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Understand mobility using SHIFT data
Combine multiple datasets to identify key patterns
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Give me a mobility overview of Bengaluru
Compare public transport across cities
Find areas with low public transport coverage
Explore congestion and travel times
Compare population and employment distribution
Explore modal share
Mobility Overview — Bengaluru
A consolidated view of key transport indicators using SHIFT data sources.
Key Observations
Major job clusters are visible in the eastern part of the city, especially along key corridors.
Several employment clusters have comparatively lower public-transport route and stop coverage.
Major corridors show higher travel times during peak hours, particularly towards the east and south-east.
Detailed Analysis — Bengaluru
Indicator-level breakdown behind the overview, with the SHIFT dataset and vintage behind each number.
Indicator Breakdown
| Indicator | Value | Vintage | Evidence |
|---|---|---|---|
| Urban population | 12.9 M | 2021 | Observed |
| Employment (jobs) | 3.8 M | 2021 | Observed |
| Bus fleet size | 6,100 | 2022 | Observed |
| PT routes | 1,240 | 2022 | Observed |
| PT stops | 5,380 | 2022 | Observed |
| Peak vs off-peak travel time | 1.8× | 2023 | Calculated |
| PT mode share | 34% | 2022 | Observed |
| Road fatalities | 1,654 | 2022 | Observed |
| Avg PT stop coverage (500 m) | 68% | 2022 | Calculated |
| Avg PT travel time to job centres | 29 min | 2023 | Calculated |
| Passenger origin–destination demand | Not available | — | Unknown |
Employment vs Public-Transport Coverage
How to read this
- 1Observed values come straight from a SHIFT dataset at the stated vintage.
- 2Calculated values are derived by SHIFT from one or more observed datasets.
- 3Unknown means no approved SHIFT dataset covers it — it is never estimated silently.
Data Sources
The approved SHIFT datasets behind this overview. External and user data is only available inside the Research Lab.
SHIFT Datasets Used
| Dataset | Vintage | Coverage | Geometry | Used for | Status |
|---|---|---|---|---|---|
| Population | 2021 | City-wide | Raster | Density, accessibility denominators | Approved |
| Employment | 2021 | City-wide | Polygon | Employment clusters and concentration | Approved |
| Public Transport Routes | 2022 | Bus network | Line | Route coverage and density | Approved |
| Public Transport Stops | 2022 | Bus network | Point | Stop coverage within 500 m / 2 km | Approved |
| Bus Fleet & Ridership | 2022 | City-wide | Table | Supply and boarding comparison | Approved |
| Road Network | 2023 | City-wide | Line | Network structure and capacity context | Approved |
| Travel Time & Congestion | 2023 | Key corridors | Raster | Peak-hour degradation | Approved |
| Modal Share | 2022 | City-wide | Table | Mode split context | Approved |
| Road Safety | 2022 | City-wide | Point | Safety context | Approved |
Only approved internal categories are available here: population, employment, PT routes and stops, bus fleet, ridership, route coverage, road network, travel time, congestion, modal share, road safety and cross-city indicators.
Authority data, ticketing, GTFS, GPS/AVL, surveys, CSV/Excel and procured datasets can only be introduced inside the Research Lab, and are labelled external wherever they appear.
Key Observations
Major job clusters are visible in the eastern part of the city, especially along key corridors.
Several employment clusters have comparatively lower public-transport route and stop coverage.
Major corridors show higher travel times during peak hours, particularly towards the east and south-east.
Selected Area Details (Observation 02)
Project Brain
Define the context for your project. This information will guide analysis, agents and outputs.
Project Goal
Assess whether major employment centres have adequate public-transport accessibility in Bengaluru.
Study Area
Key Focus Areas
Policies & Planning Guidelines
Project Rules
Data Context (SHIFT datasets)
Assumptions
- Major employment centres are defined using the SHIFT employment dataset.
- Public transport includes bus services (routes and stops) within the study period.
