COVID-19 Pandemic and Mode Choice of Alabamian Truck Drivers’ Mobility to Grocery Shopping

Background: In late December 2019, COVID-19, commonly referred to as the Coronavirus was identified in China because of the main explanation for recent human respiratory health cases. The virus was first detected in Wuhan City, and during a space of months, it had covered the whole globe. The virus has engendered huge drastic changes to world healthcare, economic, transportation, and education systems around the world. Purpose: The general purpose of this study was to investigate the intersection of demographic characteristics and how truck drivers view change in their grocery shopping (CGS) under the COVID-19 circumstances of selected counties in Alabama. Specifically, the objectives of the study are to (1) examine if there is any relationship between marital status (MS) and CGS and (2) know and assess the choice of transport mode used for grocery shopping during the covid-19 pandemic Methods: The research paper's goal necessitated the truck drivers’ views regarding commuting to the workplace. Following a summary of the literature review research phase, the researcher conducted a variety of semi-structured interviews with truck drivers in Alabama through Survey Monkey by a postgraduate student in June-July 2020. Overall, 50 truck drivers have completed the survey. The info was stored on Survey Monkey servers within the Center for Urban and Rural Research (CURR), Department of Community and Regional Planning, Alabama A&M University. Results: The data analysis reveals their main workplace before the COVID-19 pandemic 94% of the truck drivers residing in Alabama especially from Jefferson county provided information about commuting to workplaces, while 4% to the places of educations (lecture room0 and a couple of production sites. Furthermore, 92% of the truck drivers reported NO change within the means of transport in commuting trips during the COVID-19 pandemic, while 8% indicated changes within the means of transport. Implications: This research paper contributes important new empirical analysis of the truck drivers’ views regarding commuting to the workplace under the COVID-19 pandemic to some extent where there's an abundance of conceptual papers and opinion pieces but still scant evidence on the particular road safety of the pandemic for researchers to think about on potential person and situation factors related to COVID-19 that would affect road safety during and after the pandemic. Collaborative efforts by researchers and public and personal sectors are going to be needed to collect data and develop truck drivers' road safety strategies in reference to the new reality of COVID-19. Keywords: COVID‐19, health disparities, roadway safety, syndemics, truck driver

Failure Characteristics and Stability Control of Bolt Support in Thick-Coal-Seam Roadway of Three Typical Coal Mines in China

Roadways in thick coal seams are widely distributed in China. However, due to the relatively developed cracks and brittleness of coal, the support failure of thick-coal-seam roadways frequently occurs. Therefore, the study of bolt failure characteristics and new anchoring technology is very important for the safety control of thick-coal-seam roadways. Based on field observations, the failure mechanism of selected roadway failures under distinct conditions at three representative coal mines in eastern and western China was analyzed. Recommendations are provided for roadway safety control. The results show that the strength and dimension of the anchoring structure in the coal roof of thick-coal-seam roadways are the decisive factors for the resistance of the roadway convergence and stress disturbance. The thick anchoring structure in the roof constructed by flexible long bolts can effectively solve the problem of support failure caused by insufficient support length of traditional rebar bolts under the condition of extra-thick coal roof and thick coal roof with weak interlayers. The concepts and techniques presented in the paper provide a reference for the design of roadway support under similar geological conditions and dynamic load.

Automatic Roadway Features Detection with Oriented Object Detection

Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.

Developing Roadway Safety Models for Winter Weather Conditions Using a Feature Selection Algorithm

Inclement winter weather such as snow, sleet, and freezing rain significantly impacts roadway safety. To assess the safety implications of winter weather, maintenance operations, and traffic operations, various crash frequency models have been developed. In this study, several datasets, including for weather, snowplow operations, and traffic information, were combined to develop a robust crash frequency model for winter weather conditions. When developing statistical models using such large-scale multivariate datasets, one of the challenges is to determine which explanatory variables should be included in the model. This paper presents a feature selection framework using a machine-learning algorithm known as the Boruta algorithm and exhaustive search to select a list of variables to be included in a negative binomial crash frequency model. This paper’s proposed feature selection framework generates consistent and intuitive results because the feature selection process reduces the complexity of interactions among different variables in the dataset. This enables our crash frequency model to better help agencies identify effective ways to improve roadway safety via winter maintenance operations. For example, increased plowing operations before the start of storms are associated with a decrease in crash rates. Thus, pretreatment operations can play a significant role in mitigating the impact of winter storms.

Effects of Operating Speed and Traffic Flow on Severe and Fatal Crashes using the U.S. Road Assessment Program Methodology and Field Data Verification

This paper explores the effects of operating speed and traffic flow on roadway safety in light of the methodology provided by the U.S. Road Assessment Program (usRAP). Unlike traditional approaches, usRAP produces a systemic expected roadway safety performance, more specifically the likelihood of being involved in a severe or a fatal crash, that is derived purely from roadway, roadside, and traffic characteristics, without need for detailed historical crash data. Data from over 7,000 mi of segments coded using the usRAP protocols and 5 years of crash data were used to examine changes in expected safety performance with changes in operating speed and traffic volumes. Speed and flow emerged as candidates for initial exploration as their effect is explicitly considered in the usRAP formulation for all crash types. The usRAP methodology indicated a gradual increase in the frequency of expected severe and fatal crashes with an increase in the operating speed, and such trends followed those observed in the field. Increase in traffic flow was generally associated to increase in severe and fatal crashes, but to a much smaller scale compared with the effect found for speed. Effects of traffic flow were more evident at smaller ranges, both in the field and in the usRAP results, with the safety effects diminishing and even reversing as the flow approached lane capacity. Crash data were examined using a risk ratio that considers the relative frequency of severe and fatal crashes to the exposure of a given segment group, as well as star rating scores and star ratings from usRAP outputs.