Collaborative truck scheduling and appointments for trucking companies and container terminals

Appointment systems for truck arrivals at container terminals have been applied in many ports to reduce truck congestion. This study suggests a new appointment process by which trucking companies and terminals collaboratively determine truck operation schedules and truck arrival appointments. This study formulates a mathematical model involving a sub-problem for each trucking company to determine the optimal dispatching schedules for trucks and the other sub-problem for the terminal to estimate the expected truck system time in each time interval. An iterative collaboration process is proposed based on a decomposed mathematical formulation. Numerical experiments are conducted to investigate the performance of the decision process and the robustness of the process in practical operation conditions.     

Comparison of external costs of rail and truck freight transportation

In this article we estimate external costs for four representative types of freight trains. For each type of freight train, we estimate three general types of external costs and compare them with the private costs experienced by railroad companies. The general types of external costs include: accidents (fatalities, injuries, and property damage); emissions (air pollution and greenhouse gases); and noise. Resulting private and external costs are compared with those of freight trucking, estimated in an earlier article. Rail external costs are 0.24 cent to 0.25 cent (US) per ton-mile, well less than the 1.11 cent for freight trucking, but external costs for rail generally constitute a larger amount relative to private costs, 9.3–22.6%, than is the case for trucking, 13.2%.     

Impacts of highway congestion on freight operations: perceptions of trucking industry managers

To better understand how road congestion adversely affects trucking operations, we surveyed approximately 1200 managers of all types of trucking companies operating in California. More than 80% of these managers consider traffic congestion on freeways and surface streets to be either a “somewhat serious” or “critically serious” problem for their business. A structural equations model (SEM) is estimated on these data to determine how five aspects of the congestion problem differ across sectors of the trucking industry. The five aspects were slow average speeds, unreliable travel times, increased driver frustration and morale, higher fuel and maintenance costs, and higher costs of accidents and insurance. The model also simultaneously estimates how these five aspects combine to predict the perceived overall magnitude of the problem. Overall, congestion is perceived to be a more serious problem by managers of trucking companies engaged in intermodal operations, particularly private and for-hire trucking companies serving airports and private companies serving rail terminals. Companies specializing in refrigerated transport also perceive congestion to be a more serious overall problem, as do private companies engaged in LTL operations. The most problematic aspect of congestion is unreliable travel times, followed by driver frustration and morale, then by slow average speeds. Unreliable travel times are a significantly more serious problem for intermodal air operations. Driver frustration and morale attributable to congestion is perceived to be more of a problem by managers of long-haul carriers and tanker operations. Slow average speeds are also more of a concern for airport and refrigerated operations.     

Regulatory reform and freight mode choice

Reform of trucking Vehicle Weights and Dimensions (VWD) regulations in Canada is now underway. The effect these reforms will have on the freight transportation industry are only recently the subject of research. This paper is part of this new research effort, aimed at understanding how regulatory reform in the trucking sector will affect the existing competitive relationship between trucks and the railways. The paper presents the results of study and research into modelling the relationship between mode choice and the service characteristics that are affected by VWD reform.Using several periods of data, a series of econometric models are developed which help to elucidate the relative relationships between the mode service characteristics for both of the principal interprovincial freight modes. A technique is developed and presented to model railway user costs in order to overcome the complex and often unrepresentable pricing activities of Canadian railways.The strength of the developed econometric models is presented, including their significance and statistical reliability. This is further reinforced by the similarities exhibited by all the models in the series. The selected model is applied to predict market service responses required of the railway industry in order to compete with the trucking sector now adapting to the new regulations.The impact of the newly implemented vehicle weight and dimension reform on the rail transportation industry is analyzed and railway industry improvements aimed at maintaining its market share are presented. The results predicted by the model show that railway user cost reductions should be moderate, and likely less than the level of inflation over the period of time when trucking converts its fleet to take advantage of the new regulations.Abbreviations AASHTO American Association of State Highway and Transportation Officials - CCMTA Canadian Conference of Motor Transport Administrators - EPI End products, inedible commodity classification - GVW Gross Vehicle Weight - NA 1988 VWD National Agreement - RTAC Roads and Transportation Association of Canada - VWD Vehicle Weight(s) and Dimension(s) - WA 1988 VWD Western Agreement     

