Revealing True Human Mobility Pattern Using Smart Data
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The document presents an analysis of smart data applications for multimodal transportation planning in Toronto and Ontario, focusing on the benefits of using big data to understand travel patterns. It highlights various findings on vehicle behavior, sources of trips, and the integration of shared mobility, emphasizing the importance of recognizing dynamic travel patterns for effective planning. Overall, the data demonstrates a need for adjusting transportation policies to optimize infrastructure and improve urban mobility.
Revealing True Human Mobility Pattern Using Smart Data
- 1. STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL Application for Smart Data for Multimodal Transportation Planning Studies Dewan Karim Integrated Mobility Specialist, Dillon Consulting dkarim@Dillon.ca @Com_mobility
- 2. STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 2 Agenda 1. Overall Approach 2. Understanding Smart Data 3. Different Findings Using Smart Data 4. Towards Shared Multimodal Cities 5. Q&A
- 3. Why Smart Data Approach was Adopted in Toronto/Ontario since 2014 and onwards STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 3 Overall Approach Section I
- 4. Key Study Locations: Testing Different Applications of Smart Data STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 4 To r o n t o E x a m p l e s Area Plans/Gateway Mobility Studies Q U I C K LY Available in minutes O t h e r O n t a r i o E x a m p l e s Master Plan, Transit, Bridges Keele Finch Plus Don Mills Crossing Rouge National Park Transit Master Plan Fenelon Falls Crossing Study Guelph Transportation Master Plan
- 5. Multimodal City: True Nature of Human Mobility Graphics @Copyright to Dewan Karim, Prohibited to Use Replicate or Redistribute without Permission
- 6. Why We Used Big Data and Processing Software from StreetLight For These Studies STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 6 New Data Approach 1 Local Constraints 2 Moving Static to Dynamic 3 Lack of Areawide Origin- Destination, Lack of true human mobility pattern, Construction Disruption 1st Time – Our Trial Run to Test the Data Source Potential to Understand “Hard-to-Study” Dynamic Travel Patterns (i.e.: multimodal nature, trip origins, passby trip etc.)
- 7. Understanding Vehicle Behavior is Key for Multimodal Transportation Planning Where are the best opportunities to convert vehicle trips to other sustainable modes? How should shared mobility modes and new technologies be integrated? What streets/routes could create safer and comfortable active transportation? ? How might vehicle travel patterns change when new options are available ?
- 8. StreetLight InSight was Used to Obtain Four Key Types of Information Identify sources of trips, origin-destination, actual streets/ intersections used in study area 1 Develop quantitative multimodal travel demand forecasting 2 Candidate trips for transit route planning, shared mobility for 24 hr-seasonal variations 4 Measure travel times, trip length, pattern to develop multimodal mode share 3
- 9. Study Area, Trip Attributes, Calibration and Verification STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 9 Understanding Smart Data Section II
- 10. Selection of Study Zones STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 10 General selection process: Selection of Area and Zones: • Characteristics and proximity • Nature of land use • Matching local traffic zone boundary Streetlight Platform • Built-in dashboard • GIS format
- 11. Selection of Data Configuration STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 11 Streetlight Platform: • Customized dashboard to combination of attributes Origin Destination Pass-by 1. Origin-Destination 2. Trip Attributes Travel Time, Length, Speed Share of Trip Source Trip Distribution in Network Binned By: Trip Type: • Personal • Commercial Day Types: • Average Day (Mon – Sun) • Average Weekday (Mon – Fri) • Average Weekend (Sat – Sun) Day Parts: • All Day (12 am – 12 am) • Early AM (12 am – 6 am) • Peak AM ( 6 am – 9 am) • Mid-Day ( 9 am – 4 pm) • Peak PM ( 4 pm – 7 pm ) • Late PM ( 7 pm – 12 am)
