Traffic fatalities: does income inequity create an externality?

Responsible for 20 million severe injuries and/or deaths annually, few epidemics receive less attention than traffic accidents. Going beyond confirming an inverted U-shaped relationship between mean income and fatalities, we show theoretically that income inequality can positively affect fatalities in two ways. Each operates through heterogeneity between road users, and while the direct effect can be expected to evaporate with rising income, the indirect effect may prove to be an externality in that the relationship remains regardless of the level of income. Our model is supported by evidence from 79 countries between 1970 and 2000.

Impact of front-of-pack 'traffic-light' nutrition labelling on consumer food purchases in the UK

Front-of-pack ‘traffic-light’ nutrition labelling has been widely proposed as a tool to improve public health nutrition. This study examined changes to consumer food purchases after the introduction of traffic-light labels with the aim of assessing the impact of the labels on the ‘healthiness’ of foods purchased. The study examined sales data from a major UK retailer in 2007. We analysed products in two categories (‘ready meals’ and sandwiches), investigating the percentage change in sales 4 weeks before and after traffic-light labels were introduced, and taking into account seasonality, product promotions and product life-cycle. We investigated whether changes in sales were related to the healthiness of products. All products that were not new and not on promotion immediately before or after the introduction of traffic-light labels were selected for the analysis (n = 6 for ready meals and n = 12 for sandwiches). For the selected ready-meals, sales increased (by 2.4% of category sales) in the 4 weeks after the introduction of traffic-light labels, whereas sales of the selected sandwiches did not change significantly. Critically, there was no association between changes in product sales and the healthiness of the products. This short-term study based on a small number of ready meals and sandwiches found that the introduction of a system of four traffic-light labels had no discernable effect on the relative healthiness of consumer purchases. Further research on the influence of nutrition signposting will be needed before this labelling format can be considered a promising public health intervention.

Traffic fatalities and public sector corruption

Traffic accidents result in 1 million deaths annually worldwide, though the burden is disproportionately felt in poorer countries. Typically, fatality rates from disease and accidents fall as countries develop. Traffic deaths, however, regularly increase with income, at least up to a threshold level, before declining. While we confirm this by analyzing 1,356 country-year observations between 1982 and 2000, our purpose is to consider the role played by public sector corruption in determining traffic fatalities. We find that such corruption, independent of income, plays a significant role in the epidemics of traffic fatalities that are common in relatively poor countries.

Drawing lines in the sea : the laws that govern traffic on the world’s oceans can be as difficult to shape as water

Since World War II, however, the term has increasingly referred to law enforcement operations, as a means to enforce trade sanctions, to prevent the movement of weapons of mass destruction (WMDs), and particularly in the Caribbean Sea, to prevent the smuggling of illicit drugs. Such ambiguity should allow flexibility when deciding whom should be targeted, as well as allowing states with veto powers in the UN Security Council, which may legitimately ship nuclear weapons and materials, to avoid being targeted as long as they do not export WMDs to rogue states or non-state groups or individuals.2 The ISPS Code was created under the auspices of the International Maritime Organization (IMO) and is part of the 1974 Safety of Life at Sea Convention (SOLAS) concerning the safety of merchant ships.

Humanoid traffic control robot

A traffic control device in the form of a humanoid character robot, doll or dummy is used to warn driver of danger ahead on the road. The device can be used on roads, streets and in other sites where there are moving vehicles. The robotic device informs drivers of impending danger by moving its arms and sounding an acoustic alarm. In this way the robot can simulate a policeman or road flagging operator. The device may also include speed detection and preferably speed indication means. The robot may make decisions based on the detected speed of a vehicle and the limit for the area in operating the arms and sound warning means. The robot may also be equipped with a camera or video. The robot may also be controlled wirelessly.

Spam filtering for network traffic security on a multi-core environment

This paper presents an innovative fusion-based multi-classifier e-mail classification on a ubiquitous multicore architecture. Many previous approaches used text-based single classifiers to identify spam messages from a large e-mail corpus with some amount of false positive tradeoffs. Researchers are trying to prevent false positive in their filtering methods, but so far none of the current research has claimed zero false positive results. In e-mail classification false positive can potentially cause serious problems for the user. In this paper, we use fusion-based multi-classifier classification technique in a multi-core framework. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our multi-classifier-based filtering system in terms of running time, false positive rate, and filtering accuracy. Our proposed architecture also provides a safeguard of user mailbox from different malicious attacks. Our experimental results show that we achieved an average of 30% speedup at an average cost of 1.4 ms. We also reduced the instances of false positives, which are one of the key challenges in a spam filtering system, and increases e-mail classification accuracy substantially compared with single classification techniques.

