Introducing advanced electronic driver support systems: An exploration of market and technological uncertainties

R&D in the field of driver support systems is increasingly paid attention to. These systems can contribute significantly to public traffic goals. However, there is much uncertainty about future technology developments, market introduction, and impacts on driver and traffic behaviour. An international Delphi study collecting expert opinions on these issues is partly described here. The Delphi study was organized in three rounds. Opinions of 50 experts from the USA, Japan and Europe were collected. The paper is limited to market introduction, and technological and driver-behavioural barriers. The main conclusion is that future developments are less obvious than often assumed.     

The growth and patterns of international trade

Over the last 40 years, there has been an unprecedented growth in trade amongst countries, and the growth in trade shows no sign of slowing down. The increases in trade have put tremendous pressure on the maritime and port industries, and these industries have responded with innovations, investment, and greater productivity. International trade and maritime trade are synonyms, and an understanding of the determinants of international trade is central to understanding maritime trade. In this paper, we provide a review of the international trade literature with a focus on the determinants of trade and the evolution of trade modeling. We then present a broad overview of the extent and growth of trade in the context of primary determinants. The basic results are: (1) Trade is growing at a phenomenal rate; (2) Trade is dominated by relatively few countries who tend to remain dominant; (3) While trade of all products is growing, there are large differences in the growth rates, but yet, there is stability in the relative sizes of product markets; and (4) Over the last 40 years, trade has changed from major flows between the US and Europe to major flows between Asia and the US.     

“I can do perfectly well without a car!”

This article presents the results of a study exploring travellers’ preferences for middle-distance travel using Q-methodology. Respondents rank-ordered 42 opinion statements regarding travel choice and motivations for travel in general and for car and public transport as alternative travel modes. By-person factor analysis revealed four distinct preference segments for middle-distance travel: (1) choice travellers with a preference for public transport, (2) deliberate-choice travellers, (3) choice travellers with car as dominant alternative, and (4) car-dependent travellers. These preference segments differ in terms of the levels of involvement and cognitive effort in travel decision making, the travel consideration-set and underlying motivations. The study showed that for most people there is more to travel than getting from point A to point B, and that there is considerable heterogeneity in middle-distance travel preferences. Policy implications for reducing the need for travel and promoting a modal shift from car to other travel modes are discussed.     

Cluster-based correlation of severe driving events with time and location

In this article, a systematic strategy is proposed to identify severe driving events occurrence correlation with time and location. The proposed approach, which is constructed based on batch clustering and real-time clustering techniques, incorporates historical and real-time data to predict the time and location of severe driving events. Batch clustering is implemented with the combination of subtractive clustering and fuzzy c-means clustering to generate clusters representing the initial correlation patterns. Real-time clustering is then developed to create and update real-time correlation patterns on the foundation of the batch clustering using the evolving Gustafson–Kessel like (eGKL) algorithm. In both clustering processes, the correlation of the events within time domain is identified first, and then two different levels of accurate correlations are conducted for the location domain. Real-time data of operating vehicles each equipped with a data acquisition and wireless communication platform are used to validate the proposed strategy. Batch clustering results reveal the severe braking events distribution and concentration at daytime and nighttime. Real-time clustering provides and updates the variation of the correlations/intercorrelation of different regions. Drivers can be notified of the potential severe driving locations through maps showing the driving routes. Through the variation of the correlations, drivers can recognize the events occurrence at different times and locations. The generated time series can be potentially used to develop spatial-time models for regions to model and forecast the events occurrence.     

Full-slip kinematics based estimation of vehicle yaw rate from differential wheel speeds

Vehicle yaw rate is a key parameter required for various active stability control systems. Accurate yaw rate information may be obtained from the fusion of some on-vehicle sensors and GPS data. In this study, the closed-form expression of the yaw rate–written as a function of front wheel rolling speeds and steering angle–was derived via kinematic analysis of a planar four-wheel vehicle on the assumption of no longitudinal slip at the both front tires. The obtained analytical solution was primarily verified by computational simulation. In terms of implementation, the 1:10th scaled rear-wheel-drive vehicle was modified so that the front wheel rolling speeds and the steering angle could be measured. An inertial measurement unit was also installed to provide the directly measured yaw rate used for validation. Preliminary experiment was done on some extremely random sideslip maneuvers beneath the global positioning using four recording cameras. Comparing with the vision-based and the gyro-based references, the vehicle yaw rate could be well approximated at any slip condition without requiring integration or vehicle and tire models. The proposed cost-effective estimation strategy using only on-vehicle sensors could be used as an alternative way to enhance performance of the GPS-based yaw rate estimation system while the GPS signal is unavailable.     

