TCS Intelligent Planning for Railways
Optimize Rail Network Capacity
Rail transport is gaining importance in the fight to minimize carbon emissions.
Increasing passenger and freight volumes underline the growing demand for rail transport. However, building new rail infrastructure to meet the demand is capital intensive and has an extended gestation period. Hence, maximizing the utilization of existing infrastructure is crucial.
Rail organizations are looking for faster train service planning and design to maximize capacity utilization with available network and to fulfil fluctuating demand patterns. Rail Infrastructure managers need to plan for different scenarios like additional capacity, temporary unavailability of existing capacity due to maintenance and renewal works, and, finally, additional services on existing capacity. Advanced methods of operations research enable planners to rapidly do such optimization using simple descriptions of the infrastructure and the intended services.
TCS Intelligent Planning for Railways uses proprietary algorithms to maximize railway capacity, especially for cyclic services.
The solution leverages TCS’ deep domain research in railway planning and scheduling, and insights obtained through collaboration with railway industry experts.
The TCS Intelligent Planning for Railways solution helps railway organizations and government entities exploit existing infrastructure to the fullest. It also helps them decide on investments in new infrastructure by evaluating their expected capacity needs.
The state-of-the-art solution has the following components:
The TCS proprietary solution provides rail organizations with various insights to optimize capacity and make faster decisions.
It can do the following to help rail organizations manage their services better:
A partnership with TCS can help rail organizations and government entities stay on top of the most pressing challenges hindering growth.
An association with TCS will enable entities to take advantage of the following key differentiators: