1. City Mobility Overview i
High level view of city transport system performance
📅 26 Apr – 25 May 2025 ▾
Previous 8 weeks ▾
⌁ Filters
🚌
Bus Ridership
12.48M
+5.6%vs prev 8 weeks
🚇
Metro Ridership
18.32M
+7.8%vs prev 8 weeks
🚆
Suburban Rail Ridership
6.91M
+4.3%vs prev 8 weeks
👥
Public Transport Total Demand
37.71M
+6.2%vs prev 8 weeks
🚗
Road Congestion Index
1.34
+8.7%vs prev 8 weeks
Trend Overview (Last 8 Weeks)
Bus Ridership
Metro Ridership
Suburban Rail Ridership
Total PT Demand
Road Congestion Index
Key Interchange Volumes (Avg Daily)
📍
Central Interchange
385K
+8.9%vs prev 8 weeks
📍
Guindy Metro Station
226K
+6.1%vs prev 8 weeks
📍
T. Nagar Interchange
188K
+5.4%vs prev 8 weeks
📍
CMBT Interchange
162K
+4.7%vs prev 8 weeks
📍
Puratchi Thalaivar Dr. M.G.R. Central
158K
+3.9%vs prev 8 weeks
Public Transport Demand Heatmap i
Total PT Demand ▾
📈
AI Summary
Public transport demand has grown by 6.2% over the last 8 weeks.
Strongest growth observed around the West Corridor and Central Interchange.
Strongest growth observed around the West Corridor and Central Interchange.
Last updated: 26 May 2025, 08:30 AM ⟳Illustrative synthetic data — not the data of any real transport authority.
2. Bus Operations Intelligence i
Detailed operational view of bus depots and services
📅 26 Apr – 25 May 2025 ▾
All Depots (5) ▾
⌁ Filters
Depot Readiness (Tomorrow)
🚌
Buses Held
1,256
🛡️
Roadworthy Buses
1,172
93.3%
🧑✈️
Drivers Available
1,098
87.4%
🎫
Conductors Available
1,056
84.1%
📋
Duty Readiness
1,068
85.0%
Depot Energy Status
⛽ Diesel / CNG Stock
42,850 L
2.3 days of operation
Min. required: 1.5 days
🗓 Next Replenishment
27 May 2025
04:00 PM
EV charging bays: 24 / 30 available
🔧 Maintenance Status
Under Maintenance
78
(6.2%)
Due for Maintenance (Next 3 Days)
46
(3.7%)
Overdue Maintenance
9
(0.7%)
Fuel and energy are tracked as depot resources — buses do not independently visit fuel stations.
Operations Performance (Today)
🚌
Scheduled Buses
1,128
✅
Actual Buses Operated
1,038 (92.0%)
🗺️
Scheduled Trips
6,842
✅
Trips Completed
6,173 (90.2%)
📏
Scheduled KM
185,420
📏
Actual KM
167,392 (90.3%)
🎯
Route Adherence
89.1%
⊗
Cancellations
163 (2.4%)
Top 5 Routes by Schedule Adherence
| Route | Route Name | Scheduled Trips | Completed Trips | Adherence |
|---|---|---|---|---|
| 21G | Porur – Broadway | 162 | 150 | 92.6% |
| 570 | Tambaram – Koyambedu | 148 | 130 | 87.8% |
| 18B | Avadi – Parrys | 140 | 125 | 89.3% |
| 24A | Velachery – Central | 132 | 119 | 90.2% |
| 125 | Poonamallee – Thiruvanmiyur | 128 | 109 | 85.2% |
✨ AI Readiness Alert
❗
Depot 4 may fall short of tomorrow's planned service.
Main reasons: driver availability and pending maintenance clearance. Estimated shortfall ~8% of scheduled duties.
Depot-wise Readiness
| Depot | Service Readiness (Tomorrow) | Status |
|---|---|---|
| Depot 1 | 96% | On Track |
| Depot 2 | 92% | On Track |
| Depot 3 | 90% | On Track |
| Depot 4 | 78% | At Risk |
| Depot 5 | 88% | Monitor |
View All Depots
Recurring Delay Hotspots (Today)
| Location / Corridor | Impact |
|---|---|
| Anna Salai (Teynampet – Saidapet) | High |
| Poonamallee High Road | Medium |
| GST Road (Porur – Ramapuram) | Medium |
| Tambaram Railway Gate | Medium |
| Koyambedu Junction | Low |
View All Delays
Last updated: 26 May 2025, 08:30 AM ⟳Service readiness is calculated from fleet, crew, duty, energy and maintenance availability.
