Predictive Intelligence

3
AD

AI forecasts demand, crowding, delays and capacity risks across your network.

Demand Surge
+24%
vs usual today
Crowding Risk
8
locations at risk
Delay Risk
5
corridors at risk
Capacity Pressure
12
alerts active
Future Network Pulse
Normal Rising High Very High
Network Area Now8:30 AM +30 min9:00 AM +60 min9:30 AM +120 min10:30 AM
Corridor AOMR – Guindy
Normal Rising High High
Station Cluster BCentral – Egmore
Moderate High Very High Moderate
Bus Network CECR – Sholinganallur
Normal Moderate High Moderate
Interchange DKoyambedu
Normal Rising High High
Suburban Rail EBeach – Tambaram
Normal Rising High Moderate
AI Forecast Summary

Demand surge expected in
3 zones

2 interchanges approaching
crowding threshold

Delay risk increasing on
2 corridors

Capacity pressure expected
after 9:00 AM

Passenger Demand Trend
Typical Today Predicted
Now
6 AM8 AM10 AM12 PM
How it works
Live Network DataRidership, GPS, AFC, AVL
+
Historical PatternsDemand, seasonality, day & time trends
+
External SignalsWeather, events, traffic, holidays
Predict EarlySee what is coming
Prepare CapacityPlan resources in advance
Reduce DisruptionAct before it impacts riders
Improve ReliabilityDeliver a better commuter experience

Dynamic Mobility

3
AD

AI identifies service–access gaps and recommends adaptive changes to improve access.

1Detect GapAI detects mismatch between demand and existing service performance.
2Analyse & PrioritizeAI analyses impact and prioritizes areas where changes create the most value.
3Recommend AdaptationsAI suggests small service improvements to bridge the access gap.
4Improve AccessShorter walks, better coverage and more relevant public transport.
Current GapWhere the network is losing effectiveness
Stop too far from demandHigh demand area is 900 m away from nearest stop.High Impact
Route losing relevanceLow ridership on this segment for last 4 weeks.Medium Impact
Overcrowding in segmentFrequent overload during 8–10 AM on this segment.High Impact
Transfer frictionPassengers walk 700–1,000 m for next mode.Medium Impact
Underutilized coverageService available but demand is not captured.Low Impact
AI InsightsWhy this gap exists
Demand shift detectedWork & residential patterns have shifted 2–3 km towards the east.
Walking distance high41% of potential riders are beyond ideal 500–700 m walking range.
Service timing mismatchPeak demand window not aligned with current service timing.
Route deviation opportunityA slight deviation can capture 3,200+ potential daily riders.
Coverage gapThis area has low public transport availability.
Recommended AdaptationsSmall changes, big impact
↓ 18–25%Walking DistanceEstimated reduction
+ 12–18%Rider AccessPotential increase
+ 8–12%RidershipEstimated uplift
+ 10–15%On-time PerformanceEstimated improvement

Planning & Simulation

3
AD

Compare options and choose the best intervention for your network.

Planning QuestionHow should connectivity to the OMR IT Corridor from surrounding residential areas be improved?
Planning Options
A
Option AExtend Existing RouteExtend Route 19B by 5 km to cover residential cluster.
HighCoverage
MediumComplexity
MediumCost
B
Option BIntroduce New ServiceLaunch a new feeder service via key demand zones.
Very HighCoverage
HighComplexity
HighCost
C
Option CImprove Frequency & InterchangeIncrease frequency and improve interchange at key points.
MediumCoverage
LowComplexity
LowCost
Option Comparison
Criteria Option AExtend Route Option BNew Service Option CImprove Frequency Best Option
Coverage HighGood geographic reach Very HighWidest reach MediumLimited improvement B
Avg. Travel Time Impact Better↓ 8–10% Best↓ 15–20% Better↓ 5–8% B
Ridership Impact Medium+12–15% High+20–28% Medium+10–12% B
Cost / Effort Medium~ ₹1.8 Cr High~ ₹3.2 Cr Low~ ₹0.9 Cr C
Implementation Time 3–4 Months 6–7 Months 2–3 Months C
Overall Impact Score 72 / 100 92 / 100 65 / 100 B
Recommended Option
BOption BIntroduce New Service
Highest Overall Impact

Provides the best balance of coverage, ridership impact and long-term network value for the OMR IT Corridor.