- 12. Data Checking & Calibration STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 12 Calibration/Verification process: • Use traditional (TMC/AADT/Cordon) for calibration/verification • Verify percent of data in SL platform captured • Convert into approximate total trips Streetlight Platform gives • Raw data trip index • Two types of expansion facto: Local and overall • Built-in AADT Data Trip Index Trip Package Raw Data Counter Points for Each Street/ Intersections 1. With Count Data Location- specific Expansion Factor xxx xxx 2. With TTS/ Area Survey (Cordon/Screenline) Data Total Area Trips xxx xxx Overall Expansion Factor
- 13. Combining Different Data Sources STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 13 General platform facility: Overlay of census data with the Streetlight data provides additional interesting insights • Trip attributes • Traveller attributes • Premium visualization Streetlight Platform • Built-in visualization • Import external data
- 14. Solving Different Challenge Using Smart Data STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 14 Different Findings Using Smart Data Section III
- 15. Overall Area Travel: Personal vs Goods Movement STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 15 Keele-Finch Core Area Total Daily Trips 27,450 91,221 Extended Study Area 2,765 8,315 26,680 86,060 1,380 4,155 189,740 4.5% Area Total Pass-by Trips 62% 38% Overall Findings – Area Travel Patterns: – 2/3 of vehicle trips pass through the area without stopping (20-25% higher than other areas of city). – Commercial vehicles constitute 4.5% of vehicle trips, and the majority originated from Vaughan, GTA western region and Etobicoke. – Commercial O-D patterns are opposite of personal trips. Findings: Dramatically Different Travel Patterns in Keele-Finch Area
- 16. STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 16 Multimodal Infrastructure Priority Findings: Source of Traffic – Difference in Inner and Outer Suburbs Transit and Active Transportation produce maximum benefits to local community <20% External Trips >60% External Trips ~33% Internal Trips +
- 17. Internal Trips: Complete Community & Mixed-Use Policies Findings: Big Data and City Policies 60% Internal Vehicle Trips 10% 5% ~75% Total Internal Trips 10% 5% 9% 3% 3% 1%6% Using Big Data + Using TTS Data = Higher Internal Trips are associated with: • Higher population • Higher income Source: What’s Driving Your County’s Vehicles Miles Traveled? U.S. Streetlight Report, 2016. https://www.streetlightdata.com/whats-driving-your-countys-vehicles-miles-traveled Short and Shared Mobility Trip Candidates: • Connectivity Index • Walkshed • AT • Shared Mobility Modeling
- 18. Paradigm Change in Travel Demand Forecasting STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 18 Clash of Titans - Static vs. Smart Data: One city, two different travel pattern: • Short trips are unreported in TTS • Long-trips are overreported in TTS • Streetlight data shows real world travel pattern if calibrated properly Seismic Shift in Travel Demand Forecasting • Less future trips to Highway/Long-haul transit • Proper allocation of trips to short distance trips including shared mobility
- 19. Origin-Destination of Trips: Travel Pattern and Changes Findings: Drastically Different Travel Behaviour STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 19 Smart Data helps to: Big Data helps to: 1. Avoid random assumption of distribution of trips 2. Accurately project the trips to be converted to transit, specially after opening of Spadina subway 3. Enable quantitative analysis of short trip and associated infrastructures
- 20. Goods Movements: Travel Pattern and Demand Management Findings: Commercial Truck Behaviour STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 20 Smart Data helps to: 1. Temporal distribution 2. Peak travel pattern 3. Managing demand (vs. expanding infrastructure) Vaughan +North of Hwy 401 50% GTA West +Etobicoke 37%
