving insights into planning and managing transportation networks. Still, use of conventional data management tools and practices means these insights remain mostly hidden and a large part of this data is unstructured too.

The need to extract instant insights from this captured data is significant to achieve a competitive advantage, optimize CAPEX and OPEX, improve service reliability and mitigate risks. However, the volume of data being generated and the shortcomings of conventional management tools and practices – until now – have left that data underutilized, with information unreachable and the treasure trove of  insights mostly hidden.

Creating that richer, more complete picture of what’s happening on the ground is presently an opportunity for business analytics in the transportation sector, to leverage big data tools and predictive analytics to help transportation agencies improve operations, reduce costs and better serve travelers.

Below listed are some areas where big data” analytics can make significant difference to transportation industry-

  • Freight movement and routing optimization
  • Inventory Management & Capacity Optimization
  • Improved Customer Experience by develop effecting communication via insights from social media, persona segmentation & preferences
  • Reduced Environmental Impact and Increased Safety
  • Fleet Optimization and Predictive Maintenance through Real-time view of fleet operating conditions, statistics around usage and wear patterns, maintenance cycles
  • Optimized Transit schedules by predicting impact of maintenance, road-works, congestion & accidents

What do you think? Join the discussion in the comments below.

Image Credit: Nightman1965/Shutterstock

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