Multimodal Mopbility Planning Using Big Data in Toronto
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With collaborations with various City divisions and private service providers (in this case Streetlight data providers), our North York mobility innovation team uncovered several surprising suburban travel behaviour, patterns and distributions of trips that lead to meaningful and quantitative multimodal mobility planning. This presentation is a summary of project experiences and describes the key findings.
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Multimodal Mopbility Planning Using Big Data in Toronto
- 1. Dewan Karim Sr. Transportation Planner City of Toronto Multimodal Planning Beyond Toronto’s Urban Core Laura Schewel CEO StreetLight Data
- 2. -- Proprietary and Confidential -- 2 Agenda I. Introducing the Keele Finch and Don Mills Crossing Studies: Background of Study Areas and Approach II. Deep-Dive: Putting Big Data to Work III. Key Findings for Toronto IV. Q&A
- 3. Dewan Karim Introducing the Keele Finch and Don Mills Crossing Studies: Background of Study Areas and Approach
- 4. -- Proprietary and Confidential -- 4 Toronto’s Topline Planning Goals and Challenges: City Building Policies and Objectives Goals Challenges • Create vibrant neighborhoods that are part of complete communities • Create mix-use neighbourhoods around future transit infrastructures supported by active and shared mobility network • Build multimodal transportation network and infrastructure needs assessments • Reducing single occupant vehicle uses • Scarcity of areawide travel patterns • Local transport is affected by transit construction activities • Disconnected neighbourhoods and fragmented transportation network • Short study timeline and resources • Understanding how auto-oriented travel pattern will potentially change behavior
- 5. -- Proprietary and Confidential -- 5 Today, We’ll Discuss Two Planning Studies that Used Big Data to Overcome Key Challenges These study areas are “Gateway Mobility Hubs.” They are key to bringing convenient, affordable multimodal transit options to the outskirts of Toronto – beyond the urban core. Keele Finch Plus Don Mills Crossing
- 6. -- Proprietary and Confidential -- 6 Don Mills Crossing: Area Profile The study is a Phase 1 review of existing policies, strategic plans, local area characteristics, land use dynamics, travel patterns and the transportation conditions for all modes of travel. Study Goals: • Shape and manage the anticipated growth as a result of the LRT construction (currently underway) • Develop common and sustainable principles to address social inequality and guide the future transportation plan for all mobility users • Develop comprehensive multimodal mobility assessment with creative design and incorporating smart technologies
- 7. -- Proprietary and Confidential -- 7 Keele Finch Plus: Area Profile This study is a Phase 1 report that uses a qualitative and quantitative method to understand why, when, and where people travel using the transportation modes available in the study area. Study Goals: • Address existing transportation issues in this residential and industrial zone • Determine the future transportation framework for future and LRT intersecting node and find multimodal solutions • Evaluate where multimodal infrastructure should be expanded and which modes to focus on
- 8. -- Proprietary and Confidential -- 8 Why We Used Big Data and Processing Software from StreetLight For These Studies 1st Time – Our Trial Run to Test the Data Source 1 Lack of Areawide and Disrupted Local Network due to Construction 2 Potential to Understand “Hard-to-Study” Travel Patterns (i.e.: vehicle trip origins, passby trip percentages) 3
- 9. -- Proprietary and Confidential -- 9 Understanding Vehicle Behavior is Key for Multimodal Planning Where are the best opportunities to convert vehicle trips to other modes located? 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 ?
