redBus leverages AI to master complex logistics in India’s highly fragmented intercity transit market.
03 Oct 2025 | 4094 Views | By Shahkar Abidi
redBus, the 19-year-old intercity bus ticketing marketplace, is deploying machine learning and dynamic algorithms to overcome structural inefficiencies endemic to India’s sprawling road travel industry. Dealing with a supply chain composed of over 5,500 private bus operators, most of whom are small-to-medium enterprises (MSMEs) running an average fleet of just ...
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redBus, the 19-year-old intercity bus ticketing marketplace, is deploying machine learning and dynamic algorithms to overcome structural inefficiencies endemic to India’s sprawling road travel industry. Dealing with a supply chain composed of over 5,500 private bus operators, most of whom are small-to-medium enterprises (MSMEs) running an average fleet of just 10 to 12 buses, the platform is relying on sophisticated technology to optimize inventory and pricing across the 40,000 buses it lists daily.
The centerpiece of this technological approach is revMax, a dynamic pricing tool built on EIML (AI/ML). This system is designed to solve the critical problem of manual pricing, which is unfeasible given the constant flux of demand, time of day, and departure schedules.
Using the revMax algorithm, the company can change the price for a particular bus service more than 15,000 times in a single day. This high-frequency adjustment ensures that operators efficiently maximize revenue without overpricing seats.
Manoj Agarwala, Chief Business Officer (CBO) of redBus, emphasized the necessity of this automation given the industry’s structure. “It is humanly impossible for any human,” Agarwala stated, noting that operators previously relied on “gut feel” to set fares. The AI steps in to solve complex questions, such as determining if the optimal fare four days before a major holiday should be Rs 1,000, Rs 1,500, or Rs 2,000.
To achieve this granular pricing, revMax ingests multiple data streams. It uses historical patterns and forecasting to predict booking behavior at the route and individual bus level. Critically, it analyzes real-time signals gathered from the 2 to 3 million searches the redBus platform sees daily. If searches surge on a specific route, the system registers the increased demand instantly and suggests price hikes. The complexity is further compounded as different seat categories within a single bus viz., front, back, or aisle-- are priced differently, similar to airline fare tiers.
The technological focus extends beyond pricing to improving service quality, a crucial factor in the highly fragmented market. redBus has provided operators with B2B tools like redPro, an analytics console that allows them to track booking patterns and customer feedback relative to market performance.
redBus also pushed the mandatory inclusion of GPS tracking across its platform to address the unstructured nature of private bus boarding and dropping points. Over 90% of listed buses now have tracking, enabling customer-centric features like wake-up alarms provided 20 minutes before arrival and navigation assistance to pick-up spots. This comprehensive effort has helped lift the average customer rating on the platform from 3.45 several years ago to almost 4.
From an automotive industry perspective, redBus is attempting to integrate itself into the supply side, assisting operators with the transition to electric fleets. While the company maintains an asset-light marketplace model, it facilitates relationships between operators and leasing/finance companies to help mitigate the high capital expenditure required for electric vehicles. Operators can also access B2B services, such as purchasing GPS devices, through the company’s NOVA marketplace.
redBus operates globally in eight countries.
Despite technological advancements, the industry's biggest challenge remains the lack of infrastructure, including formalized boarding and dropping points. Nevertheless, with the Indian intercity bus market projected to grow at a CAGR of 12–15%, Agarwala believes that their deep investment in AI/ML is positioning the company as a critical engine for efficiency and quality improvement in a mode of transport essential for the middle and lower economic strata.