Heavy-duty trucking and the energy efficiency paradox: Evidence from focus groups and interviews

Theory suggests that profit maximizing firms have an incentive to incorporate cost-effective technologies into their products. However, simple net present value calculations comparing upfront costs of fuel-saving technologies to future savings suggest this is not always the case. This puzzle is commonly referred to as the “energy efficiency paradox.” A growing number of empirical studies examine why households may under-invest in energy efficiency. Fewer studies examine similar undervaluation by businesses. We explore investment decisions within the heavy-duty trucking sector for fuel-saving technologies via focus groups and interviews to gain insight into what factors might explain apparent underinvestment in fuel-saving technologies. We find some evidence that market failures related to lack of information about technology performance and network externalities contribute to slow adoption of some technologies. However, information about new technologies for tractors seems to generate limited spillovers. There is also some evidence of split incentives between owners and drivers, though companies have invested in a variety of technologies and approaches in an attempt to address these effects. Other factors important in trucking investment decisions that are not classic market failures include tradeoffs between fuel economy and other valued truck attributes, as well as uncertainty and risk associated with new technologies if decision-makers are loss averse.     

Toxic air pollutants and trucking productivity in the US

This paper introduces toxic air pollutants into the measurement of trucking productivity to obtain true productivity growth. Our results show that omitting or ignoring toxic air pollutants in measuring trucking productivity yields statistically significant biased productivity estimates in for 2002-2005. Trucking productivity growth was understated by the traditional productivity measure, because the latter did not account for reductions in truck air pollution over time. We also find that the difference between traditional and environmental efficiency scores was negligible, suggesting that environmental constraint did not distort efficiency in the trucking sector.     

A dynamic empty equipment and crew allocation model for long-haul networks

This research develops a realistic and efficient operational model to optimize empty equipment and crew movements in long-haul trucking networks with consolidation, where returning drivers home within a reasonable amount of time is an important issue. The problem can be stated as follows. On a network of consolidation centers, demand is expressed as a set of trailer-loads that need to be moved between each pair of consolidation centers in each time period and the objective is to optimize trailer, tractor and driver movements while ensuring that drivers return home within a pre-determined period of time. In this paper, a dynamic integer programming model is developed and an efficient approximate solution method is proposed, which involves column generation and branch-and-bound. The algorithm switches from a combination of network and primal simplex to dual simplex to overcome the degeneracy problem, which is very common for dynamic networks. This novel approach enables solving large problems with many intervals. We solved problems with up to 30 nodes and 48 periods successfully by using real data provided by a less-than-truckload company, and by generating statistical forecasts based on the real data.     

A rule for allocating joint truck-carrier costs

This paper presents a rule for allocating joint vehicle costs between fronthaul and backhaul truck trips on a given route. The rule is derived by extending the De Vany and Saving model of a competitive trucking industry operating in uncertain markets. The rule is based upon the existence of inventory costs related to truck trips and demand interdependency between fronthaul and backhaul truck trips. The rule is compared to an earlier joint cost allocation rule developed by Walters.     

Fuel price elasticities in the U.S. combination trucking sector

This paper estimates fuel price elasticities of combination trucking operations in the United States between 1970 and 2012. We evaluate trucking operations in terms of vehicle miles traveled and fuel consumption for combination trucks. Our explanatory variables include measures of economic activity, energy prices, and indicator variables that account for important regulatory shifts and changes in data collection and reporting in national transportation datasets. Our results suggest that fuel price elasticities in the United States’ trucking sector have shifted from an elastic environment in the 1970s to a relatively inelastic environment today. We discuss the importance of these results for policymakers in light of new policies that aim to limit energy consumption and reduce greenhouse gas emissions from heavy-duty vehicles.     

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.     

