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Smarter Urban Mobility Systems Around the Pacific Rim. Jerry Walters Fehr & Peers. Virtuous Cycles in City Planning and Operation. How Scale Matters Urban Forms that Reduce Traffic, Energy and Emissions . D ensity D iversity D esign D estinations D istance to Transit D evelopment Scale - PowerPoint PPT Presentation
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Smarter Urban Mobility SystemsAround the Pacific Rim
Jerry WaltersFehr & Peers
Virtuous Cycles in City Planning and Operation
Simulate
Plan
Iterate /Implement
Approve
Evaluate Analyze
Design
Build
Operate
Monitor +
Manage
How Scale MattersUrban Forms that Reduce Traffic, Energy and Emissions
Dimensions of Urban Form and Vehicle Use
1. Density
2. Diversity
3. Design
4. Destinations5. Distance to Transit
6. Development Scale
7. Demographics
8. Demand Management
National Evidence on MXD Travel Generation
GROSS TRIP GENERATION TRIP GENERATION REDUCTIONS
64,036 5,288 6,168
SAMPLE DEVELOPMENT PROJECT PRELIMINARY TRANSPORTATION SUMMARY
Residential
Commercial
Others
Daily
Residential
Commercial
Others
AM
Residential
Commercial
Others
PM
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Daily AM PM
44,0
54
3,90
3
4,36
8
31% 26% 29%Reductions Net Trips
SUMMARY OF VMT REDUCTIONS (FROM MXD)External Vehicle Trips
Daily
AMPM
-27%
-22%-25%
External Walk Trips
External Transit Trips
Internal Trip Capture
Net External Auto Trips
SUMMARY OF TRIPS (FROM MXD)
Daily
AMPM
10,790 729 934
3,658 287 334
5,534 369 532
44,0543,9034,368
SHARED PARKING MODEL*
-
2,000
4,000
6,000
8,000
10,000
12,000
6AM
7AM
8AM
9AM
10AM
11AM
12PM
1 PM 2 PM 3 PM 4 PM 5 PM 6 PM 7 PM 8 PM 9 PM 10PM
11PM
12AM
Weekday Peak Demand Weekend Peak Demand
11,170 9,929
Weekday
Shared Parking Demand ReductionWeekend
18% 27%
Potential Cost Savings
Surface LotAbove Ground
StructureSubterranean
$6,150,000 $51,250,00 $82,000,000
GHG EmissionsCO2 Equivalent/Day
41.6 Metric Tons
TOP ISSUES/CONCERNS• Congestion at freeway ramps and intersections adjacent to the project .• Parking intrusion into the neighborhood north of the project.• Cut-through traffic through the neighborhood.
POTENTIAL TDM OPPORTUNITIES• Ride Share Programs• Parking Cash Out• Flextime/Telecommuting Opportunities
DailyAMPM
-31%Daily
AM -26%
PM -29%
Raw Net270,046 196,72726,499 20,68427,455 20,589
Vehicle Miles Travel (VMT)
DailyAMPM
64,036 44,0545,288 3,9036,168 4,368
Raw Net
GrossNet 44,054 3,903 4,368
Network Data, Analytics and Simulation
Kunming-Chenggong New Town
• 2,800 acres• 232,300 population• 243,300 jobs
Objectives Energy efficiency Emissions and GHG reduction Economic and fiscal performance Health and safety
Kunming New Town / Sustainable Objectives
… and Challenges
Macro: Pace of development Planning and performance mandates Car culture Lack of data and models
Integrated Zoning, Circulation Systems
Sesame Street Quiz
Inter-Connected Network Option
Conventional Network Plan
Kunming Urban Form / Network Form
Network Simulation
(Ignores benefits of 9% trip reduction and traffic dispersion to parallel routes)
Operational Comparison
Measure Standard Arterial Couplet
Pedestrian Crossing Distance 35.0 meters 12.7 meters
Min. Pedestrian Crossing Time 37.3 seconds 13.6 seconds
Number of Signal Phases 4 to 8 2 to 5
# of LOS E/F Intersections 4 of 4 (100%) 5 of 16 (31%)
Sustainability Indicators
Measure Standard Arterial Couplet
East-West Travel Time 8 minutes 6 minutes (-25%)
Vehicle Hours of Delay (VHD) 860 VHD 640 VHD (-25%)
Fuel Consumption 9,100 liters 7,500 liters (-18%)
California Legislated Mandates
• AB 32 – Greenhouse gas reduction targets, Cap + Trade • SB 97 – CEQA requirements for GHG assessment
• SB 375 - Linkages among: GHG targets regional transportation sustainable communities strategies
• SB 732 – Grant funding for sustainable communities
• SB226 –Approval streamlining for infill development
• Location Efficiency
• Network Management
• Multi-Modal Focus
• Speed Suitability
Caltrans Smart Mobility Performance Measures
Global Cap Road Pricing
| | |
Max Reduction Work, School:
25%/ 65%
Max Reduction (all VMT): 25%
| | | | | || | | | | |
Land Use/ Location
Neighborhood/ Site Enhancements
Parking Policy/ Pricing
Transit System Improvements
Commute Trip Reduction (CTR)
Progams(assuming mixed-use
Road Pricing/ Management