3. Metro & Rail Demand Intelligence i
Demand overview for the metro and suburban rail network
📅 26 Apr – 25 May 2025 ▾
All Networks ▾
⌁ Filters
👥
Total Daily Ridership (Metro + Rail)
18.32M
+7.9%vs prev 8 weeks
📈
Average Weekday Ridership
19.11M
+8.3%vs prev 8 weeks
🕒
Average Frequency (Metro)
3.6 min
No change
⇄
Total Interchange Passengers (Daily)
3.42M
+6.7%vs prev 8 weeks
Top Stations by Daily Ridership
| Station | Daily Entries | Daily Exits | Total (Daily) | vs prev 8 wks | Trend |
|---|---|---|---|---|---|
| Central Station | 98K | 105K | 203K | +11.4% | |
| Station A | 82K | 88K | 170K | +14.2% | |
| T. Nagar | 71K | 74K | 145K | +6.8% | |
| Anna Nagar Tower | 66K | 69K | 135K | +7.1% | |
| Porur Junction | 54K | 57K | 111K | +4.3% |
View All Stations
Ridership Trend (Last 8 Weeks)
Central Station
Station A
T. Nagar
Anna Nagar Tower
Porur Junction
Station A — Demand Overview
Ridership Growth
+14.2%
vs prev 6 months
Daily Entries & Exits
Entries
Exits
⇄ Interchange Passengers (Daily Avg)
48K
+12.6% vs prev 8 weeks
🚕 First / Last Mile Demand (3–6 km catchment)
High
Increasing trend
📉
Existing bus connectivity within the 3–6 km catchment is limited in the western and north-western sectors.
✨ AI Insights
📈
Station A ridership has grown 14% over the last 6 months, while surrounding bus connectivity has remained almost unchanged.
📍
Rail Station B generates high passenger volumes, but 3–6 km catchments show weak public-transport connectivity.
👥
Interchange volumes at Central Station are increasing due to strong metro demand and suburban rail commuters.
View All Insights
Feeder Connectivity Around Top Stations
| Station | Score (0–100) | Level |
|---|---|---|
| Central Station | 78 | Good |
| Station A | 52 | Moderate |
| T. Nagar | 71 | Good |
| Anna Nagar Tower | 46 | Weak |
| Porur Junction | 58 | Moderate |
View Connectivity Map
Underserved 3–6 km Zones
West Catchment (Station A)Gap: High
North Catchment (Central)Gap: Moderate
East Catchment (Rail Station B)Gap: High
Porur CatchmentGap: Moderate
QPo does not manage train operations. Metro and rail are used as structured demand anchors.
Last updated: 26 May 2025, 08:30 AM ⟳Ridership aggregated from metro AFC, rail ticketing and official authority sources.
4. Multimodal Correlation i
Understand how bus, metro, rail and road patterns influence each other
📅 26 Apr – 25 May 2025 ▾
Central & West Zone ▾
⌁ Filters
🚇
Central Station Ridership Growth
+11.0%
vs prev 8 weeks
🚌
Nearby Bus Corridor Growth
+8.7%
vs prev 8 weeks
👥
Major Interchange Volume
342K
daily average
⚠️
Weak Catchments Identified
2
catchments
🚶
First / Last-Mile Demand Trend
Rising
vs prev 8 weeks
Mode Correlation Overview i
🚇
Metro / Rail Ridership
🚌
Bus Routes & Ridership
🚗
Road Traffic & Congestion
🔍
Journey Search / OD Signals
🚶
First / Last-Mile Connectivity
🕸️
Multimodal Intelligence
Unified view of connected demand across modes and catchments
📈
Growth at Central Station is increasing surface-transport demand toward the western residential catchment.
Trend Alignment (Last 8 Weeks) i
Central Station Ridership
Route 21G Ridership
Route 570 Ridership
Road Congestion Index
Linked Corridors & Catchments i
| Rank | Corridor / Catchment Link | Bus Growth | Connectivity | Opportunity |
|---|---|---|---|---|
| 1 | Central Station → West Catchment | +9.1% | Weak | 🚐 Feeder |
| 2 | Central Station → North Catchment | +6.4% | Moderate | 📋 Route review |
| 3 | Rail Station B → East Catchment | +4.2% | Weak | 🚶 First / Last-Mile |
| 4 | Anna Nagar → Porur Catchment | +2.6% | Moderate | 🚐 Feeder |
| 5 | T. Nagar → West Catchment | +1.4% | Strong | ✓ Maintain |
View All Links
✨ AI Insights
📈
Central Station demand growth is strongly correlated with rising ridership on Routes 21G and 570.
👥
Two nearby residential catchments remain underserved despite higher interchange demand.
🔁
Western-zone feeder opportunity appears stronger than direct route expansion in the current network.
Associations are correlational. QPo does not claim absolute causality between modes.
💬
AI Summary
Metro and rail demand growth is increasingly influencing nearby bus corridors and first/last-mile demand, especially around Central Station.
Last updated: 26 May 2025, 08:30 AM ⟳Correlation derived from aggregated metro, rail, bus, traffic and journey-demand signals.
5. External City Context i
External factors influencing mobility demand and network performance
📅 26 Apr – 25 May 2025 ▾
⌁ Filters
Traffic Overview (Today)
High Congestion Corridors
18
↑ 3 vs yesterday
Junctions with High Delay
27
↑ 5 vs yesterday
Road Closures
6
No change
Average City Congestion
46%
↓ 4% vs yesterday
Top Congested Corridors
| Corridor | Avg Speed (km/h) | Congestion Level | Change vs yesterday |
|---|---|---|---|
| OMR (Sholinganallur – Tidel Park) | 18 | High | ↑ 12% |
| GST Road (Guindy – Tambaram) | 19 | High | ↑ 9% |
| Poonamallee High Road | 24 | Moderate | ↑ 6% |
| Mount Road (Anna Salai) | 26 | Moderate | ↓ 3% |
| ECR (Thiruvanmiyur – Panaiyur) | 28 | Moderate | No change |
View Traffic Map
Factor Classification
Short-term
Weather, holidays, events, closures
Medium-term
Festival periods, seasonal patterns, exams
Long-term
IT parks, malls, universities, hospitals, housing
Only external factors that materially improve transport analysis are shown.
Education / Holiday Calendar
🎓
27 May 2025 — College closure
Multiple colleges along the Porur – Guindy – Taramani corridor are closed tomorrow. This is the primary driver of the Route 570 demand forecast.
Data Sources
Traffic APIsGovernment Open Data
Weather Services (IMD)GIS
Urban Development AuthoritiesAcademic Calendars
✨
AI Context Insight
Opening of Global Tech Park in Porur is expected to increase metro station demand by 7–9%, bus corridor demand by 8–10% and junction congestion around Porur Junction by ~12% during peak hours.
Last updated: 26 May 2025, 08:30 AM ⟳Data sources: Traffic APIs, Government Open Data, Weather Services, GIS, Urban Development Authorities
6. Demand & Operations Forecast i
AI-powered forecast of how demand and operations are likely to change over the next day, week and service period.
📅 27 May 2025 (Tomorrow) ▾
⌁ Forecast Settings
⤓ Export Report
Forecast Horizon
Forecast Confidence
Medium Confidence
Model based on recent patterns and external signals
Overall City Demand Outlook (Tomorrow)
↓ 4–7%
Below normal Tuesday
Compared to last 4 Tuesdays
Key Corridor & Route Forecasts (Tomorrow vs Normal Tuesday)⤓ View All Routes
| Route / Corridor | Mode | Expected Demand Change | Main Reason | Planning Review Suggestion |
|---|---|---|---|---|
Route 570 Porur – Guindy – Taramani |
Bus | ↓ 5 – 8% Lower than normal |
🎓 Colleges along the corridor are closed tomorrow |
Review peak period buses: Current 32 → consider operating 29–30 buses (subject to approval) |
Route 21G Koyambedu – Airport |
Bus | ↓ 4 – 6% Lower than normal |
🌧️ Rain forecast may reduce non-essential travel |
Maintain current service. Review running time buffer. |
Route 18B Washermenpet – Velachery |
Bus | ↑ 6 – 9% Higher than normal |
🏟️ Event at City Stadium, evening (5 PM onwards) |
Consider additional buses in evening peak (4 PM – 8 PM). |
Metro Station: Guindy Blue Line |
Metro | ↑ 7 – 10% Higher than normal |
🌧️ Rain forecast + IT corridor office commute |
Review first/last-mile feeder readiness (autos / vans). |
Metro Station: Koyambedu Green Line |
Metro | ↓ 5 – 7% Lower than normal |
🎓 Colleges nearby not working |
Monitor feeder demand. No change in core service. |
GST Road Corridor All Services |
Road Traffic | ↑ 10 – 15% Higher congestion |
🚧 Roadwork on Inner Ring Road → diverting traffic |
Expect higher running time. Add buffer in schedule. |
Demand Forecast Trend (All Public Transport)
Bus
Metro
Suburban Rail
Overall PT
Reason Impact on Demand Change
Education Closure−2.8%
Rain Forecast−1.5%
Traffic Conditions+1.2%
Events+0.8%
Seasonal / Other+0.6%
Negative values reduce demand, positive values increase demand.
