A freight transport price optimization model with multi bounded-rational customers

Transportation - To analyze the effect of customer enterprises’ behavior on freight transport markets with various products, a bi-level programming model for freight pricing is developed. The...     

A disaggregate model of auto-type choice

Previous models of auto-type choice have not been able to disentangle very much of the structure of the household's auto-choice decision: the models assumed that very few auto characteristics affect choice, and often these few parameters were estimated with low precision. Hence the models had only limited use in forecasting the effects of government policies to influence transportation energy consumption. The present paper introduces a multinomial logit model for the type of car that households will choose to buy. The model includes a large variety of auto characteristics as explanatory variables, as well as a large number of characteristics of the household and the driving environment. The model fits the data quite well, and all of the variables enter with the correct signs and plausible magnitudes.     

Environmental benefits from better freight transport management: freight traffic in a VAR model

Most macro empirical studies on the price elasticity in the freight transport sector focus on the demand for transport. However, most of the external costs from the freight transport sector are more closely linked with the traffic volumes. In the paper an explicit distinction between traffic and transport demand is made by regarding traffic as an input in the shippers’ production of transport services, while transport demand is derived from firms’ production of output. An empirical analysis based on aggregate time series is carried out in a VAR model, where the stationary long run relationships are estimated using the so-called ‘Johansen Procedure’. The estimated price elasticity with respect to traffic (−0.81) is considerably higher than the elasticity with respect to transport (−0.47).     

Calculation of external costs for freight transport

Abstract

The negative impacts of transport are in general associated with costs. These costs are usually denoted as ‘external costs’ or ‘externalities’. This paper presents a tool for calculating external costs for freight transport together with its application to a number of case studies. The categories considered include: air pollution, greenhouse gases, noise, accidents and congestion. Results are presented for a number of different transport alternatives as total costs and divided into categories. The uncertainties in the results are discussed. The assessment of these costs is essential for predicting future transport costs.     

A validation test of a disaggregate mode choice model

A model of work trip mode choice was developed on a sample of workers taken before Bay Area Rapid Transit (BART) opened for service. Validation tests of the model were performed on a sample of workers taken after BART service began. Two validation methods were used: (1) the actual mode shares in the post-BART sample were compared to the mode shares predicted by the models estimated on the pre-BART sample, and (2) the parameters of models estimated on the post-BART sample were compared with the parameters of the models estimated pre-BART. Three possible reasons were explored for the differences in actual and predicted shares and in the pre- and post-BART model parameters: (1) failure of the independence from irrelevant alternatives (IIA) property of the multinomial logit model, (2) non-genericity and incorrect data contributed substantially to the incorrect data for walk times. It was found that non-genericity and incorrect data contributed substantially to the mispredictions, while failure of the IIA property contributed less. The present study concerns only one model and one transportation environment. The results of this test, however, can be viewed along with the results of other validation studies to obtain a sense of the predictive ability of disaggregate mode choice models.     

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.     

A stochastic model for highway traffic

A nonlinear model for unidirectional flow of heavy traffic on a two-lane highway is considered. Features such as entrance, exit and lane transfer with time-dependent parameters are incorporated into the model, with the result that a number of previous models employed in the study of traffic flow become special cases of ours. Using the method of system-size expansion, an asymptotic analysis of the problem, including the time evolution of both deterministic and stochastic aspects of the traffic system, is carried out. In addition, a scheme for obtaining the moments of the probability distribution for systems of finite size is developed and a comparison is made with the exact results appropriate to a particular model. The agreement between the two sets of results turns out to be remarkably good.     

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.     

A Stated preference freight mode choice model

Transport policy aims to assist the transport system to work more efficiently and effectively. An understanding of the reasons why people choose to move freight in a certain manner is critical to the development of appropriate policies. This article outlines a data collection approach and the development of a disaggregate mode choice model applicable to the analysis of freight shipper decision making. It focuses on the choice between rail and road in Java, Indonesia. The model indicates that safety, reliability and responsiveness are major attributes influencing rail/road freight mode choice. Transport policies aimed at improving these dimensions should increase the attractiveness of rail transport.     

灰色BP网络串联式组合模型在铁路客货运输量预测中的应用

针对铁路客货运输量发展趋势的研究,建立一种基于灰色理论和BP神经网络的串联式组合预测模型。该模型首先用同一组数据序列建立不同参数的灰色方程,然后用各灰色方程分别预测,最后将各灰色方程预测的结果进行BP神经网络非线性组合,形成串联式组合预测模型。对湖南省铁路客货运量进行分析预测,结果表明:该组合模型预测的准确性高于单独使用灰色模型的准确性,是一种可靠有效的预测方法。     

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.     

Energy conservation and intercity freight transport

In this paper, the characteristics of intercity freight modal operations are investigated to evaluate the potential for achieving energy savings. It is determined that the greatest opportunities for conserving energy appear to rest with achieving modal shifts and operational improvements in truck and rail transport. To test this hypothesis, intercity truck and rail freight operations are analyzed to determine the relationships between energy consumption and the delivery of transport service. The energy consumption impact of alternative conservation measures are calculated, and in turn, evaluated in light of a series of institutional constraints. As such, this study goes beyond the characteristic cataloging of alternative energy conservation measures by conducting a disaggregated assessment of the effectiveness and feasibility of implementing such measures. This paper concludes that the potential for achieving energy conservation in the movement of intercity freight in general, and by truck and rail systems in particular, is limited, as well as shrouded by the complexity of the nature of the commodity itself, the commodity flow characteristics, and the market and institutional structure.     