Key Insights
Strong demand clusters identified along 2 new corridors.
New service can capture ~3,200 daily potential riders.
Interchange improvement in Medavakkam can reduce transfer time by ~25%.
Option B provides 28% higher ridership impact vs Option A.
How planning works
Understand DemandAI analyses demand and mobility patterns
Generate OptionsAI creates multiple plausible interventions
Compare & ScoreAI evaluates using impact, cost, effort and time
Recommend BestAI recommends the option with highest overall value
Simulate ImpactTest the recommended option in Digital Twin

Mobility Digital Twin

3
AD

Test interventions virtually and understand the likely impact before implementation.

1Select InterventionChoose a network / service change to evaluate.
2Configure ScenarioSet parameters like frequency, span, fleet, etc.
3Run SimulationDigital Twin simulates network behaviour and outcomes.
4Analyse ImpactReview results across key performance metrics.
5Compare & DecideCompare alternatives and choose the best option.
Selected Intervention
Extend Bus Route 19BBfrom Sholinganallur to Siruseri SIPCOT
Intervention TypeRoute Extension
New Length+5.4 km
Additional Stops4
Planned Frequency8 mins
Service Span5:30 AM – 11:00 PM
Fleet Required+6 Buses
Scenario NotesExtension to improve first-mile connectivity for IT corridor workforce and resident communities.
Simulation Visualization
ExistingProposed
Sholinganallur Sathyabama Karapakkam Navalur Siruseri Jn Egattur Kazhipattur Padur SIPCOT Siruseri Existing Route 19BB 14.6 km | 22 Stops Proposed Extension +5.4 km | 4 New Stops
Demand ModelLive + Historical
Time PeriodWeekday Avg
Simulation Runs50 Iterations
Confidence Level90%
Last run: 14 May 2025, 09:15 AM
Simulated Impact Summary
vs Existing
Daily RidershipEstimated+28%
CoveragePopulation+21%
Average Travel TimeEnd-to-end−12%
First-mile AccessibilityWithin 500 m+35%
Transfers ReducedPer trip−18%
Operating CostEstimated+9%
12,800+People benefit daily (estimated)
1.8 kmAvg. first-mile walk reduction
9 minsAvg. time saved per trip
15.6 TonnesCO₂ reduction per day
₹18.4 LakhsAnnual economic benefit (est.)

AI Services for Multimodal Apps

3
AD

Intelligent mobility layer that apps and platforms can plug into.

Inputs
Public Transport Data
Real-time Services
Network & Operational Data
External Signals
QPo
AI Mobility Layer APIs & Services
Natural Language Understanding
Journey Planning Intelligence
Real-time Context Awareness
Anomaly & Disruption Detection
Recommendations Engine
Delivered to
Multimodal Apps
Mobility Platforms
Super Apps
Web / IVR Channels
For CommutersSmarter journeys, in natural language
How do I reach Tidel Park by 9:15 AM?
Here are the best options based on live data
Fastest Option 56 min · ₹45
8 min18 min22 min6 min
Least Walking 63 min · ₹30
Bus → Metro → Walk 300 m
Natural Language SearchAsk in your language. Get instant answers.
Multimodal Journey PlannerBus, Metro, Train, Share Rides, Walk.
Best Option FinderFastest, Cheapest, Least Walking.
Live UpdatesDelays, platform changes, disruptions.
Personalized SuggestionsBased on your preferences & patterns.
For AuthoritiesActionable intelligence, when you need it
What are the key issues this morning?
Here is the operational summary for 8:00 AM
Crowding at Guindy MetroHigh crowding detected on Platform 2
Delay on OMR CorridorAverage bus delay is 12 mins
Low Bus OccupancyRoute 501 is underutilized
Ask anything…
Ask in Natural LanguageGet answers the way you ask.
Operational SummariesKey issues, hotspots and trends.
Root Cause AnalysisUnderstand why it is happening.
Action RecommendationsWhat to do, where and when.
Reports in One ClickDaily, weekly, or custom reports.
APIs & ServicesSeamless integration for any platform
Journey Planning API ETA & Real-time API Disruption & Alerts API Places & Network API AI Chat / Voice API Analytics API
Better User ExperienceSmarter, faster journeys
Higher EngagementMore usage, more retention
Operational EfficiencyLess support, better automation
Data-driven DecisionsPlan, operate and grow better
Scalable & Future ReadyAI layer that evolves with you

AI Copilot for Transport Authorities

3
TATransport AuthorityAdmin

Ask anything about your network. Get instant, actionable intelligence.

