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