- Accessibility is measured using proximity and network connectivity, not surveyed demand.
- Analysis is performed at city and zone level.
Project Progress
Define project context to start the investigation.
Project Contents
- Project Details6/6
- Policies & Guidelines1
- Assumptions4
- Selected Datasets9
- Evidence (auto-collected)0
- Key Findings0
- Open Questions0
The Project Brain maintains the context for your project. The AI will use this information across every agentic investigation and final output.
Based on the project context and analysis of 9 SHIFT datasets, here are the likely initial concerns for Bengaluru regarding public-transport accessibility to employment centres.
Major employment clusters in the eastern and outer zones are visible, but public-transport route and stop coverage is comparatively lower.
| Employment (2 km) | 82,000 |
| PT routes (2 km) | 8 |
| PT stops (2 km) | 24 |
Several employment clusters show higher travel times during peak hours, particularly along east and south-east corridors.
| Peak vs off-peak | 1.8× |
| Major affected corridors | 5 |
| Employment clusters | 7 |
Large residential areas, especially in the south and south-east, show relatively weaker public-transport connectivity to major employment centres.
| Population (2 km) | 45,000 |
| PT accessibility | Low |
| Connectivity to clusters | Moderate |
Agentic Project Studio Beta
Your project objective is now a structured research roadmap. Multiple AI agents work together to investigate, correlate findings and build project intelligence.
Assess whether major employment centres have adequate public-transport accessibility in Bengaluru.
Bengaluru
Research Roadmap
Stage 1 — Establish city baseline
Understanding the overall mobility and spatial context of Bengaluru using SHIFT datasets.
Agentic Investigation Beta
Multiple specialist agents work together to investigate, correlate findings and build project intelligence.
Datasets Being Used
Agents are selecting and processing relevant SHIFT datasets for this investigation.
Agent Execution Status
Current Task
Next Tasks
Transport Gap Analysis
Areas where public transport supply appears weak relative to population or employment concentration.
City-wide Indicators
(for comparison)AI Findings
These areas show comparatively weaker public-transport coverage.
These areas have moderate PT coverage but longer peak-hour travel times.
Weak public-transport supply and peak-hour congestion overlap in the same corridors.
Identified Transport Gap Zones (Top 5)
| # | Area / Zone | Employment | Population | PT Coverage | Gap Index |
|---|
Accessibility Investigation
Analyse accessibility between population areas and major employment centres using public transport.
Accessibility Overview — Whitefield – ITPL
Accessibility Map (Public Transport)
Accessibility Gaps (Top 5 Areas around Whitefield – ITPL)
| # | Area / Zone | Population (2 km) | Nearest PT Stop | Avg PT Travel Time to ITPL | Access Level |
|---|---|---|---|---|---|
| 1 | Kadugodi | 42,000 | 1.2 km | 38 min | Low |
| 2 | Belathur | 34,000 | 1.6 km | 41 min | Low |
| 3 | Channasandra | 48,000 | 1.1 km | 36 min | Low |
| 4 | Soukya Road | 29,000 | 1.8 km | 47 min | Low |
| 5 | Varthur | 38,000 | 1.4 km | 43 min | Low |
Key Insights
- 1Lower residential-area access to employment around Whitefield – ITPL, with low public-transport density close to the centre.
- 2Several employment areas around ITPL are well connected, but surrounding residential zones show relatively weaker limited-PT access.
- 3Access to other major employment centres (Marathahalli, Electronic City) requires higher travel times.
- 4Additional routes or improved coverage in eastern residential zones could significantly improve accessibility.
Congestion and Network Context Analysis
Understand why this area appears problematic by analysing travel conditions and road-network structure along with employment and public-transport accessibility.
Why does this area appear problematic?
Whitefield – ITPL shows high employment concentration, significant peak-hour travel time and comparatively weaker public-transport route and stop coverage. The combination of high activity levels, limited PT access and constrained road-network capacity contributes to longer travel times and lower accessibility.
