A unified dataset for the city-scale traffic assignment model in 20 U.S. cities.

City-scale traffic data, such as traffic flow, speed, and density on every road segment, are the foundation of modern urban research. However, accessing such data on a city scale is challenging due to the limited number of sensors and privacy concerns. Consequently, most of the existing traffic datasets are typically limited to small, specific urban areas with incomplete data types, hindering the research in urban studies, such as transportation, environment, and energy fields. It still lacks a city-scale traffic dataset with comprehensive data types and satisfactory quality that can be publicly available across cities. To address this issue, we propose a unified approach for producing city-scale traffic data using the classic traffic assignment model in transportation studies. Specifically, the inputs of our approach are sourced from open public databases, including road networks, traffic demand, and travel time. Then the approach outputs comprehensive and validated citywide traffic data on the entire road network. In this study, we apply the proposed approach to 20 cities in the United States, achieving an average correlation coefficient of 0.79 in average travel time and an average relative error of 5.16% and 10.47% in average travel speed when compared with the real-world data.

Enhancement of the organic acid content and antioxidant capacity of yellow whey through fermentation with Lacticaseibacillus casei YQ336.

Fermentation is considered an effective tool for improving the functional characteristics of food. In this study, Lacticaseibacillus casei YQ336 was used to ferment yellow whey, and physical and chemical analysis was performed to identify the changes in the nutritional components and antioxidant activity of the fermented yellow whey. Non-targeted metabolomics was used to study the transformation of small molecular substances in the fermented yellow whey. After 48 h of pure culture fermentation with L. casei YQ336, the pH of yellow whey decreased significantly (p < 0.05). Meanwhile, the content of total acids, organic acids, sugars, total phenols, and total flavonoids and the antioxidant activity showed a significant increase (p < 0.05). A total of 628 differential metabolites were identified between fermented and unfermented yellow whey samples, of which 293 were upregulated and 335 were downregulated. After fermentation, due to the growth and metabolic activity of L. casei YQ336, meaningful metabolites such as homovanillic acid, lactic acid, oxalic acid, L-glutamic acid, and phenylalanine, as well as phenyllactic acid, gallic acid, and genistein were produced. This increased the organic acid content and antioxidant activity of yellow whey. The findings provide a theoretical and practical basis for further research on the bio-functional activity of yellow whey and the recycling and utilization of food by-products.

Fermentation improves antioxidant capacity and γ-aminobutyric acid content of Ganmai Dazao Decoction by lactic acid bacteria.

Introduction: Ganmai Dazao Decoction is a traditional Chinese recipe, and is composed of licorice, floating wheat, and jujube. Methods: Effects of lactic acid bacteria fermentation on the physicochemical properties, antioxidant activity, and γ-aminobutyric acid of Ganmai Dazao Decoction were studied. The changes of small and medium molecules in Ganmai Dazao Decoction before and after fermentation were determined by LC-MS non-targeted metabolomics. Results: The results showed that the contents of lactic acid, citric acid, acetic acid, and total phenol content increased significantly, DPPH free radical clearance and hydroxyl free radical clearance were significantly increased. γ-aminobutyric acid content was 12.06% higher after fermentation than before fermentation. A total of 553 differential metabolites were detected and identified from the Ganmai Dazao Decoction before and after fermentation by partial least squares discrimination and VIP analysis. Discussion: Among the top 30 differential metabolites with VIP values, the content of five functional substances increased significantly. Our results showed that lactic acid bacteria fermentation of Ganmai Dazao Decoction improves its antioxidant effects and that fermentation of Ganmai Dazao Decoction with lactic acid bacteria is an innovative approach that improves the health-promoting ingredients of Ganmai Dazao Decoction.

Effect of lactic acid bacteria co-fermentation on antioxidant activity and metabolomic profiles of a juice made from wolfberry and longan.

