Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD

Investing for Reliability and Security in Transportation Networks

Alternative transportation investment policies can lead to very different network forms in the future. The desirability of a transportation network should be assessed not only by its economic efficiency but also by its reliability and security, because the cost of an incidental capacity loss in a road network can be massive. This research concerns how investment rules shape the hierarchical structure of roads and affect network fragility to natural disasters, congestion, and accidents and vulnerability to targeted attacks. A microscopic network growth model predicts the equilibrium road networks under two alternative policy scenarios: investment based on beneÞtÐcost analysis and investment based on bottleneck removal. A set of Monte Carlo simulation runs, in which a certain percentage of links was removed according to the type of network degradation analyzed, was carried out to evaluate the equilibrium road networks. It was found that a hierarchy existed in road networks for reasons such as economic efficiency but that an overly hierarchical structure had serious reliability problems. Throughout the equilibrating or evolution process, the grid network studied under beneÞtÐcost analysis had better efficiency performance, as well as error and attack tolerance. The paper demonstrates that reliability and security considerations can be integrated into the planning of transportation systems.

Evolutionary algorithms: Overview and applications to European transport

The present paper aims to analyse the research potential of Evolutionary Algorithms (EAs) in the light of their possible applications in the space-economy. For this purpose the first part of the paper will be devoted to an overview and illustration of EAs, also in comparison with other recent tools emerging form bio-computing, like Neural Networks (NNs). The second part of the paper will then focus on empirical applications concerning analyses and forecasts of European freight transport flows (at a regional level). In this context, the results stemming from an integrated approach combining EAs with NNs will be compared with those from conventional methodologies, like logit models, as well as with the "usual" NN models. We will analyze the sensitivity of various results by using different environmental policy on scenarios on European transport. The empirical experiments highlight the advantages and limitations of these approaches from both a methodological and empirical viewpoint, by offering a plausible range of values of outcomes that may be useful for planners and operators in this field.

Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics

The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns. The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%

Dynamic Congestion and Tolls with Mobile Source Emission

This paper proposes a dynamic congestion pricing model that takes into account mobile source emissions. We consider a tollable vehicular network where the users selfishly minimize their own travel costs, including travel time, early/late arrival penalties and tolls. On top of that, we assume that part of the network can be tolled by a central authority, whose objective is to minimize both total travel costs of road users and total emission on a network-wide level. The model is formulated as a mathematical program with equilibrium constraints (MPEC) problem and then reformulated as a mathematical program with complementarity constraints (MPCC). The MPCC is solved using a quadratic penalty-based gradient projection algorithm. A numerical study on a toy network illustrates the effectiveness of the tolling strategy and reveals a Braess-type paradox in the context of traffic-derived emission.Comment: 23 pages, 9 figures, 5 tables. Current version to appear in the Proceedings of the 20th International Symposium on Transportation and Traffic Theory, 2013, the Netherland

Analysis of Consumer Behavior towards Electric Vehicles: Intentions, Concerns, and Policies

Despite the acceptance of electric vehicles (EVs) by consumers in developed countries, consumers' intentions towards these smart devices (SD) and the steps that can be taken to expand in this market continue to be investigated in developing countries such as Turkey. In this study, policies and incentives for the purchase of Electric Vehicles in different countries were examined, consumer concerns before the adoption of SDs were evaluated, and then consumer intentions in adopting EVs with models such as reasoned action theory, planned behavior theory, and technology acceptance model were evaluated with bibliometric analysis through conducted studies. Data from 63 publications accessed from Scopus, Web of Science, and DergiPark databases were used in the field mapping process. The results provide insights into increasing the market share of electric vehicles, which are critical in reducing the carbon footprint, by recommending the issues that need to be highlighted to the industry and researchers