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Smart transportation, railway systems, and mobility solutions
6 Problem Statements in this Domain (5 Standard + 1 Open Innovation)
Rail fastening components (clips, pads, bolts) that are missing, loose, or damaged are a known contributor to track safety incidents, and current inspection relies substantially on manual walking inspection along thousands of kilometers of track, which cannot achieve high-frequency coverage of every fastening point across the network. Vision-based automated detection is appealing but must cope with enormous variability in the field: rust, mud, vegetation encroachment, variable lighting from full sun to overcast to tunnel darkness, and worn/aged components that look visually different from new reference imagery — making this a genuinely difficult robust-detection problem, not a simple defect classifier.
Wheel-rail interface wear and developing defects (wheel flats, rail corrugation, subsurface cracking) contribute to ride quality issues and, in severe cases, safety risk, but current detection relies on periodic specialized inspection runs rather than continuous monitoring, meaning developing defects can progress significantly between inspection cycles. Acoustic emission signals from wheel-rail contact carry information about developing defects, but the signal is heavily confounded by variable train speed, load (freight vs. passenger, variable freight loading), and track condition — making reliable defect signature extraction a genuinely hard signal-processing problem, not a straightforward threshold-based alarm.
Unauthorized track crossing and trespassing (especially in areas without formal level crossings) is a major cause of railway fatalities in India, and remote/unmanned sections cannot be economically covered by continuous human patrol or expensive dedicated fencing/sensor infrastructure at national scale, while candidate sensing approaches (fiber-optic sensing, vision, seismic) must reliably distinguish genuine human/animal intrusion from the very high volume of benign vibration and movement (passing trains, wind, wildlife, vegetation) along a live rail corridor.
Overhead traction (catenary) wire sag, wear, and misalignment can cause pantograph-catenary interaction failures leading to service disruption and, in severe cases, safety incidents, and current inspection relies on periodic specialized measurement trains that cannot provide the inspection frequency needed across India's rapidly expanding electrified network. A workable continuous or high-frequency monitoring approach must detect subtle geometric changes in overhead wire position/tension from a scalable, lower-cost sensing method — not requiring a dedicated specialized measurement train for every inspection cycle.
Indian urban traffic corridors carry an unusually heterogeneous mix of vehicle types (cars, two-wheelers, auto-rickshaws, buses, cycles, pedestrians, occasional animal-drawn carts) with far less lane discipline than the relatively homogeneous, lane-based traffic assumed by most established traffic-flow models, meaning small local disruptions can cascade into corridor-wide congestion through mechanisms poorly captured by standard models. Understanding and predicting how localized disruption cascades through this genuinely heterogeneous, weakly-lane-disciplined traffic mix is a substantially harder modeling problem than conventional homogeneous-traffic congestion prediction.
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