Data Sources Used
📊 Historical Ridership (AFC/ETM)
📡 AVLS (Real-time)
🎟️ ETM / AFCS Boardings
🌧️ Weather Forecast (IMD)
🎓 College / School Calendars
📅 Holidays & Events
🚧 Traffic Feeds & Closures
📈 Seasonality Patterns
Top Influencing Factors (Tomorrow)View All
🎓
HighCollege & Institution Closure
Multiple colleges along key corridors
🌧️
HighRain Forecast
Moderate to heavy rain (06:00 – 11:00)
🚦
MediumTraffic & Road Conditions
Higher congestion on 2 major corridors
🏟️
MediumMajor Events
City Stadium event (evening)
📅
LowRegular Weekday Pattern
Normal Tuesday pattern
Forecast by Horizon (All PT)View Details
Tomorrow (27 May)↓ 4 – 7% Medium
Next 7 Days↓ 2 – 5% Medium
Next 30 Days↑ 3 – 6% Medium
Next 90 Days↑ 5 – 10% Low
Route 570 — Forecast Detail
Expected demand−5% to −8%
Confidence72%
Main signalsHistorical ETM demand,
education calendar, AVLS pattern,
weather forecast
education calendar, AVLS pattern,
weather forecast
🧠
Forecast is an estimate, not a commitment. Use alongside operational judgement and local knowledge for final planning decisions.
Last updated: 26 May 2025, 08:30 AM ⟳Models run on QPo Intelligence Engine · Data refresh: every 15 minutes
7. Test the Impact (Digital Twin Simulation) i
Simulate and evaluate the impact of forecast-based adjustments before making operational changes.
📅 27 May 2025 (Tomorrow) ▾
⌁ Scenario Library
⤓ Export Report
Selected Corridor / Route
🚌 Route 570
Porur – Guindy – Taramani
Adjustment Being Tested
Reduce peak period buses
32 → 29 Buses
(−3 buses, ~9.4%)
Simulation Type
📦 Near-term Operational
Tomorrow (27 May 2025)
Forecast Signal
Expected demand
↓ 5 – 8%
Colleges along corridor closed
Confidence
72%
Medium
Scenario Comparison — current schedule vs simulated adjustment
| Metric | Current Plan (32 Buses) | Test Scenario (29 Buses) | Change | Impact Assessment |
|---|---|---|---|---|
| 👥 Expected Passengers (Peak Period) | 18,150 | 17,450 | ↓ 700 (−3.9%) | Minimal |
| 🕒 Average Passenger Waiting Time | 3.8 min | 4.6 min | ↑ 0.8 min | Minimal |
| 📊 Peak Load Factor (Capacity Utilisation) | 68% | 72% | ↑ 4 pp | Acceptable |
| 🚷 Passengers Not Able to Board | 45 | 78 | ↑ 33 (+73%) | Low Risk |
| 📏 Operating Kilometres | 512 km | 464 km | ↓ 48 km (−9.4%) | Positive |
| ⛽ Estimated Fuel / Energy Savings | – | ₹20,500 – ₹24,000 | – | Positive |
| 🚌 Fleet Released | 0 | 3 Buses | +3 Buses | Positive |
| 🛡️ Service Reliability Risk | Low | Low – Medium | Slight increase | Acceptable |
Simulation Outcome
✅
Minor adjustment appears feasible.
Reducing 3 peak period buses is expected to maintain service quality within acceptable limits and deliver meaningful operational savings.
Key Takeaways
- Passenger impact is minimal; waiting-time increase within acceptable range.
- Load factor remains within comfortable limits.
- Small increase in not-boarded passengers; still low risk.
- ~9.4% reduction in operating kilometres and fuel.
- 3 buses can be redeployed for maintenance or reserve.
✨ AI Recommendation
This adjustment is recommended for planner review. Proceed to Planning Options to compare alternatives.
