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The COVID-19 pandemic is a public health crisis that also fuels the pervasive social inequity in the United States. Existing studies have extensively analyzed the inequity issues on mobility across different demographic groups during the lockdown phase. However, it is unclear whether the mobility inequity is perennial and will continue into the mobility recovery phase. This study utilizes ride-hailing data from Jan 1st, 2019, to Mar 31st, 2022, in Chicago to analyze the impact of various factors, such as demographic, land use, and transit connectivity, on mobility inequity in the different recovery phases. Instead of commonly used statistical methods, this study leverages advanced time-series clustering and an interpretable machine learning algorithm. The result demonstrates that inequity still exists in the mobility recovery phase of the COVID-19 pandemic, and the degree of mobility inequity in different recovery phases is varied. Furthermore, mobility inequity is more likely to exist in the census tract with more families without children, lower health insurance coverage, inflexible workstyle, more African Americans, higher poverty rate, fewer commercial land use, and higher Gini index. This study aims to further the understanding of the social inequity issue during the mobility recovery phase of the COVID-19 pandemic and help governments propose proper policies to tackle the unequal impact of the pandemic.

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Objective: To investigate the effect and the mechanism of Astragalus membranaceus (Huangqi in Chinese, HQ) extract on the intestinal absorption of six alkaloids of Aconitum carmichaelii (Fuzi in Chinese, FZ) in rats with spleen deficiency and provide novel insights into the application of HQ on modulating intestinal barrier. Methods: Four-week-old male Sprague-Dawley rats were fed with Xiaochengqi Decoction to induce the spleen deficiency model for 40 d. Single-pass intestinal perfusion model were used to study the effects of HQ extract on the absorption of alkaloids. Protein expression and mRNA levels of MRP2 and BCRP and tight junction proteins (TJ, including Claudin-1, Occludin and ZO-1) were measured using Western blot and real-time PCR, respectively. The location and expression of TJ protein was also investigated by the immunofluorescence method. Results: Compared with the normal group, the protein expression of MRP2, BCRP and TJ proteins in the model group were significantly down-regulated. After oral administration of HQ, the alkaloid absorption in intestinal villi was inhibited, MRP2, BCRP and TJ proteins were up-regulated, the green fluorescence staining of Claudin-1, Occludin, and ZO-1 was enhanced, and a thick layer of mucus was deposited on the surface of the epithelium of the intestinal cavity. Conclusion: HQ as an intestinal barrier modulator improves the physiological changes of the intestinal environment of spleen deficiency to reduce the absorption of toxic components, leading to a decrease in the absorption of drug-like molecules.

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The working standard of shared office spaces has evolved in recent years. Due to the ongoing COVID-19 pandemic, many companies have instituted work from home (WFH) policies in accordance with public health guidelines in order to increase social distancing and decrease the spread of COVID-19. As the pandemic and WFH-related policies have continued for more than a year, there has been a rise in people becoming accustomed to the remote environments; however, others are more enthusiastic about returning to in-person work environments, reflecting the desire to restore pre-pandemic environments. As working from home is related to transportation issues such as changing commuting patterns and decreased congestion, motorized trips, and emission, there is a need to explore the extent of public attitudes on this important issue. This study used unique open-source survey data that provides substantial information on this topic. Using an advanced categorical data analysis method known as cluster correspondence analysis, this study identified several key findings. Not having prior WFH experiences, being eager to interact with colleagues, difficulties with adapting to virtual meeting technologies, and challenges with self-discipline while WFH were strongly associated with individuals who refused to continuously WFH at all after the pandemic. Individuals holding a strong view against the seriousness of the COVID-19 pandemic were also largely associated with never choosing WFH during and after the pandemic. For individuals with some prior WFH experiences, the transition to WFH every day in response to the outbreak was much easier, compared to those without prior experiences. Moreover, being forced to WFH during the COVID-19 pandemic positively influences the choice of WFH after the pandemic. The findings of this study will be beneficial to help policymakers and sustainable city planners understand public opinions about WFH.

