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.     

European freight transport and the environment: empirical applications and scenarios

Environmental issues have received a prominent place in transport policies of most European countries. The coordination of such policies however, is fraught with many difficulties. The increasing freight flows after the European integration are a source of concern, but have not yet led to straightforward and effective environmental strategies.The paper focuses on the Trans Alpine freight transport systems in the light of the future integration of single national transport systems into the European transport network. The environmental, social and institutional peculiarities of this ‘region’ have favoured—in the past—the development of strong nationally-oriented policies, partly in contrast with the goals promoted by the European Union. The present analysis aims to highlight opportunities, and limits inherent, in the implementation of various infrastructure projects oriented towards a drastic change of the Alpine transport systems structure. The Alpine countries, viz. Austria and Switzerland, play a central role in the promotion of environmental benign modes of transport of goods, with a clear focus on rail. The route choice and modal split of freight flows in Europe are taking place simultaneously. In this paper the results of European freight flow models (based on logit analysis and neural networks) will be presented. An important exercise is then to assess the consequences of various types of eco-taxes on road transport in Europe. In this context, several policy scenarios will be dealt with.     

Freight Data Assembling and Modeling: Methodologies and Practice

Abstract

In comparison to personal travel, freight movements within large metropolitan areas are much less studied. Most conventional transportation models and planning analysis that disregarded freight flows have been criticized on the plausibility of their results and conclusions. To alleviate these problems, this study proposes a non-survey based approach to assemble and process freight data in a systematic way. A freight origin–destination (OD) matrix of freight flows can be developed using secondary data sources. The estimated freight flows can be loaded together with conventional passenger flows onto the regional highway network of a large metropolitan area. As a case study, this non-survey based approach was applied to build a freight OD and study the traffic flows in Los Angeles. It concluded that this approach can be used to analyze urban freight movement in a low-cost way in which planning agencies can overcome the common omission of freight flow information in their transportation plans.     

Exchange rate and transport cost sensitivities of bilateral freight flows between the US and China

This paper explores the long-run impacts of gross domestic product, exchange rate, and transport costs on bilateral air and ocean freight flows between the US and China. The study employs a cointegration framework by using export and import data over the period of 2003:Q1-2014:Q2. Results show that gross domestic product is the key determinant of bilateral freight flows, indicating that real income of a trading partner is a driving force of the bilateral freight flows between the US and China. In examining the sensitivities of the bilateral trade flows, air freight flows are found to be more responsive to a real income change than ocean freight flows. The bilateral exchange rate is a significant factor affecting the freight flows from China to the US, suggesting that a US dollar appreciation against the Chinese yuan increases the inflows of Chinese commodities to the US. The impacts of the bilateral exchange rate and transport cost are found to vary at industry and commodity levels. These findings support the importance of employing disaggregate data in the bilateral freight flow analysis.     

Impacts of information technology and urbanization on less-than-truckload freight flows in China: An analysis considering spatial effects

Understanding the relationship between socioeconomic factors and the Less-than-Truckload (LTL) freight flows is important for transportation planners and policy makers. This paper explores the impacts of information technology, urbanization on LTL freight flows by using a spatial autocorrelation model with freight flow data from a leading LTL company in China. The results show that all IT variables and urbanization variables have positive effects on freight flows. Distance, as expected, is negatively correlated with the freight flow volume. The application of the spatial autocorrelation model further shows that origin dependence, destination dependence and OD dependence are all significant, justifying the consideration of spatial interdependence. Finally, policy implications are discussed based on the estimated results. These findings shed light on the impacts of internet and urbanization on freight transportation, and contribute to the design of freight policies and the development of the LTL industry.     

