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The red-light running (RLR) behaviors of urban mixed e-bike groups (delivery and ordinary e-bike) have become the main cause of traffic accidents at signalized intersections. The primary purpose of this study is to identify influencing factors of e-bike riders' RLR behaviors, focusing on the role of delivery e-bike riders in mixed e-bike rider groups. Crossing behaviors of 4,180 e-bike samples (2006 delivery e-bikes and 2174 ordinary e-bikes) at signalized intersections are observed in Xi'an, China. The random parameter multinomial logit model is employed to capture the unobserved heterogeneous effects, and the effects of interaction terms are also considered. The results indicate that delivery e-bike riders are more likely to run red lights than ordinary e-bike riders. E-bike type, riders' age, waiting position, traffic volume, traffic light type, and time of day are associated with crossing behaviors in urban mixed e-bike groups. In addition, the variable of traffic light status is found to account for unobserved heterogeneity. Findings are indicative to the development of effective implications in improving e-bikes' traffic safety level at signalized intersections. [ABSTRACT FROM AUTHOR]

Rapid population growth and agricultural development are generating a considerable amount of effluents, which poses threats to the quality of rural water resources as well as sanitary conditions. However, with a range of rural wastewater treatment (WT) technologies available, one major problem facing the practitioners is which to choose as the most favorable option suited to specific areas. In this study, a novel decision-making framework is proposed to evaluate and select the optimal alternative in rural areas of Xi'an within multiple consecutive time periods. Firstly, an evaluation index system is constructed and picture fuzzy numbers (PFNs) are used to represent both evaluation levels and experts' refusal due to limitation of knowledge. Secondly, fuzzy analytical hierarchy process (FAHP) is applied to derive weights of criteria, which enables experts to assign fuzzy numbers to express their preferences for comparison judgments. Thirdly, evidence theory is utilized to obtain the aggregated values from multiple time periods. Finally, based on the belief intervals obtained, sequencing batch reactor (A4) is determined as the optimal rural WT technology in Xi'an from 2006 to 2020, whereas the membrane bio-reactor (A2) is the last option. The effectiveness of the proposed framework is further validated by comparative analysis. This research can hopefully serve as useful guidance for the assessment of rural WT technologies in various regions. [ABSTRACT FROM AUTHOR]

The inadequate reuse and recycle of construction and demolition waste (C&DW) has been identified as a major issue, as it implies the depletion of natural aggregates, massive occupation of landfill and inefficiency of construction industry. However, a holistic view involving economic, technical and comprehensive dimensions has rarely been adopted for C&DW utilization evaluation against both qualitative and quantitative criteria. In this study, we propose a framework in a heterogeneous group decision-making environment to solve the C&DW utilization mode selection problem by integrating the Choquet integral and the elimination and choice translating reality (ELECTRE) method with two data forms, that is, interval-valued hesitant fuzzy numbers and real numbers. Firstly, an evaluation index system is constructed to assess multiple representative C&DW utilization modes in Xi'an, China. Secondly, a novel method is proposed to apply the Choquet integral for aggregation of evaluation values and fuzzy ELECTRE for outranking of C&DW utilization modes after the heterogenous evaluation data are unified into interval numbers. Thirdly, to illustrate the application of the proposed framework, this paper evaluates four typical C&DW utilization modes based on a case study in Xi'an, China, and the result shows that the most preferable mode is A3 (off-site crushing, roadbed backfilling). Finally, a comparative analysis is conducted to verify the effectiveness of this framework. This study presents useful findings to C&DW practitioners and can help avoid intuitive selection among available C&DW utilization modes. [ABSTRACT FROM AUTHOR]