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INTRODUCTION: Given the tremendous number of lives lost or injured, distracted driving is an important safety area to study. With the widespread use of cellphones, phone use while driving has become the most common distracted driving behavior. Although researchers have developed safety performance functions (SPFs) for various crash types, SPFs for distraction-affected crashes are rarely studied in the literature. One possible reason is the lack of critical distracted behavior information in the commonly used safety data (i.e., roadway inventory, traffic, and crash counts). Recently, the frequency of phone use while driving (referred to as phone use data) is recorded by mobile application companies and has become available to safety researchers. The primary objective of this study is to examine if phone use data can potentially predict distracted-affected crashes. METHOD: The authors first integrated phone use data with roadway inventory, traffic, and crash data in Texas. Then, the Random Forest (RF) algorithm was applied to assess the significance of the feature - phone use while driving - for predicting the number of distraction-affected crashes on a road segment. Further, this study developed two SPFs for distraction-affected crashes with and without the phone use data, separately. Both SPFs were assessed in terms of model fitting and prediction performances. RESULTS: RF results rank the frequency of phone use as an important factor contributing to the number of distraction-affected crashes. Performance evaluations indicated that the inclusion of phone use data in the SPFs consistently improved both fitting and prediction abilities to predict distracted-affected crashes. Practical Applications: The phone use data provide new insights into the safety analyses of distraction-affected crashes, which cannot be achieved by only using the conventional roadway inventory and crash data. Therefore, safety researchers and practitioners are encouraged to incorporate the emerging data sources in reducing distraction-affected crashes.

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In response to the coronavirus disease 2019 (COVID-19) pandemic, governments have encouraged and ordered citizens to practice social distancing, particularly by working and studying at home. Intuitively, only a subset of people have the ability to practice remote work. However, there has been little research on the disparity of mobility adaptation across different income groups in US cities during the pandemic. The authors worked to fill this gap by quantifying the impacts of the pandemic on human mobility by income in Greater Houston, Texas. We determined human mobility using pseudonymized, spatially disaggregated cell phone location data. A longitudinal study across estimated income groups was conducted by measuring the total travel distance, radius of gyration, number of visited locations, and per-trip distance in April 2020 compared to the data in a baseline. An apparent disparity in mobility was found across estimated income groups. In particular, there was a strong negative correlation (ρ = -0.90) between a traveler's estimated income and travel distance in April. Disparities in mobility adaptability were further shown since those in higher income brackets experienced larger percentage drops in the radius of gyration and the number of distinct visited locations than did those in lower income brackets. The findings of this study suggest a need to understand the reasons behind the mobility inflexibility among low-income populations during the pandemic. The study illuminates an equity issue which may be of interest to policy makers and researchers alike in the wake of an epidemic.

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This study aims to explore the associations between near-crash events and road geometry and trip features by investigating a naturalistic driving dataset and a corresponding roadway inventory dataset using an association rule mining method - the Apriori algorithm. To provide more insights into near-crash behavior, this study classified near-crash events into two severity levels: trivial near-crash events (-7.5 g ≤ deceleration rate ≤ -4.5 g) and non-trivial near-crash events (≤-7.5 g). From the perspective of descriptive statistics, the frequency of the itemsets, a set of categories of various variables, generated by the Apriori algorithm suggests that near-crash events are highly associated with several factors, including roadways without access control, driving during non-peak hours, roadways without a shoulder or a median, roadways with the minor arterial functional class, and roadways with a speed limit between 30 and 60 mph. By comparing the frequency of the occurrence of the itemset during trivial and non-trivial near-crash events, the results indicate that the length of the trip is a strong indicator of the near-crash event type. The results show that non-trivial near-crash events are more likely to occur if the trip is longer than 2 h. After applying the association rule mining algorithm, more interesting patterns for the two near-crash events were generated through the rules. The main findings include: 1) trivial near-crash events are more likely to occur on roadways without a median and shoulder that have a relatively lower functional class; 2) relatively higher functional roadways with relatively wide medians and shoulders could be an intriguing combination for non-trivial near-crash events; 3) non-trivial near-crash events often occur on long trips (more than 2 h); 4) congestion on roadways that have a lower functional class is a dominant rule associating with the high frequency of non-trivial near-crash events. This study associates near-crash events and the corresponding road geometry and trip features to provide a unique understanding of near-crash events.

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The engagement of secondary tasks, like using a phone or talking to passengers while driving, could introduce considerable risks to driving safety. This study utilizes a near-crash dataset extracted from a naturalistic driving study to explore the patterns of near-crash events with or without the involvement of secondary tasks as a surrogate approach to understand the impact of these behaviors on traffic safety. The dataset contains information about driver behaviors, such as secondary tasks, vehicle maneuvers, other conflict vehicles' maneuvers before and during near-crash events, and the driving environment. The patterns for near-crashes with or without the involvement of secondary tasks are mined by adopting the apriori association rule algorithm. Finally, the mined rules for the near-crash events with or without the involvement of the secondary tasks are analyzed and compared. The results demonstrate that near-crashes with the involvement of secondary tasks often occur with drivers in a relatively stable and presumably predictable environment, such as an interstate highway with a constant speed. This type of near-crash is highly associated with the leading vehicle's sudden slowing or stopping since there is no expectation of any interruptions for these drivers performing the secondary tasks. The most common evasive maneuver in this kind of emergency is braking. Near-crashes without the involvement of secondary tasks is often associated with lane-changing behavior and sideswipe incidents. With shorter reaction time and awareness of the driving environment, the drivers in this type of near-crash can often make more complex maneuvers, like braking and steering, to avoid a collision. Understanding the patterns of these two types of near-crash incidents could help safety researchers, traffic engineers, and even vehicle designers/engineers develop countermeasures for minimizing potential collisions caused by secondary tasks or improper lane changing behaviors.