- 10. Laura Schewel Deep-Dive: Putting Big Data to Work
- 11. -- Proprietary and Confidential -- 11 StreetLight Delivered Several Key Advantages for Understanding Vehicle Trips Larger sample size than local surveys 1 More complete O-D information than sensors 2 Can analyze commercial truck behavior specifically 4 Possible to create detailed routing analyses 3 Ability to collect data without sending teams into the field 6 Ability to iterate and optimize 5
- 12. -- Proprietary and Confidential -- 12 The City of Toronto Put Big Data to Work with the StreetLight InSight® Platform Source of Commercia l Trips Source of Personal Trips StreetLight InSight® Metrics by Day Type and Day Part: • Origin-Destination • Select Link • Travel Times • Commercial Trips Massive Mobile Data + Contextual Data Constructio n Impact Assessmen t Travel Times to Key Activity Centers Passthroug h (Pass-by) Trip Analysis Identifying Alternate Routes for Congested CorridorsOrigins and Destination s for Corridors Travel Demand Mgmt. Calibration to Counts Commercia l Truck Volumes
- 13. -- Proprietary and Confidential -- 13 The City of Toronto Used StreetLight InSight to Obtain Four Key Types of Information Identify source of personal and commercial vehicle trips 1 Understand origins and destinations of trips on specific corridors 2 Determine share of truck trips and their origins and destinations 4 Measure travel times from key activity centers to the study area 3
- 14. -- Proprietary and Confidential -- 14 Navigation-GPS Data Helped the City Better Understand Personal and Commercial Vehicle Trips Technical Characteristics Spatial Precision ~5 meters Frequency of Pings Regularly; every 1 sec – 1 min Type of Travel Personal vehicle and commercial truck trips Navigation-GPS Data Creation
- 15. -- Proprietary and Confidential -- 15 Location-Based Services Data are Now Viable for This Type of Project Technical Characteristics Spatial Precision ~5 meters – 25 meters Frequency of Data Pings Variable; usually triggered by location change Type of Travel Behavior Personal trips; aggregate home work locations; trip purposes Key Advantage Large Sample Size – Nearly ¼ of and Canadian Adult Populations LBS Data Creation
- 16. -- Proprietary and Confidential -- 16 But The Data Alone Is Not Enough: Using the Right Processing Software is Critical Iterate and Optimize Calibrate to Counts For Don Mills, Toronto planners ran 14 StreetLight InSight studies in just two weeks –that’s much faster than it takes to run most traditional studies. Toronto planners uploaded local sensor data into StreetLight InSight so that the platform automatically scaled index values Metrics to estimated counts.
- 17. Dewan Karim Key Learnings for the City of Toronto
- 18. -- Proprietary and Confidential -- 18 Study Area: Selecting Data Data Configuration - Area Selection Selection of Area and Zones: – Characteristics and proximity – Nature of land use – Matching local traffic zone boundary
- 19. -- Proprietary and Confidential -- 19 Layering Sub Area: Data Selection and Boundaries Data Configuration – Sub-Area Selection – Regional influence – Local neighbourhoods – Immediate study area
- 20. -- Proprietary and Confidential -- 20 Data Checking Data Configuration – Entry Point Selection – Data calibration – Trips distribution in fine detail – Trip conversion
- 21. -- Proprietary and Confidential -- 21 Origin Destination Pass-by 1. Origin-Destination 2. Trip Attributes Travel Time, Speed Share of Trip Source Trip Distribution in Network Data Contents Data Configuration: Attributes and Temporal Distribution 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)
- 22. -- Proprietary and Confidential -- 22 Counter Points for Each Street/ Intersections 1. With Count Data Expansion Factor xxx xxx 2. With TTS/ Survey Data Total Area Trips xxx xxx Data Checking Calibration and Validation Trip Index Trip PackageRaw Data
- 23. -- Proprietary and Confidential -- 23 Overall Area Travel: Personal vs Goods Movement Findings: Dramatically Different Travel Patterns 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.
