Factors influencing support for local transportation sales tax measures

Sales tax measures passed at the local level and dedicated to transportation projects have become increasingly popular in the United States. While revenues from fuel taxes stagnate, growth of local transportation sales taxes (LTSTs), most approved in local elections, has led to a gradual shift of the financial base for transportation projects away from user fees and toward broader-based taxes. In this study, the relationship between voter support and the social, political, and geographic characteristics of the voters is explored. Using precinct-level voting data and census demographic data for three local transportation sales tax elections in Sonoma County, in the San Francisco Metropolitan area of California, regression models were constructed to analyze this relationship. In addition, the relationship between the outcomes of the three measures was explored to better understand which transportation projects might have garnered more support for the successful measure. It was found that the closer voters lived to the transportation projects to be funded, the greater their support. Higher incomes were also positively related to support, controlling for other variables. Political leanings were found to affect support, with the direction of the effect dependent upon the project list in each measure’s expenditure plan. Finally, it appears that the latest measure, which passed successfully, benefited greatly from its multi-modal expenditure plan.     

Simulating the impacts of household travel on greenhouse gas emissions,urban air quality,and population exposure

This paper establishes a link between an activity-based model for the Greater Toronto Area (GTA), dynamic traffic assignment, emission modelling, and air quality simulation. This provides agent-based output that allows vehicle emissions to be tracked back to individuals and households who are producing them. In addition, roadway emissions are dispersed and the resulting ambient air concentrations are linked with individual time-activity patterns in order to assess population exposure to air pollution. This framework is applied to evaluate the effects of a range of policy interventions and 2031 scenarios on the generation of vehicle emissions and greenhouse gases in the GTA. Results show that the predicted increase of approximately 2.6 million people and 1.3 million jobs in the region by 2031 compared to 2001 levels poses a major challenge in achieving meaningful reductions in GHGs and air pollution.     

Isolating high-priority metro and feeder bus transfers using smart card data

Fixed-rail metro (or ‘subway’) infrastructure is generally unable to provide access to all parts of the city grid. Consequently, feeder bus lines are an integral component of urban mass transit systems. While passengers prefer a seamless transfer between these two distinct transportation services, each service’s operations are subject to a different set of factors that contribute to metro-bus transfer delay. Previous attempts to understand transfer delay were limited by the availability of tools to measure the time and cost associated with passengers’ transfer experience. This paper uses data from smart card systems, an emerging technology that automatically collects passenger trip data, to understand transfer delay. The primary objective of this study is to use smart card data to derive a reproducible methodology that isolates high priority transfer points between the metro system and its feeder-bus systems. The paper outlines a methodology to identify transfer transactions in the smart card dataset, estimate bus headways without the aid of geographic location information, estimate three components of the total transfer time (walking time, waiting time, and delay time), and isolate high-priority transfer pairs. The paper uses smart card data from Nanjing, China as a case study. The results isolate eight high priority metro-bus transfer pairs in the Nanjing metro system and finally, offers several targeted measures to improve transfer efficiency.     

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.     

Certificateless message recovery signatures providing girault’s level-3 security

A digital signature with message recovery is a signature that the message itself (or partial of the message) is not required to be transmitted together with the signature. It has the advantage of small data size of communication comparing with the traditional digital signatures. In this paper, combining both advantages of the message recovery signatures and the certificateless cryptography, we propose the first certificatelss signature scheme with message recovery. The remarkable feature of our scheme is that it can achieve Girault’s Level-3 security while the conventional certificateless signature scheme only achieves Level-2 security. The security of the scheme is rigorously proved in the random oracle model based on the hardness of the k bilinear Diffie-Hellman inverse (k-BDHI) problem.     