A framework to evaluate policy options for supporting electric vehicles in urban freight transport

Electric vehicles (EVs) are considered as a feasible alternative to traditional vehicles. Few studies have addressed the impacts of policies supporting EVs in urban freight transport. To cast light on this topic, we established a framework combining an optimization model with economic analysis to determine the optimal behavior of an individual delivery service provider company and social impacts (e.g., externalities and welfare) in response to policies designed to support EVs, such as purchase subsidy, limited access (zone fee) to congestion/low-emission zones with exemptions for EVs, and vehicle taxes with exemptions for EVs. Numerical experiments showed that the zone fee can increase the company’s total logistics costs but improve the social welfare. It greatly reduced the external cost inside the congestion/low-emission zone with a high population, dense pollution, and heavy traffic. The vehicle taxes and subsidy were found to have the same influence on the company and society, although they have different effects with low tax/subsidy rates because their different effects on vehicle routing plans. Finally, we performed a sensitivity analysis. Local factors at the company and city levels (e.g., types of vehicle and transport network) are also important to designing efficient policies for urban logistics that support EVs.     

Modelling mode choice for freight transport using advanced choice experiments

In this paper we use advanced choice modelling techniques to analyse demand for freight transport in a context of modal choice. To this end, a stated preference (SP) survey was conducted in order to estimate freight shipper preferences for the main attributes that define the service offered by the different transport modes. From a methodological point of view, we focus on two critical issues in the construction of efficient choice experiments. Firstly, in obtaining good quality prior information about the parameters; and secondly, in the improved quality of the experimental data by tailoring a specific efficient design for every respondent in the sample.With these data, different mixed logit models incorporating panel correlation effects and accounting for systematic and random taste heterogeneity are estimated. For the best model specification we obtain the willingness to pay for improving the level of service and the elasticity of the choice probabilities for the different attributes. Our model provide interesting results that can be used to analyse the potential diversion of traffic from road (the current option) to alternative modes, rail or maritime, as well as to help in the obtaining of the modal distribution of commercial traffic between Spain and the European Union, currently passing through the Pyrenees.     

An intermodal freight transport system for optimal supply chain logistics

Complexity in transport networks evokes the need for instant response to the changing dynamics and uncertainties in the upstream operations, where multiple modes of transport are often available, but rarely used in conjunction. This paper proposes a model for strategic transport planning involving a network wide intermodal transport system. The system determines the spatio-temporal states of road based freight networks (unimodal) and future traffic flow in definite time intervals. This information is processed to devise efficient scheduling plans by coordinating and connecting existing rail transport schedules to road based freight systems (intermodal). The traffic flow estimation is performed by kernel based support vector mechanisms while mixed integer programming (MIP) is used to optimize schedules for intermodal transport network by considering various costs and additional capacity constraints. The model has been successfully applied to an existing Fast Moving Consumer Goods (FMCG) distribution network in India with encouraging results.     

Rebound effects in UK road freight transport

This paper analyses aggregate time-series data to estimate the direct rebound effect in UK road freight over the period 1970–2014. We investigate 25 different model specifications, conduct a comprehensive set of diagnostic tests to evaluate the robustness of these specifications and estimate the rebound effect using three different elasticities. Using the mean of the statistically significant estimates from these specifications, we estimate a direct rebound effect of 61% - which is larger than previous estimates in the literature and almost twice as large as the consensus estimate of direct rebound effects in road passenger transport. Using the mean of the estimates from our most robust models, we estimate a slightly lower direct rebound effect of 49%. Our estimates are fairly consistent between different model specifications and different metrics, although individual estimates range from 21% to 137%. We also find that an increasing proportion of UK road freight is being undertaken by foreign registered vehicles, and that increases in the vehicle weight limits have encouraged more freight activity. We highlight the significant limitations imposed by the use of aggregate time series data and recommend that further studies in this area employ data from vehicle use surveys.     

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.     

Achieving transport modal split targets at intermodal freight hubs using a model predictive approach

The increase of international freight commerce is creating pressure on the existing transport network. Cooperation between the different transport parties (e.g., terminal managers, forwarders and transport providers) is required to increase the network throughput using the same infrastructure. The intermodal hubs are locations where cargo is stored and can switch transport modality while approaching the final destination. Decisions regarding cargo assignment are based on cargo properties. Cargo properties can be fixed (e.g., destination, volume, weight) or time varying (remaining time until due time or goods expiration date). The intermodal hub manager, with access to certain cargo information, can promote cooperation with and among different transport providers that pick up and deliver cargo at the hub. In this paper, cargo evolution at intermodal hubs is modeled based on a mass balance, taking into account hub cargo inflows and outflows, plus an update of the remaining time until cargo due time. Using this model, written in a state-space representation, we propose a model predictive approach to address the Modal Split Aware – Cargo Assignment Problem (MSA–CAP). The MSA–CAP concerns the cargo assignment to the available transport capacity such that the final destination can be reached on time while taking into consideration the transport modality used. The model predictive approach can anticipate cargo peaks at the hub and assigns cargo in advance, following a push of cargo towards the final destination approach. Through the addition of a modal split constraint it is possible to guide the daily cargo assignment to achieve a transport modal split target over a defined period of time. Numerical experiments illustrate the validity of these statements.     

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 model to design a national high-speed rail network for freight distribution

High-speed rail is often touted as a means to reduce congestion on the United States’ highways by removing passenger car traffic. But highway congestion can also be reduced by reducing the amount of freight traffic. So, given the advances in high-speed rail, the potential exists for developing a national high-speed network for freight distribution. To design such a network considering highway traffic and transit times, we present an uncapacitated network design model with a post-processing step for the capacity constraint. To illustrate how our modeling approach could be used by policy makers to evaluate the impacts of a high-speed rail network, we apply our models with preliminary data on high-speed rail operating parameters for freight applications and from current data on shipments from a major truckload carrier and the US Census Bureau.