- 24. -- Proprietary and Confidential -- 24 Internal Trips: Complete Community & Mixed-Use Policies Findings: Big Data and City Pologies 60% Internal 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
- 25. -- Proprietary and Confidential -- 25 Multimodal Infrastructure Priority Findings: Source of Traffic Transit and Active Transportation produce maximum benefits to local community <20% External Trips >60% External Trips ~33% Internal Trips +
- 26. -- Proprietary and Confidential -- 26 Traffic Infiltration Findings: Source of Traffic New Approach to “Traffic Infiltration” • Typical approach is license plate tracing, but there is no information on origin, destination and streets being used • Big Data clearly identifies actual source, path and scale of traffic infiltration ~50% Local Trips ~75% Inner Zone Trips <25% Long-distance Pass-by Trips
- 27. -- Proprietary and Confidential -- 27 Origin-Destination of Trips: Travel Pattern and Changes Findings: Drastically Different Travel Behaviour 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
- 28. -- Proprietary and Confidential -- 28 Long-range Travel Behaviour: Overall Travel Time Findings: Duration of Trips to Key Activity Centers Big Data helps to reveal: 1. Relative travel time to/from study area 2. Route circuity 3. Alternative modes for most congested routes
- 29. -- Proprietary and Confidential -- 29 Goods Movements: Travel Management Findings: Commercial Truck Behaviour Big Data helps with: 1. Temporal distribution 2. Peak travel pattern 3. Managing demand (vs. expanding infrastructure) Vaughan +North of Hwy 401 50% GTA West +Etobicoke 37%
- 30. -- Proprietary and Confidential -- 30 Travel Patterns within Study Area Findings: Local Behaviour and Construction Impact Big Data was able to show: 1. Travel patterns at every street or intersection 2. Share of trips and source at every location 3. Distribution for microscopic modeling 4. Before and after impact of subway 5. Impact of transit construction -17% -29% Construction Impact
- 31. -- Proprietary and Confidential -- 31 Our Key Takeways: The Advantages of Using StreetLight’s Big Data + Software Solution • When we combined the Big Data with more other data sources, we got the best results • The Big Data not only provided new insights, it helped make existing information more valuable • Big Data provided key findings that weren’t available from other sources (vehicle trip origins, pass through trip percentages) 1 2 3
- 32. -- Proprietary and Confidential -- 32 Challenges and Future Opportunities for the City of Toronto’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
- 33. Thank You!
- 34. Questions? Dewan Karim Sr. Transportation Planner City of Toronto Laura Schewel CEO StreetLight Data
Editor's Notes
- Tori to introduce webinar. Explain Q&A process. Introduce Dewan and Laura as our panelists.
- Tori to walk through agenda. Structure: Introducing The Keele and Finch and Don Mills Crossing Studies Dewan to take Why Toronto Used Big Data Dewan to take Deep-Dive: How Using Big Data Worked Laura to take Key Takeaways for Toronto Dewan to take Beyond Toronto Q&A Tori to manage; Laura and Dewan to respond
- Tori introduces Dewan
- Goals: Create vibrant neighborhoods that are part of complete communities Create mix-use neighbourhoods around future transit infrastructures supported by active and shared mobility network Build multimodal transportation network and infrastructure needs assessments Challenges: Reducing single occupant vehicle uses Scarcity of areawide travel pattern Local transport is affected by transit construction activities Disconnected neighbourhoods and fragmented transportation network Short study timeline and resources Understanding how auto-oriented travel pattern will potentially change behavior
- Today, we’re going to dive into two Planning Studies that used Big Data to overcome key challenges. These are the Keele Finch Plus and Don Mills Crossing studies. These two areas are similar because: they’re both “Gateway Mobility Hubs” to the City of Toronto. We call these two areas of Toronto” Gateway Mobility Hubs. They are key nodes in the regional transportation system located where two or more current or planned regional rapid transit lines intersect and where there is expected to be significant passenger activity (4,500 or more forecasted combined boardings and alightings in the 2031 in the morning peak period). In addition, these areas are generally forecasted to achieve mixed-use environment with higher density, seamless integration between the rapid transit stations, and high levels of pedestrian priority with attractive public realm.
- Dewan: Describes study area Points to key trouble spots and interesting aspects of the study area Further information from the report: The Don Mills-Eglinton area is identified by Metrolinx's Mobility Hub Guidelines as a “Gateway Hub”, an interchange of two rapid transit lines where transit-oriented activities and intensification takes place. One of these lines is the Crosstown LRT, currently under construction, that will connect along Eglinton Avenue from Kennedy Road to Mount Dennis station. Don Mills Road has been identified as rapid transit corridor in Toronto's Official Plan, and its intersection with the Crosstown includes a bus terminal connected to the LRT stop. These local and regional connections are expected to draw new transportation demand and offer more convenient transit alternatives potentially encouraging a modal shift from private automobile usage.
- Dewan: Describes study area Points to key trouble spots and interesting aspects of the study area Further information from the report: The Don Mills-Eglinton area is identified by Metrolinx's Mobility Hub Guidelines as a “Gateway Hub”, an interchange of two rapid transit lines where transit-oriented activities and intensification takes place. One of these lines is the Crosstown LRT, currently under construction, that will connect along Eglinton Avenue from Kennedy Road to Mount Dennis station. Don Mills Road has been identified as rapid transit corridor in Toronto's Official Plan, and its intersection with the Crosstown includes a bus terminal connected to the LRT stop. These local and regional connections are expected to draw new transportation demand and offer more convenient transit alternatives potentially encouraging a modal shift from private automobile usage.