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Phone use while driving (PUWD) is one of the most crucial factors of distraction related traffic crashes. This study utilized an unsupervised learning method, known as factor analysis, on a unique distracted driving dataset to understand PUWD behavior from the roadway geometry and operational perspectives. The results indicate that the presence of a shoulder, median, and access control on the relatively higher functional class roadways could encourage more PUWD events. The roadways with relatively lower speed limits could have high PUWD event occurrences if the variation in operating speed is high. The results also confirm the correlations between the frequency of PUWD events and the frequency of distracted crashes. This relationship is strong on urban roadways. For rural roadways, this correlation is only strong on the roadways with a large amount of PUWD events. The findings could help transportation agencies to identify suitable countermeasures in reducing distraction related crashes. Moreover, this study provides researchers a new perspective to study PUWD behavior rather than only focus on drivers' personalities.

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Objective: Astragalus Radix (AR, Huangqi in Chinese) has been widely used as a qi (energy) restoring herb that is thought to act through reinvigorating the spleen and lung. Aconite is used to rebalance the body temperature during illness and played an irreplaceable role in disease control since ancient times, but it is limited by its strong neuro and cardiotoxicity. Since the Song Dynasty (1227), the two herbs have been commonly used as herbal pairs including in the famous Qifu Decotion, from the "Wei's Family Prescription". However, many ancient texts also record that they are not compatible using together, suggesting they can have negative outcomes when mixed. This study investigated whether Astragali Radix had either positive or negative effects on absorption of six different active alkaloids derived from aconite. Methods: Single intestinal perfusion model was used to study the effects of Astragali Radix on aconite alkaloids absorption. Response of ABC transporters and distribution of three tight junction proteins on the surface of intestinal enothelium were assessed by Reverse Transcription-Polymerase Chain Reaction (RT-PCR), Western blot and immunofluorescence microscopy, respectively. Results: The results showed that aconite alkaloids absorption could be inhibited, and different concentrations of Astragali Radix considerably increased the expression levels of the ABC transporters and tight junction proteins with Astragali Radix treatment. Conclusion: These results suggest that Astragali Radix can block absorption of aconite alkaloids through the upregulation expression of ATP-binding cassette transporters (ABC transporters) and tight junction proteins. It demonstrates that co-administration of Astragali Radix with other drugs might change the absorption profile of the second drug which is important to know in clinic therapy.

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Speeding is considered as one of the most significant contributing factors to severe traffic crashes. Understanding the associations between trip/driving/roadways features and speeding behavior is crucial for both researchers and practitioners. This research utilized naturalistic driving data collected by the Safety Pilot Model Deployment (SPMD) program and roadway features from a road inventory dataset - Highway Performance Monitoring System (HPMS), provided by the United States Department of Transportation (USDOT), to investigate the hidden rules that associated trip/driving/roadway features with speeding behavior. A classification-based association (CBA) algorithm was adopted to explore the hidden rules from two perspectives of speeding: speeding duration and speeding pattern. Results indicate that the combinations of longer trips (more than 60 min), driving on the roadways with a relatively higher functional class are highly associated with longer speeding events (speeding longer than 2 min). The moderate speeding events (speeding longer than 2 min and longer than 30 s) are found highly associated with the combination of driving on roadways with lower functional class, absence of a median and relatively short trip time (less than 30 min). The research also found the combinations of driving on roadways with relatively lower functional class, experienced congestion before a speeding event, and the presence of a median is a leading cause that triggers a higher speeding pattern (speeding more than 5mph above the speed limit). Furthermore, the moderate speeding pattern (speeding more than 1mph above the speed limit and less than 5mph of the speed limit) is associated with the combinations of factors like experiencing congestion before a speed event, driving on roadways with higher functional class and a relatively shorter trip (less than 30 min). The findings can help practitioners understand the composite effect of these factors more comprehensively and provide corresponding countermeasures to mitigate the negative consequences of speeding wherever possible. These can also help in calibrating driver behavior parameters for transportation-related simulation tools.

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