mag new generation freight model shrp2 c20 iap project vladimir livshits, ph.d. 2014 ampo annual...
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MAG New Generation Freight Model
SHRP2 C20 IAP Project
Vladimir Livshits, Ph.D.
2014 AMPO Annual Conference, Atlanta, GAOctober 23, 2014
Freight Session
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The Need
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• Planning efforts at ADOT, MAG and PAG have to address new scale of geography – Arizona’s Sun Corridor Mega-Region– Very significant growth in the freight
traffic– Part of the global supply chain
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• Modeling areas reflect mega-regional approach. MAG/PAG ABM includes Maricopa, Pinal, Pima and portions of Yavapai and Gila counties
Source: http://www.jpacaz.org/map.asp
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Freight Planning
Safety and Efficiency
of shipping
Congestion and
Hazards
Economic Impact
Communities and Social
Equity
Integration with Regional
Planning – Freight
Corridors
Land Use and Environmental
Impact
Freight Mobility
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The Legacy
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State of the Practice Truck Models served us well but fall short in answering future freight planning needs• Aggregated model – does not provide for detailed land use, infrastructure, community
planning scenario analysis. Insensitive to agent-based economic scenarios, does not account for evolutionary developments of economic agents in the forecast, insensitive to fine-grained network changes.
• Does not include supply chain models and is not suitable for development of economic scenarios on mega-regional scale. Insensitive to mega-regional supply chain scenarios and technological shifts.
• Static model - does not provide for integration with dynamic traffic simulations, detailed safety analysis, and agent-based passenger travel demand models with continuous timeline.
• Trip-based – is not consistent with operational behavior of carriers and as a result insensitive to operational scenarios or relevant network improvements.
• Does not account for multi-modal aspect of supply chains, does not forecast multimodal freight flows and is not sensitive to multimodal freight issues.
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The Vision
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SHRP2 C20 Guidance
Strategic objectives of SHRP2-C20 project:
• Improve and expand the knowledge base• Develop modeling methods to reflect actual supply chain management practices• Develop modeling methods based on sound economic principles• Maximize use of freight tools by public sector for planning and programming• Improve availability and visibility of data between public and private sectors
In order to achieve the above objectives, the following SHRP2 research initiatives will be addressed in this work:
• Establish techniques and standard practices to validate freight forecasts.• Establish modeling approaches for “behavior-based” freight movement.• Establish analytical approaches that describe how elements of the freight transportation system operate, perform, and impact the
larger overall transportation system.• Determine how economic, demographic, and other factors/conditions drive freight patterns and characteristics. Document economic
and demographic changes related to freight choices.• Advance research to effectively integrate logistics practices (private sector) with transportation policy, planning, and programming
(public sector).
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The Approach
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Main Methodological Principles
• Agent-based micro-simulation model• Multi-modal freight model• Behavioral model, including economic
behavior of establishments, shiuppers, carriers in travel generation and tour formation
• Integration with activity-based passenger model
• Industry-specific model
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Tour Generation
Heavy truck tour rates by industry type
Stop Generation
1 stop
2 stops
……..
11 stops
Tour Completion
Yes – return to home base
No – does not return
Stop Purpose
One of 10 stop types•Retail•Constr.•Farming•Resid.•Govt.•Warehs.•Transp.•Office• Industrial•Service
Stop Location
One of 3,000 TAZs
Stop TOD Choice
1st Stop TOD (24 1-hr periods)
Next Stop TOD (24 1-hr periods)
Source: Kuppam, A. et al. Development of a Tour-Based Truck Travel Demand Model using Truck GPS Data. Presented at the 2014 TRB 93 rd Annual Meeting. January 2014.
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ATRI GPS All Truck IDsApril 2011
All Trucks in April 2011GPS Events = 3,429,603
Truck Tours = 58,637Trucks = 22,657
ATRI GPS Truck ID 3570452April 2011
One Truck (ID 357042) in April 2011GPS Events = 719Truck Tours = 40
Trucks = 1
Source: Kuppam, A. et al. Development of a Tour-Based Truck Travel Demand Model using Truck GPS Data. Presented at the 2014 TRB 93 rd Annual Meeting. January 2014.
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Contractual work and administration
Work plan and organization of TAG
Data Collection – GPS data
Data requirements and data sources
Commodity flow data acquisition
GPS data analysis and processing
Collection and analysis of traffic counts
List of main traffic generators and industries
Establishments/ firm synthesis models
Behavioral establishment interaction model/ distribution models
Tour formation models for different truck types/industries
Establishment survey, commercial vehicles survey
Multimodal network and corresponding documentation.
Mode choice models
Firm Evolution Model
Model Structure and methods
Employment and land use projections
Quality control and Assurance