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.     

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.     

Storage space allocation in container terminals

Container terminals are essential intermodal interfaces in the global transportation network. Efficient container handling at terminals is important in reducing transportation costs and keeping shipping schedules. In this paper, we study the storage space allocation problem in the storage yards of terminals. This problem is related to all the resources in terminal operations, including quay cranes, yard cranes, storage space, and internal trucks. We solve the problem using a rolling-horizon approach. For each planning horizon, the problem is decomposed into two levels and each level is formulated as a mathematical programming model. At the first level, the total number of containers to be placed in each storage block in each time period of the planning horizon is set to balance two types of workloads among blocks. The second level determines the number of containers associated with each vessel that constitutes the total number of containers in each block in each period, in order to minimize the total distance to transport the containers between their storage blocks and the vessel berthing locations. Numerical runs show that with short computation time the method significantly reduces the workload imbalance in the yard, avoiding possible bottlenecks in terminal operations.     

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. ©     

A multi-criteria decision support methodology for implementing truck operation strategies

This study proposes a multi-criteria decision support methodology to enable the prioritization of potential alternative transportation system operations strategies and then demonstrates the effectiveness of the methodology using a case study involving truck operations. The primary feature of this methodology is its ability to help policymakers consider economic, public, and private sector standpoints simultaneously. The economic criterion is cost to the public sector where four criteria related to truck impacts on the transportation system are incorporated. These are traffic congestion, safety hazards, air pollution, and pavement damage. In addition, reliability and productivity are regarded as metrics representing the private sector viewpoint since they can significantly affect profitability. The methodology combines qualitative and quantitative aspects of these standpoints. In order to demonstrate the applicability of this methodology, a corridor with some of the highest truck traffic in the US is selected as a case study and three forms of left lane restrictions for trucks are considered. For qualitative analysis, survey data were collected from two groups classified as public agency and transportation industry professionals who are experts in trucking. In addition, a micro traffic simulation model was used to produce various performance measurements that can describe quantitative impacts. As a result, the methodology provides a rational argument for prioritizing potential alternative truck strategies.     

Truck backhauling on networks with many terminals

Truck backhauling reduces empty truck-miles by having drivers haul loads on trips back to their home terminal. This paper develops a model to help coordinate backhauling between many (more than two) terminals. Two mathematical programming formulations of this backhauling problem are given. One formulates it as a “matching” problem that leads to a heuristic for solving the very large backhauling problems that arise in practice. Using Lagrangian relaxation, the other formulation allows a very tight bound on the optimal solution to be calculated. The quality of the heuristic solution can be determined by comparison with this bound. A large scale example based on actual truck shipments demonstrates how the model might be used in planning truck backhauling. For this example, the heuristic yields a solution within 1% of optimal.     

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.     

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.     

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.     

Optimal fueling strategies for locomotive fleets in railroad networks

Railroad companies spend billions of dollars each year to purchase fuel for thousands of locomotives across the railroad network. Each fuel station charges a site-dependent fuel price, and the railroad companies must pay an additional flat contracting fee in order to use it. This paper presents a linear mixed-integer mathematical model that integrates not only fuel station location decisions but also locomotive fueling schedule decisions. The proposed model helps railroads decide which fuel stations to contract, and how each locomotive should purchase fuel along its predetermined shipment path, such that no locomotive runs out of fuel while the summation of fuel purchasing costs, shipment delay costs (due to fueling), and contracting charges is minimized. A Lagrangian relaxation framework is proposed to decompose the problem into fueling schedule and facility location selection sub-problems. A network shortest path formulation of the fueling schedule sub-problem is developed to obtain an exact optimal solution to the fueling schedule sub-problem. The proposed framework is applied to a large-scale empirical case and is shown to effectively reduce system costs.     

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.     

Dynamic green bike repositioning problem – A hybrid rolling horizon artificial bee colony algorithm approach

This paper introduces a new dynamic green bike repositioning problem (DGBRP) that simultaneously minimizes the total unmet demand of the bike-sharing system and the fuel and CO₂ emission cost of the repositioning vehicle over an operational period. The problem determines the route and the number of bikes loaded and unloaded at each visited node over a multi-period operational horizon during which the cycling demand at each node varies from time to time. To handle the dynamic nature of the problem, this study adopts a rolling horizon approach to break down the proposed problem into a set of stages, in which a static bike repositioning sub-problem is solved in each stage. An enhanced artificial bee colony (EABC) algorithm and a route truncation heuristic are jointly used to optimize the route design in each stage, and the loading and unloading heuristic is used to tackle the loading and unloading sub-problem along the route in a given stage. Numerical results show that the EABC algorithm outperforms Genetic Algorithm in solving the routing sub-problem. Computation experiments are performed to illustrate the effect of the stage duration on the two objective values, and the results show that longer stage duration leads to higher total unmet demand and total fuel and CO₂ emission cost. Numerical studies are also performed to illustrate the effects of the weight and the loading and unloading times on the two objective values and the tradeoff between the two objectives.     

