How do economies of density in container handling operations affect ships’ time and emissions in port? Evidence from Norwegian container terminals

Efficient port services are prerequisites for competitive and sustainable maritime transports. This paper makes advances in studying the determinants of the time that ships spend in port and the associated emissions to air. We estimate a production model for cargo handling based on a unique dataset containing each port of call at the largest container terminals in Norway in 2014. In turn, we use auxiliary engine emission factors to estimate particulate matter and nitrogen oxide emissions from ships at berth, to determine how the corresponding damage costs of air pollution vary with container throughput, location, and terminal investments. We find that Norwegian container terminals operate under increasing returns to density. Small ships that unload few containers are far from reaping economies of density, leading to high marginal time requirements for container handling and consequently high marginal external costs. From a Pigouvian taxation perspective, port charges should therefore be regressive in the number of containers handled. Moreover, we find that the external costs of maritime transports are severely understated when port operations are ignored. Our model allows determining the marginal productivities of port facilities. Thereby, it is instrumental in designing port charges that are diversified according to the quantity of containers handled and the service quality (i.e., the speed of handling operations). Regarding contextual factors, we find that establishing high-frequent liner services improves the ship working rate, while simultaneous calls at a terminal impede productivity. The type of container (loading/unloading; empty/laden) also appears to influence the duration of ship working.     

A multioutput cost function for the services of Spanish ports' infrastructure

A long run multioutput cost function for the infrastructure services of Spanish ports is estimated using 286 observations on 26 ports during 11 years. Cargo specific marginal costs and the degree of economies of scale and scope are calculated up to a port level. Results show that liquid bulk and non-containerised general cargo present the lowest and largest marginal cost, respectively. Increasing returns to scale are present in general and for each and every port. A scope analysis indicates that port specialisation is not appropriate from the viewpoint of infrastructure.     

Multioutput analysis of cargo handling firms: An application to a Spanish port

Cargo handling in ports is a multioutput activity, as freight can arrive in many forms such as containers, bulk, rolling stock, or non-containerised general cargo. In this paper, the operation of cargo handling firms in a Spanish port is analysed through the estimation of a multioutput cost model that uses monthly data on three representative firms located at the Las Palmas port. This permits the calculation of product specific marginal costs, economies of scale (general and by firm) and economies of scope, which help identifying optimal pricing policies and the potential cost advantages of increasing production.     

Schedule-based transit assignment model with vehicle capacity and seat availability

In this paper, we propose a new schedule-based equilibrium transit assignment model that differentiates the discomfort level experienced by sitting and standing passengers. The notion of seat allocation has not been considered explicitly and analytically in previous schedule-based frameworks. The model assumes that passengers use strategies when traveling from their origin to their destination. When loading a vehicle, standing on-board passengers continuing to the next station have priority to get available seats and waiting passengers are loaded on a First-Come-First-Serve (FCFS) principle. The stimulus of a standing passenger to sit increases with his/her remaining journey length and time already spent on-board. When a vehicle is full, passengers unable to board must wait for the next vehicle to arrive. The equilibrium conditions can be stated as a variational inequality involving a vector-valued function of expected strategy costs. To find a solution, we adopt the method of successive averages (MSA) that generates strategies during each iteration by solving a dynamic program. Numerical results are also reported to show the effects of our model on the travel strategies and departure time choices of passengers.     

Evaluation of an existing bus network using a transit network optimisation model: a case study of the Hiroshima City Bus network

This study evaluates an existing bus network from the perspectives of passengers, operators, and overall system efficiency using the output of a previously developed transportation network optimisation model. This model is formulated as a bi-level optimisation problem with a transit assignment model as the lower problem. The upper problem is also formulated as bi-level optimisation problem to minimise costs for both passengers and operators, making it possible to evaluate the effects of reducing operator cost against passenger cost. A case study based on demand data for Hiroshima City confirms that the current bus network is close to the Pareto front, if the total costs to both passengers and operators are adopted as objective functions. However, the sensitivity analysis with regard to the OD pattern fluctuation indicates that passenger and operator costs in the current network are not always close to the Pareto front. Finally, the results suggests that, regardless of OD pattern fluctuation, reducing operator costs will increase passenger cost and increase inequity in service levels among passengers.     