Max Reduction = 65% (urban), 30% (compact infill), 10% (suburban
center), 5% (suburban)
Max Reduction =5% (without NEV)15% (with NEV)
Max Reduction = 20% Max Reduction = 10% Max Reduction = 25% work VMT Max Reduction =25%
Density (30%) Pedestrian Network (2%)
Parking Supply Limits (12.5%)
Network Expansion (8.2%)
CTR Program<Required> (21% work VMT)
<Voluntary> (6.2% work VMT)
Cordon Pricing (22%)
Design (21.3%) Traffic Calming (1%)Unbundled
Parking Costs (13%)
Service Frequency/Speed
(2.5%)
Transit Fare Subsidy (20% work VMT)
Traffic Flow Improvements
(45% CO2)
Location Efficiency (65%)
NEV Network (14.4%)<NEV Parking>
On-Street Market Pricing (5.5%)
Bus Rapid Transit (3.2%)
Employee Parking Cash-Out (7.7% work VMT)
Required Contributions by
Project
Diversity (30%) Car Share Program (0.7%)
Residential Area Parking Permits
Access Improvements
Workplace Parking Pricing (19.7% work VMT)
Destination Accessibility (20%)
Bicycle Network <Bike Lanes> <Bike Parking>
<Land Dedication for Bike Trails>
Station Bike ParkingAlternative Work Schedules and
Telecommute Program (5.5% work VMT)
Transit Accessibility (25%)
Urban Non-Motorized Zones Local Shuttles CTR Marketing (4.0% work
VMT)
Global Max Reduction (all VMT)75% (urban), 40% (compact infill), 20% (suburban center or suburban with NEV), 15% (suburban)
Cross-Category Max Reduction (all VMT)70% (urban), 35% (compact infill), 15% (suburban center or suburban with NEV), 10% (suburban)
CAPCOA Best Management Practices
High Speed Rail Sustainability Evaluation
Micro/ Macro Analysis of 3-B-L Performance
Data / Analytics
Benefits of Sustainable Transport/ Land Use
Better-Informed Transportation Decisions ASAP
Simulate
Plan
Measure
Approve
Evaluate Analyze
Design
Build
Operate
Manage +
Monitor
Thank You!
Jerry Walters Fehr & Peers
Operationalizing Smarter Urban Mobility Systems
Jerry WaltersFehr & Peers
Goals for Smarter Mobility Systems
• Decisions ASAP and better informed
• Better infrastructure design decisions
• Accurate impact assessments of land development
• User-oriented transit service plans and station designs
• Optimal sizing and integration of on-demand systems
• Tailored mobility services to optimize TDM effectiveness
Lab Work
• Bus reliability by route segment
• GHG, fuel use at traffic signals, roundabouts, stop signs
• Biases/inaccuracies in self-reported journey times
Field Work
• Transit ridership optimization
• Neighborhood parking management enforcement
• Bike station analytics: opportunity effectiveness assessment
Leading-Edge Work
• Express-lane bottleneck removal through GPS-calibrated simulation
• Regional traffic modeling through video and cell O/D identification
• Simulation of campus operation, expansion options
Next Gen Studies for Smart Mobility
Next Gen Studies for Smart Mobility
• Why and how people travel – Longitudinal measurement – Traveler demographics, market segmentation– O/D data vs built environment (D’s)– Models with complex AI objective functions
• Safety studies: – Road, signing and traffic conditions, driver attention
Next Gen Studies for Smart Mobility
• Operational improvements
– Traffic queues and delays, simulation models
– Cruising for parking
– Traveler-weighted transit service level
– Un-served markets • Service availability, traveler characteristics of transit non-users• Demographics of bike-share users, demographics and journey
characteristics of non-users• Comprehensive bicyclist route choice factors, including safety
and security
Data Aggregation and Synthesis Needed
A Dozen Data Desires
1. Higher fidelity traffic flow data
2. Complete traveler O/D movements by all modes
3. Operating flow, interactions and incidents among modes
4. Longitudinal data: before/after stimulus
5. Land use and employment inventories by parcel
6. Over-the-net accessibility: time, cost, reliability, uncertainty
• * calibrate/ validate
A Dozen Data Desires
7. GPS verification of household and workplace surveys
8. Geo-correlation of travel surveys with built context
9. Transaction data to discern travel purpose
10.Consistent variable definitions to allow cross-walking data
11. Consistent sample rates by region
12.Open data from synthesizers via clients and from big actor
data sources
• * calibrate/ validate
Multi-Modal Network Simulation - Silicon Valley
Multi-Modal Network Simulation – Kunming China
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