- 21. Transit Master Plan Findings: High Demand Routes – Windsor STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 21 Smart Data helps to: 1. Highest demand origin-destination 2. Which route are used for real world travel by vehicle i.e. replicate same route for transit 3. Scale of demand to increase transit service frequency 4. Analysis time and budget was significantly lower than traditional transit master plan ChryslerCanada CityCentre Devonshire DevonshireMall EastRiverside EastWindsor Fontainbleu ForestGlade_Ind ForestGlade_Res Malden Ojibway RemingtonPark Riverside Roseland Sandwich SandwichSouth SouthCameron SouthCentral SouthWalkerville SouthWindsor StClairCollege TecumsehMall University UniversityofWindsor WalkerFarm Walkerville Lasalle TecumsehInd TecumsehRes GrandTotal Chrysler Canada 0.0% 0.0% 0.2% 0.0% 0.1% 0.2% 0.2% 0.1% 0.1% 0.0% 0.0% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.0% 0.1% 1.6% City Centre 0.0% 0.0% 0.1% 0.0% 0.0% 0.2% 0.1% 0.1% 0.1% 0.0% 0.0% 0.1% 0.1% 0.0% 0.2% 0.0% 0.1% 0.4% 0.1% 0.1% 0.0% 0.1% 0.2% 0.2% 0.1% 0.4% 0.1% 0.1% 0.1% 3.0% Devonshire 0.2% 0.1% 0.0% 0.1% 0.0% 0.1% 0.2% 0.2% 0.1% 0.0% 0.1% 0.4% 0.1% 0.2% 0.1% 0.1% 0.1% 0.2% 0.2% 0.3% 0.1% 0.0% 0.1% 0.0% 0.3% 0.1% 0.2% 0.3% 0.1% 4.0% Devonshire Mall 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.4% East Riverside 0.1% 0.4% 0.1% 0.0% 0.0% 0.1% 0.1% 0.2% 0.7% 0.0% 0.0% 0.2% 0.4% 0.1% 0.0% 0.1% 0.0% 0.1% 0.0% 0.1% 0.0% 0.1% 0.0% 0.1% 0.2% 0.1% 0.0% 0.1% 0.6% 4.0% East Windsor 0.3% 0.4% 0.3% 0.1% 0.0% 0.0% 0.8% 0.4% 0.4% 0.0% 0.0% 0.3% 0.4% 0.1% 0.3% 0.1% 0.1% 0.2% 0.3% 0.1% 0.1% 0.1% 0.1% 0.1% 0.4% 0.5% 0.2% 0.5% 0.2% 7.0% Fontainbleu 0.2% 0.2% 0.3% 0.1% 0.0% 0.5% 0.0% 0.5% 0.2% 0.0% 0.0% 0.2% 0.3% 0.1% 0.1% 0.0% 0.0% 0.2% 0.1% 0.2% 0.0% 0.1% 0.0% 0.1% 0.4% 0.2% 0.1% 0.2% 0.2% 4.7% Forest Glade_Ind 0.0% 0.0% 0.1% 0.0% 0.0% 0.2% 0.2% 0.0% 0.2% 0.0% 0.0% 0.0% 0.2% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.0% 0.0% 0.1% 1.7% Forest Glade_Res 0.2% 0.3% 0.2% 0.1% 0.1% 0.2% 0.3% 0.6% 0.0% 0.0% 0.1% 0.2% 0.5% 0.0% 0.1% 0.1% 0.0% 0.2% 0.3% 0.1% 0.1% 0.3% 0.0% 0.0% 0.4% 0.2% 0.1% 0.2% 0.5% 5.3% Malden 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.5% Ojibway 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.5% Remington Park 0.2% 0.2% 0.2% 0.1% 0.0% 0.2% 0.1% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.0% 0.1% 0.3% 0.2% 0.2% 0.1% 0.0% 0.1% 0.1% 0.1% 0.2% 0.1% 0.1% 0.1% 3.2% Riverside 0.3% 0.5% 0.2% 0.1% 0.2% 0.7% 0.5% 0.8% 0.8% 0.0% 0.1% 0.3% 0.0% 0.1% 0.2% 0.1% 0.1% 0.2% 0.2% 0.1% 0.1% 0.2% 0.0% 0.1% 0.5% 0.6% 0.2% 0.2% 0.3% 7.7% Roseland 0.2% 0.3% 0.8% 0.1% 0.0% 0.2% 0.2% 0.1% 0.1% 0.0% 0.1% 0.3% 0.0% 0.0% 0.1% 0.0% 0.2% 0.2% 0.1% 1.0% 0.3% 0.0% 0.2% 0.2% 0.5% 0.2% 0.5% 0.7% 0.2% 6.9% Sandwich 0.0% 0.3% 0.1% 0.1% 0.0% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.2% 0.1% 0.0% 0.0% 0.0% 0.2% 0.2% 0.2% 0.1% 0.1% 0.0% 0.4% 0.3% 0.2% 0.1% 0.2% 0.2% 0.1% 3.5% Sandwich South 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.1% 0.6% South Cameron 0.0% 0.4% 0.1% 0.1% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.2% 0.1% 0.1% 0.3% 0.0% 0.0% 0.2% 0.1% 0.5% 0.1% 0.0% 0.3% 0.3% 0.1% 0.1% 0.1% 0.2% 0.0% 3.5% South Central 0.1% 0.7% 0.1% 0.0% 0.0% 0.2% 0.1% 0.0% 0.2% 0.0% 0.0% 0.4% 0.1% 0.1% 0.1% 0.0% 0.1% 0.0% 0.3% 0.1% 0.1% 0.0% 0.2% 0.1% 0.1% 0.4% 0.2% 0.2% 0.0% 3.8% South Walkerville 0.2% 0.2% 0.3% 0.0% 0.0% 0.2% 0.3% 0.1% 0.1% 0.0% 0.0% 0.3% 0.1% 0.0% 0.1% 0.0% 0.0% 0.4% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.1% 0.2% 0.1% 0.1% 0.1% 3.1% South Windsor 0.2% 0.5% 0.5% 0.1% 0.0% 0.1% 0.3% 0.1% 0.1% 0.0% 0.1% 0.5% 0.1% 0.3% 0.4% 0.1% 0.3% 0.3% 0.2% 0.0% 0.2% 0.0% 0.3% 0.1% 0.3% 0.2% 0.2% 0.4% 0.1% 6.1% St Clair College 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.3% Tecumseh Mall 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.5% University 0.0% 0.5% 0.1% 0.0% 0.0% 0.1% 0.1% 0.1% 0.1% 0.0% 0.0% 0.2% 0.1% 0.1% 0.4% 0.0% 0.2% 0.3% 0.1% 0.1% 0.1% 0.0% 0.0% 0.3% 0.1% 0.2% 0.1% 0.1% 0.0% 3.4% University of Windsor 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.5% Walker Farm 