- 1st Time – Our Trial Run to Test the Data Source Lack of Areawide and Disrupted Local Network due to Construction Potential to Understand “Hard-to-Study” Travel Patterns (i.e.: vehicle trip origins, passby trip percentages)
- For these two studies, Big Data was used to understand vehicle behavior. This might seem counterintuitive, but creating multimodal options also means reducing vehicle demand and providing options for travel without cars. (Questions planners are asking themselves)
- Larger sample size than local surveys More complete O-D information than sensors Possible to create detailed routing analyses Could analyze commercial truck behavior specifically Ability to iterate and reiterate on the study to optimize results Ability to collect data without sending people into harm’s way on roadsides “in the field”
- Investigate the source of personal and commercial vehicle trips within the transportation area of influence Understand origins and destinations of personal vehicle trips on specific corridors Measure travel times from key activity centers to the study area Determine the share of commercial truck trips in the study area, and where they begin and end their trips It’s important to note that we used this data resource in combination with other data sources – surveys, sensors, etc.
- Next, let’s dive into the details on the type of data that Toronto used for the Don Mills Crossing study. Navigation-GPS data Derived from connected cars and navigation apps 5-meter spatial precision Ideal for understanding personal vehicle and commercial truck trips
- Since this study was completed, another data source has come out – Location-Based Services data, or LBS data. If it had been available, it would have been a great complement to the navigation-GPS data. This data source However, LBS data does not provide information about commercial truck trips.
- The city of Toronto ran 14 discrete studies to obtain the key information required for Don Mills Crossing in just two weeks. They could start high-level to identify areas of interests, then drill down on those key areas and intersections in greater detail (Screenshots show high-level O-D of study area Don Mills Crossing study area, then directional O-D analysis that drills down on key arterials) The ease of use of our platform allows users to optimize, re-run, and optimize more – in just minutes to hours No other provider can offer this combination of speed, ease of use, and customization They also used our calibration tool to automatically scale our normalized index values to counts (Screenshot shows location of every calibration zone in StreetLight InSight. Then it shows a map of the commercial vehicle traffic scaled to counts from their report.) Scaling to counts made the data derived from sensors much more useful In short, our platform makes putting Big Data to work simple and easy
- *Will split this into two slides) Dewan: Walks through results provided by navigation-GPS data From the report: Nearly one-fourth of trips along Don Mills Road and Eglinton Avenue East originate outside of Toronto. The share of outside trips is roughly 10~15% at other collector or locals streets (Exhibit 4-12). In addition, roughly 60% of vehicles pass-though the transportation influence area without stopping at any destinations (Exhibit 4-14). These findings from smart data provides strong evidence that vehicles generated from outside of Toronto mainly uses major arterials and Don Valley Parkway, adding additional stress on the top of local traffic. overall travel times by vehicle to the transportation area of influence were estimated based on average travel distance. Although the study area is located roughly in the middle of the City, vehicles travelling to and from Etobicoke or other western area face the highest delays (50 min to more than an hour) due to lack of direct connections to the study area (Exhibit 4-13). Direct connections through Don Valley Parkway provide faster connections to downtown. In the future, direct Crosstown LRT connections will provide better alternative and encourage shifting mode towards transit to address longer travel time under existing conditions. Commercial activities are only 2.5% of total vehicles although nearly 10% of truck traffic volume of total traffic is observed on Millwood Road that links to Leaside Business area (Exhibit 4-15). Slightly higher truck usage is observed along Don Valley Parking ramps and major intersections along Lawrence Avenue East. Although majority of the commercial trips remain within the transportation area of influence (52% to 61%), downtown contributes to the second highest share of trip origins (11~12%). A relatively small share (13~16%) of commercial trips originates from outside of Toronto. The pattern of commercial origin and destination remains the same regardless of peak hours during the day. Unlike other business areas in the city, overlapping personal and commercial vehicle peak periods poses a challenge to manage goods movement delivery which could be typically done during off-peak periods. Further detail information will be needed to manage goods movement in the area.