Source: SHIFT Employment Dataset (2021)
Source: SHIFT Travel Time Dataset (2023)
Source: SHIFT PT Routes & Stops (2022)
Combined analysis of employment, PT supply and travel time
Network Context
Limited high-capacity access connected by arterials.
Significant congestion on approaches from Whitefield Main Rd.
Fewer PT routes and stops relative to employment concentration.
Key Indicators for Whitefield – ITPL (2 km)
| Indicator | Whitefield – ITPL | Bengaluru Avg | Difference |
|---|---|---|---|
| Employment (2 km) | 82,000 | 24,000 | 3.4× higher |
| Population (2 km) | 45,000 | 38,000 | 1.2× higher |
| PT Stops (2 km) | 24 | 31 | 23% lower |
| PT Route Length (km) | 18 | 25 | 28% lower |
| Avg PT Travel Time to major job centres | 42 min | 29 min | 1.4× higher |
Supporting Analysis
- 1Whitefield – ITPL is a major employment centre with high activity levels, particularly in the IT and business-services sector.
- 2Peak-hour travel times are significantly higher due to network concentration on a few corridors.
- 3Public transport coverage is comparatively lower than employment concentration, with fewer direct routes and stops.
- 4The road network has limited high-capacity alternatives, leading to higher congestion and longer travel times.
Correlation Intelligence
Connect findings across analyses to understand relationships, identify patterns and uncover new research questions.
Connected Findings for Whitefield – ITPL
Source: SHIFT Employment (2021)
Source: SHIFT PT Routes & Stops (2022)
Source: SHIFT Travel Time (2023)
- Is the current PT supply sufficient for the level of employment activity?
- Are there gaps in network coverage limiting PT attractiveness?
- Would additional route or network capacity measurably improve accessibility?
- What role does network congestion play in limiting PT performance?
Supporting Evidence for the Correlated Finding
Source: SHIFT Employment (2021)
Source: SHIFT PT Routes & Stops (2022)
Source: SHIFT Travel Time (2023)
Source: SHIFT Road Network (2023)
Related Findings in Other Areas
| Area / Zone | Correlation Pattern | Key Issue |
|---|---|---|
| Sarjapur Corridor | High employment + weak PT + high peak-hour time | High access pressure |
| Electronic City | High employment + moderate PT + high travel time | Moderate pressure |
| Outer Ring Rd (E) | Moderate employment + weak PT + high travel time | High access pressure |
| Marathahalli | High employment + moderate PT + high travel time | Moderate pressure |
| Hebbal | Growing employment + moderate PT + rising travel time | Emerging pressure |
Anomaly Detection and Contradiction Analysis
Identify unusual patterns, contradictions and unexpected relationships across transport and urban datasets.
Detected Anomalies (Top 5)
| Area / Zone | Anomaly Type | Key Indicators | Confidence |
|---|---|---|---|
| Sarjapur Corridor | High ridership, low route coverage | 18,500 boardings/day · 14 routes | High |
| Whitefield – ITPL | High employment, low PT coverage | 82,000 jobs · 8 routes (2 km) | High |
| Outer Ring Rd (E) | High congestion, limited PT | 1.9× peak factor · 11 routes | High |
| Yelahanka | High employment growth, stable PT | +18% jobs · 0 new routes | Medium |
| Kengeri / Mysore Rd | High population, low ridership | 96,000 pop · 7,400 boardings | Medium |
Anomaly Detected
This area shows high bus boardings relative to the number of routes and stops serving the zone, which is unusual compared with other employment corridors in the city.
Ridership vs Route Coverage (Comparison)
Possible Explanations
- 1Demand is concentrated on a small number of high-demand corridors.
- 2Existing routes may be operating at or beyond practical capacity.
- 3Limited coverage may mean riders are travelling unusually long distances to reach available routes.
- 4A mismatch between service supply and spatial distribution of demand.
- 5Additional data (e.g. route frequency and capacity, boarding counts) is required to confirm.
