Multi-modal travel in India: A big data approach for policy analytics

Abstract

Multi-modal travel is becoming prominent amongst Indian Passengers due to the advance of low-cost air travel, increasing disposable income, and connectivity by rail, bus, and air across various cities. This is a huge opportunity for all stakeholders within transport sector like Rail, Aviation, and Surface transport to operate seamlessly to boost domestic transportation and ultimately offer passengers the best of breed travel solution. In this paper, we will propose a framework for policy analytics for Rail and Air connectivity and discuss how big data can play a key role to analyze the existing datasets like routes, schedules, booking information, benchmark studies, economic characteristics, and passenger demographics. Big data tools are very useful in processing unstructured data sets by analyzing them and providing meaningful visualizations. Policy analytics can combine the power of information technology, operations research, statistical modeling and machine learning to modernize and equip policy makers for better data-driven decisions while drafting policies. This would ultimately enable Government's vision on smart cities, seamless transport hubs, and interchanges that provide seamless connectivity and high passenger satisfaction.