Dispatching control at transfer stations in multi‐hub transit networks

Among dispatching control approaches, the holding option has attracted the most attention in bus control. However, holding a vehicle at a transfer station may exacerbate the delays because more passengers might accumulate at downstream stations and may also affect other connecting routes at other transfer stations. Our problem is to minimize the total costs of dispatching ready vehicles at each transfer station along coordinated routes in a multi‐hub transit network. The total costs include the waiting cost for on‐board passengers, the missed connection costs for late arrival passengers at the subject transfer station and possible transfer costs at downstream transfer stations. We develop a heuristic algorithm to optimize the holding times based on real time information about late vehicles. The results show that ready vehicles should be held longer when the arrival variances of late vehicles are small or when many late connecting passengers are expected.     

A practical method to estimate the benefits of improved road network reliability: an application to departing air passengers

This paper develops and applies a practical method to estimate the benefits of improved reliability of road networks. We present a general methodology to estimate the scheduling costs due to travel time variability for car travel. In contrast to existing practical methods, we explicitly consider the effect of travel time variability on departure time choices. We focus on situations when only mean delays are known, which is typically the case when standard transport models are used. We first show how travel time variability can be predicted from mean delays. We then estimate the scheduling costs of travellers, taking into account their optimal departure time choice given the estimated travel time variability. We illustrate the methodology for air passengers traveling by car to Amsterdam Schiphol Airport. We find that on average planned improvements in network reliability only lead to a small reduction in access costs per trip in absolute terms, mainly because most air passengers drive to the airport outside peak hours, when travel time variability tends to be low. However, in relative terms the reduction in access costs due to the improvements in network reliability is substantial. In our case we find that for every 1 Euro reduction in travel time costs, there is an additional cost reduction of 0.7 Euro due to lower travel time variability, and hence lower scheduling costs. Ignoring the benefits from improved reliability may therefore lead to a severe underestimation of the total benefits of infrastructure improvements.     

On the relationship between airport pricing models

Airport pricing papers can be divided into two approaches. In the traditional approach the demand for airport services depends on airport charges and on congestion costs of both passengers and airlines; the airline market is not formally modeled. In the vertical-structure approach instead, airports provide an input for an airline oligopoly and it is the equilibrium of this downstream market which determines the airports’ demand. We prove, analytically, that the traditional approach to airport pricing is valid if air carriers have no market power, i.e. airlines are atomistic or they behave as price takers (perfect competition) and have constant marginal operational costs. When carriers have market power, this approach may result in a surplus measure that falls short of giving a true measure of social surplus. Furthermore, its use prescribes a traffic level that is, for given capacity, smaller than the socially optimal level. When carriers have market power and consequently both airports and airlines behave strategically, a vertical-structure approach appears a more reasonable approach to airport pricing issues.     

Control strategies for departure process delays at airport passenger terminals

This paper explores the characteristics of process delays at airport passenger terminals and establishes a queuing model for both passengers and baggage served by different connecting type facilities. The impact of delay propagation on other processes and flights is investigated using an analytical approach. In addition, the extra costs incurred on passengers, process operators, and airlines are examined using the delay cost functions. To reduce the impact of process delays, various delay-controlled strategies are proposed, such as setting scheduled times for completion of a process, increasing the number of service counters, and priority service for emergent flights. Taoyuan International Airport in Taiwan is used as a case study when facing special events. Results showed that the model can effectively and efficiently estimate delay propagation and its costs. In addition, processes that are not consecutive allow more buffer time between different operations, which helps ease propagation of delays caused by previous services.     

