Time taken for residents to adopt a new public transport service: examining heterogeneity through duration modelling

When a new public transport service is introduced it would be valuable for public authorities, financing organisations and transport operators to know how long it will take for people to start to use the service and what factors influence this. This paper presents results from research analysing the time taken for residents living close to a new guided bus service to start to use (or adopt) the service. Data was obtained from a sample of residents on whether they used the new service and the number of weeks after the service was introduced before they first used it. Duration modelling has been used to analyse how the likelihood of starting to use the new service changes over time (after the introduction of the service) and to examine what factors influence this. It is found that residents who have not used the new service are increasingly unlikely to use it as time passes. Those residents gaining greater accessibility benefits from the new service are found to be quicker to use the service, although the size of this effect is modest compared to that of other between-resident differences. Allowance for the possibility that there existed a proportion of the sample that would never use the new service was tested using a split population model (SPD) model. The SPD model indicates that 36% of residents will never use the new service and is informative in differentiating factors that influence whether Route 20 is used and when it is used.

Second best toll and capacity optimisation in networks: solution algorithm and policy implications

This paper looks at the first and second best jointly optimal toll and road capacity investment problems from both policy and technical oriented perspectives. On the technical side, the paper investigates the applicability of the constraint cutting algorithm for solving the second best problem under elastic demand which is formulated as a bilevel programming problem. The approach is shown to perform well despite several problems encountered by our previous work in Shepherd and Sumalee (Netw. Spat. Econ., 4(2): 161–179, 2004). The paper then applies the algorithm to a small sized network to investigate the policy implications of the first and second best cases. This policy analysis demonstrates that the joint first best structure is to invest in the most direct routes while reducing capacities elsewhere. Whilst unrealistic this acts as a useful benchmark. The results also show that certain second best policies can achieve a high proportion of the first best benefits while in general generating a revenue surplus. We also show that unless costs of capacity are known to be low then second best tolls will be affected and so should be analysed in conjunction with investments in the network.

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.

Regulation of the informal transport sector in Rio de Janeiro,Brazil: welfare impacts and policy analysis

In Brazil, the explosion of informal transport activity during the past decade has had profound effects on formal public transport systems and is a source of great controversy in the urban transportation sector. A variety of policies have been proposed to manage the growth of the sector. This study seeks to understand how proposed policies will impact the users of these systems. A corridor in Rio de Janeiro with substantial informal activity was used as a case study. Measures of welfare changes in a discrete choice framework were used to estimate proposed policies’ impacts on users. Eleven candidate policies were evaluated, ranging from the eradication of the informal modes and investment in formal modes, to the legalization of the informal modes. Benefits were compared with costs and the distribution of benefits across income classes was explored. Net benefits from some policies were found to be substantial. Legalizing the informal sector was found to benefit users slightly but further investments in the sector are probably inefficient. Users benefited most from improvements in formal mass transit modes, at roughly 100–200 dollars per commuter per year. Finally, policies to foster a competitive environment for the delivery of both informal and formal services were shown to benefit users about 100 dollars per commuter per year. Together, the regulation of the informal sector and investments in the formal sector serve to reinforce the movement towards competitive concessions for services and help reduce the impacts of cartelization and costly in-road competition.

Fault diagnosis for singular stochastic systems

This paper studies the fault diagnosis of singular stochastic systems. The probability distribution of output is measured by probability density functions (PDFs), which are modeled by a square root B-spline expansion. An adaptive nonlinear observer is proposed to estimate the size of the fault occurring in systems. Furthermore, the linear matrix inequality (LMI) approach is applied to establish sufficient conditions for the existence of the observer. Finally, the simulation results are given to indicate the method for diagnosing the fault.     

Vocational and academic approaches to maritime education and training (MET): Trends,challenges and opportunities

Traditional seafarer training has always focused on the acquisition and use of practical skills. The prevailing view is that, while this approach addresses a degree of cognitive skills, it focuses on and gives much more emphasis to the acquisition of hands-on practical skills for the performance of specific tasks. On the other hand, academic education has been seen to be much more focused on the development of in-depth analytical and critical thinking skills; cognitive skills that are less reliant on hands-on task-oriented training, but stress critical reading and discussion. The global trend in maritime education and training is increasingly to link an essentially vocational education that provides specific and restricted competence outcomes with more general or deeper academic components leading to an academic qualification. This trend has led to some dilemmas for curriculum development, for training legislation in a global industry, and for achieving desired learning outcomes in a professional setting (in the shipping industry). This paper discusses some of the challenges arising from this trend and the opportunities the trend offers.     

Rapid first-stage tests of on-ship infection

Protection against on-ship infectious disease—whether due to mishap or to harmful purpose—faces special situational problems. Sometimes, when infection levels on board have reached threshold levels, emergency actions are required. Often, the most thorough strategies for responding to threat are not feasible. A rapid first-stage test (RFT) is a fast, minimally invasive procedure used to rule out from possible infection a large percentage of an infection-threatened group. Prevention and control of on-ship infection need to combine various interconnected tactics. When timely criterion tests are not possible, the medical team must adopt fast alternative measures. The methods used to summarize protection against on-ship infectious agents included a scientific literature review and a web search. The fields of the review were maritime, health, and technology sources. Special attention was paid to material dealing with risks and threats of on-ship penetration by infectious agents, on-ship infection prevalence thresholds, and rapid diagnostic screens. The Bayes rule and the law of large numbers were applied to the analysis, for large on-ship populations, of RFT indications of crossing of an infection prevalence threshold. The increasing risk of serious on-ship infection—either accidental or purposeful—calls for a multi-layered protection approach. RFTs are a key part of the outer layer of such a defense. Well-designed and well-administered RFTs provide several advantages for defense against on-ship infection: low-cost, non-invasive, fast, and focuses on a drastically smaller number of infection possibilities.     

Brittleness analysis and important nodes discovery in large time-evolving complex networks

The brittleness analysis and important nodes detection have been a hot spot in the complex networks. How to get the overall feature of the whole network and how to find out some important nodes are requisites to solve these problems. In this paper, we adopt the trace of the adjacency matrix and the centrality of the complex networks to give a quantitative and qualitative analysis of networks being studied. Results show that the k-shell plays a more important role than the degree centrality and the betweenness in finding important nodes, and it can also be used to give direction on the immunization and maintenance of complex networks.     

The cost saving potential of carsharing in a US context

Carsharing is a vehicle sharing service for those with occasional need of private transportation. Transportation planners are beginning to see great potential for carsharing in helping to create a more diversified and sustainable transport system. While it has grown quickly in the US in recent years, it is still far from the level where it can deliver significant aggregate benefits. A key element to the potential growth of carsharing is its ability to provide cost savings to those who adopt it in favor of vehicle ownership. This research seeks to quantify these potential cost savings. The costs of carsharing and vehicle ownership are compared based on actual vehicle usage patterns from a large survey of San Francisco Bay Area residents. The results of this analysis show that a significant minority of Bay Area households own a vehicle with a usage pattern that carsharing could accommodate at a lower cost. Further research is required to indentify how these cost savings translate to the adoption of carsharing.