Lactic acid bacteria (LAB) fermentation is frequently employed to improve the nutritional, functional, and sensory characteristics of foods. Our study explored the effects of co-fermentation with Lacticaseibacillus paracasei ZH8 and Lactococcus lactis subsp. lactis YM313 on the physicochemical properties, antioxidant activity, and metabolomic profiles of wolfberry-longan juice (WLJ). Fermentation was carried out at 35 °C for 15 h. The results suggest that WLJ is a favorable substrate for LAB growth, reaching a total viable count exceeding 8 log CFU/mL after fermentation. LAB fermentation increased acidity, reduced the sugar content, and significantly impacted the juice color. The total phenolic and flavonoid contents of the WLJ and the antioxidant capacities based on 2,2-diphenyl-1-picrylhydrazyl (DPPH), ABTS radical scavenging abilities and FRAP were significantly improved by LAB fermentation. Nontargeted metabolomics analysis suggested that the contents of small molecule substances in WLJ were considerably affected by LAB fermentation. A total of 374 differential metabolites were identified in the juice before and after fermentation, with 193 significantly upregulated metabolites and 181 siginificantly downregulated metabolites. The regulation of metabolites is important for improving the flavor and functions of juices, such as L-eucylproline, Isovitexin, Netivudine, 3-Phenyllactic acid, vanillin, and ethyl maltol, ect. This study provides a theoretical foundation for developing plant-based foods fermented with LAB.

Fermentation improves flavors, bioactive substances, and antioxidant capacity of Bian-Que Triple-Bean Soup by lactic acid bacteria.

The ancient traditional Chinese drink Bian-Que Triple-Bean Soup made by fermentation (FTBS) of Lactococcus lactis subsp. lactis YM313 and Lacticaseibacillus casei YQ336 is a potential functional drink. The effect of fermentation on the flavor and biological activity of FTBS was evaluated by analyzing its chemical composition. Five volatile flavors were detected in modified FTBS. Fermentation decreased the proportion of nonanal (beany flavor substances) but significantly increased the total flavone contents, phenol contents and many bioactive small molecule substances in FTBS. The changes of these substances led to the significant improvement of FTBS sensory evaluation, antioxidant activity and prebiotic potential. This research provides a theoretical basis for the application of Lactic acid bacteria (LAB) in the fermentation of edible plant-based foods and transformation from traditional food to industrial production.

Incorporating multiple congestion levels into spatiotemporal analysis for the impact of a traffic incident.

Traffic incidents occurring on the road interrupt the smooth mobility of traffic flow and lead to traffic congestion. Although there has been a proliferation of studies that attempt to estimate the spatiotemporal impact of a traffic incident, most, if not all, of them focus exclusively on the differentiation of bi-level traffic status. In this research, we propose to incorporate multiple congestion levels in the spatiotemporal analysis for the impact of a traffic incident, which is new to the literature. The input to our model includes the historical speed on a given road and the occurrence time and location of the incident. The model then outputs the spatiotemporal impact region with multiple congestion levels. We first use a discriminant indicator to initially indicate the traffic status according to the travelling speed of probe vehicles. We then develop an integer programming model with a set of novel constraints to estimate the spatiotemporal region impacted by the incident. Unlike existing studies that only distinguish between uncongested and congested status, our model allows us to determine the impact region with diverse congestion levels. We validate our model using both simulation and real data. Results demonstrate that our approach can not only ensure the consistency of the propagation of shockwaves even when multiple congestion levels are incorporated, but also produce more accurate estimation of the delay caused by the incident when compared to the current state-of-the-art approach.

Determinants of the congestion caused by a traffic accident in urban road networks.

Non-recurrent congestion is frustrating to travelers as it often causes unexpected delay, which would result in missing important meetings or appointments. Major causes of non-recurrent congestion include adverse weather conditions, natural hazards, and traffic accidents. Although there has been a proliferation of studies that investigate how adverse weather conditions and natural hazards impact road congestion in urban road networks, studies that look into determinants of the congestion caused by a traffic accident are scarce. This research fills in this gap in the literature. When a traffic accident occurs on an urban link, the congestion would propagate to and affect adjacent links. We develop a modified version of the Dijkstra's algorithm to identify the set of links in the neighborhood of the accident. We first measure the level of congestion caused by the traffic accident as the reduction in traveling speed on those links. As the impact of congestion varies both in space and in time, we then estimate a generalized linear mixed-effects model with spatiotemporal panel data to identify its determinants. Finally, we conduct a case study using real data in Beijing. We find that: (1) the level of congestion is mostly associated with the types of the traffic accidents, the types of vehicles involved, and the occurrence time; (2) for the three types of traffic accidents, namely, scrape among vehicles, collisions with fixed objects, and rear-end collisions, the level of congestion associated with the first two types are comparable, while that associated with the third type is 8.43% more intense; (3) for the types of vehicles involved, the level of congestion involving buses/trucks is 6.03% more intense than those involving only cars; (4) for the occurrence time, the level of congestion associated with morning peaks and afternoon peaks are 5.87% and 6.57% more intense than that associated with off-peak hours, respectively.