Estimating the marginal cost of railway track renewals using corner solution models

Economic theory advocates marginal cost pricing for efficient utilisation of transport infrastructure. A growing body of literature has emerged on the issue of rail marginal infrastructure wear and tear costs, but the majority of the work is focused on costs for infrastructure maintenance. Railway track renewals are a substantial part of an infrastructure manager’s budget, but in disaggregated statistical analyses they cause problems for traditional regression models since there is a piling up of values of the dependent variable at zero. Previous econometric work has sought to circumvent the problem by aggregation in some way. In this paper we instead apply corner solution models to disaggregate (track-section) data, including the zero observations. We derive track renewal cost elasticities with respect to traffic volumes and in turn marginal renewal costs using Swedish railway renewal data over the period 1999–2009. This paper is the first attempt in the literature to apply corner solution models, and in particular the two-part model, to disaggregate renewal cost data in railways. It is also the first paper that we are aware of to report usage elasticities specifically for renewal costs and therefore adds important new evidence to the previous literature where there is a paucity of studies on renewals and considerable uncertainty over the effects of rail traffic on renewal costs. In the Swedish context, we find that the inclusion of marginal track renewal costs in the track access pricing regime, which currently only reflects marginal maintenance costs, would add substantially to the existing track access charge. EU legislation requires that access charges reflect the ‘costs directly incurred as a result of operating the train service’, which should include a marginal renewal cost component. This change would also increase the cost recovery ratio of the Swedish infrastructure manager, thus meeting a policy objective of the national government.     

Equity-oriented skip-stopping schedule optimization in an oversaturated urban rail transit network

In this study, we focus on improving system-wide equity performance in an oversaturated urban rail transit network based on multi-commodity flow formulation. From the system perspective, an urban rail transit network is a distributed system, where a set of resources (i.e., train capacity) is shared by a number of users (i.e., passengers), and equitable individuals and groups should receive equal shares of resources. However, when oversaturation occurs in an urban rail transit network during peak hours, passengers waiting at different stations may receive varying shares of train capacity leading to the inequity problem under train all-stopping pattern. Train skip-stopping pattern is an effective operational approach, which holds back some passengers at stations and re-routes their journeys in the time dimension based on the available capacity of each train. In this study, the inequity problem in an oversaturated urban rail transit network is analyzed using a multi-commodity flow modeling framework. In detail, first, discretized states, corresponding to the number of missed trains for passengers, are constructed in a space-time-state three-dimensional network, so that the system-wide equity performance can be viewed as a distribution of all passengers in different states. Different from existing flow-based optimization models, we formulate individual passenger and train stopping pattern as commodity and network structure in the multi-commodity flow-modeling framework, respectively. Then, we aim to find an optimal commodity flow and well-designed network structure through the proposed multi-commodity flow model and simultaneously achieve the equitable distribution of all passengers and the optimal train skip-stopping pattern. To quickly solve the proposed model and find an optimal train skip-stopping pattern with preferable system-wide equity performance, the proposed linear programming model can be effectively decomposed to a least-cost sub-problem with positive arc costs for each individual passenger and a least-cost sub-problem with negative arc costs for each individual train under a Lagrangian relaxation framework. For application and implementation, the proposed train skip-stopping optimization model is applied to a simple case and a real-world case based on Batong Line in the Beijing Subway Network. The simple case demonstrates that our proposed Lagrangian relaxation framework can obtain the approximate optimal solution with a small-gap lower bound and a lot of computing time saved compared with CPLEX solver. The real-world case based on Batong Line in the Beijing Subway Network compares the equity and efficiency indices under the operational approach of train skip-stopping pattern with those under the train all-stopping pattern to state the advantage of the train skip-stopping operational approach.     

The econometric estimation of airports’ cost function

The econometric estimation of cost functions has been proposed in the literature as a suitable approach in order to obtain estimations of marginal costs, efficiency levels and scale elasticities for transport industries. However, regarding the airport industry, no significant attention has been paid in developing an airport-specific estimation methodology rather than adapting the procedures applied to other industries. The lack of comparable airport data is one of the causes which could explain the scarcity of this literature in the past, as well as the use of very limited approaches to explain airport technology. This paper tries to overcome these limitations by developing an airport-specific methodology to estimate a multi-output long-run cost function using an unbalanced pooled database on 161 airports worldwide. The specification of hedonically-adjusted aircraft operations, domestic and international passengers, cargo and commercial revenues in the output vector, as well as the calculation of input prices are discussed. Both technical and allocative inefficiencies are specified in the model using a Stochastic Frontier method that has been estimated through Bayesian Inference and Markov Chain Monte Carlo methods.     