- *Will split this into two slides) Dewan: Walks through results provided by navigation-GPS data From the report: Nearly one-fourth of trips along Don Mills Road and Eglinton Avenue East originate outside of Toronto. The share of outside trips is roughly 10~15% at other collector or locals streets (Exhibit 4-12). In addition, roughly 60% of vehicles pass-though the transportation influence area without stopping at any destinations (Exhibit 4-14). These findings from smart data provides strong evidence that vehicles generated from outside of Toronto mainly uses major arterials and Don Valley Parkway, adding additional stress on the top of local traffic. overall travel times by vehicle to the transportation area of influence were estimated based on average travel distance. Although the study area is located roughly in the middle of the City, vehicles travelling to and from Etobicoke or other western area face the highest delays (50 min to more than an hour) due to lack of direct connections to the study area (Exhibit 4-13). Direct connections through Don Valley Parkway provide faster connections to downtown. In the future, direct Crosstown LRT connections will provide better alternative and encourage shifting mode towards transit to address longer travel time under existing conditions. Commercial activities are only 2.5% of total vehicles although nearly 10% of truck traffic volume of total traffic is observed on Millwood Road that links to Leaside Business area (Exhibit 4-15). Slightly higher truck usage is observed along Don Valley Parking ramps and major intersections along Lawrence Avenue East. Although majority of the commercial trips remain within the transportation area of influence (52% to 61%), downtown contributes to the second highest share of trip origins (11~12%). A relatively small share (13~16%) of commercial trips originates from outside of Toronto. The pattern of commercial origin and destination remains the same regardless of peak hours during the day. Unlike other business areas in the city, overlapping personal and commercial vehicle peak periods poses a challenge to manage goods movement delivery which could be typically done during off-peak periods. Further detail information will be needed to manage goods movement in the area.
- *Will split this into two slides) Dewan: Walks through results provided by navigation-GPS data From the report: Nearly one-fourth of trips along Don Mills Road and Eglinton Avenue East originate outside of Toronto. The share of outside trips is roughly 10~15% at other collector or locals streets (Exhibit 4-12). In addition, roughly 60% of vehicles pass-though the transportation influence area without stopping at any destinations (Exhibit 4-14). These findings from smart data provides strong evidence that vehicles generated from outside of Toronto mainly uses major arterials and Don Valley Parkway, adding additional stress on the top of local traffic. overall travel times by vehicle to the transportation area of influence were estimated based on average travel distance. Although the study area is located roughly in the middle of the City, vehicles travelling to and from Etobicoke or other western area face the highest delays (50 min to more than an hour) due to lack of direct connections to the study area (Exhibit 4-13). Direct connections through Don Valley Parkway provide faster connections to downtown. In the future, direct Crosstown LRT connections will provide better alternative and encourage shifting mode towards transit to address longer travel time under existing conditions. Commercial activities are only 2.5% of total vehicles although nearly 10% of truck traffic volume of total traffic is observed on Millwood Road that links to Leaside Business area (Exhibit 4-15). Slightly higher truck usage is observed along Don Valley Parking ramps and major intersections along Lawrence Avenue East. Although majority of the commercial trips remain within the transportation area of influence (52% to 61%), downtown contributes to the second highest share of trip origins (11~12%). A relatively small share (13~16%) of commercial trips originates from outside of Toronto. The pattern of commercial origin and destination remains the same regardless of peak hours during the day. Unlike other business areas in the city, overlapping personal and commercial vehicle peak periods poses a challenge to manage goods movement delivery which could be typically done during off-peak periods. Further detail information will be needed to manage goods movement in the area.