Determinants of modal choice in freight transport

In the next few years, exciting developments in the field of freight transport are likely to occur. The Channel Tunnel will be perceived as giving railways much greater distance of operation, compared to the current train ferry to/from Great Britain. The further development of swap-body technology will allow easier modal transfer and the creation, in 1992, of a single market in Europe will transform the pattern of trade. All of these are likely to have significant impacts on modal choice, and hence modal split, in freight transport. Reappraisal by many firms of the modes of transport used is likely but will it result in a net transfer of freight from road to rail and, if so, to what extent? To answer such questions, an accurate and reliable method of predicting modal split is required. Research in the past has concentrated on the development of modal split models based on generalised costs. These fail to explain adequately the prevalence of road freight in the UK. From surveys of freight managers within industry, it is clear that models to date rely too heavily on the economic cost factor and too little on behavioural factors (Jeffs 1985). This paper derives from a recent study of freight transport modal choice from the standpoint of the transport decision-maker within the firm. It attempts to shed light on the actual parameters which should be incorporated into a modal split model. Many variables appear to exert an influence on modal choice decision-making process. However, it is possible to categorise them into six main groups, namely: customer-requirements; product-characteristics; company structure/organisation; government interventions; available transport facilities; and perceptions of the decision-maker him/herself. It is the interactions and inter-relationships between these which ultimately determine freight modal split. This study has shown that the relationship between the outcome of the transport decision process and the values of particular determinants of modal split is not straight-forward, due to the complexity and variety of interactions involved. Perhaps one of the main reasons for researchers' failure hitherto to develop a successful modal-split model has been the preoccupation with techniques that rely on the development of common metric (e.g. generalised cost), which has led to the exclusion of some important explanatory variables along quite different dimensions. Another important issue concerns the appropriate level of aggregation. In order not to reduce the explanatory power of the key variables, it is important to work at a disaggregate level, although this does make substantial demands on data. The use of factor analysis enables both the aggregation of information without loss of behavioural reality and the specification of variables in terms of a common metric. In conclusion, freight transport has usually been examined within too narrow a framework. It must be placed firmly within the context of the total industrial process. The demand for freight transport is directly influenced by the level, composition and geographical distribution of production and consumption activities. Freight flows are complex and so it is highly unlikely that a universal mode-choice model can ever be developed. Future research should, therefore, be directed towards developing partial models in response to specific needs of those involved in decision-taking in the freight sector.     

A velocity and payload dependent emission model for heavy-duty road freight transportation

The paper develops a forecasting model of emissions from traffic flows embracing the dynamics of driving behavior due to variations in payload. To measure of emissions at the level of individual vehicles under varying payloads a portable emission measurement system is used. This paper reports on a model based on data at the level of individual vehicles for a representative road trajectory. The model aggregates the data to the level of a homogeneous flow dependent of velocity and specific power, which is dependent on payload weight. We find a lean specification for the model that provides emission factors for CO₂, NO_x, HC, CO, and NO₂. The results indicate that, in comparison with earlier models, NO_x emissions in particular tend to be underestimated.     

A multi-layer scenario analysis for sustainable international transport

This article considers the development of the international transport sector based on four globalization scenarios. These four images of the future transportation market are constructed at three different levels (global, European and Dutch). The possible consequences of these scenarios are mapped out not only by key aspects such as modal split and spatial organization but also by providing empirical insights into expected transport flows for both passenger and freight transport in 2020 based on data from 1995.     

Commodity flow estimation for a metropolitan scale freight modeling system: supplier selection considering distribution channel using an error component logit mixture model

Freight forecasting models have been significantly improved in recent years, especially in the field of goods vehicle behavior modeling. On the other hand, the improvements to commodity flow modeling, which provide inputs for goods vehicle simulations, were limited. Contributing to this component in urban freight modeling systems, we propose an error component logit mixture model for matching a receiver to a supplier that considers two-layers in supplier selection: distribution channels and specific suppliers. The distribution channel is an important element in freight modeling, as the type of distribution channel is relevant to various aspects of shipments and vehicle trips. The model is estimated using the data from the Tokyo Metropolitan Freight Survey. We demonstrate how typical establishment survey data (i.e. establishment and outbound shipment records) can be used to develop the model. The model captures the correlation structure of potential suppliers defined by business function and provides insights on the differences in the supplier choice by distribution channel. The reproducibility tests confirm the validity of the proposed approach, which is currently integrated into a metropolitan-scale agent-based freight modeling system, for practical use.

     

Use of sequential learning for short-term traffic flow forecasting

Accurate short-term traffic flow forecasting has become a crucial step in the overall goal of better road network management. Previous research [H. Kirby, M. Dougherty, S. Watson, Should we use neural networks or statistical models for short term motorway traffic forecasting, International Journal of Forecasting 13 (1997) 43–50.] has demonstrated that a straightforward application of neural networks can be used to forecast traffic flows along a motorway link. The objective of this paper is to report on the application and performance of an alternative neural computing algorithm which involves ‘sequential or dynamic learning’ of the traffic flow process. Our initial work [H. Chen, S. Clark, M.S. Dougherty, S.M. Grant-Muller, Investigation of network performance prediction, Report on Dynamic Neural Network and Performance Indicator development, Institute for Transport Studies, University of Leeds Technical Note 418, 1998 (unpublished)] was based on simulated data (generated using a Hermite polynomial with random noise) that had a profile similar to that of traffic flows in real data. This indicated the potential suitability of dynamic neural networks with traffic flow data. Using the Kalman filter type network an initial application with M25 motorway flow data suggested that a percentage absolute error (PAE) of approximately 9.5% could be achieved for a network with five hidden units (compared with 11% for the static neural network model). Three different neural networks were trained with all the data (containing an unknown number of incidents) and secondly using data wholly obtained around incidents. Results showed that from the three different models, the ‘simple dynamic model’ with the first five units fixed (and subsequent hidden units distributed amongst these) had the best forecasting performance. Comparisons were also made of the networks’ performance on data obtained around incidents. More detailed analysis of how the performance of the three networks changed through a single day (including an incident) showed that the simple dynamic model again outperformed the other two networks in all time periods. The use of ‘piecewise’ models (i.e. where a different model is selected according to traffic flow conditions) for data obtained around incidents highlighted good performance again by the simple dynamic network. This outperformed the standard Kalman filter neural network for a medium-sized network and is our overall recommendation for any future application.     

