The use of equilibrium network models in logistics management: With application to the U.S. coal industry

This paper discusses the use in logistics management of the freight network equilibrium model, developed in Harker and Friesz (1985a, b), called the Generalized Spatial Price Equilibrium Model, or GSPEM. After this discussion, computational techniques for solving this model are presented. The application of GSPEM to the analysis of the U.S. coal economy is then presented and future extensions of this line of research are discussed.     

The evolution of U.S. rail freight pricing in the post-deregulation era: revenues versus marginal costs for five commodity types

There have now been over three decades of experience with rate-making freedom for all modes of intercity freight transport in the United States. Most evidence suggests that regulatory change has been beneficial for the rail industry and its users. Despite evidence of positive impacts of regulatory reform of U.S. freight transport, there is limited evidence related to long-term pricing trends by commodity in the deregulated era. Moreover, U.S. shipper groups have called for increased regulation of U.S. railroads, citing increased rates and profits, and monopoly pricing to “captive shippers.” This study estimates U.S. railroad revenue-marginal cost ratios for seven different commodities between 1986 and 2008. Interestingly, we find no significant increase in revenue-cost margins for commodities thought to be “most captive” (coal and chemicals), while finding large increases for some commodities thought to be “non-captive.” These results may provide insight into the impacts of regulatory reform in other countries, where there are similar concerns of equitable pricing and financial viability. They suggest that a move toward a more market-based pricing system can enhance railroad viability without harming those with fewer transport options.     

Advanced freight transportation systems for congested urban areas

Urban freight transportation constitutes both an extremely important and a rather disturbing activity. Increasingly, one observes efforts to measure and control freight movements within city centers. We introduce a possible organizational and technological framework for the integrated management of urban freight transportation and identify important associated planning and operation issues and models. We then describe a formulation for one of these problems, the design of the proposed logistical structure, and discuss algorithmic and implementation issues. Our model city and challenge is Rome.     

A time-dependent freight tour synthesis model

This paper introduces a model of urban freight demand that seeks to estimate tour flows from secondary data sources e.g., traffic counts, to bypass the need for expensive surveys. The model discussed in this paper, referred as Freight Tour Synthesis (FTS), enhances current techniques by incorporating the time-dependent tour-based behavior of freight vehicles, and the decision maker’s (e.g., metropolitan planning agency planner) preferences for different sources of information. The model, based on entropy maximization theory, estimates the most likely set of tour flows, given a set of freight trip generation estimates, a set of traffic counts per time interval, and total freight transportation cost in the network. The type of inputs used allows the assessment of changes in infrastructure, policy and land use. The ability of the model to replicate actual values is assessed using the Denver Region (CO) as a case study.     

Trucking industry demand for urban shared use freight terminals

The issue of shared use urban freight facilities first received attention during the 1970s when it was observed that, while inter-urban freight movements were becoming increasingly efficient, there were significant diseconomies in the movement of freight via truck within urban areas. Early research suggested that shared urban freight facilities should be constructed so that trucking companies could consolidate smaller shipments into larger ones. In the past few years, the concept of Urban Ports has gained increasing attention, not just for carriers who need to load and unload freight, but to provide a place near the urban center for truckers to wait out peak traffic periods. In this paper, using recently developed survey data, we examine trucking company interest in such facilities by examining the results of an ordered probit demand model.     

A data-driven approach to manpower planning at U.S.–Canada border crossings

We investigate the staffing problem at Peace Arch, one of the major U.S.–Canada border crossings, with the goal of reducing time delay without compromising the effectiveness of security screening. Our data analytics show how the arrival rates of vehicles vary by time of day and day of week, and that the service rate per booth varies considerably by the time of day and the number of active booths. We propose a time-varying queueing model to capture these dynamics and use empirical data to estimate the model parameters using a multiple linear regression. We then formulate the staffing task as an integer programming problem and derive a near-optimal workforce schedule. Simulations reveal that our proposed workforce policy improves on the existing schedule by about 18% in terms of average delay without increasing the total work hours of the border staff.     