The regulation of the trucking industry—deterministic replacement model application

An attempt has been made to analyze the impact of a country's regulatory policy of its trucking industry on the correlation between the preferable usage of trucks according to national interests and its counterpart according to private interests, by using a deterministic replacement model. Operational data has been obtained from time series derived from a cross‐sectional survey of the country's truck fleet. Trucks were grouped into 15 strata according to characteristics of load capacity, technological specification, and type of fuel. For each stratum the optimal replacement age was determined according to the criteria of minimal average cost of hauling ton × km, maximal average profit, and minimal average annual cost, and the corresponding costs or profits were calculated for both interests. By comparing the optimal conditions in the various strata, the preferable fuel types and load capacities were determined for the national interest and recommendations for regulation have been provided coinciding the national and private preferences in utilizing trucks.     

Truck backhauling on two terminal networks

Truck backhauling reduces empty truck-miles by having drivers haul loads on trips back to their home terminal. This paper 1) examines the impact on backhauling opportunities of terminal locations and directional imbalances in the flow of freight from the terminals, and 2) develops a method for determining which truckloads should be backhauled. Backhauling is studied for two terminals sending full truckloads to many customers under steady-state conditions. This research develops two backhauling models. The first is a continuous model that makes simplifying assumptions about customer locations and travel distances. It results in formulae showing that 1) savings from backhauling increase at a decreasing rate as the directional flow of freight between two terminals becomes more balanced and 2) backhauling is an important, but often ignored, factor in terminal (e.g. trucking terminal, warehouse, or plant) location and supplier selection decisions. The second model is a more general discrete model that determines which loads should be backhauled to minimize empty truck-miles.     

Stochastic freight flow patterns: implications for fleet optimization

Trucking, rail and other types of transportation networks share the common feature of moving equipment and crews between spatially separated terminals to accommodate the transportation of goods or people. This paper develops measures for temporal and spatial imbalances in freight flows, and applies these measures to a major trucking network. Fundamentally, the randomness inherent to a system of terminals is mitigated by pooling freight flows among terminal groups, and by pooling freight flows over many time periods. In the terminal network that we examined, long-run freight imbalances ensure that empty equipment movements must equal or exceed 13.3% of loaded movements at individual terminals and 8.2% of loaded movements at terminal groups. Due to short-run freight imbalances, the number of empty movements could increase by about 50% over the long-run average; greater increases would occur if equipment flows must be balanced on each travel lane. ©     

External Costs of Domestic Container Transportation: Short‐Sea Shipping versus Trucking in Taiwan

Abstract

This paper explores the external costs of domestic container transportation in Taiwan by analysing the origin and destination of current container cargoes. After reviewing an extensive literature survey of methods of external cost, a comparison of external costs between trucking and short sea shipping (SSS) by corridor is made by using a model developed in this paper. Based on the findings that external costs of SSS are considerably lower than truck transport and can be a viable alternative to current domestic container cargo transportation, we discuss the significance and managerial implications of SSS from the perspective of green logistics. In so doing, a top‐down approach is employed for developing government policies, which aim to not only reduce the external costs of domestic container transportation but also promote SSS in Taiwan.     

Multioutput analysis of trucking operations using spatially dissaggregated flows

The operation of a trucking system at a national scale is analyzed by means of an operating multioutput cost function, which recognizes flows with different origin and destination as distinct components of the transportation product. The original, fully disaggregated, information on O-D specific flows is used to generate distance-weighted and pure-flow output vectors, whose dimension allows for the estimation of flexible cost functions. Different models are compared in terms of O-D specific marginal costs and second derivatives in common units, obtained through manipulations of both the estimated parameters and their variance-covariance matrix. Results indicate that marginal costs are not proportional to distance. Constant returns prevail in the operation of the system, simultaneously with economies of spatial scope and cost complementarity between flow pairs, with one exception which suggests some regional specialization. On methodological grounds, the bias on scale economies introduced by the single aggregated output measure (ton-km) becomes evident, and partial aggregation in terms of distance-weighted zonal flows seems more appropriate than pure flow aggregation when distances are heterogeneous. The multioutput formulation with O-D specific flows under a flexible form of the cost function, is recommended for meaningful policy analysis of transportation systems.     

