Citizen Science for Traffic Monitoring: Investigating the Potentials for Complementing Traffic Counters with Crowdsourced Data

Traffic counts are among the most frequently employed data to assess the traffic patterns and key performance indicators of next generation sustainable cities. Automatised counting is often based on conventional traffic monitoring systems such as inductive loop counters (ILCs). These are costly to install, maintain, and support. In this paper, we investigate the possibilities to complement and potentially replace the existing traffic monitoring infrastructure with crowdsourcing solutions. More precisely, we investigate the capabilities to predict the ILC-obtained data using Telraam counters, low-cost camera counters voluntarily employed by citizens and freely accessible by the general public. In this context, we apply different exploratory data analysis approaches and demonstrate a regression procedure with a selected set of regression models. The presented analysis is demonstrated on different urban and highway road segments in Slovenia. Our results show that the data obtained from low-cost and easily accessible counters can be used to replace the existing traffic monitoring infrastructure in different scenarios. These results confirm the prospective to directly apply the citizen engagement in the process of planning and maintaining sustainable future cities.

Relationships between litter size, sex ratio and within-litter birth weight variation in a sow herd and consequences on weaning performance

Routine selection for litter size has resulted in increase in the proportion of lightweight piglets. There is a need to balance prolificacy with litter uniformity in order to maximise profit. A total of 3 465 piglets from 310 litter records obtained from 2016 until 2019 at the Pig Industry Board research unit, Arcturus, Zimbabwe, were used to determine the relationships between litter size, sex ratio and within-litter birth weight variation in the sow herd and consequences on performance at weaning. The regression procedure of SAS was used to determine the relationships between litter size, sex ratio and within-litter birth weight variation. The regression procedure was also used to determine the relationships between number born alive, within-litter birth weight variation, and sex ratio, and litter performance traits at weaning. Parity of sow, year and month of farrowing did not affect sex ratio (P > 0.05). The number born alive and number of piglets born had no relationship with sex ratio (P > 0.05). As the sex ratio increased, percent survival of piglets at weaning also increased linearly (P < 0.05). As the proportion of males in litters increased, within-litter birth weight variation and within-litter weaning weight variation increased reaching maximum as the proportion of males in litters approached 0.5 and then decreased onwards. As the proportion of males in litters approached 1, within-litter birth weight variation and within-litter weaning weight variation reached their least values. In conclusion, within-litter sex ratio does not vary with parity, year and month of farrowing. Within-litter weight variation is highest in litters with equal number of male and female piglets and lowest in unisex litters. This implies that production of unisex litters can help to reduce the variation in the weight of pigs at birth, weaning, and marketing which is one of the biggest economic challenges faced by pork producers.

Monthly Rainfall-Runoff Modeling for Sondur Reservoir

This study presents the application of Artificial Neural Network (ANN) to modeling the rainfall-inflow relationship for Sondur Reservoir located in Chhattisgarh State of India. ANNs are usually assumed to be powerful tools for nonlinear mapping in various applications. ANN is superior to linear regression procedure used for rainfallinflow modeling. For model development twenty nine years data of monthly rainfall and inflow have been used. The results extracted from study indicated that the ANN model is efficient for rainfall-inflow modeling.

Testing the Integrity of the Middle and Later Stone Age Cultural Taxonomic Division in Eastern Africa

The long-standing debate concerning the integrity of the cultural taxonomies employed by archaeologists has recently been revived by renewed theoretical attention and the application of new methodological tools. The analyses presented here test the integrity of the cultural taxonomic division between Middle and Later Stone Age assemblages in eastern Africa using an extensive dataset of archaeological assemblages. Application of a penalized logistic regression procedure embedded within a permutation test allows for evaluation of the existing Middle and Later Stone Age division against numerous alternative divisions of the data. Results suggest that the existing division is valid based on any routinely employed statistical criterion, but that is not the single best division of the data. These results invite questions about what archaeologists seek to achieve via cultural taxonomy and about the analytical methods that should be employed when attempting revise existing nomenclature.

Covariance regression for operational modal analysis

This article proposes a covariance regression procedure for operational modal analysis. The whole work is mainly twofold. On the one hand, two identities on the covariance are presented and they reveal that the covariance at different times is linearly dependent through both scalar and matrix coefficients. On the other hand, based on the two identities, the scalar covariance regression approach and the matrix covariance regression approach are naturally invoked. In proceeding so, the scalar or matrix coefficients are first acquired through covariance regression, and then, the modal parameters are simply extracted from the coefficients. Numerical examples and a field test case are studied to see the effectiveness of the proposed covariance regression procedure, and the ability to deal with harmonic load, large damping, and closely spaced modes is clearly verified.

A weighted Fama-MacBeth two-step panel regression procedure: asymptotic properties, finite-sample adjustment, and performance

Purpose In a recent paper, Yoon and Lee (2019) (YL hereafter) propose a weighted Fama and MacBeth (FMB hereafter) two-step panel regression procedure and provide evidence that their weighted FMB procedure produces more efficient coefficient estimators than the usual unweighted FMB procedure. The purpose of this study is to supplement and improve their weighted FMB procedure, as they provide neither asymptotic results (i.e. consistency and asymptotic distribution) nor evidence on how close their standard error estimator is to the true standard error. Design/methodology/approach First, asymptotic results for the weighted FMB coefficient estimator are provided. Second, a finite-sample-adjusted standard error estimator is provided. Finally, the performance of the adjusted standard error estimator compared to the true standard error is assessed. Findings It is found that the standard error estimator proposed by Yoon and Lee (2019) is asymptotically consistent, although the finite-sample-adjusted standard error estimator proposed in this study works better and helps to reduce bias. The findings of Yoon and Lee (2019) are confirmed even when the average R2 over time is very small with about 1% or 0.1%. Originality/value The findings of this study strongly suggest that the weighted FMB regression procedure, in particular the finite-sample-adjusted procedure proposed here, is a computationally simple but more powerful alternative to the usual unweighted FMB procedure. In addition, to the best of the authors’ knowledge, this is the first study that presents a formal proof of the asymptotic distribution for the FMB coefficient estimator.