Passenger Impact Distribution (Test Scenario)
Change in expected demand vs normal (Tomorrow)
Lower Demand
No Change
Higher Demand
ⓘ Most routes are expected to see lower demand.
Waiting Time Change Distribution
Change in average waiting time vs current plan
Improved
Minimal (±1 min)
Higher
ⓘ Majority of routes have minimal change in waiting time.
Top Corridors ImpactedView All
| Corridor / Route | Demand | Waiting | Impact |
|---|---|---|---|
| Route 570 (Porur – Taramani) | ↓ 5–8% | ↑ 0.8 min | Minimal |
| Route 21G (Koyambedu – Airport) | ↓ 4–6% | ↑ 0.6 min | Minimal |
| Route 18B (Washermenpet – Velachery) | ↑ 6–9% | ↑ 1.2 min | Monitor |
| Route 45B (Madhavaram – T. Nagar) | ↓ 3–5% | ↑ 0.3 min | Minimal |
| Route 12C (Poonamallee – Guindy) | ↑ 4–6% | ↑ 1.1 min | Monitor |
Simulation Assumptions
🎟️ ETM / AFCS and AVLS — latest data up to today 23:59
🌦️ Weather forecast — IMD for tomorrow
🎓 College / school closures — as per academic calendar
📅 Normal Tuesday baseline — last 4 Tuesdays average
🏟️ No major event on corridor tomorrow
🚦 Traffic conditions — normal unless stated
The Digital Twin does not generate the original forecast. It answers: “if we follow this suggestion, what is likely to happen?”
Last updated: 26 May 2025, 08:30 AM ⟳Models run on QPo Intelligence Engine · Data refresh: every 15 minutes
8. Planning Options i
Compare practical service adjustment options based on forecast and digital twin simulation results.
📅 27 May 2025 (Tomorrow) ▾
⌁ Scenario Library
⤓ Export Report
✨
AI Recommendation: Option B — Minor Adjustment is recommended for planner review.
Balanced approach with minimal passenger impact and meaningful operational savings.
Selected Corridor / Route
🚌 Route 570
Porur – Guindy – Taramani
Forecast Signal
Expected demand
↓ 5 – 8%
Colleges along corridor closed
Current Service (Baseline)
Peak Period (07:00 – 11:00)
32 Buses
Headway ~6.5 min · Load factor 68%
Tested Scenario (Digital Twin)
32 → 29 Buses
−3 buses (−9.4%)
FeasibleSimulation Outcome
✅ Minor adjustment appears feasible
Passenger impact minimal · Operational savings achievable
Service Adjustment Options Comparison
| Parameters | Option A Keep Full Schedule (No Change) |
★ Option B Minor Adjustment (Recommended) |
Option C Larger Adjustment (Not Recommended) |
|---|---|---|---|
| 🚌 Peak Period Buses | 32 Buses (No Change) | 29 – 30 Buses (−2 to −3 buses) | 25 – 26 Buses (−6 to −7 buses) |
| 👥 Expected Passenger Impact | No change | Minimal (Waiting ↑ ~1–2 min) | Moderate (Waiting ↑ ~4–6 min) |
| 📊 Peak Load Factor | 68% (Current) | 72 – 74% | 82 – 87% |
| 🕒 On-time Performance | 91% (Current) | 89 – 91% | 82 – 86% |
| 📏 Operating KMs (Peak) | 512 km (No Change) | 464 – 480 km (−6% to −8%) | 400 – 420 km (−18% to −22%) |
| ⛽ Estimated Fuel / Energy Savings | – | ₹18,000 – ₹22,000 | ₹55,000 – ₹65,000 |
| 🚌 Fleet Released | 0 | 2 – 3 Buses | 6 – 7 Buses |
| 🛡️ Service Reliability Risk | Low (No Change) | Low (Manageable) | Medium (Higher risk) |
| 🔧 Implementation Effort | None | Low (Adjust 2–3 low-demand trips) | Medium (Reschedule multiple trips) |
| 🎯 Best For | Risk-averse operating day No demand deviation | Small demand deviation days (Weather / holidays / events) | Large demand drop days (After further validation) |
What Each Option Means
Option A — Keep Full Schedule
- Maintains highest capacity
- No risk to passengers
- Higher operating cost
✅ Option B — Minor Adjustment (Recommended)
- Reduce 2–3 buses on low-demand trips
- Maintains core timetable and frequency
- Minimal passenger impact
- Meaningful efficiency savings
⚠️ Option C — Larger Reduction
- More savings
- Higher passenger wait times
- Risk of overcrowding
- Use only if demand drop is significant
Planner's Notes
Final decision remains with the transport authority.