Evaluation of fuel cell auxiliary power units for heavy-duty diesel trucks

A large number of heavy-duty trucks idle a significant amount. Heavy-duty line-haul truck engines idle about 20–40% of the time the engine is running, depending on season and operation. Drivers idle engines to power climate control devices (e.g., heaters and air conditioners) and sleeper compartment accessories (e.g., refrigerators, microwave ovens, and televisions) and to avoid start-up problems in cold weather. Idling increases air pollution and energy use, as well as wear and tear on engines. Efforts to reduce truck idling in the US have been sporadic, in part because it is widely viewed in the trucking industry that further idling restrictions would unduly compromise driver comfort and truck operations. The auxiliary power units (APUs) available to replace the idling of the diesel traction engine all have had limited trucking industry acceptance. Fuel cells are a promising APU technology. Fuel cell APUs have the potential to greatly reduce emissions and energy use and save money. In this paper, we estimate costs and benefits of fuel cell APUs. We calculate the payback period for fuel cell APUs to be about 2.6–4.5 years. This estimate is uncertain since future fuel cell costs are unknown and cost savings from idling vary greatly across the truck fleet. The payback period is particularly sensitive to diesel fuel consumption at idle. Given the large potential environmental and economic benefits of fuel cell APUs, the first major commercial application of fuel cells may be as truck APUs.     

Evaluating gravel transport sustainability: A case study of Taiwan’s northeast corridor

We use social-eco-efficient analysis in the form of SEEbalance to evaluate gravel transport sustainability for trucking and two kinds of intermodal transportation. Results show that switching from trucks to intermodal transportation can improve the sustainability of gravel transportation in the northeast corridor of Taiwan. Sensitivity analysis shows that rail combined with truck intermodal transportation has competitive advantage despite the terminal’s location factor.     

Marginal cost pricing of truckload services: A comparison of two approaches

A useful input to the pricing of truckload trucking services is the marginal benefit to the entire system of an additional load going from one region to the next. Two approaches are reviewed for estimating the marginal system benefit. The first is based on a well-known deterministic network formulation in which certain easily obtainable shadow prices provide an estimate of the marginal benefit. The second is based on a nonlinear stochastic formulation which incorporates uncertainty in demand forecasts. Each procedure is reviewed in detail and evaluated in terms of the accuracy with which the actual change in total system profit (found by increasing the demand on a link and completely reoptimizing) matches the estimated benefit.     

Trucking industry perceptions of congestion problems and potential solutions in maritime intermodal operations in California

Efficient maritime transportation is heavily dependent on the smooth operation of land transportation. Swift modal transfers are key to successful intermodal operations. In this paper we examine the efficiency of maritime intermodal transfer facilities in California, from the point of view of the trucking companies that use these facilities. We also examine the perceived effects of traffic network congestion on intermodal carriers' operations. Conclusions are based on a recent survey of nearly 1200 private and for-hire carriers operating in California. Over 450 of the companies surveyed had operations involving maritime ports in California. These provided a rich sample of responses and significant insights into the current state of the industry.     

Measuring supply chain risk: Predicting motor carriers’ ability to withstand disruptive environmental change using conjoint analysis

Supply chain risk measurement is an expanding research stream that considers the ability of networked firms to anticipate and respond to significant environmental risks, including major disruptions and unexpected events. However measuring and quantifying supply chain risk has proved an enormous challenge and this research contributes to this goal by developing a risk assessment scorecard, using conjoint analysis, for motor carrier firms. The resultant motor-carrier scorecard has been scaled from 300 to 900, to resemble the well-known FICO score for assessing consumer creditworthiness. Our scoring model enables motor carriers – and the firms that depend upon them in intermodal supply chains – to assess carriers’ ability to withstand major disruptive events, which are broadly defined as events which might lead to a significant drop in carriers’ income and profitability (e.g., such as that which occurred on September 11, 2001). Carriers with weaker risk scores (<600, on a 300–900 scale) are more likely to experience financial distress (and as a result possibly exit the industry itself); those with scores above 600 are less likely to depart. The model correctly identified 77 percent of motor carriers that ultimately exited the trucking industry following the significant environmental disruption caused by 9/11. Our computational experience indicates that the model accuracy, quantified in terms of Type I and Type II errors, compares favorably to prior results reported in the credit scoring literature.