In a city like Delhi that's choking under air-pollution, vehicular traffic, etc. public conveyance & the metro seems like the only viable solution to the mess. For many like me, the metro is a rather convenient way to travel back & forth to work daily.
Interestingly, during peak hours not all coaches get equally packed. Certain coaches, typically the ones close to the staircases, are much more congested. Now if only the passengers were notified in advance about the occupancy factor across coaches of the upcoming trains, they might be able to move a little bit on the platform & board a less congested one.
This could be achieved via existing sensors on the train that capture weight, footfall, etc. or via video feeds from the on-board cameras (see references below) within the coaches. Just need to relay this feed in real-time to a screen/ dashboard on the platform (& an app) visible to the customer. These feeds needn't be super accurate, and a reasonable estimate (Low, Medium, High, Very High) of the occupancy should do. This data can also reveal other interesting insights on occupancy across days of the week, events, festivals, seasonality, etc.
Another observation is that typically low to medium occupancy trains follow/ trail the high occupancy ones. Perhaps there's a general tendency in people to board the first available train that shows up. On the other hand, if the feed could also show occupancy stats along with arrival timings of next two to three trains that might help the passenger to wait a few minutes & board a less congested one.
Surprisingly, the expected arrival timings of next two or three trains, fairly common elsewhere (like Singapore MRT), is not available on the monitors here. This should probably be easy to introduce right away, even if the other one with the occupancy indicator takes time.
Working out the right moment to switch between a 6-coach & an 8-coach (or other smaller) variant seems like a solution to a cost (running) minimization problem while maximizing users' comfort. Key factors being peak hours timings, occupancy levels, end-to-end runtime of the train, cost/ kg to ply the trains, time to hook/ unhook additional coaches, available parking space for spare coaches and so on. Very much worth a look at by data science folks.
The nearest metro station from home is about half-an-hours drive which I cover via one of the connectors or in an auto. The metro ride after that is well managed & regular. At times, there are slowdowns, delayed arrivals, etc. but on the whole the Delhi metro is doing a fantastic job helping several million people travel across the lengths & breadths of the city everyday. Sharing a few thoughts on what could make the journey more comfortable for travellers in the future.
Peak Hours Rush, 100+% occupancy
All regulars will tell you to avoid the metro at peak/ office hours. Metro can get really full & crowded at these hours. To encourage off-peak hours travel, metro also offers travel discounts for these hours.
Interestingly, during peak hours not all coaches get equally packed. Certain coaches, typically the ones close to the staircases, are much more congested. Now if only the passengers were notified in advance about the occupancy factor across coaches of the upcoming trains, they might be able to move a little bit on the platform & board a less congested one.
This could be achieved via existing sensors on the train that capture weight, footfall, etc. or via video feeds from the on-board cameras (see references below) within the coaches. Just need to relay this feed in real-time to a screen/ dashboard on the platform (& an app) visible to the customer. These feeds needn't be super accurate, and a reasonable estimate (Low, Medium, High, Very High) of the occupancy should do. This data can also reveal other interesting insights on occupancy across days of the week, events, festivals, seasonality, etc.
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| Fig 1: Occupancy Across Coaches |
Another observation is that typically low to medium occupancy trains follow/ trail the high occupancy ones. Perhaps there's a general tendency in people to board the first available train that shows up. On the other hand, if the feed could also show occupancy stats along with arrival timings of next two to three trains that might help the passenger to wait a few minutes & board a less congested one.
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| Fig 2: Occupancy & Arrival Timings |
Surprisingly, the expected arrival timings of next two or three trains, fairly common elsewhere (like Singapore MRT), is not available on the monitors here. This should probably be easy to introduce right away, even if the other one with the occupancy indicator takes time.
Optimizing Number of Coaches
The current logic to ply trains having 6-coaches in place of 8-coaches should also be improved in the future. Perhaps to reduce costs by roughly 75% (6/8), 6-coach trains are run during off-peak hours. Invariably though, back to back 6-coach trains show up during peak hours leading to overcrowding inside the trains & long spiralling queues at the stations.
Working out the right moment to switch between a 6-coach & an 8-coach (or other smaller) variant seems like a solution to a cost (running) minimization problem while maximizing users' comfort. Key factors being peak hours timings, occupancy levels, end-to-end runtime of the train, cost/ kg to ply the trains, time to hook/ unhook additional coaches, available parking space for spare coaches and so on. Very much worth a look at by data science folks.
Beyond the 8-Coaches Barrier
A bigger challenge for the Metro is handling additional/ future load from a growing population. The metro stations are built for the current size of trains having at most 8-coaches. There's no way to increase this length without undertaking massive reconstruction of the platforms at huge costs, time, etc. Increasing frequency of trains is already being explored, & there's very little scope left with trains running just a few minutes apart. Improving coverage certainly helps to an extent. Beyond that the options seem limited.
Probably adding coaches to existing trains could work. These coaches would have to be either attached to the ends of the train. Since they'll be positioned beyond the platform limits, they'll have to be door-less. Entry/ exit would be from adjacent coaches positioned on the platforms having doors. Doable in theory, though the additional movement across the train aisles, etc. will pose newer engineering & security challenges.
Probably adding coaches to existing trains could work. These coaches would have to be either attached to the ends of the train. Since they'll be positioned beyond the platform limits, they'll have to be door-less. Entry/ exit would be from adjacent coaches positioned on the platforms having doors. Doable in theory, though the additional movement across the train aisles, etc. will pose newer engineering & security challenges.
Another option could be to set-up each train to have two well synchronized halts per station. First halt to board coaches 1 to 6. After which the train moves forward & makes a second halt to allow boarding for coaches 6 to 12. Another idea could be to increase capacity vertically instead with double decker trains. Various practical aspects need to be figured out for fanciful ideas like these to work. But given the way we are going & growing might need to solve this very soon. Worthwhile therefore thinking of ideas, even if they seem weird today some might get applied in the future!
References:
Counting Number of People In A Video
- RIVA/ VCA Counting
- People Counting Demonstration
- Stable Multi-Target Tracking in Real-Time Surveillance Video (CVPR 2011)
- Motion-Based Multiple Object Tracking
- Crowd Size Estimation
- Counting in Extremely Dense Crowd Images
- Algorithm to count people in a crowd
- People detection from above
- Which Algorithm is used to count the number of people in a video?