Suggested Next Steps
- 1Analyse route frequency and capacity for the existing services.
- 2Investigate surrounding land use and employment distribution.
- 3Compare with similar corridors to understand whether the pattern is systemic.
- 4Validate against planning documents and published policy objectives.
Policy Context Analysis
Understand how the current findings relate to relevant policies and planning standards.
Policy Compliance Indicators
(Sarjapur Corridor)within 500 m
served by high-capacity PT
to nearest employment centre
with adequate PT access
Relevant Policy / Planning Context
Bengaluru Metropolitan Region Development Authority (BMRDA) · 2021
- 1Improve public-transport accessibility to major activity and employment centres.
- 2Enhance first and last-mile connectivity to public transport.
- 3Increase public-transport mode share and reduce dependence on private vehicles.
- 4Provide equitable access to transport, with focus on underserved areas.
Findings vs Policy Assessment
| Policy Objective | Relevant Finding from Project | Assessment |
|---|---|---|
| Improve public-transport accessibility to major activity and employment centres | Three high-employment zones (including Sarjapur Corridor) show comparatively weak PT coverage. | Not Met Needs further investigation |
| Enhance first and last-mile connectivity to public transport | Residential areas east of Sarjapur show limited PT stop availability within 500 m. | Partially Aligned Improvement required in boundary communities |
| Increase public-transport mode share | High transition congestion and limited PT supply may constrain mode shift. | Not Met Requires network and service enhancements |
| Provide equitable access to transport, with focus on underserved areas | Residential zones with lower-income profiles show weaker access to employment centres. | Potential Gap Indicates spatial inequity in current PT provision |
Evidence and Data-Gap Analysis
Understand the strength of evidence behind current findings and identify what is known, inferred and missing.
Accessibility Evidence Layers
Source: SHIFT Population (2021)
Source: SHIFT Employment (2021)
Source: SHIFT PT Routes & Stops (2022)
Source: SHIFT Travel Time (2023)
Evidence Summary for Sarjapur Corridor
Source: SHIFT Employment Dataset (2021)
Source: SHIFT PT Routes & Stops (2022)
Source: SHIFT Travel Time (2023)
Combined analysis of employment, PT supply and travel time
Key Data Gap
The certified accessibility gap is derived from the spatial relationship between population, employment, PT supply and travel-time/network analysis, and does not confirm actual passenger demand. SHIFT can indicate where service and accessibility gaps appear to exist; it cannot confirm who is travelling, from where, to where, or in what volume.
Confidence Assessment
| Finding | Evidence Type | Data Source | Confidence |
|---|---|---|---|
| High employment concentration | Observed | SHIFT Employment (2021) | High |
| Low PT route coverage | Calculated | SHIFT PT Routes & Stops (2022) | Medium |
| High peak-hour travel time | Observed | SHIFT Travel Time (2023) | High |
| Potential accessibility issue | Inferred | Combined analysis | Medium |
| Actual passenger OD demand | Unknown | Not available | Low |
Implications for the Project
- 1Findings provide strong evidence of potential accessibility issues in the Sarjapur Corridor.
- 2Additional data such as ticketing data, GTFS or survey-based OD can help validate demand patterns.
- 3Current conclusions should be interpreted as indicative service gaps, not confirmed passenger demand.
Research Checkpoint
A summary of the current investigation status, key findings, evidence and what remains to be explored.
What We Started With
Assess whether major employment centres have adequate public-transport accessibility.
Bengaluru (BBMP boundary)
Public transport + employment accessibility
What We Found
- 1Three employment areas with weak PT accessibility in major employment centres.
- 2High employment concentration with comparatively limited public-transport coverage.
- 3Peak-hour congestion overlaps with weaker-access employment clusters.
- 4Potential first/last-mile connectivity gap in surrounding residential areas.