The evolution and competitiveness of air cargo transportation: the case of Israel's airborne trade

Abstract

Air freight traffic has grown rapidly in the last decade. Although, in terms of volume carried, air cargo represents only a small fraction of the total trade, in value terms, its share is very significant. Against the background of the development of the world's air freight traffic and the factors behind it, this paper reviews the growth of air cargo movements in Israeli international trade and analyses the major components of this transport mode. Because of Israel's geopolitical position, air transport is a significant mode in the country's foreign trade. Air and sea transport are the only modes available for any trade going to and from Israel. The proportion of airborne trade in the total Israeli foreign trade is reviewed in light of the factors affecting the competition between air and sea transport.     

Incorporating spatial interactions in zero-inflated negative binomial models for freight trip generation

This paper formulates a spatial autoregressive zero-inflated negative binomial model for freight trip productions and attractions. The model captures the following freight trip characteristics: count data type, positive trip rates, overdispersion, zero-inflation, and spatial autocorrelation. The spatial autoregressive structure is applied in the negative binomial part of the models to obtain unbiased estimates of the effects of different regressors. Further, we estimate parameters using the full information maximum likelihood estimator. We perform empirical analysis with an establishment based freight survey conducted in Chennai. Separate models are estimated for trips generated by motorised two-wheelers and three-wheelers, and pickups besides an aggregate model. Spatial variables such as road density and indicator of geolocation are insignificant in all the models. In contrast, the spatial autocorrelation is significant in all of the models except for the freight trips attracted and produced by pickups. From a policy standpoint, the elasticity results show the importance of considering spatial autocorrelation. We also highlight the bias due to aggregation of vehicle classes, based on the elasticities.

     

Breakthrough innovations in intermodal freight transport

Breakthrough innovations, whether technological, organizational or both, are a necessity if the market share of intermodal freight transport is to expand. The main growth potential lies in the markets for flows over short distances, for perishable and high-value commodities, for small consignments, and for flows that demand speed, reliability and flexibility. It will take radical innovations to produce a breakthrough in the modal split and allow these new markets to be conquered. This special issue is based on papers presented at an international conference on freight transport automation and multimodality, held in Delft in May 2002, that are illustrative of the direction of breakthrough research and development (R&D) aimed at increasing the market share for intermodal transport.     

Logistical restructuring and road freight traffic growth

The forecasting of road freight traffic has relied heavily on the close correlation between GDP and road tonne-kilometers. It has not been rooted in an understanding of the causes of freight traffic growth. The research reported in this paper has investigated this process of traffic growth in two ways: first, by analysing official data on the production, consumption and movement of food and drink products, and second, by conducting a survey of the changing freight transport requirements of 88 large British-based manufacturers.The analysis of secondary data shows how, in the food and drink sector, the relationship between the real value of output and road vehicle-kms hinges on four key parameters: value density, handling factor, average length of haul and consignment size. An attempt is made to explain variations in these parameters.The survey of manufacturers suggests that the growth of lorry traffic is the net result of a complex interaction between factors operating at four levels of logistical management: strategic planning of logistical systems, choice of suppliers and distributors, scheduling of product flow and the management of transport resources. Changes in the frequency and scheduling of freight deliveries in response to tightening customer service requirements and just-in-time management appear to have become a more prevalent cause of freight traffic growth than the physical restructuring of logistical systems. Manufacturers anticipate that their road freight demand will broadly increase in line with sales and be largely unaffected by road transport cost increases at the levels currently proposed. The paper concludes by examining their likely reactions to a much sharper increase in the cost of road freight movement.     

Evaluation of Rail Freight Facilities Grant Funding in Britain

Abstract

Rail freight has been generally in decline in many European Union countries in recent years, contrary to European transport policy. State support for railway operations is commonplace in most countries, and this paper establishes the background to targeted rail freight grant funding in Britain. Through desk‐based analysis of Freight Facilities Grant (FFG) awards, together with a survey of recipient companies, the paper assesses the extent to which the planned flows expected from these awards have materialized and it evaluates the role of the grants in influencing rail freight volumes. The evidence suggests that FFG funding has been largely successful, attracting considerable private sector investment. Overall, FFGs have played an important role in developing or retaining rail freight flows, although the processes could be made more transparent and consistent. As other European countries liberalize their rail freight markets as a result of European Union legislation, such targeted funding may be an appropriate alternative to more general government subsidy of freight operations.     

Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census

The need for acquiring the current-year traffic data is a problem for transport planners since such data may not be available for on-going transport studies. A method is proposed in this paper to predict hourly traffic flows up to and into the near future, using historical data collected from the Hong Kong Annual Traffic Census (ATC). Two parametric and two non-parametric models have been employed and evaluated in this study. The results show that the non-parametric models (Non-Parametric Regression (NPR) and Gaussian Maximum Likelihood (GML)) were more promising for predicting hourly traffic flows at the selected ATC station. Further analysis encompassing 87 ATC stations revealed that the NPR is likely to react to unexpected changes more effectively than the GML method, while the GML model performs better under steady traffic flows. Taking into consideration the dynamic nature of the common traffic patterns in Hong Kong and the advantages/disadvantages of the various models, the NPR model is recommended for predicting the hourly traffic flows in that region.     

Intermodal freight transport planning – A receding horizon control approach

This paper investigates intermodal freight transport planning problems among deep-sea terminals and inland terminals in hinterland haulage for a horizontally fully integrated intermodal freight transport operator at the tactical container flow level. An intermodal freight transport network (IFTN) model is first developed to capture the key characteristics of intermodal freight transport such as the modality change phenomena at intermodal terminals, physical capacity constraints of the network, time-dependent transport times on freeways, and time schedules for trains and barges. After that, the intermodal freight transport planning problem is formulated as an optimal intermodal container flow control problem from a system and control perspective with the use of the proposed IFTN model. To deal with the dynamic transport demands and dynamic traffic conditions in the IFTN, a receding horizon intermodal container flow control (RIFC) approach is proposed to control and to reassign intermodal container flows in a receding horizon way. This container flow control approach involves solving linear programming problems and is suited for transport planning on large-sized networks. Both an all-or-nothing approach and the proposed RIFC approach are evaluated through simulation studies. Simulation results show the potential of the proposed RIFC approach.     

Forecasting containerized traffic for the port of Montreal (1981–1995)

This paper presents an approach to making long-term forecasts of overseas container traffic in the port of Montreal. The paper discusses, first, why performance models explaining traffic variations in ports when postulating a fixed hinterland may not be appropriate for the port of Montreal. Then, the forecasting approach itself is presented. Using 1981 as the base year, projections of container traffic through the port of Montreal until 1995 are developed by considering separately the traffic flows associated with the Canadian and the United States hinterlands. Our approach takes account of anticipated changes in Canadian and U.S. international trade volume, containerization rates and regional growth within Canada. It yields detailed forecasts of containerized traffic, by origin and destination, for 78 commodity groups, 7 world regions and 11 North American regions (i.e. the 10 Canadian provinces plus the United States). Two forecasting scenarios are considered and the aggregate results for 1995 are reported. Finally, a comparison of forecasts with recent data available for 1984 is made, suggesting that the internation trade projections used to generate our forecasts may be too conservative.     

A dynamic approach to road freight flows modeling in Spain

This paper presents an innovative approach to analyzing road vehicle freight traffic that uses a dynamic panel data specification derived from a gravity model. This dynamic approach, which has recently been employed in international goods trade models in lieu of the traditional static specification, is applied to the case of Spain using data for the countrys 15 NUTS-3 regions between 1999 and 2009. Using the system general method of moments approach, we obtained significant evidence that the flow of vehicles carrying commodities by road has a strong persistence effect when controlling for unobserved heterogeneity. We also found that the quality of road transport infrastructure has a significant impact on vehicle trips. According to our findings, we suggest that this type of specification be employed in distribution models in which fixed effects and lags of the dependent variable are included to account for unobserved heterogeneity and persistence effects, respectively.     

A multivariate cointegrating vector auto regressive model of freight transport demand: evidence from Indian railways

In this paper long run structural relationship for freight transport demand is derived for railways in India using annual time series data for 1960–1995. Some of the recent developments in multivariate dynamic econometric time series modelling have been employed such as estimation of long-run structural cointegrating relationship, short-run dynamics and measurement of the effects of shocks and their persistence during the evolution of dynamic freight transport demand system. The models are estimated using a cointegrating vector autoregressive (VAR) modelling framework, which allows for endogeneity of regressors. Results indicate high GDP elasticity and low price elasticity, with real freight rate, i.e. the price variable behaving exogenously with respect to the system. Any disequilibrium in the short-run is likely to be corrected in the long run via adjustments in freight transport demand and GDP. Further, the demand system seems to be stable in the long run and converges to equilibrium in a period of around 3 years after a typical system-wide shock.