What are the key issues this morning?Asked at 08:02 AM
AI SummaryAs of 08:00 AM
Crowding AlertGuindy MetroHigh crowding detected on Platform 2.High Impact
Delay AlertOMR CorridorAverage bus delay is 12 mins.Medium Impact
Transfer IssueChennai CentralLong waiting time between Metro and Suburban Rail.Medium Impact
Service GapVelachery ZoneLow last-mile connectivity in 3 pockets.Low Impact
Disruption RiskRain ForecastHeavy rain expected after 12:00 PM.Monitor
Detailed Analysis
Crowding at
Guindy Metro
Delay on
OMR Corridor
Transfer Issue at
Chennai Central
Service Gap in
Velachery Zone
Rain Forecast
Impact
Crowding at Guindy MetroHigh Impact
Why is it happening?
    What is the likely impact?
      What is recommended?
        Affected Passengers (est.)18,500+
        Expected Duration2 – 3 hours
        Confidence Level85%
        Chat History

        What are the key issues this morning?

        08:02 AM

        Show me yesterday's delay summary

        Yesterday

        Which corridors have highest crowding this week?

        12 May

        Generate weekly performance report

        11 May
        Quick Actions
        Crowding Heatmap
        Delay Analysis
        Ridership Trends
        Download Daily Report
        Ask a New Question
        Faster InsightsMinutes, not hours
        Data-Driven DecisionsAct with confidence
        Proactive OperationsSolve issues before they escalate
        Better ReportsAutomated & accurate
        Continuous LearningSmarter every day

        AI Dynamic Hotspots & Ride Dispatch

        3
        AD

        Reduce walking gaps to public transport using dynamic hotspots and existing mobility supply.

        Understand Demand
        Detect Walking Gaps
        Create Dynamic Hotspots
        Group & Optimize
        Dispatch Ride
        Seamless Access
        Data & Signals
        Station entries / exits
        Bus / train arrivals
        Live commuter demand
        Walking-gap information
        Traffic conditions
        Mobility supply availability
        QPo AI Engine
        Demand intelligence
        Walking-gap detection
        Dynamic hotspot generation
        Request optimization
        Multi-provider routing
        Dispatch via Existing Ecosystem
        RapidoBike / Auto
        OlaAuto / Cab
        UberAuto / Cab
        ONDC NetworkOpen network
        Local OperatorsShared autos, e-rickshaw fleets
        Illustrative provider routing / potential integrations. Not live integrations.
        Commuter Journey
        Before QPo1.4 kmwalk to station
        After QPo300 mwalk to dynamic hotspot
        Ready to match supply
        HomeResidential pocket, Perungudi
        Dynamic Hotspot Pickup300 m walk · generated from live demand
        RideGrouped with 2 nearby requests
        Metro / Bus / Rail StationSeamless interchange
        DestinationTidel Park, OMR
        Walking DistanceReduced
        Station AccessImproved
        PT CatchmentExpanded
        AffordabilityImproved by grouping
        Fleet UtilizationHigher
        PT AdoptionIncreased

        QPo Mobility AI

        AD

        Predict · Adapt · Plan · Simulate · Assist · Connect

        PredictKnow what is coming.
        AdaptImprove the existing network.
        PlanChoose the right intervention.
        SimulateTest before implementation.
        AssistTurn intelligence into useful journeys and decisions.
        ConnectClose the first / last-mile gap.
        Final Outcome
        Better public transport accessibility
        Better network utilization
        More reliable operations
        Smarter planning
        Easier journeys
        Higher public transport adoption

        One AI intelligence and orchestration layer for
        better public transport and multimodal mobility.

        QPo Mobility AI

        Predict Adapt Plan Simulate Assist Connect

        AI intelligence and orchestration for public transport
        and multimodal mobility.