- *Will split this into two slides) Dewan: Walks through results provided by navigation-GPS data From the report: Nearly one-fourth of trips along Don Mills Road and Eglinton Avenue East originate outside of Toronto. The share of outside trips is roughly 10~15% at other collector or locals streets (Exhibit 4-12). In addition, roughly 60% of vehicles pass-though the transportation influence area without stopping at any destinations (Exhibit 4-14). These findings from smart data provides strong evidence that vehicles generated from outside of Toronto mainly uses major arterials and Don Valley Parkway, adding additional stress on the top of local traffic. overall travel times by vehicle to the transportation area of influence were estimated based on average travel distance. Although the study area is located roughly in the middle of the City, vehicles travelling to and from Etobicoke or other western area face the highest delays (50 min to more than an hour) due to lack of direct connections to the study area (Exhibit 4-13). Direct connections through Don Valley Parkway provide faster connections to downtown. In the future, direct Crosstown LRT connections will provide better alternative and encourage shifting mode towards transit to address longer travel time under existing conditions. Commercial activities are only 2.5% of total vehicles although nearly 10% of truck traffic volume of total traffic is observed on Millwood Road that links to Leaside Business area (Exhibit 4-15). Slightly higher truck usage is observed along Don Valley Parking ramps and major intersections along Lawrence Avenue East. Although majority of the commercial trips remain within the transportation area of influence (52% to 61%), downtown contributes to the second highest share of trip origins (11~12%). A relatively small share (13~16%) of commercial trips originates from outside of Toronto. The pattern of commercial origin and destination remains the same regardless of peak hours during the day. Unlike other business areas in the city, overlapping personal and commercial vehicle peak periods poses a challenge to manage goods movement delivery which could be typically done during off-peak periods. Further detail information will be needed to manage goods movement in the area.
- *Will split this into two slides) Dewan: Walks through results provided by navigation-GPS data From the report: Nearly one-fourth of trips along Don Mills Road and Eglinton Avenue East originate outside of Toronto. The share of outside trips is roughly 10~15% at other collector or locals streets (Exhibit 4-12). In addition, roughly 60% of vehicles pass-though the transportation influence area without stopping at any destinations (Exhibit 4-14). These findings from smart data provides strong evidence that vehicles generated from outside of Toronto mainly uses major arterials and Don Valley Parkway, adding additional stress on the top of local traffic. overall travel times by vehicle to the transportation area of influence were estimated based on average travel distance. Although the study area is located roughly in the middle of the City, vehicles travelling to and from Etobicoke or other western area face the highest delays (50 min to more than an hour) due to lack of direct connections to the study area (Exhibit 4-13). Direct connections through Don Valley Parkway provide faster connections to downtown. In the future, direct Crosstown LRT connections will provide better alternative and encourage shifting mode towards transit to address longer travel time under existing conditions. Commercial activities are only 2.5% of total vehicles although nearly 10% of truck traffic volume of total traffic is observed on Millwood Road that links to Leaside Business area (Exhibit 4-15). Slightly higher truck usage is observed along Don Valley Parking ramps and major intersections along Lawrence Avenue East. Although majority of the commercial trips remain within the transportation area of influence (52% to 61%), downtown contributes to the second highest share of trip origins (11~12%). A relatively small share (13~16%) of commercial trips originates from outside of Toronto. The pattern of commercial origin and destination remains the same regardless of peak hours during the day. Unlike other business areas in the city, overlapping personal and commercial vehicle peak periods poses a challenge to manage goods movement delivery which could be typically done during off-peak periods. Further detail information will be needed to manage goods movement in the area.
- *Will split this into two slides) Dewan: Walks through results provided by navigation-GPS data From the report: Nearly one-fourth of trips along Don Mills Road and Eglinton Avenue East originate outside of Toronto. The share of outside trips is roughly 10~15% at other collector or locals streets (Exhibit 4-12). In addition, roughly 60% of vehicles pass-though the transportation influence area without stopping at any destinations (Exhibit 4-14). These findings from smart data provides strong evidence that vehicles generated from outside of Toronto mainly uses major arterials and Don Valley Parkway, adding additional stress on the top of local traffic. overall travel times by vehicle to the transportation area of influence were estimated based on average travel distance. Although the study area is located roughly in the middle of the City, vehicles travelling to and from Etobicoke or other western area face the highest delays (50 min to more than an hour) due to lack of direct connections to the study area (Exhibit 4-13). Direct connections through Don Valley Parkway provide faster connections to downtown. In the future, direct Crosstown LRT connections will provide better alternative and encourage shifting mode towards transit to address longer travel time under existing conditions. Commercial activities are only 2.5% of total vehicles although nearly 10% of truck traffic volume of total traffic is observed on Millwood Road that links to Leaside Business area (Exhibit 4-15). Slightly higher truck usage is observed along Don Valley Parking ramps and major intersections along Lawrence Avenue East. Although majority of the commercial trips remain within the transportation area of influence (52% to 61%), downtown contributes to the second highest share of trip origins (11~12%). A relatively small share (13~16%) of commercial trips originates from outside of Toronto. The pattern of commercial origin and destination remains the same regardless of peak hours during the day. Unlike other business areas in the city, overlapping personal and commercial vehicle peak periods poses a challenge to manage goods movement delivery which could be typically done during off-peak periods. Further detail information will be needed to manage goods movement in the area.