Estimation of urban bus transit marginal cost without cost data

We develop a method to study the industrial structure of urban bus transit without using cost data. To do so, we estimate the marginal cost function under the assumption that firms compete on frequency and adjust frequency to maximize profits. Our methodology is applied to Santiago, Chile. In this case, demand is modeled with a simplified model of transit network assignment. The goal is to consider how frequency, capacity, and on-board passengers affect the bus line’s demand. The marginal cost function is estimated by using the first-order conditions of the firm’s profit maximization problem, using the results of the demand model as data. We conclude that the urban bus transit industry in Santiago exhibits increasing returns to scale for low levels of demand and that these returns are exhausted rapidly at a moderate demand level. Additionally, firms exhibit economies of network expansion, on average.     

Marginal costs of freeway traffic congestion with on-road pollution exposure externality

The health cost of on-road air pollution exposure is a component of traffic marginal costs that has not previously been assessed. The main objective of this paper is to introduce on-road pollution exposure as an externality of traffic, particularly important during traffic congestion when on-road pollution exposure is highest. Marginal private and external cost equations are developed that include on-road pollution exposure in addition to time, fuel, and pollution emissions components. The marginal external cost of on-road exposure includes terms for the marginal vehicle’s emissions, the increased emissions from all vehicles caused by additional congestion from the marginal vehicle, and the additional exposure duration for all travelers caused by additional congestion from the marginal vehicle. A sensitivity analysis shows that on-road pollution exposure can be a large portion (18%) of marginal social costs of traffic flow near freeway capacity, ranging from 4% to 38% with different exposure parameters. In an optimal pricing scenario, excluding the on-road exposure externality can lead to 6% residual welfare loss because of sub-optimal tolls. While regional pollution generates greater costs in uncongested conditions, on-road exposure comes to dominate health costs on congested freeways because of increased duration and intensity of exposure. The estimated marginal cost and benefit curves indicate a theoretical preference for price controls to address the externality problem. The inclusion of on-road exposure costs reduces the magnitudes of projects required to cover implementation costs for intelligent transportation system (ITS) improvements; the net benefits of road-pricing ITS systems are increased more than the net benefits of ITS traffic flow improvements. When considering distinct vehicle classes, inclusion of on-road exposure costs greatly increases heavy-duty vehicle marginal costs because of their higher emissions rates and greater roadway capacity utilization. Lastly, there are large uncertainties associated with the parameters utilized in the estimation of health outcomes that are a function of travel pollution intensity and duration. More research is needed to develop on-road exposure modeling tools that link repeated short-duration exposure and health outcomes.     

Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach

In a heavily congested metro line, unexpected disturbances often occur to cause the delay of the traveling passengers, infeasibility of the current timetable and reduction of the operational efficiency. Due to the uncertain and dynamic characteristics of passenger demands, the commonly used method to recover from disturbances in practice is to change the timetable and rolling stock manually based on the experiences and professional judgements. In this paper, we develop a stochastic programming model for metro train rescheduling problem in order to jointly reduce the time delay of affected passengers, their total traveling time and operational costs of trains. To capture the complexity of passenger traveling characteristics, the arriving ratio of passengers at each station is modeled as a non-homogeneous poisson distribution, in which the intensity function is treated as time-varying origin-to-destination passenger demand matrices. By considering the number of on-board passengers, the total energy usage is modeled as the difference between the tractive energy consumption and the regenerative energy. Then, we design an approximate dynamic programming based algorithm to solve the proposed model, which can obtain a high-quality solution in a short time. Finally, numerical examples with real-world data sets are implemented to verify the effectiveness and robustness of the proposed approaches.     

Measuring marginal congestion costs of urban transportation: Do networks matter?