Supporting Evidence
- Employment data (2021)
- Population data (2021)
- PT routes and stops (2022)
- Fleet and ridership data
- Travel time and congestion (2023)
- Road network
What Remains Unknown
- Actual passenger-level OD demandGapCannot be derived from current SHIFT datasets
- Detailed service frequency and capacityGapBy route
- Behavioural factors and mode choiceGapNot covered by core datasets
- Impact of upcoming projects (e.g. metro expansion)ScopeOut of current scope
Key Finding Map
Top Priority Areas Identified
| # | Area / Zone | Key Issue | Key Indicators | Priority |
|---|---|---|---|---|
| 1 | Whitefield – ITPL | High employment + weak PT coverage | 82,000 jobs · 8 PT routes (2 km) | High |
| 2 | Sarjapur Corridor | Growing employment + limited PT access | 56,000 jobs · 14 PT routes | High |
| 3 | Electronic City | High employment + high travel-time degradation | 66,000 jobs · PT travel time 1.7× | Medium |
| 4 | Outer Ring Rd (E) | Moderate employment + coverage gaps | 37,000 jobs · 11 PT routes | Medium |
| 5 | Yelahanka | High population + weak connectivity to job centres | 2.0 lakh population · 7 PT routes | Lower |
Project Timeline and Progress
Next Steps
- 1Investigate identified priority areas in more detail.
- 2Explore potential first/last-mile connectivity solutions.
- 3Assess impact of upcoming metro/BRT projects.
- 4If passenger-level OD data is needed, continue in Research Lab.
Project Synthesis
A structured, evidence-backed output generated from the complete investigation.
Project Summary
Assess whether major employment centres have adequate public-transport accessibility.
Bengaluru (BBMP boundary) · ~741 km²
Public transport + employment accessibility
9 SHIFT datasets (2021–2023): population, employment, PT routes & stops, travel time, road network, modal share, road safety
Key Findings
Three major employment centres show comparatively weak public-transport accessibility.
Peak-hour congestion significantly degrades public-transport travel times in key corridors.
Residential areas in the east and south-east show weaker connectivity to major employment centres.
Limited route coverage and road network capacity jointly constrain accessibility.
Key Findings Map
Evidence Summary
- Population data (2021)SHIFT Population Dataset
- Employment data (2021)SHIFT Employment Dataset
- PT routes and stops (2022)SHIFT PT Dataset
- Fleet and ridership dataSHIFT Fleet Dataset
- Travel time and congestion (2023)SHIFT Travel Time Dataset
- Road networkSHIFT Road Network
- Administrative boundaryBBMP Boundary
Report Outline
Assessing public-transport accessibility to major employment centres
This study analyses public-transport accessibility to major employment centres in Bengaluru using SHIFT datasets. The analysis identifies key areas where employment concentration is high but public-transport accessibility is comparatively weak, with several corridors also showing peak-hour travel-time degradation.
Export Project
Prepare a professional handoff package based on your role and requirements.
GIS Planner Package
RecommendedAnalyst Package
Export structured datasets and indicators for further analysis.
- All zone-level indicators
- Analysis and comparison tables
- Key findings and evidence labels
- Analysis metadata
Research Package
Complete research package with methodology and evidence.
- All source datasets
- Analysis methodology
- Key findings and maps
- Evidence and limitations
- Project documentation
Decision Brief
Executive summary and decision-ready brief.
- Project objective and context
- Key findings and maps
- Priority areas
- Conclusions and next steps
Research Lab
Add and analyse additional datasets to explore identified data gaps in a controlled environment.
Core SHIFT Project
Read-onlyUses SHIFT-approved datasets (population, employment, PT routes, travel time, road network etc.). The objective, study area, findings and data gaps from the core project are carried forward here unchanged.
Research Lab
Additional DataAdd external and user datasets such as ticketing, GTFS, survey or authority datasets. These are explicitly labelled as external and kept outside the core SHIFT environment and its exports.
