The determinants of long distance travel in Great Britain

This study analyses of the determinants of long distance travel in Great Britain using data from the 1995-2006 National Travel Surveys (NTSs). The main objective is to determine the effects of socio-economic, demographic and geographic factors on long distance travel. The estimated models express the distance travelled for long distance journeys as a function of income, gender, age, employment status, household characteristics, area of residence, size of municipality, type of residence and length of time living in the area. A time trend is also included to capture common changes in long distance travel over time not included in the explanatory variables. Separate models are estimated for total travel, travel by each of four modes (car, rail, coach and air), travel by five purposes (business, commuting, leisure, holiday and visiting friends and relatives (VFRs)) and two journey lengths (<150 miles and 150+ miles one way), as well as the 35 mode-purpose-distance combinations.The results show that long distance travel is strongly related to income: air is most income-elastic, followed by rail, car and finally coach. This is the case for most journey purposes and distance bands. Notable is the substantial difference in income elasticities for rail for business/commuting as opposed to holiday/leisure/VFR. In addition, the income elasticity for coach travel is very low, and zero for the majority of purpose-distance bands, suggesting coach travel to be an inferior mode in comparison to car, rail and air. Regarding journey distance, we find that longer distance journeys are more income elastic than shorter journeys.For total long distance travel, the study indicates that women travel less than men, the elderly less than younger people, the employed and students more than others, those in one adult households more than those in larger households and those in households with children less than those without. Long distance travel is also lowest for individuals living in London and greatest for those in the South West, and increases as the size of the municipality declines.     

Bush-based sensitivity analysis for approximating subnetwork diversion

Subnetwork analysis is often used in traffic assignment problems to reduce the size of the network being analyzed, with a corresponding decrease in computation time. This is particularly important in network design, second-best pricing, or other bilevel problems in which many equilibrium runs must be solved as a subproblem to a master optimization program. A fixed trip table based on an equilibrium path flow solution is often used, but this ignores important attraction and diversion effects as drivers (globally) change routes in response to (local) subnetwork changes. This paper presents an approach for replacing a regional network with a smaller one, containing all of the subnetwork, and zones. Artificial arcs are created to represent “all paths” between each origin and subnetwork boundary node, under the assumption that the set of equilibrium routes does not change. The primary contribution of the paper is a procedure for estimating a cost function on these artificial arcs, using derivatives of the equilibrium travel times between the end nodes to create a Taylor series. A bush-based representation allows rapid calculation of these derivatives. Two methods for calculating these derivatives are presented, one based on network transformations and resembling techniques used in the analysis of resistive circuits, and another based on iterated solution of a nested set of linear equations. These methods are applied to two networks, one small and artificial, and the other a regional network representing the Austin, Texas metropolitan area. These demonstrations show substantial improvement in accuracy as compared to using a fixed table, and demonstrate the efficiency of the proposed approach.     

Employability skills of maritime business graduates: industry perspectives

The maritime industry underpins international business and world trade. As to be expected, business management is critical for the maritime industry, requiring highly trained individuals and teams to lead the development, implementation and control of sound contemporary management practices. Maritime business degrees are developed by universities to meet such demand by providing graduates with sufficient skills for the onshore business-related roles. This empirical study conducted in Australia, USA and Canada, investigates current and future industry employability skills for maritime business graduates through focus groups, individual interviews and an online survey with senior managers in maritime organisations. This study found the important employability skills for maritime business graduates which include communication, problem solving, adaptability, self-management, team work, and digital literacy and technology. Demand for digital literacy and technology knowledge and skills have increased due to the maritime industry having a trend of moving towards digitalisation and automation. However, the survey findings revealed that a skills focus for maritime business graduates will not be the technology itself but the use and management of technology. In relation to future skills/knowledge required from maritime business degree graduates in 10 years’ time, communication and adaptability are recognised as being the most important. Employers expect that maritime business graduates should be able to adopt new technology and be competent in communication, and be more adaptable given the highly dynamic nature of the maritime industry. Moreover, they require graduates to be equipped with a higher level of computer skills, have a strong work ethic and multilingual skills.     

Robust network pricing and system optimization under combined long-term stochasticity and elasticity of travel demand

Network pricing serves as an instrument for congestion management, however, agencies and planners often encounter problems of estimating appropriate toll prices. Tolls are commonly estimated for a single-point deterministic travel demand, which may lead to imperfect policy decisions due to inherent uncertainties in future travel demand. Previous research has addressed the issue of demand uncertainty in the pricing context, but the elastic nature of demand along with its uncertainty has not been explicitly considered. Similarly, interactions between elasticity and uncertainty of demand have not been characterized. This study addresses these gaps and proposes a framework to estimate nearest optimal first-best tolls under long-term stochasticity in elastic demand. We show first that the optimal tolls under the deterministic-elastic and stochastic-elastic demand cases coincide when cost and demand functions are linear, and the set of equilibrium paths is constant. These assumptions are restrictive, so three larger networks are considered numerically, and the subsequent pricing decisions are assessed. The results of the numerical experiments suggest that in many cases, optimal pricing decisions under the combined stochastic-elastic demand scenario resemble those when demand is known exactly. The applications in this study thus suggest that inclusion of demand elasticity offsets the need of considering future demand uncertainties for first-best congestion pricing frameworks.     

Book reviews

The European Community Transport Policy: Towards a Common Transport Policy. By Jurgen Erdmenger (Gower Publishing Co. 1984) [Pp. 155.] £14.50.

Transport Reform: Changing the Rules. By Stephen Plowden. P.S.I. No. 642 (London: Institute, 1985.) [Pp. 250.] £6.50. ISBN 0–85374–258–8.

The Atlas of British Railway History. By Michael Freeman and Derek Aldcroft (London: Croom Helm, 1985.) [Pp. 128.] £12.95.

