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Transcript of Strategic Planning of National/Regional Freight Transportation Systems : An Analysis TG Crainic, J...
Strategic Planning of National/Regional Freight Transportation
Systems : An Analysis
TG Crainic, J Damay, M Gendreau, R Namboothiri
June 15, 2009
Talk Outline
Problem Motivation Multi-product Multi-modal Freight Transportation Systems Strategic National Planning Research Questions
Models & Methodologies Modeling & Formulation Solution Framework Algorithms & Techniques
Instances & Computational Experiments Conclusions & Future Research
Multi-product Multi-modal Transportation Systems
Provides efficient, smooth, and timely movement of people, goods and information
Supports almost all social or economic activities Vital for society’s existence
System Stakeholders
Shippers Require transportation services to move various commodities in
order to meet consumer demands Chooses services based on cost and quality of service
Carriers Meet demand for transportation by supplying infrastructure and
services Offers services at various levels of service and price.
Governments Supply infrastructure – roads, terminals, ports, rail facilities Regulate and tax the industry
Interaction results in the flow of commodities
Multi-product Multi-modal Transportation Systems
Complex network Strong feedback loops exist among various stakeholders
Multi-product Multi-modal Freight Transportation Systems
System Characteristics Each stakeholder makes individual decisions to optimize
their objective and adjusts operations accordingly Individual user decisions impact the entire system,
modifying the behavior of other users Observed performance is an equilibrium between the
decisions of many participants
Performance Measures System performance measures – capacity of
infrastructure and services User performance measures - efficiency measures for
users
Strategic National Planning
• Analysis and planning of multimodal, multiproduct transportation systems
• Traffic assignment of demand flows for different commodities from specific origins to specific destinations on an inter/multi-modal network
• Integrated, multimodal view of transportation• Understand and predict the behavior of the system and its
components, considering various social, economic, and technological trends and forecasts
• Plan the sustainable development of an efficient system by measuring the impact of contemplated investments or policies on the system and its users
National Planning Models
Comprehensive representation of a multimodal transportation system, its main components and their interactions
Achieve a sufficiently good simulation of the global behavior of the system to offer a realistic representation of the current situation
Broad focus – strategic planning issues at the international, national and regional levels
Adequate analysis tool for planned or forecast scenarios and policies
Research Questions
Evolution of a given transportation system and its response to various modifications in its environment Transportation infrastructure
New modal or intermodal facilities Expansion /Removal(rail lines) of existing ones
Socio-Economic characteristics of the region Population distribution Patterns and volumes of production, consumption and trade
Policies and legislation Environment-related taxation Security related regulation
Technology Hardware – New/Enhanced vehicular and infrastructure technologies Software – Intelligent Transportation Systems
Research Questions
• How do the various non-linear methodologies available in the literature compare for solving this complex problem?
• Can we gain a better understanding of the interactions between the data collection, the data aggregation, the modeling of the different components and their interactions, and the performance measures?
• Can we develop state of the art, new generation models for this challenging problem?
Modeling Methodologies
• Requirements• Efficiently and correctly identify and represent the
fundamental components of the system - demand, supply, performance measures and decision criteria, and their interactions.
• Powerful, flexible and adaptable to the scope of studies encompassing broad range of geographical dimensions
• Analyze the impact of issues identified now, as well as planned or forecast scenarios and policies
• Build new evaluation procedures as the needs arise• Traffic Assignment Models
• Already been presented and transferred into practice for freight transportation systems
• Derived from the nonlinear programming literature• From studies related to passenger transportation• Devised or revisited by us
Model Components
Supply modeling Represent transportation modes, infrastructure, carriers, services
intermodal facilities, capacities and congestion
Demand modeling Identify producers, shippers and intermediaries, and represent
production, consumption and regional distribution volumes, as well as mode choice
Assignment Multi-commodity flows(demand model) to the multimodal
network(supply model)
Data manipulation tools Analysis, fusion, validation and updating of information Result analysis capabilities
Four-step planning method
Mathematical Model
• Demand• O-D demand matrix for each product p Є P• Demand quantity and mode choice is exogenous
• Supply Network• N nodes; M modes; T transfers; A links
• Variables
Path Flow Formulation
More intuitive and provide more comprehensive results in comparison with the link formulations
Effective use of improved computing capabilities and implementation tools
User Optimum vs. System Optimum
Wardrop’s first principle (User Optimum) “The journey times on all the routes actually used are equal, and less
than those which would be experienced by a single flow unit on any unused route”
Objective function
Wardrop’s second principle (System Optimum) “At equilibrium the average journey time is minimum”
Objective function
Wardrop’s first principle reformulated with marginal costs
Solution Framework
Local Improvement Algorithms
Generate a succession of feasible solutions , starting from an initial solution
Involves shortest path calculations Algorithms
Frank-Wolfe Path Equilibration Schemes
Convex Simplex Method Reduced Gradient Method Projected Gradient Method
Multimodal Shortest Path Search
Decomposition Techniques
Build disjoint and complementary sets of O-D pairs Whole
All O-D pairs of current product p
Unitary Decomposition Each O-D pair (Singleton sets) sequentially
Decomposition by class Sort O-D pairs into several classes based on O-D demand, and
process the classes in a pre-defined sequence
Generic Algorithm
Instance Generation
Challenge : Large amounts of data required Input-output structure of the economy and reliable data on
shipments of all products in order to build demand models Synthetic realities for case studies of these systems Scenarios of different sizes
Nodes/Zones Typically disaggregated instance - > 1000 zones Completely aggregated instance - < 50 zones
Modes LTL carriers, TL carriers, barges, container ships, diesel trains
Products P = 1 - Passenger transportation system
Density of Network Density of Demand
Instance Generation
Computational Experiments
Treat the various instances generated with Specific Decomposition Specific Local Improvement Algorithm User behavior assumptions
Output parameters Number of times equilibrium has been reached CPU times required Number of major cycles Number of shortest path searches Number of line search iterations Costs
After Initialization After one major cycle At the end of execution
Conclusions & Future Research
Strategic National Planning of Freight Transportation Systems Comprehensive and realistic models representing current
state of the system Exhaustive evaluation platform encompassing various
solution methodologies
Examining impact of policy-level and technological
advancement initiatives Environment related laws Intelligent Transportation Systems
Questions