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.     

Simulation-based assessment methodology to improve ground service operations at a hub airport

ABSTRACT

Airport terminals are dynamic environments and security/passport services generally constitute costly bottlenecks in terminals. Increases in the number of airline passengers compels airport terminals to provide more efficient services to its customers under space and resource limitations. This study examines the level of service of passenger processes at Istanbul Atatürk Airport by constructing a comprehensive simulation model. It focuses mainly on passport control services and passenger transfer security services because of the airport's hub status and the strategy of Turkish Airlines. The increasing number of transfer passengers may cause disruptions in departure flight schedules due to slow passenger processes. After validating the model, we investigate the consequences of three main alternative solutions, including 17 sub-scenarios, to capture target quality levels. Finally, we provide the results for each scenario to investigate the optimum allocation of resources to terminal operations.     

Trucking industry adoption of information technology: a multivariate discrete choice model

The objective of this research is to understand the demand for information technology among trucking companies. A multivariate discrete choice model is estimated on data from a large-scale survey of the trucking industry in California. This model is designed to identify the influences of each of twenty operational characteristics on the propensity to adopt each of seven different information technologies, while simultaneously allowing the seven error terms to be freely correlated. Results showed that the distinction between for-hire and private fleets is paramount, as is size of the fleet and the provision of intermodal maritime and air services.     

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.     

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.     

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%.     

A heuristic for the train pathing and timetabling problem

In a railroad system, train pathing is concerned with the assignment of trains to links and tracks, and train timetabling allocates time slots to trains. These important tasks were traditionally done manually, but there is an increasing move toward automated software based on mathematical models and algorithms. Most published models in the literature either focus on train timetabling only, or are too complicated to solve when facing large instances. In this paper, we present an optimization heuristic that includes both train pathing and train timetabling, and has the ability to solve real-sized instances. This heuristic allows the operation time of trains to depend on the assigned track, and also lets the minimum headway between the trains to depend on the trains’ relative status. It generates an initial solution with a simple rule, and then uses a four-step process to derive the solution iteratively. Each iteration starts by altering the order the trains travel between stations, then it assigns the services to the tracks in the stations with a binary integer program, determines the order they pass through the stations with a linear program, and uses another linear program to produce a timetable. After these four steps, the heuristic accepts or rejects the new solution according to a Threshold Accepting rule. By decomposing the original complex problem into four parts, and by attacking each part with simpler neighborhood-search processes or mathematical programs, the heuristic is able to solve realistic instances. When tested with two real-world examples, one from a 159.3 km, 29-station railroad that offers 44 daily services, and another from a 345 km, eight-station high-speed rail with 128 services, the heuristic obtained timetables that are at least as good as real schedules.     

Integrated airline planning: Robust update of scheduling and fleet balancing under demand uncertainty

The airline schedule planning problem is defined as the sequence of decisions that need to be made to obtain a fully operational flight schedule. Historically, the airline scheduling problem has been sequentially solved. However, there have already been many attempts in order to obtain airline schedules in an integrated way. But due to tractability issues it is nowadays impossible to determine a fully operative and optimal schedule with an integrated model which accounts for all the key airline related aspects such as competitive effects, stochastic demand figures and uncertain operating conditions. Airlines usually develop base schedules, which are obtained much time in advance to the day of operations and not accounting for all the related uncertainty. This paper proposes a mathematical model in order to update base schedules in terms of timetable and fleet assignments while considering stochastic demand figures and uncertain operating conditions, and where robust itineraries are introduced in order to ameliorate miss-connected passengers. The proposed model leads to a large-scale problem which is difficult to be solved. Therefore, a novel improved and accelerated Benders decomposition approach is proposed. The analytical work is supported with case studies involving the Spanish legacy airline, IBERIA. The presented approach shows that the number of miss-connected passengers may be reduced when robust planning is applied.