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.1% 0.0% 1.1% Walkerville 0.1% 1.1% 0.3% 0.1% 0.0% 0.5% 0.3% 0.3% 0.2% 0.0% 0.1% 0.4% 0.2% 0.1% 0.2% 0.1% 0.1% 0.8% 0.5% 0.2% 0.1% 0.1% 0.2% 0.1% 0.2% 0.0% 0.2% 0.2% 0.1% 6.7% Lasalle 0.3% 0.6% 0.4% 0.1% 0.0% 0.3% 0.2% 0.2% 0.3% 0.4% 0.4% 0.5% 0.1% 0.4% 0.7% 0.1% 0.2% 0.3% 0.3% 0.4% 0.3% 0.1% 0.1% 0.3% 0.4% 0.2% 0.0% 0.6% 0.1% 8.4% Tecumseh Ind 0.0% 0.1% 0.2% 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.0% 0.1% 0.0% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.0% 0.1% 1.5% Tecumseh Res 0.3% 0.6% 0.3% 0.1% 0.5% 0.3% 0.2% 0.5% 0.8% 0.0% 0.1% 0.3% 0.3% 0.0% 0.1% 0.2% 0.2% 0.1% 0.2% 0.1% 0.0% 0.1% 0.0% 0.1% 0.4% 0.2% 0.1% 0.4% 0.0% 6.4% Grand Total 3.0% 7.6% 5.2% 1.5% 1.4% 4.9% 4.4% 4.6% 4.8% 0.6% 1.5% 5.4% 3.4% 2.0% 4.0% 1.2% 2.3% 4.8% 3.7% 3.9% 1.8% 1.2% 2.7% 2.8% 5.2% 4.6% 3.1% 5.3% 3.2% 100.0% Weekday AM Peak – OD Matrix
- 22. Transit Master Plan Findings: Transit Demand Visualization – Windsor STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 22 StreeLight platform offers: 1. Built-in visualization tools 2. Visualization of O-D Pair 3. Visualization in converted transit demand trip intensification
- 23. Bridge Assessment Findings: Bridge trip activities – Fenelon Falls STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 23 StreeLight data reveals: 1. Isolate trips that specifically use the bridge 2. Highlight zones with high percent of trip Origins and/or Destinations 3. Review breakdown of trips by time of day, day of week, and season
- 24. Bridge Assessment Findings: Bridge trip activities – Fenelon Falls STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 24 Streelight data reveals Internal trips within Kawartha Lakes Region versus external trips: 1. 5% - 10% of trips on the Bridge are travelling between External Areas 2. 15% - 21% of trips are between Kawartha Lakes and External Areas 3. 69% - 81% of trips are within the Kawartha Lakes Region Average Summer Day Average Summer Weekday Average Summer Friday Average Summer Friday PM PEAK Average Summer Weekend Day Average Summer Weekend Midday PEAK External to External 6% 5% 8% 9% 7% 6% Internal 77% 81% 74% 69% 72% 73% External to Internal 8% 7% 11% 14% 9% 10% Internal to External 9% 8% 7% 7% 12% 11%
- 25. Active Transportation by Smart Data Potential Application: How people travel to Rouge Park STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 25 Streelight data is expected to useful for: 1. What are common routes/entry points to park 2. True nature of walking and cycling to park destinations 3. Where and what to improve to reduce environmentally sensitive lands 4. How to reduce pollution from transportation modes 5. Making easy access by transit, walking, cycling and potential new mobility modes
- 26. Smart Data insights lead to different planning principles STREETLIGHT DATA PROPRIETARY & CONFIDENTIAL | 26 Towards Shared Multimodal Cities Section IV
- 27. >65% Short Trips in Urban Areas Smart Data Application of Smart Data: Candidate Trips for Multimodal Mobility Mobility’s Missing Middle Graphics @Copyright to Dewan Karim, Prohibited to Use Replicate or Redistribute without Permission
- 28. EcoMobility Hub Elements Context 1.On-Street 2.Bus-stop 3.MobilityPark 6.Private/ Off-street/ Underground5.TransitStation Application of Smart Data: EcoMobility Hubs 4.Boulevard
- 29. Innovative Space Design Multimodal Buildings Application of Smart Data: Redesign and Reinvent Planning Areas
- 30. Challenges and Future Opportunities for the City’s Use of Big Data Opportunities: • Trip purpose with larger dataset • Integrated multimodal dataset • Full application of Big Data for planning studies • Integration with operational planning • Opportunity to use Big Data for predictive analytics drastically reducing time & resources • Incorporation of new modes and technologies • Understanding multimodal & trip-chain issues Challenges: • Data processing skill set • Understanding data contents and calibration • Integrated multimodal data set • Demand modeling and database • Predictive analytics • Parallel sets of database