• Delivery e-bike riders are more likely to violate traffic lights at signalized intersections than ordinary e-bike riders. • Account for the effects of different influencing factors on RLR behaviors of e-bike riders by correlated mixed logit model. • Age, traffic light status, observation time and waiting position among DEB riders are connected to unobserved heterogeneity. The red-light running (RLR) behavior of delivery e-bike (DEB) riders in cities has become the primary cause of traffic accidents associated with this group at signalized intersections. This study aimed to explore the influencing factors of red light running behavior and identify the differences between the DEB riders and the ordinary e-bike (OEB) riders to aid the development of countermeasures. In this study, the mixed (random parameter) binary logistic model was employed to capture the effects of unobserved heterogeneity. With this approach, factors including individual characteristics, behavioral variables, characteristics of signalized intersections, and the traffic environment were examined. Additionally, to account for the combined influence on the RLR occurrence, mixed logit framework was developed to reveal the correlations among the random parameters. The data of e-bike riders' crossing behaviors at four signalized intersections in Xi'an, China were collected, and 3335 samples were recorded. The results indicated showed that DEB riders are more likely to run red lights than OEB riders. Factors that affect RLR behaviors of the two groups are different. Factors associated with the unobserved heterogeneity include red-light stage, observation time, age and waiting position of the rider. The joint influence among random parameters further illustrates the complexity of the contributing factors of riders' crossing behavior. Results from the models provide insights into the development of intervention systems to improve the traffic safety of e-bike riders at intersections. [ABSTRACT FROM AUTHOR]

• The theory of planned behavior was used to investigate the behavior of delivery riders running red lights. • The structural equation model was used to predict delivery riders running red lights. • Attitude, conformity tendency and the traffic environment were significant predictors in the modified TPB model. • The results of multiple group analysis show that differences between the means of psychological variables of different population groups. • Some intervention measures are obtained by analyzing the influencing factors of running red lights. Delivery riders, an occupation that has emerged from China's booming E-commerce industry, have attracted widespread attention due to their red-light running (RLR) and high accident rates. This study aimed to utilize the theory of planned behavior (TPB) to investigate the psychological characteristics of delivery riders' RLR intentions. A survey questionnaire was designed to collect data, including information regarding the extended variables, the basic components of the TPB and demographic characteristics. The survey was conducted in Xi'an, and 228 complete questionnaires were collected. Structural equation modeling was used to examine the data, and a multiple group analysis of the demographic variables was conducted. The results showed that the expanded TPB model had a better model fit and higher variance explanation than the original TPB model. Extended constructs, i.e., conformity tendency (CT) and the traffic environment (TE), were significant predictors, and attitude was the strongest predictor of all the examined variables related to RLR intentions. Finally, the path parameters of the expended TPB model were adapted for different demographic groups, and some differential effects were also found. These results could provide a basis for the design of intervention measures and safety education schemes by delivery platforms and traffic management departments to reduce RLR behavior among delivery riders. [ABSTRACT FROM AUTHOR]

• Pedestrians were exposed to large temporal and spatial variation of PM2.5, BC and UFP concentration. • Teens inhaled higher doses of PM2.5 and BC than adults' on all the streets studied. • Gender-related inhaled dose differences between teens were more significant than those for adults. • Background concentrations, traffic and pollution hotspots were identified as major factors affecting the pollutant concentrations. To evaluate the exposure of adults and teens to fine particles (PM 2.5), black carbon (BC), and ultrafine particles (UFP) as they walked along urban roads, an investigation was performed using mobile measurement devices on two types of urban roads (arterial and collector) in Xi'an. The inhalation dose model, considering exposure concentration, inhalation rate, and trip time, was employed to estimate the personal inhaled dose of pollutants for adults and teens. Multivariate linear regression was used to explore impact factors that contributed to their exposure variability. Results showed large spatial and temporal pollutant concentration variations along the designed route. Pedestrians experienced higher exposure concentrations on the arterial road than on the collector road. Teens faced higher PM 2.5 and BC inhaled doses than adults on all the streets studied, although sometimes being exposed to lower concentrations than adults. In addition, gender-related inhaled dose differences between teens were more significant than those for adults. The overall background concentrations explained the greatest variability in pollutant exposures, from 18.7% for BC to 40.4% for PM 2.5. Ambient concentrations and traffic volume as well as pollution hotspots (pedestrian cigarette smokers, restaurants, and open burning) were identified as major factors affecting the pollutant concentrations. [ABSTRACT FROM AUTHOR]