Joint user and antenna selection for multiuser MIMO downlink with block diagonalization

User selection is necessary for multiuser multiple-input multiple-output (MIMO) downlink systems with block diagonalization (BD) due to the limited free spatial transmit dimensions. The pure user selection algorithms can be improved by performing receive antenna selection (RAS) to increase sum rate. In this paper, a joint user and antenna selection algorithm, which performs user selection for sum rate maximization in the first stage and then performs antenna selection in the second stage, is proposed. The antenna selection process alternately drops one antenna with the poorest channel quality based on maximum determinant ranking (MDR) from the users selected during the first stage and activates one antenna with the maximum norm of projected channel from the remaining users. Simulation results show that the proposed algorithm significantly outperforms the algorithm only performing user selection as well as the algorithm combining user selection with MDR receive antenna selection in terms of sum rate.     

3D human motion tracking by using interactive multiple models

Of different model-based methods in vision based human tracking, many state of the art works focus on the stochastic optimization method to search in a very high dimensional space and try to find the optimal solution according to a proper likelihood function. Seldom works perform a framework of interactive multiple models (IMM) to track a human for challenging problems, such as uncertainty of motion styles, imprecise detection of feature points and ambiguity of joint location. This paper presents a two-layer filter framework based on IMM to track human motion. First, a method of model based points location is proposed to detect key feature points automatically and the filter in the first layer is performed to estimate the undetected points. Second, multiple models of motion are learned by the prior motion data with ridge regression and the IMM algorithm is used to estimate the quaternion vectors of joints rotation. Finally, experiments using real images sequences, simulation videos and 3D voxel data demonstrate that this human tracking framework is efficient.     

Chaff cloud jamming suppression based on wavelet transform

Radar target signals and chaff cloud jamming signals have different characters by the wavelet transform. The wavelet coefficients of radar target signals are highly correlated with its near-and-near-scale wavelet coefficients, however the correlativity between the wavelet coefficients of chaff cloud jamming signals and its near-and-near scale wavelet coefficients is less significant. Based on the binary-base discrete wavelet transform and the correlation algorithm, the method of target entropy to estimate standard variance of the jamming signals and each scale is proposed to ensure reasonable threshold, to suppress chaff cloud signals and finally to reconstruct mixed signals by the improved spatially selective noise filtration (ISSNF) method. The extensive simulation results show that the proposed method can availably suppress chaff cloud jamming and decontaminate target echo.     

The enforcement of shipping standards under UNCLOS

This article discusses the enforcement of shipping standards, with a particular focus on Part XII of UNCLOS. Section 6 of Part XII contains the only comprehensive set of vessel-related enforcement provisions in the Convention, but the Part’s scope is limited to “the preservation and protection of the marine environment.” Therefore, not all shipping standards fall clearly within Part XII’s ambit, including those centered on safety, security, and crewing considerations. The enforcement provisions of Section 6 are favorable to flag states and their vessels, and Section 7 contains a number of safeguards for their benefit, so it is in the interests of these parties to have coastal state enforcement governed by Part XII. However, the ability of coastal states to establish and enforce shipping standards that apply within the territorial sea extends to more than just environmental matters. The result is that different standards give rise to different enforcement powers, depending on the maritime zone in which a vessel is located, even if those standards arise from the same international agreement. It is suggested that to remedy these problems, UNCLOS would need to be amended in order to provide a comprehensive enforcement regime for the enforcement of shipping standards.     

Color image segmentation by edge linking and region grouping

A novel method toward color image segmentation is proposed based on edge linking and region grouping. Firstly, the edges extracted by the Canny detector are linked to form regions. Each of the end points of edges is connected by a direct line to the nearest pixel on another edge segment within a sub-window. A new distance is defined based on the feature that the edge tends to preserve its original direction. By sampling the lines to the image, the image is over-segmented to labeled regions. Secondly, the labeled regions are grouped both locally and globally. A decision tree is constructed to decide the importance of properties that affect the merging procedure. Finally, the result is refined by user’s selection of regions that compose the desired object. Experiments show that the method can effectively segment the object and is much faster than the state-of-the-art color image segmentation methods.