Joint optimization of freight facility location and pavement infrastructure rehabilitation under network traffic equilibrium

Establishment of industry facilities often induces heavy vehicle traffic that exacerbates congestion and pavement deterioration in the neighboring highway network. While planning facility locations and land use developments, it is important to take into account the routing of freight vehicles, the impact on public traffic, as well as the planning of pavement rehabilitation. This paper presents an integrated facility location model that simultaneously considers traffic routing under congestion and pavement rehabilitation under deterioration. The objective is to minimize the total cost due to facility investment, transportation cost including traffic delay, and pavement life-cycle costs. Building upon analytical results on optimal pavement rehabilitation, the problem is formulated into a bi-level mixed-integer non-linear program (MINLP), with facility location, freight shipment routing and pavement rehabilitation decisions in the upper level and traffic equilibrium in the lower level. This problem is then reformulated into an equivalent single-level MINLP based on Karush–Kuhn–Tucker (KKT) conditions and approximation by piece-wise linear functions. Numerical experiments on hypothetical and empirical network examples are conducted to show performance of the proposed algorithm and to draw managerial insights.     

Impacts of trade related sustainability strategies on freight transportation: Modelling framework and application for France

The circular and functional economies are being presented in the literature as potential strategies for future sustainable societies. In terms of the consequences for supply chains, they will promote a much more dispersed and diversified, local and network based usage of goods than the current economy, which is comparatively linear, concentrated, long distance oriented and scale economy based. A gap in the literature is the assessment of the effects of these systems on freight transport flows. In our paper, we present a first attempt at estimating this impact using freight transport scenario building and quantitative modelling. In order to translate the main parameters that characterize these systems into factors determining freight transportation volumes, we develop a framework based on a typology of goods categories describing functional and spatial proximity between producers and consumers. In order to simulate changes in the economy, we develop scenarios for the shifting of goods from one category to another and, additionally, include internalization policies that should guide their realization. We calculate the impacts on freight flows using a new interregional transport model for France that includes distribution chains and produces estimates of external costs of transport. Our results show that circular and functional economies could lead to a 2–5% reduction of air pollutant emissions and up to a 14–26% reduction if combined with the internalization of external costs. The scenario with ongoing mass production for differentiated demand is found to lead to a 5% increase of environmental impacts compared to the baseline.     

Transport deregulation and cross‐border trucking in North America

Internationally, recent years have seen a spread of deregulatory policies with respect to transport. Equally, transport movements across national boundaries have expanded as trade has grown. Despite these two quite noticeable trends comparatively little has been written on the problems that are created for cross‐border traffic as the result of differential changes in national regulatory policies. This paper is concerned with looking specifically at what has happened in the context of road freight transport movements across the U.S.‐Canadian border and the wider (especially for the formulation of a Common Transport Policy for the EEC) lessons which can be learned from it.     

Adaptive road border crossing system management: the role of priority programs

Land border crossings in North America, such as those between Canada and U.S.A., are expected to experience severe imbalance of travel demand and capacity of processors. During peak travel periods, this is already the case at high traffic locations. The land border crossing authorities have to address problems of congestion, national security and environmental impacts in the operation of the existing systems and to continue to address these problems as a part of infrastructure expansion plans. There is a need to adapt the crossing system management in order to accommodate efficiency and productivity‐oriented priority crossing measures. From a methodological perspective, it is a challenge to evaluate the role of priority crossing measures within the complex border crossing system. This paper reports research on modelling priority crossing initiatives. A microsimulation approach was used to model and analyse integrated processors of the Peace Bridge crossing system between Fort Erie (Ontario) and Buffalo (New York) under different scenarios of travel demand, customs processing times, priority crossing and queue jump lanes for automobile and truck traffic. Findings show the extent to which a border crossing system with priority crossing and queue jump lanes is more efficient and productive than one without these innovations. Copyright © 2011 John Wiley & Sons, Ltd.     

Effect of mass on multimodal fuel consumption in moving people and freight in the U.S.

The United States transportation sector consumes 5 billion barrels of petroleum annually to move people and freight around the country by car, truck, train, ship and aircraft, emitting significant greenhouse gases in the process. Making the transportation system more sustainable by reducing these emissions and increasing the efficiency of this multimodal system can be achieved through several vehicle-centric strategies. We focus here on one of these strategies – reducing vehicle mass – and on collecting and developing a set of physics-based expressions to describe the effect of vehicle mass reduction on fuel consumption across transportation modes in the U.S. These expressions allow analysts to estimate fuel savings resulting from vehicle mass reductions (termed fuel reduction value, FRV), across modes, without resorting to specialized software or extensive modeling efforts, and to evaluate greenhouse gas emission and cost implications of these fuel savings. We describe how FRV differs from fuel intensity (FI) and how to properly use both of these metrics, and we provide a method to adjust FI based on mass changes and FRV. Based on this work, we estimate that a 10% vehicle mass reduction (assuming constant payload mass) results in a 2% improvement in fuel consumption for trains and light, medium, and heavy trucks, 4% for buses, and 7% for aircraft. When a 10% vehicle mass reduction is offset by an increase in an equivalent mass of payload, fuel intensity (fuel used per unit mass of payload) increases from 6% to 23%, with the largest increase being for aircraft.     

