a b s t r a c t a r t i c l e i n f o Available online xxxxIn 2013, 346 out of 616 fatal crashes in Louisiana were single vehicle crashes with Run-Off-Road (ROR) crashes being the most common type of single vehicle crash. In order to create effective countermeasures for reducing the number of fatal single vehicle ROR crashes, it is important to identify any associated key factors that can quantitatively assess the performance of roads, vehicles and humans. This research uses Multiple Correspondence Analysis (MCA), a multidimensional descriptive data analysis method that associates a combination of factors based on their relative distance in a two dimensional plane, to analyze eight years (2004-2011) of fatal ROR crashes in Louisiana. This method measures important contributing factors and their degree of association. The results revealed that drivers of lightweight trucks, drivers on undivided state highways, male drivers in passenger-vehicles at dawn, older female (65-74) drivers in non-passenger vehicles, older drivers facing hardship to yield in partial access control zones, and drivers with poor reaction time due to impaired driving were closely associated with fatal ROR crashes. Results of the MCA method can help researchers select the most effective crash countermeasures. Further work on the degree of association between the identified crash contributing factors can help safety management systems develop the most efficient crash reduction strategies.

In the United States, about 14% of total crash fatalities are pedestrian related. In 2012, 4,743 pedestrians were killed, and 76,000 pedestrians were injured in vehicle–pedestrian crashes in the United States. Vehicle– pedestrian crashes have become a key concern in Louisiana as a result of the high percentage of fatalities there in recent years. In 2012, pedestrians accounted for 17% of total crash fatalities in the state. This study used multiple correspondence analysis (MCA), an exploratory data analysis method used to detect and represent underlying structures in a categorical data set, to analyze 8 years (2004 to 2011) of vehicle–pedestrian crashes in Louisiana. Pedestrian crash data are best represented as transactions of multiple categorical variables, so the use of MCA was a unique choice to determine the relationship of the variables and their significance. The findings indicated several nontrivial focus groups (e.g., drivers with high-occupancy vehicles, female drivers in bad weather conditions, and drivers distracted by mobile phone use). The associated geometric factors were hillcrest roadways, dip or hump aligned roadways, roadways with multiple lanes, and roadways with no lighting at night. Male drivers were seen to be relatively susceptible to severe and moderate injury crashes. Fatal pedestrian crashes were correlated to two-lane roadways with no lighting at night. The MCA method helped measure significant contributing factors and degrees of association between the factors through the analysis of the systematic patterns of variation with categorical data sets of pedestrian crashes. The findings from this study will help transportation professionals improve countermeasure selection strategies.

Coronavirus 2019, or COVID-19, is a contagious disease triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With origins in Wuhan, China, this disease has since spread globally, resulting in the ongoing 2019-2020 coronavirus pandemic. As of May 3, 2020, the Ministry of Health and Family Welfare confirmed a total of 39,980 positive COVID-19 cases and 1,301 deaths in India (more than 3.42 million positive COVID-19 cases resulting in more than 243,000 deaths worldwide). To flatten the curve, India has been locking down its country from March 24 to May 17, 2020. This study collected "COVID-19 in India" related tweets (totaling 410,643 tweets in English) from March 22 to April 21, 2020 to gauge the unknowns and contexts associated with public sentiments during the lockdown. This work contributes to the growing body of studies on COVID-19 social media mining by extracting emotions and sentiments over time, which could potentially shed some lights on the contexts of expressions during pandemic.

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The collective knowledge system has been advancing rapidly in the recent past. The digitalization of information in many online media—such as blogs, social media, articles, webpages, images, audios, and videos—provides an unprecedented opportunity for the extraction and identification of a knowledge trend. Prominent journal and conference proceedings usually contain extensive amounts of textual data that can be used to examine the research trends for various topics of interest and to understand how this research has helped in the advancement of a subject such as transportation engineering. The exploration of the unstructured contents in journal or conference papers requires sophisticated algorithms for knowledge extraction. This paper presents text mining techniques to analyze compendiums of papers published from TRB annual meetings, the largest and most comprehensive transportation conferences in the world. Topic models are algorithms designed to discover hidden thematic structure from massive collections of unstructured documents. This study used a popular topic model, latent Dirichlet allocation, to reveal research trends and interesting histories of the development of research by analyzing 15,357 compendiums of papers from 7 years (2008 to 2014) of TRB annual meetings.

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