Mohammed Maaz · BCA, Shree Veda College, Bangalore
TransitPulse — Can Smart Sensors and Predictive Routing Fix Public Transport's Reliability and Comfort Problems?
Summary
Methods
Disciplinary perspectives
This project breaks down daily public transport frustration into two linked problems: a reliability crisis, where riders can't trust when a bus will actually arrive, and a comfort deficit, where the ride itself is overcrowded and physically demanding. It then proposes an Integrated Smart Mobility Framework — a three-part technology system designed to make fleets more predictable and journeys more comfortable at the same time.
The case for action is backed by two figures from the research: 68% of daily transit riders report arrival uncertainty as their primary source of travel stress, and commuters record 3.2× higher fatigue levels on unventilated, overcrowded journeys.
Approach and Methodology
The analysis splits the operational breakdown of urban transit into two pillars. The first is the reliability crisis: commuters face unpredictable delays caused by unmonitored road congestion and a lack of accurate GPS tracking (ETA volatility and fleet blindspots), while irregular bus arrivals compound over a journey into missed work connections, lower productivity, and chronic commute anxiety (schedule drift and cumulative delays).
The second is the comfort deficit: unmanaged passenger surges during rush hours cause dangerous boarding bottlenecks and physical discomfort (peak-hour overcrowding), while inadequate seating design and poor ventilation turn daily city travel into demanding physical strain (cabin environment stress).
Treating reliability and comfort as two separate but connected pillars — rather than one general complaint about "bad buses" — makes it possible to target each with a distinct technical fix, instead of a single blunt solution.
Proposal / Outcome
The response is a 3-pillar Integrated Smart Mobility Framework engineered to optimise fleet predictability and passenger journey quality.
Step 1 — Predictive Telemetry: integrates IoT GPS fleet tracking with machine learning traffic models to deliver hyper-accurate arrival ETAs directly to commuter apps, combining live vehicle tracking with ML delay predictions.
Step 2 — Live Capacity Sensing: deploys Automated Passenger Counter (APC) sensors to broadcast coach occupancy levels, giving riders real-time seat availability and crowd-level indicators so they can choose more comfortable trips.
Step 3 — Dynamic Dispatch: an automated fleet re-routing protocol that detects crowd density surges in real time and automatically dispatches backup vehicles, giving automated fleet rerouting and relief at peak strain.
Beyond Outcomes
Read together, the three pillars close the loop between fleet operations and commuter experience: Predictive Telemetry and Dynamic Dispatch work on the operator's side to keep the fleet responsive to real conditions, while Live Capacity Sensing hands that same information back to riders so they can make better decisions about their own journey.
Framed this way, the project's real claim is that reliability and comfort are not separate transit problems to be solved by separate teams — they share the same underlying fix: better real-time data, flowing in both directions between the fleet and the passenger.
About me
Mohammed Maaz
BCA, Shree Veda College, Bangalore
Hi, my name is Mohammed Maaz. I am currently pursuing my Bachelor of Computer Applications at Shree Veda College in Bangalore. I have a strong foundation in software and computing, and I pride myself on my analytical problem-solving skills and ability to adapt quickly to new challenges. I'm currently looking for internship opportunities where I can apply my technical knowledge and strong work ethic to contribute to real-world tech projects.
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