The drinking driver: a medical view

Accidents are the third main cause of death in Europe with 40% of them due to road traffic accidents (RTAs). These victims are mainly young male drivers/riders and elderly pedestrians. One quarter of the deaths and 10% of the injuries are associated with alcohol. Consumption of alcohol is generally increasing. Ignorance of the effects of alcohol on the body and on the performance of skilled mental and physical tasks contributes to the number of road users who are impaired by alcohol. Alcohol is a depressant drug which impairs information processing in the brain. The risk of being involved in a RTA increases very rapidly with increasing concentration of alcohol in the body. There are probably two groups of drivers, the majority who do not drink, or who drink very little, before driving and those who regularly drink whether or not they are going to drive. The former (the social drinker) can do without his drink, the latter (the problem drinker) cannot. Various legislative measures have been introduced in different countries with varying success. Most developed countries now make it illegal to drive with more than a stipulated concentration of alcohol in the body. Much discussion centres round what further legal steps should be taken to reduce drinking and driving, especially the more widespread use of random roadside breath testing of drivers. Doctors are much concerned in this public health problem which causes such misery and waste of resources and are involved in research on the physical and psychological effects of alcohol, the best methods of educating drivers on these effects and, where necessary, identifying those who are misusing alcohol to the extent that they should be kept off the road.     

The Development of a Visualization Methodology for Integrated Coastal Management

Coastal management information is frequently communicated to stakeholders and the public through complicated management documents and engineering plans. With the recognition that public involvement in coastal decision-making processes should be widened have come calls to develop new techniques to communicate complicated coastal information. Using Virtual Reality Geographical Information Systems and visualization packages, such information may be presented using formats more suitable for public consultation and information dissemination exercises than those currently employed. Using a site on the north Norfolk coast of England, an integrated Geographical Information Systems based methodology is presented that allows the visualization of proposed coastal management interventions. Visualizations have been produced that can be published in traditional paper-based management documents, or electronically. The different visualizations are compared and the technical issues surrounding their use discussed. It is argued that the methodology has clear advantages over traditional communication methods, although further research is necessary to determine how it may be practically employed by coastal managers.     

Modelling and simulation of transportation system effectiveness to reduce traffic congestion: a system dynamics framework

ABSTRACT

This paper is designed to evaluate and improve the effectiveness of transportation systems and reduce traffic congestion through the use of simulation models and scenario development. A system dynamics framework is used to test and evaluate the alternatives of future strategies for the city of Surabaya, Indonesia. Some factors affecting the effectiveness of transport systems include operational effectiveness and service effectiveness, as well as uncertainty. To improve the effectiveness of transportation systems, several strategies can be implemented, such as subsidizing public transportation, increasing the cost of private vehicle parking fees, raising taxes on private vehicles, and reducing delays in public transportation through scenario development. Scenario results show that, by pursuing these strategies, effectiveness could be improved by 80% as the impact of the increase in operational and service effectiveness, helping to mitigate traffic congestion. Congestion could be reduced to 70% (on average) due to the decrease in daily traffic.     

Forecasting automobile petrol demand in Australia: An evaluation of empirical models

Transport fuel consumption and its determinants have received a great deal of attention since the early 1970s. In the literature, different types of modelling methods have been used to estimate petrol demand, each having methodological strengths and weaknesses. This paper is motivated by an ongoing need to review the effectiveness of empirical fuel demand forecasting models, with a focus on theoretical as well as practical considerations in the model-building processes of different model forms. We consider a linear trend model, a quadratic trend model, an exponential trend model, a single exponential smoothing model, Holt’s linear model, Holt–Winters’ model, a partial adjustment model (PAM), and an autoregressive integrated moving average (ARIMA) model. More importantly, the study identifies the difference between forecasts and actual observations of petrol demand in order to identify forecasting accuracy. Given the identified best-forecasting model, Australia’s automobile petrol demand from 2007 through to 2020 is presented under the “business-as-usual” scenario.