An inter‐Urban freight mode choice model

The Australian Railway Research and Development Organisation is conducting a study with one of its objectives being to determine factors affecting freight mode use. Part of this has included the development and calibration of freight mode choice models. This paper outlines the results obtained from the application of an Elimination‐by‐Aspects (EBA) model to this task. The paper describes the theoretical background to the EBA model, within the context of a general choice process, and then describes the results of the model when applied to three samples of shippers involved in regional freight transport.     

The European freight railway system as a hub-and-spoke network

This paper addresses a hub-and-spoke network problem for railroad freight, where a central planner is to find transport routes, frequency of service, length of trains to be used, and transportation volume. Hub-and-spoke networks, often found in air freight, have not been favoured by railways in the past. Such a structure could be profitable, however, if there exist concentrated freight flows on some service links. We formulate a linear integer programming model whose objective function includes not only the typical operational cost, but also cost due to the transit time spent by freight in the network. We then develop heuristic algorithms to solve large scale instances occurring in rail freight systems in France plus Italy; Germany; and a 10-country European network. By assuming that every node is equipped with consolidation capability, we let the final solution naturally reveal potential hub locations, the impact of several of which is studied by sensitivity analysis.     

Quantifying the environmental impacts of increasing high occupancy vehicle lanes in the United States

To what extent will increasing High Occupancy Vehicle (HOV) lane-kilometer incentivize carpooling and reduce emissions of air pollutants and greenhouse gases? To answer these questions, we develop a multiple regression model relating HOV lanes and other socioeconomic factors to carpooling propensity in all 50 U.S. states and the District of Columbia, then calculate the extent to which increasing HOV lane-kilometers would lead to reductions in carbon dioxide equivalent (CO₂e) and major air pollutants across the U.S., by state. Increasing HOV lane extent has the greatest potential to reduce annual CO₂e in the District of Columbia, followed by Hawaii and New York. The smallest potential is found in states with the lowest population density, led by North Dakota. We then explore the extent to which recommendations made at one level of data aggregation (that of individual states) may be valid for another level, such as individual counties. The only state with sufficient data available to disaggregate the model to the county level is California, where we found a lower potential for state-wide CO₂e emission reductions under the county-level model as compared to the state-level model (0.69% as compared to 1.08%, under the same hypothetical scenario), albeit with significant differences in emission reduction potential between counties with higher vs. lower population densities. This analysis demonstrates the potential to generate generalizable insight into the magnitude of vehicle emission reductions that might be achieved through expanding HOV lanes, and highlights the importance of data disaggregation in identifying the optimal locations for potential reductions.     

Incorporating Logistics in Freight Transport Demand Models: State-of-the-Art and Research Opportunities

Freight transport demand is a demand derived from all the activities needed to move goods between locations of production to locations of consumption, including trade, logistics and transportation. A good representation of logistics in freight transport demand models allows us to predict the effects of changes in logistics systems on future transport flows. As such it provides better estimations of the costs of interaction and allows to predict changes in spatial patterns of freight transport flows more accurately. In recent years, the attention for freight modelling has been growing and new research work has appeared aimed at incorporating logistics in freight models. In this paper we review the state of the art in the representation of logistics considerations in freight transport demand models. Our focus is on the service and cost drivers of changes in logistics networks and how these affect freight transport. Our review proceeds along a conceptual framework for modelling that goes beyond the conventional 4-step modelling approach. We identify promising areas for freight modelling that have an integrative function within this framework, such as spatial computable general equilibrium models, supply chain choice models and hypernetwork models.     

