Student's perspective on distance learning during COVID-19 pandemic: A case study of Western Michigan University, United States.

As the distance learning process has become more prevalent in the USA due to the COVID-19 pandemic, it is important to understand students' experiences, perspectives, and preferences. Our study's purpose is to reveal students' perspectives and preferences on distance learning due to the dramatic change that happened in the education process. Western Michigan University is used as the case study to achieve that purpose. Participants completed an online survey that investigated two measures: distance learning and instructional methods with a set of scales associated with each. Students reported negative experiences of distance learning such as lack of social interaction and positive experiences such as time and location flexibility. These findings may help WMU and higher educational institutions to improve distance learning education.

Discovering latent themes in traffic fatal crash narratives using text mining analytics and network topology.

The proliferation of digital textual archives in the transportation safety domain makes it imperative for the inventions of efficient ways of extracting information from the textual data sources. The present study aims at utilizing crash narratives complemented by crash metadata to discern the prevalence and co-occurrence of themes that contribute to crash incidents. Ten years (2009-2018) of Michigan traffic fatal crash narratives were used as a case study. The structural topic modeling (STM) and network topology analysis were used to generate and examine the prevalence and interaction of themes from the crash narratives that were mainly categorized into pre-crash events, crash locations and involved parties in the traffic crashes. The main advantage of the STM over the other topic modeling approaches is that it allows the researchers to discover themes from documents and estimate how the topic relates to the document metadata. Topics with the highest prevalence for the angle, head-on, rear-end, sideswipe and single motor vehicle crashes were crash at stop-sign, crossing the centerline, unable to stop, lane change maneuver and run-off-road crash, respectively. Eigenvector centrality measure in network topology showed that event-related topics were consistently central in articulating the crash occurrence. The centrality and association between topics varied across crash types. The efficacy of generated topics in classifying crashes by type was tested using a machine learning algorithm, Random Forest. The classification accuracy in the held-out sample ranged between 89.3 % for sideswipe crashes to 99.2 % for single motor vehicle crashes. High classification accuracy suggests that automation of crash typing and consistency checks can be accomplished effectively by using extracted latent themes from the crash narratives.

Development of systemwide pedestrian safety performance function using stratified random sampling and a proxy measure of pedestrian exposure.

The lack of pedestrian counts at a systemwide level prompts the need to find other innovative ways of assessing pedestrian traffic crash risks using proxy measures of exposure. This study aims to formulate the methodology for developing pedestrian safety performance functions (SPF) using the proxy measure of pedestrian exposure and stratified random sampling. The case study was all urban intersections in Michigan State that comprise of collector and arterial roads. The stratified random sampling strategy was deployed to select the sample which is representative of all urban intersections in the state of Michigan. Factor analysis was used to develop a proxy measure of pedestrian exposure at urban intersections using a walkability measure (walk score), among other factors. The performances of various count models were compared using the goodness of fit measures based on the Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and Vuong test. The final pedestrian SPFs was formulated using the Zero-Inflated Poisson (ZIP) model with AADT at a major approach, AADT at the minor approach, and a proxy measure of pedestrian exposure. The proposed methodology in this study can benefit transportation agencies that have embarked on systemwide planning of pedestrian facilities to improve the safety of pedestrians but lack systemwide analytical tools and pedestrian counts to make data-driven decisions.

Assessing the safety impacts of raising the speed limit on Michigan freeways using the multilevel mixed-effects negative binomial model.

Objectives: The main objective of this study was to assess the change in crash patterns associated with speed limit changes from 55 mph to 70 mph that occurred on some of Michigan freeway segments between year 2005 and year 2010.Method: Many of the statistical methods used in the past to evaluate the safety impacts of raising the speed limit on freeways lack the ability to address one or more critical issues inherent in count data, such as omitted-sample bias, over-dispersion and regression-to-the-mean bias. This study used multilevel mixed-effects negative binomial regression to address these limitations, with an additional advantage of controlling for intra-cluster correlation of crashes on each freeway corridor and segments nested in the same corridor. Changes in the crash patterns between the year 2000 and year 2015 were investigated on test sites that had a change of speed limit from 55 mph to 70 mph, relative to control sites where the speed limit was maintained at 55 mph.Results: The inclusion of random effects improved the model's ability in explaining observed crash variations on the selected freeway segments, as indicated by test statistics such as the log-likelihood ratio test, the Akaike information criterion and Bayesian information criterion (BIC)values. Further, random effects improved the significant speed limit change fixed effects during model calibration. The final mixed-effects model indicated a significant increase in fatal and injury crashes (FI), total crashes (KABCO) and road departure crashes by 11.9 percent, 21.0 percent and 23.3 percent, respectively, on freeway segments where the speed limit was raised from 55 mph to 70 mph. The increase in road departure crashes was more pronounced on curved freeway segments with the raised speed limit compared to straight segments with no speed limit changes.Conclusions: The 15 mph increase in the speed limit on Michigan freeways had a significant association with the increase in fatal and injury crashes, total crashes and road departure crashes. The elevated crash risks associated with the speed limit increase suggest that further studies are needed to understand changes in drivers' behaviors following a speed limit increase.