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1 – 10 of over 6000Hilde Meersman, Eddy Van de Voorde and Thierry Vanelslander
Ports are widely recognised as crucial nodes in international trade and transport. However, for various reasons, capacity does not always match demand: sometimes there is…
Abstract
Ports are widely recognised as crucial nodes in international trade and transport. However, for various reasons, capacity does not always match demand: sometimes there is overcapacity, whereas in other cases, demand exceeds capacity and there is a shortage of the latter. This chapter therefore looks at where port congestion occurs, both globally and in the port-calling chain; it analyses actual responses by various chain actors, and it sheds some light on potential future evolution and reaction patterns.
Congestion, in general, can feature various forms of appearance: it can be more or less hidden, featuring congestion costs, or it can be visually present, featuring queues which are building up. The chapter discerns eight zones in the port-calling chain where congestion may emerge. As a result of a wide literature search, supplemented with a survey, it can first of all be observed that quite some congestion seems to occur, globally spread, and hitting larger as well as smaller ports. Most of the congestion is generated at the terminals, hinterland connection points and hinterland transport itself.
In terms of reaction patterns, one would assume that pricing throughout the system is adapted in such way that demand equals capacity. In practice, prices are hardly making any effort to make marginal revenue equal marginal cost. The reason is mainly that the power balance is quite strongly in favour of shipping companies, who impose on port and port operators the need to expand capacity at low fees. Port operators, in turn, apply various kinds of technical and procedural adaptations. The same is true for hinterland operators.
Looking towards the future, it seems that with the increase in world trade, the risk of port congestion will be even more outspoken, be it in some parts of the world more than in others. It is also very much likely that most problems will occur landside, as this is the part of the chain where solutions are least easy: who is going to take the initiative, how will co-ordination take place and where will the funding come from? Most actors seem to be aware of this trend, and seek for solutions like dedicated terminals and vertical integration or co-operation.
With the above observations, the chapter sheds some light on where the future needs and trends in the abatement of capacity will lie. It is therefore useful from a scientific point of view as well as with an eye on policy-making and operational port management.
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Xavier Fageda and Ricardo Flores-Fillol
We investigate the relationship between airline network structure and airport congestion. More specifically, we study the ways in which airlines adjust capacity to delays…
Abstract
We investigate the relationship between airline network structure and airport congestion. More specifically, we study the ways in which airlines adjust capacity to delays depending on the network type they operate. We find some evidence suggesting that airlines operating hub-and-spoke structures react less to delays than airlines operating fully connected configurations. In particular, network airlines have incentives to keep frequency high even if this is at the expense of a greater congestion at their hub airports. We also show that airlines in slot-constrained airports seem to react to higher levels of congestion by using bigger aircraft at lower frequencies; thus, we conclude that conditioning the number of available slots on the levels of delays at the airport seems an effective measure that creates the right incentives for airlines to reduce the congestion they generate.
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Priyanka Chawla, Rutuja Hasurkar, Chaithanya Reddy Bogadi, Naga Sindhu Korlapati, Rajasree Rajendran, Sindu Ravichandran, Sai Chaitanya Tolem and Jerry Zeyu Gao
The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives…
Abstract
Purpose
The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.
Design/methodology/approach
This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.
Findings
The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.
Research limitations/implications
The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.
Practical implications
To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.
Social implications
To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.
Originality/value
The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.
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David Besanko, Johannes Horner and Ed Kalletta
Describes the events leading up to the imposition of the London congestion charge. Views about the congestion charge, both pro and con, are presented. Also discusses, in general…
Abstract
Describes the events leading up to the imposition of the London congestion charge. Views about the congestion charge, both pro and con, are presented. Also discusses, in general terms, the economics of traffic congestion, pointing out that an unregulated market for driving will not reach the social optimum. Contains sufficient data to estimate the deadweight loss in an unregulated market and the reduction of the deadweight loss due to the imposition of the congestion charge in 2003.
To provide a good illustration of how an unregulated market with negative externalities can lead to an overprovision of a good (in this case driving). Also, to show how an externality tax (in this case, London's congestion charge) can lead to an improvement in social welfare.
