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In-lane street hawking is the intermittent entry of signalized intersections by traders to sell groceries to drivers and passengers. Studies have shown that hawkers get exposed to traffic injuries but the lack of quantitative analysis of their lane entry and exit behaviors in signalized intersections makes it difficult to improve traffic safety. This study analyzes the significant predictors of in-lane street hawkers' (1) lane entry within 30 s after the red signal illumination, (2) lane exit within 30 s after the green signal illumination, and (3) probability of getting injuries during the green signal time. Drone-based trajectory data were collected from a selected signalized intersection in Accra, Ghana. A Weibull accelerated failure time duration model incorporating Gamma frailty was used to evaluate hawkers' behaviors. Overall, the majority of hawkers exhibited red-light running behaviors exposing them to traffic injuries. An increase in traffic speed, especially beyond 20 km/h, exposed hawkers to injury risks significantly. Notably, hawkers' lane entry decreased significantly as the traffic speed increased. Their lane exit duration was significantly predicted by the queue lengths and traffic volumes. Accordingly, safety practitioners can enhance traffic regulation and control methods in addition to pro-poor social interventions to demotivate hawking at signalized intersections. [ABSTRACT FROM AUTHOR]

Alcoholic liver disease (ALD) is a disease with high incidence, limited therapies, and poor prognosis. The present study aims to investigate the effect of riboflavin on ALD and explore its potential therapeutic mechanisms. C57BL/6 mice were divided into the control, alcohol, and alcohol+ riboflavin groups. 16S rRNA-seq and RNA-seq analysis were utilized to analyze the polymorphism of intestinal microbiota and the transcriptome heterogeneity respectively. KEGG and GO enrichment analysis were performed. CIBERSORTx was applied to evaluate the immune cell infiltration level. Publicly available transcriptome data of ALD was enrolled and combined with the RNA-seq data to identify the immune subtypes of ALD. Pathological and histology analysis demonstrated that riboflavin reversed the progression of ALD. 16S rRNA-seq results showed that riboflavin could regulate alcohol-induced intestinal microbiota alteration. Intestinal microbiota polymorphism analysis indicated that VLIDP may contribute to the progression of ALD. Based on the VLIDP pathway, two subtypes were identified. Immune microenvironment analysis indicated that the upregulated inflammatory factors may be important regulators of ALD. In conclusion, intestinal microbiota homeostasis was associated with the protective effect of riboflavin against ALD, which was likely mediated by modulating inflammatory cell infiltration. Riboflavin emerges as a promising therapeutic candidate for the management of ALD. Riboflavin attenuates alcoholic liver disease progression, regulates alcohol-induced changes in intestinal microbiota, and affects inflammatory cell infiltration suggesting that riboflavin may be a potentially effective agent for treating ALD [ABSTRACT FROM AUTHOR]

It is widely accepted that an unintended lateral attack is inevitable in the intelligent connected vehicle environment. This paper explores the feasibility of a reinforcement learning method named PPO (Proximal Policy Optimization) to handle the unintended lateral attack and keep the vehicle stay in the ego lane. Based on the China highway design guide, the discrete speed variants of 120km/h, 100km/h, and 80km/h were selected, along with different curvatures ranging from 250m to 1200m in every 50m as combinations of speed-curvature test. The tests were implemented in the Open.ai CarRacing-v0 simulation environment with an external racing wheel attached to simulate the unintended lateral attack. The simulation results show that the PPO can handle the unintended lateral attack on the standard-designed highway in China. The results can be applied to the intelligent connected vehicle to be mass-produced in the future., Author(s): Luoyi Huang [1,2]; Wanjing Ma (corresponding author) [1]; Ling Wang [1]; Kun An [1] 1. Introduction Intelligent connected vehicle is shaping the automotive industry. It allows the vehicle to [...]

It is commonly seen that buses are blocked by the ones in front serving passengers and have to queue outside a curbside bus stop although there are vacant berths at the stop. The resultant bus delays degrade the service level of urban public transportation. A potential solution is to reschedule the arrivals of the buses at the stop for full utilization of the berths with the aid of connected vehicle technologies. This study proposes a mixed-integer linear programming model to optimize the scheduling of bus arrivals and the bus-berth matching at a curbside stop under the connected vehicle environment. The objective is the minimization of the bus delays weighted by the number of passengers on the buses. Bus arrival times at the stop and the assignment of berths are optimized together with bus departure times from the stop. Bus punctuality is also taken into consideration. The proposed model could be applied dynamically to cater to time-varying traffic conditions. Numerical studies validate the advantages of the proposed model over the “first-come-first-service” strategy and the relaxed model without bus punctuality in terms of weighted bus delays and bus punctuality. Sensitivity analyses show that (1) the proposed model is robust to the fluctuation of bus service time and (2) a smaller number of berths may be preferred on the condition that the bus demand does not exceed the stop capacity., Author(s): Shiqi Ou [1]; Wanjing Ma [1]; Chunhui Yu (corresponding author) [1] 1. Introduction Urbanization has increased the population of human beings and vehicles in cities. This has resulted in [...]

Pricing directly affects the sustainable development of the flexible bus service. This study proposes a profit maximization model and a social welfare maximization model for the flexible bus operator based on the cumulative prospect theory. Fares and uncertain travel time due to unforeseen detours in serving passengers jointly affect passengers' mode choice. On the other hand, fares and passengers' probabilistic choices over the flexible bus jointly determine the profits of the flexible bus company and social welfare. This study explores the relationship between the probability of passengers choosing the flexible bus, trip fares, and uncertain travel time. Serving more passengers indicates more profits, which also results in longer detour time thus decreasing the probability of passengers choosing the flexible bus. Considering the interactive influence among passengers, we further calculate the detour time distribution. Finally, a pricing model is established to compensate for the side effects of the detour. The results show that heterogeneous fares can help the flexible bus company to obtain higher profits but have negligible influence on social welfare. In addition, the development of long-distance services and regulations over the detour time can also help to obtain more profits., Author(s): Wanjing Ma [1]; Yuhang Guo [1]; Kun An (corresponding author) [1]; Lei Wang [1,2] 1. Introduction The flexible bus can be regarded as a bridging mode between the conventional [...]

The bus transit system is promising to enable electric and autonomous vehicles for massive urban mobility, which relies on high-level automation and efficient resource management. Besides the on-road automation, the in-depot automated scheduling for battery recharging has not been adequately studied yet. This paper presents an integrated in-depot routing and recharging scheduling (IDRRS) problem, which is modeled as a constraint programming (CP) problem with Boolean satisfiability conditions (SAT). The model is converted to a flexible job-shop problem (FJSP) and is feasible to be solved by a CP-SAT solver for the optimal solution or feasible solutions with acceptable performance. This paper also presents a case study in Shanghai and compares the results from the FJSP model and the first-come first-serve (FCFS) method. The result demonstrates the allocation of routes and chargers under multiple scenarios with different numbers of chargers. The results show that the FJSP model shortens the delay and increases the time conservation for future rounds of operation than FCFS, while FCFS presents the simplicity of programming and better computational efficiency. The multiple random input test suggests that the proposed approach can decide the minimum number of chargers for stochastic charging requests. The proposed method can conserve the investment by increasing the utilization of automated recharging devices, improving vehicles' in-depot efficiency., Author(s): Lei Wang [1,2]; Wanjing Ma [2]; Ling Wang (corresponding author) [2]; Yongli Ren [3]; Chunhui Yu [2] 1. Introduction Developing high-efficient, safe, clean, and sustainable public transportation is always [...]