Add External Data
or
Supported formats: CSV, XLSX, GTFS, GeoJSON, SHP
Max file size 2 GB
- BMTC Operations APIConnectedLive ticketing and AVL endpoints
- GTFS Feed URLConnectedScheduled import, refreshed weekly
- Authority Data WarehousePendingRequires access approval
- Survey Platform ExportNot linkedHousehold travel survey responses
Starter datasets you can add without procuring external data first.
- Sample ticketing extract30 days · 1.2 M boardings · CSV
- Sample GTFS feedFull bus network · 1,240 routes
- Sample household survey12,400 households · XLSX
- Sample AVL/GPS traces4,200 vehicles · 7 days
Configured Datasets
| Dataset Name | Type | Year | Coverage | Status |
|---|---|---|---|---|
| BMTC Ticketing Data | Ticketing | 2023 | City-wide | Ready |
| BMTC GTFS Feed | GTFS | 2023 | Bus network | Ready |
| Household Travel Survey | Survey | 2022 | 12,400 households | Ready |
| Mobile Movement Data | Mobility | 2023 | Anonymised traces | Processing |
| BMTC AVL/GPS Data | AVL | 2023 | 4,200 vehicles | Ready |
Analyse with Research Lab Agents
Data Gap Analysis
| Data Gap from Core Project | Additional Data Used | Status | Insight |
|---|---|---|---|
| Passenger OD demand | Ticketing, mobile traces | Filled | Observed OD pairs identified; corridor demand now quantifiable |
| Service frequency & capacity | GTFS, AVL | Filled | Peak headways and crowding now measurable per route |
| Behaviour / mode choice | Household survey | Partial | Preference signals available at sample level only |
| Upcoming project impact | Authority plans | Open | Still requires published project timelines and alignments |
Research Insights
- 1Ticketing data confirms high passenger demand on a limited number of corridors.
- 2Riders are travelling significantly further than expected to reach major employment clusters.
- 3Some zones with high observed accessibility gaps also show low observed PT usage, indicating service or access barriers.
- 4Metro and bus-lane improvements could materially improve measured accessibility in east and south Bengaluru.
- 1Validate actual passenger demand for identified accessibility gaps.
- 2Validate service and capacity improvements for priority corridors.
- 3Explore impact of proposed service or network changes on measured accessibility.
- 4Prepare findings for final analysis and feed an updated report back into the Decision Lab.
My Projects
Projects turn a surfaced observation into a persistent, agent-driven investigation.
Agentic Studio Beta
The specialist agents available to every project, how they are orchestrated, and what they have run.
Agent Library
Orchestration Model
Every agent reads the same project context and writes findings, evidence and open questions back to it. Nothing an agent produces sits outside that record, which is what keeps evidence labels consistent from investigation through to export.
Investigations
Data Catalogue
Approved SHIFT datasets available to every project in the core environment.
Approved SHIFT Datasets
| Dataset | Vintage | Coverage | Geometry | Typical use | Status |
|---|---|---|---|---|---|
| Population | 2021 | City-wide | Raster | Density, accessibility denominators | Approved |
| Employment | 2021 | City-wide | Polygon | Employment clusters and concentration | Approved |
| Public Transport Routes | 2022 | Bus network | Line | Route coverage and density | Approved |
| Public Transport Stops | 2022 | Bus network | Point | Stop coverage within 500 m / 2 km | Approved |
| Bus Fleet & Ridership | 2022 | City-wide | Table | Supply and boarding comparison | Approved |
| Road Network | 2023 | City-wide | Line | Network structure and capacity context | Approved |
| Travel Time & Congestion | 2023 | Key corridors | Raster | Peak-hour degradation | Approved |
| Modal Share | 2022 | City-wide | Table | Mode split context | Approved |
| Road Safety | 2022 | City-wide | Point | Safety context | Approved |
Everything above is available to any project without further approval. Passenger-level origin–destination demand is not in the catalogue, so projects report it as a data gap rather than estimating it.
Authority data, ticketing, GTFS, GPS/AVL, surveys and procured datasets are introduced only inside the Research Lab, where they stay labelled as external.