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Subramania Raju Rajasulochana and Domenica Matranga
The purpose of this paper is to assess congestion as the simultaneous occurrence of desirable health output (e.g. maternal admissions) along with undesirable output (e.g. still…
Abstract
Purpose
The purpose of this paper is to assess congestion as the simultaneous occurrence of desirable health output (e.g. maternal admissions) along with undesirable output (e.g. still births (SB)), in emergency obstetric care settings of public hospitals in Tamil Nadu.
Design/methodology/approach
The study is based on a cross-sectional data set of 97 public hospitals collected by the statistical cell of Tamil Nadu Health Systems Project for the year 2013–2014. The study uses three inputs – beds, doctors and nurses; three desirable outputs – maternal admissions, neonatal admission and live births; and four undesirable outputs – SB, intra-uterine deaths, neonatal deaths and maternal deaths. Congestion analysis, a variant of the data envelopment analysis (DEA) method and slack analysis, has been applied to detect an excessive use of some inputs or a shortfall in some outputs across these hospitals. Furthermore, the association between congestion and some contextual factors has been examined.
Findings
On an average, the hospitals in our sample can increase the total amount of outputs by 62.8 percent by improving overall efficiency, and about 34.2 percent of this inefficiency can be attributed to congestion. Analysis of sub-samples showed that government hospitals at the taluk level have higher congestion than district headquarter hospitals. Congestion seems to decrease with greater hospital volume up to a limit; beyond that, it increases in obstetric care settings.
Originality/value
Hospital-based efficiency studies in the Indian context, so far, have estimated relative efficiency among hospitals using the classical DEA method, but ignoring adverse health outcomes. Congestion analysis, an advance in the DEA method, considers how much the desirable outputs can be increased as also how much undesirable outputs affect efficiency.
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Irvine Lapsley and Filippo Giordano
The aim of this paper is to add to understanding of how cities function. Specifically, through the lens of power relationships in political organisations, it seeks to study the…
Abstract
Purpose
The aim of this paper is to add to understanding of how cities function. Specifically, through the lens of power relationships in political organisations, it seeks to study the manner in which accounting and politics are involved in the development of city transport strategies.
Design/methodology/approach
The paper uses a comparative case study approach in which documents and media coverage are key elements of the visualising of the city.
Findings
The findings are on a number of levels. First, the study explains the efficacy of congestion charging systems. Second, in the politicised organisation of the city, the context in which policy makers sit is crucial in the elaboration of strategies. Third, the adoption of calculative practices such as congestion charging may reflect political rationality rather than actual need.
Originality/value
The focus of the study has been cities – a neglected field, but one with considerable research potential. Second, the mobilisation of concepts of power, as articulated by Clegg, Flyvbjerg and Clegg, represent a novel contribution to the accounting literature.
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Afzal S. Siddiqui, Emily S. Bartholomew, Chris Marnay and Shmuel S. Oren
The physical nature of electricity generation and delivery creates special problems for the design of efficient markets, notably the need to manage delivery in real time and the…
Abstract
The physical nature of electricity generation and delivery creates special problems for the design of efficient markets, notably the need to manage delivery in real time and the volatile congestion and associated costs that result. Proposals for the operation of the deregulated electricity industry tend towards one of two paradigms: centralized and decentralized. Transmission congestion management can be implemented in the more centralized point‐to‐point approach, as in New York state, where derivative transmission congestion contracts (TCCs) are traded, or in the more decentralized flowgate‐based approach. While it is widely accepted that theoretically TCCs have attractive properties as hedging instruments against congestion cost uncertainty, whether efficient markets for them can be established in practice has been questioned. Based on an empirical analysis of publicly available data from years 2000 and 2001, it appears that New York TCCs provided market participants with a potentially effective hedge against volatile congestion rents. However, the prices paid for TCCs systematically diverged from the resulting congestion rents for distant locations and at high prices. The price paid for the hedge not being in line with the congestion rents, i.e., unreasonably high risk premiums are being paid, suggests an inefficient market. The low liquidity of TCC markets and the deviation of TCC feasibility requirements from actual energy flows are possible explanations.
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