In determining the marginal cost of congestion, economists have traditionally relied upon directly measuring traffic congestion on network links, disregarding any “network effects,” since the latter are difficult to estimate. While for simple networks the comparison of the network-based congestion costs with the link-based ones can be done within a theoretical framework, it is important to know whether such network effects in real large-scale networks are quantitatively significant.In this paper we use a strategic transportation planning model (START) to compare marginal congestion costs computed link-by-link with measures taking into account network effects. We find that while in aggregate network effects are not significant, congestion measured on a single link is a poor predictor of total congestion costs imposed by travel on that link. Also, we analyze the congestion proliferation effect on the network to see how congestion is distributed within an urban area.     

The marginal social cost of headway for a scheduled service

This brief paper derives the marginal social cost of headway for a scheduled service, i.e. the cost for users of marginal increases to the time interval between departures. In brief we may call it the value of headway in analogy with the value of travel time and the value of reliability. Users have waiting time costs as well as schedule delay costs measured relative to their desired time of arrival at the destination. They may either arrive at the station to choose just the next departure or they may plan for a specific departure in which case they incur also a planning cost. Then planning for a specific departure is costly but becomes more attractive at longer headways. Simple expressions for the user cost result. In particular, the marginal cost of headway is large at short headways and smaller at long headways. The difference in marginal costs is the value of time multiplied by half the headway.     

Fuel saving and ridesharing in the US: Motivations,limitations, and opportunities

Ridesharing can reduce the fuel consumed in noncommercial passenger highway vehicles by grouping individuals into fewer vehicles and reducing the number of miles that vehicles must travel. We estimate the potential fuel savings that could result from an increase in ridesharing in the US. If no additional travel is required to pick up passengers, adding one additional passenger for every 100 vehicles would reduce annual fuel consumption by 0.80–0.82 billion gallons of gasoline per year; if one passenger were added in every 10 vehicles, the potential savings would be 7.54–7.74 billion gallons per year. However, ridesharing may require extra travel to pick up additional passengers, which can reduce and possibly eliminate potential fuel savings. The tradeoff between saving fuel and spending time to pick up additional passengers is investigated, finding that, on average, ridesharing may not be attractive to travelers, but can be made more attractive by increasing per-vehicle-trip costs such as parking and tolls.     

Frequency-based transit assignment considering seat capacities

This paper proposes a frequency-based assignment model that considers travellers probability of finding a seat in their perception of route cost and hence also their route choice. The model introduces a “fail-to-sit” probability at boarding points with travel costs based on the likelihood of travelling seated or standing. Priority rules are considered; in particular it is assumed that standing on-board passengers will occupy any available seats of alighting passengers before newly boarding passengers can fill any remaining seats. At the boarding point passengers are assumed to mingle, meaning that FIFO is not observed, as is the case for many crowded bus and metro stops, particularly in European countries. The route choice considers the common lines problem and an user equilibrium solution is sought through a Markov type network loading process and the method of successive averages. The model is first illustrated with a small example network before being applied to the inner zone of London’s underground network. The effect of different values passengers might attach to finding a seat are illustrated. Applications of the model for transit planning as well as for information provision at the journey planner stage are discussed.     

The potential of turboprops for reducing aviation fuel consumption

To assist in aviation systems planning in the context of fuel price uncertainty and environmental regulation, we take a total logistics cost approach and evaluate three representative aircraft (narrow body, regional jet, and turboprop) for operating and passenger preference costs over a range of fuel prices. Homogenous fleets of each vehicle category are compared for operating and passenger costs over a range of fuel prices and route distances and the minimum cost fleet mix is determined. In general, as fuel prices increase, the turboprop offers a lower operating cost per seat over a wider range of distances when compared with both jet aircraft models. The inclusion of passenger costs along with operating costs decreases the number of fuel price and distance pairs where the turboprop exhibits the lower cost. This analysis shows that the aircraft that exhibits the lowest cost is highly sensitive to fuel prices and passenger costs and points to the important balance between saving fuel and serving passengers.