Impacts of combining partial and general equilibrium modelling in freight transport analyses – a forest sector case study from Norway

Abstract

The forest sector in Norway is very transport intensive, accounting for approximately 14% of total domestic freight transport traffic on Norwegian roads. This paper presents an analysis linking a general equilibrium freight transport modelling tool with a partial equilibrium model of the forest sector. The freight transport model predicts transport costs, modal split and transport patterns, and the results are treated as inputs to the forest sector model. The objective of the paper is to analyse the modelling effect of taking forest sector model effects back into the freight transport model and treated as new demand. Compared to a base scenario for the year 2020, we compare analyses with and without this new demand from the forest sector model back into the freight transport modelling tool.     

Profit-based maritime container assignment models for liner shipping networks

We propose the problem of profit-based container assignment (P-CA), in which the container shipment demand is dependent on the freight rate, similar to the “elastic demand” in the literature on urban transportation networks. The problem involves determining the optimal freight rates, the number of containers to transport and how to transport the containers in a liner shipping network to maximize the total profit. We first consider a tactical-level P-CA with known demand functions that are estimated based on historical data and formulate it as a nonlinear optimization model. The tactical-level P-CA can be used for evaluating and improving the container liner shipping network. We then address the operational-level P-CA with unknown demand functions, which aims to design a mechanism that adjusts the freight rates to maximize the profit. A theoretically convergent trial-and-error approach, and a practical trial-and-error approach, are developed. A numerical example is reported to illustrate the application of the models and approaches.     

Development of a differential route share model for railroad freight traffic

This paper summarizes work undertaken towards development and calibration of a model to predict the distribution of rail freight traffic among competing routes. The model is designed for use in analyzing the traffic effects of changes in the level-of-service on selected rail lines. The model predicts route shares based on the overall network configuration of each railroad participating in a given market. The model selects feasible routes, discards those routes which appear to be too circuitous or costly, and then assigns traffic to the remaining routes in accordance with several network characteristics. It is designed to be sensitive to level-of-service changes, and to simulate the response of shippers and railroads to a competitive environment. A multiple route-finding algorithm was used to find possible routes based on the number of railroads operating at the originating and terminating end of a market. Multiple routes were determined and matched with observed traffic flows from the ICC One-Percent Waybill Sample. Physical network characteristics for each route, including distance, junction frequency, and “impedance,” were calculated from the network model and were correlated with the traffic share observed on each route in the market. A two-stage model was developed to find feasible routes from the set of possible routes and to allocate traffic to feasible routes based on levels-of-service. The model was calibrated on 9,793 routes from 1,199 markets with twenty or more carloads from the 1977 One-Percent Carload Waybill Sample. Model calibration supported the hypothesis that network route characteristics did indeed influence shipper choice of route, and that a normative model could be used to assess relative attractiveness of routes under various railroad corporate ownership restructuring scenarios.     

An agent-based framework for cooperative planning of intermodal freight transport chains

The recent development of Intelligent Transportation Systems offers the possibility of cooperative planning of multi-actor systems in a distributed framework, by enabling prompt exchange of information among actors. This paper proposes a modeling framework for cooperation in intermodal freight transport chains as multi-actor systems. In this framework, the problem of optimizing freight transportation is decomposed into a suitable set of sub-problems, each representing the operations of an actor which are connected using a negotiation scheme. A Discrete Event model is developed which optimizes the system on a rolling horizon basis to account for the dynamics of intermodal freight transport operations. This framework allows for an event driven short/medium term planning of intermodal freight transport chains. The proposed methodology is evaluated using a realistic case study, and the results are compared against the First-Come-First-Served strategy, highlighting the significance of cooperation in systems operating close to capacity.     

Comprehensive regional modeling for long-range planning: linking integrated urban models and geographic information systems

This study demonstrates the sequential linking of two types of models to permit the comprehensive evaluation of regional transportation and land use policies. First, we operate an integrated urban model (TRANUS), which represents both land and travel markets with zones and networks. The travel and land use projections from TRANUS are outlined, to demonstrate the general reasonableness of the results, as this is the first application of a market-based urban model in the US. Second, the land use projections for each of the 58 zones in the urban model were fed into a Geographic Information System (GIS)-based land allocation model, which spatially allocates the several land uses within each zone according to simple accessibility rules. While neither model is new, this is one of the first attempts to link these two types of models for regional policy assessments. Other integrated urban models may be linked to other GIS land allocation models in this fashion. Pairing these two types of models allows the user to gain the advantages of the urban models, which represent spatial competition across a region and produce measures of user welfare (traveler and locator surplus), and the advantages of the GIS land allocation models, which produce detailed land use maps that can then be used for environmental impact assessment.