Service Adjustment Principles (QPo AI Guidance)
🛡️ Protect Core Network
Ensure essential trips and peak connections are maintained.
⌁ Adjust at the Margin
Make small, reversible changes at the margin, not wholesale.
📊 Data-Driven, Not Rigid
Recommendations are probabilistic, not absolute instructions.
🌦️ Context Aware
Every suggestion considers weather, events and ground reality.
👥 Human in the Loop
Final decision always rests with the transport authority.
Last updated: 26 May 2025, 08:30 AM ⟳Recommendations are based on forecast, simulation (Digital Twin) and current system constraints.
9. AI Multimodal Dispatch & First/Last-Mile Layer i
Use multimodal demand intelligence to orchestrate first/last-mile options through existing government or metro mobility apps.
📅 27 May 2025 (Tomorrow) ▾
⌁ Scenario Library
⤓ Export Report
✨
AI dispatch is designed as a complement to structured public transport, not a replacement.
Predict demand, identify underserved catchments, cluster passengers and orchestrate external mobility supply through partner integrations.
Dispatch Architecture
1
Government / Metro Multimodal App
📱
- Journey planning
- Ticket / pass context
- User destination
- First/last-mile need identified
2
QPo AI Demand Intelligence
🧠
- Metro ridership history
- Bus connectivity data
- Station entry/exit trends
- Journey search / OD patterns
- External context
Predictive layer
3
QPo AI Dispatch Engine
🕸️
- Passenger clustering
- Shared-route matching
- Pickup hotspot selection
- Seat / vehicle allocation
- Detour minimisation
Orchestration layer
4
Approved External Mobility Supply
🚕
Ride-hailing Partner A
Ride-hailing Partner B
Shared Auto
Feeder Vans
Local Operator
5
Options Returned to Multimodal App
📋
- Shared ride options
- ETA / fare estimate
- Pickup point
- Mode choice
- Confirm booking
Partner integrations (e.g. Ola, Uber, Rapido, local operators) are subject to APIs and commercial arrangements.
Example Station Insight
Station: Central Metro — West Catchment
Historical exitsHigh
Bus connectivityLimited in 3–5 km zone
Journey-search signalRepeated westbound demand
Likely peak6:15 PM – 7:45 PM
Suggested pickupExit 2
Recommended modeShared auto / van
Demand cluster statusPersistent last-mile gap
🛡️ Operational Principle
The authority or metro app owns the rider experience. QPo provides the AI intelligence and orchestration layer behind it.
✨ What the AI Predicts
1 Where first-mile demand will emerge📍
2 Where last-mile demand is structurally weak⚠️
3 Likely peak period and direction🕒
4 Approximate feeder capacity required👥
5 Suitable mobility mode for the catchment🚐
⚙️ What the Dispatch Engine Does
1 Group compatible passengers
2 Select common pickup hotspot
3 Match riders to available supply
4 Optimise shared seat allocation
5 Return choices to the authority app
Last updated: 26 May 2025, 08:30 AM ⟳Partner integrations are subject to commercial arrangements and approved APIs.
10. AI Mobility Copilot ✨
Your AI assistant for planning, insights and decision support across the city mobility ecosystem.
📅 27 May 2025 (Tomorrow) ▾
🕘 Conversation History
⤓ Export Brief
Ask me anything about your mobility network
✨ Copilot Response08:30 AM
Key Insights at a GlanceView All Insights
📊
Demand
3 corridors likely below normal demand (5–8%).
🌧️
Weather
Heavy rain expected. Running time may increase on 2 corridors.
🚶
First / Last-Mile
Koyambedu, Central & Porur Junction have high F/L-M gaps.
🚌
Fleet & Operations
3 buses can be released with minor adjustment.
🎓
Events & Education
Colleges closed along Route 570 corridor.
Quick Actions
Recent Briefs & ConversationsView All
📄
Tomorrow Planning Brief – 27 May 2025
Generated at 08:30 AM
⌁
Route 570 Scenario Comparison
Generated at 08:12 AM
📈
Weekly Operations Summary (19 – 25 May)
Generated at 26 May, 09:00 AM
Last updated: 26 May 2025, 08:30 AM ⟳Data sources: AVLS, ETM/AFCS, AFC, GTFS, Weather API, Traffic Feeds, Academic Calendars, Events, GIS