Losing Track. By Kerry Hamilton and Stephen Potter (Routledge & Kegan Paul in association with Channel Four, 1985.) [Pp. viii + 152.] £7.95. Paperback.     

An advanced risk analysis approach for container port safety evaluation

Risk analysis in seaports plays an increasingly important role in ensuring port operation reliability, maritime transportation safety and supply chain distribution resilience. However, the task is not straightforward given the challenges, including that port safety is affected by multiple factors related to design, installation, operation and maintenance and that traditional risk assessment methods such as quantitative risk analysis cannot sufficiently address uncertainty in failure data. This paper develops an advanced Failure Mode and Effects Analysis (FMEA) approach through incorporating Fuzzy Rule-Based Bayesian Networks (FRBN) to evaluate the criticality of the hazardous events (HEs) in a container terminal. The rational use of the Degrees of Belief (DoB) in a fuzzy rule base (FRB) facilitates the implementation of the new method in Container Terminal Risk Evaluation (CTRE) in practice. Compared to conventional FMEA methods, the new approach integrates FRB and BN in a complementary manner, in which the former provides a realistic and flexible way to describe input failure information while the latter allows easy updating of risk estimation results and facilitates real-time safety evaluation and dynamic risk-based decision support in container terminals. The proposed approach can also be tailored for wider application in other engineering and management systems, especially when instant risk ranking is required by the stakeholders to measure, predict and improve their system safety and reliability performance.     

Ocean Citizenship: An Emergent Geographical Concept

Ocean citizenship describes a relationship between our everyday lives and the health of the coastal and marine environment. Through our everyday lives we affect, and are affected by, the marine and coastal environment in numerous ways. As such, individuals have a responsibility to make informed lifestyle choices to minimize this impact. In doing so, the actions of individuals can contribute to the amelioration of large-scale and seemingly insurmountable geographical problems. This article outlines the concept of ocean citizenship within the context of the public understanding of marine environmental issues. The article draws heavily on the experience of the National Maritime Museum as an important contributor to the development of ocean citizenship in the United Kingdom. Specifically, the Planet Ocean initiative will be examined, in which the Museum has adopted a multimodal approach to public engagement through exhibitions, educational resources, and specific research publications. The article concludes by highlighting the importance of geography in the development and sustainability of ocean citizenship.     

Experimental study on a model azimuthing podded propulsor in ice

The objective of this study was to investigate the performance of a model azimuthing podded propulsor in ice-covered water. Model tests were carried out with two different depths of cut into the ice (15 and 35 mm), two different ice conditions (presawn and pack ice conditions), and four different azimuthing angles. The depth of cut is the maximum penetration depth of the propeller blade into the ice block. The 0.3-m-diameter model propeller was operated in a continuous ice milling condition. Ice loads were measured by several sensors which were installed in various positions on the model. Six one-axis pancake-style load cells on the top of the model measured the global loads and two six-component dynamometers were installed on the shaft to measure the shaft loads. One six-component dynamometer was attached to the one of the propeller blades inside the hub to measure the blade loads. The pod unit and propeller performance in ice are presented. Ice-related loads, which were obtained when the blade was inside the ice block, are introduced and discussed. During the propeller–ice interaction, a blade can experience the path generated by the previous blade, which is called the shadowing effect. The effects of shadowing, depth of cut, azimuthing angle, and advance coefficient on propulsor performance are presented and discussed.     

A new methodology for incident detection and characterization on surface streets

In this paper, a new methodology is presented for real-time detection and characterization of incidents on surface streets. The proposed automatic incident detection approach is capable of detecting incidents promptly as well as characterizing incidents in terms of time-varying lane-changing fractions and queue lengths in blocked lanes, lanes blocked due to incidents, and incident duration. The architecture of the proposed incident detection approach consists of three sequential procedures: (1) Symptom Identification for identification of incident symptoms, (2) Signal Processing for real-time prediction of incident-related lane traffic characteristics and (3) Pattern Recognition for incident recognition. Lane traffic counts and occupancy are the only two major types of input data, which can be readily collected from point detectors. The primary techniques utilized in this paper include: (1) a discrete-time, nonlinear, stochastic system with boundary constraints to predict real-time lane-changing fractions and queue lengths and (2) a pattern-recognition-based algorithm employing modified sequential probability ratio tests (MSPRT) to detect incidents. Off-line tests based on simulated as well as video-based real data were conducted to assess the performance of the proposed algorithm. The test results have indicated the feasibility of achieving real-time incident detection using the proposed methodology.     

Investigating effects of asphalt pavement conditions on traffic accidents in Tennessee based on the pavement management system (PMS)

Pavement maintenance is essential for ensuring good riding quality and avoiding traffic congestion, air pollution, and accidents. Improving road safety is one of the most important objectives for pavement management systems. This study utilized the Tennessee Pavement Management System (PMS) and Accident History Database (AHD) to investigate the relationship between accident frequency and pavement distress variables. Focusing on four urban interstates with asphalt pavements, divided median types, and 55 mph speed limits, 21 Negative Binomial Regression models were developed for predicting various types of traffic accident frequencies based on different pavement condition variables, including rut depth (RD), International Roughness Index (IRI), and Present Serviceability Index (PSI). The modeling results indicated that the RD models did not perform well, except for predicting accidents at night and accidents under rain weather conditions; whereas, IRI and PSI were always significant prediction variables in all types of accident models. Comparing the models goodness‐of‐fit results, it was found that the PSI models had a better performance in crash frequency prediction than the RD models and IRI models. This study suggests that the PSI accident prediction models should be considered as a comprehensive approach to integrate the highway safety factors into the pavement management system. Copyright © 2010 John Wiley & Sons, Ltd.