- *Will split this into two slides) Dewan: Walks through results provided by navigation-GPS data From the report: Nearly one-fourth of trips along Don Mills Road and Eglinton Avenue East originate outside of Toronto. The share of outside trips is roughly 10~15% at other collector or locals streets (Exhibit 4-12). In addition, roughly 60% of vehicles pass-though the transportation influence area without stopping at any destinations (Exhibit 4-14). These findings from smart data provides strong evidence that vehicles generated from outside of Toronto mainly uses major arterials and Don Valley Parkway, adding additional stress on the top of local traffic. overall travel times by vehicle to the transportation area of influence were estimated based on average travel distance. Although the study area is located roughly in the middle of the City, vehicles travelling to and from Etobicoke or other western area face the highest delays (50 min to more than an hour) due to lack of direct connections to the study area (Exhibit 4-13). Direct connections through Don Valley Parkway provide faster connections to downtown. In the future, direct Crosstown LRT connections will provide better alternative and encourage shifting mode towards transit to address longer travel time under existing conditions. Commercial activities are only 2.5% of total vehicles although nearly 10% of truck traffic volume of total traffic is observed on Millwood Road that links to Leaside Business area (Exhibit 4-15). Slightly higher truck usage is observed along Don Valley Parking ramps and major intersections along Lawrence Avenue East. Although majority of the commercial trips remain within the transportation area of influence (52% to 61%), downtown contributes to the second highest share of trip origins (11~12%). A relatively small share (13~16%) of commercial trips originates from outside of Toronto. The pattern of commercial origin and destination remains the same regardless of peak hours during the day. Unlike other business areas in the city, overlapping personal and commercial vehicle peak periods poses a challenge to manage goods movement delivery which could be typically done during off-peak periods. Further detail information will be needed to manage goods movement in the area.
- *Will split this into two slides) Dewan: Walks through results provided by navigation-GPS data From the report: Nearly one-fourth of trips along Don Mills Road and Eglinton Avenue East originate outside of Toronto. The share of outside trips is roughly 10~15% at other collector or locals streets (Exhibit 4-12). In addition, roughly 60% of vehicles pass-though the transportation influence area without stopping at any destinations (Exhibit 4-14). These findings from smart data provides strong evidence that vehicles generated from outside of Toronto mainly uses major arterials and Don Valley Parkway, adding additional stress on the top of local traffic. overall travel times by vehicle to the transportation area of influence were estimated based on average travel distance. Although the study area is located roughly in the middle of the City, vehicles travelling to and from Etobicoke or other western area face the highest delays (50 min to more than an hour) due to lack of direct connections to the study area (Exhibit 4-13). Direct connections through Don Valley Parkway provide faster connections to downtown. In the future, direct Crosstown LRT connections will provide better alternative and encourage shifting mode towards transit to address longer travel time under existing conditions. Commercial activities are only 2.5% of total vehicles although nearly 10% of truck traffic volume of total traffic is observed on Millwood Road that links to Leaside Business area (Exhibit 4-15). Slightly higher truck usage is observed along Don Valley Parking ramps and major intersections along Lawrence Avenue East. Although majority of the commercial trips remain within the transportation area of influence (52% to 61%), downtown contributes to the second highest share of trip origins (11~12%). A relatively small share (13~16%) of commercial trips originates from outside of Toronto. The pattern of commercial origin and destination remains the same regardless of peak hours during the day. Unlike other business areas in the city, overlapping personal and commercial vehicle peak periods poses a challenge to manage goods movement delivery which could be typically done during off-peak periods. Further detail information will be needed to manage goods movement in the area.
- Tori to thank presenters. Explain that slides and recording will go out in 24 hours. Begin Q&A session.