An optimal transportation routing approach using GIS-based dynamic traffic flows

This paper examines the value of real-time traffic information gathered through Geographic Information Systems for achieving an optimal vehicle routing within a dynamically stochastic transportation network. We present a systematic approach in determining the dynamically varying parameters and implementation attributes that were used for the development of a Web-based transportation routing application integrated with real-time GIS services. We propose and implement an optimal routing algorithm by modifying Dijkstra’s algorithm in order to incorporate stochastically changing traffic flows. We describe the significant features of our Web application in making use of the real-time dynamic traffic flow information from GIS services towards achieving total costs savings and vehicle usage reduction. These features help users and vehicle drivers in improving their service levels and productivity as the Web application enables them to interactively find the optimal path and in identifying destinations effectively.

'Traffic-light' nutrition labelling and 'junk-food' tax : a modelled comparison of cost-effectiveness for obesity prevention

Introduction: Cost-effectiveness analyses are important tools in efforts to prioritise interventions for obesity prevention.
Modelling facilitates evaluation of multiple scenarios with varying assumptions. This study compares the cost-effectiveness of
conservative scenarios for two commonly proposed policy-based interventions: front-of-pack ‘traffic-light’ nutrition labelling
(traffic-light labelling) and a tax on unhealthy foods (‘junk-food’ tax).
Methods: For traffic-light labelling, estimates of changes in energy intake were based on an assumed 10% shift in consumption
towards healthier options in four food categories (breakfast cereals, pastries, sausages and preprepared meals) in 10% of adults. For the ‘junk-food’ tax, price elasticities were used to estimate a change in energy intake in response to a 10% price increase in seven food categories (including soft drinks, confectionery and snack foods). Changes in population weight and body mass index by sex were then estimated based on these changes in population energy intake, along with subsequent impacts on disability-adjusted life years (DALYs). Associated resource use was measured and costed using pathway analysis, based on a health sector perspective (with some industry costs included). Costs and health outcomes were discounted at 3%. The cost-effectiveness of each intervention was modelled for the 2003 Australian adult population.
Results: Both interventions resulted in reduced mean weight (traffic-light labelling: 1.3 kg (95% uncertainty interval (UI): 1.2;
1.4); ‘junk-food’ tax: 1.6 kg (95% UI: 1.5; 1.7)); and DALYs averted (traffic-light labelling: 45 100 (95% UI: 37 700; 60 100);
‘junk-food’ tax: 559 000 (95% UI: 459 500; 676 000)). Cost outlays were AUD81 million (95% UI: 44.7; 108.0) for traffic-light
labelling and AUD18 million (95% UI: 14.4; 21.6) for ‘junk-food’ tax. Cost-effectiveness analysis showed both interventions were
‘dominant’ (effective and cost-saving).
Conclusion: Policy-based population-wide interventions such as traffic-light nutrition labelling and taxes on unhealthy foods are
likely to offer excellent ‘value for money’ as obesity prevention measures.

An automatic application signature construction system for unknown traffic

Identifying applications and classifying network traffic flows according to their source applications are critical for a broad range of network activities. Such a decision can be based on packet header fields, packet payload content, statistical characteristics of traffic and communication patterns of network hosts. However, most present techniques rely on some sort of apriori knowledge, which means they require labor-intensive preprocessing before running and cannot deal with previously unknown applications. In this paper, we propose a traffic classification system based on application signatures, with a novel approach to fully automate the process of deriving signatures from unidentified traffic. The key idea is to integrate statistics-based flow clustering with payload-based signature matching method, so as to eliminate the requirement of pre-labeled training data sets. We evaluate the efficiency of our approach using real-world traffic trace, and the results indicate that signature classifiers built from clustered data and pre-labeled data are able to achieve similar high accuracy better than 99%.

Discriminating DDoS attack traffic from flash crowd through packet arrival patterns

Current DDoS attacks are carried out by attack tools, worms and botnets using different packet-transmission strategies and various forms of attack packets to beat defense systems. These problems lead to defense systems requiring various detection methods in order to identify attacks. Moreover, DDoS attacks can mix their traffics during flash crowds. By doing this, the complex defense system cannot detect the attack traffic in time. In this paper, we propose a behavior based detection that can discriminate DDoS attack traffic from traffic generated by real users. By using Pearson's correlation coefficient, our comparable detection methods can extract the repeatable features of the packet arrivals. The extensive simulations were tested for the accuracy of detection. We then performed experiments with several datasets and our results affirm that the proposed method can differentiate traffic of an attack source from legitimate traffic with a quick response. We also discuss approaches to improve our proposed methods at the conclusion of this paper.

Internet traffic classification using machine learning : a token-based approach

Due to the increasing unreliability of traditional port-based methods, Internet traffic classification has attracted a lot of research efforts in recent years. Quite a lot of previous papers have focused on using statistical characteristics as discriminators and applying machine learning techniques to classify the traffic flows. In this paper, we propose a novel machine learning based approach where the features are extracted from packet payload instead of flow statistics. Specifically, every flow is represented by a feature vector, in which each item indicates the occurrence of a particular token, i.e.; a common substring, in the payload. We have applied various machine learning algorithms to evaluate the idea and used different feature selection schemes to identify the critical tokens. Experimental result based on a real-world traffic data set shows that the approach can achieve high accuracy with low overhead.