Objective: To investigate the available evidence referring to the effectiveness of digital countdown timers (DCTs) in improving the safety and operational efficiency of signalized intersection.
Methods: A systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement guidelines. Relevant literature was searched from electronic databases using key terms. Based on study selection and methodological quality assessment, 14 studies were included in the review. Findings of the studies were synthesized in a narrative analysis.
Results: Three types of DCT had different effects on intersection safety and operational efficiency. Green signal countdown timers (GSCTs) reduced red light violations, type I dilemma zone distributions, and rear-end collision likelihood but increased crossing after yellow onset and had mixed impacts on type II dilemma zone distributions and intersection capacity. In contrast, red signal countdown timers (RSCTs) increased intersection capacity, although their effectiveness in reducing red light violations dissipated over time. Likewise, continuous countdown timers (CCTs) significantly enhanced intersection capacity but had mixed influences on red light violations and crossing after yellow onset.
Conclusions: Due to the limited and inconsistent evidence regarding DCTs' effects on intersection safety and efficiency, it is not sufficient to recommend any type of DCT to be installed at signalized intersections to improve safety and operational efficiency. Nevertheless, it is apparent that both RSCTs and CCTs enhance intersection capacity, though their impacts on intersection safety are unclear. Future studies need to further verify those anticipated safe and operational benefits of DCTs with enriched field observation data. 相似文献
The life cycle assessment of the mobile phone housing in Motorola(China) Electronics Ltd. was carried out, in which materials flows and environmental emissions based on a basic production scheme were analyzed and assessed. In the manufacturing stage, such primary processes as polycarbonate molding and surface painting are included, whereas different surface finishing technologies like normal painting, electroplate, IMD and VDM etc. were assessed. The results showed that housing decoration plays a significant role within the housing life cycle. The most significant environmental impact from housing production is the photochemical ozone formation potential.Environmental impacts of different decoration techniques varied widely, for example, the electroplating technique is more environmentally frieodly than VDM. VDM consumes much more energy and raw material. In addition, the results of two alternative scenarios of dematerialization showed that material flow analysis and assessment is very important and valuable in selecting an environmentally friendly process. 相似文献
● Data acquisition and pre-processing for wastewater treatment were summarized. ● A PSO-SVR model for predicting CODeff in wastewater was proposed. ● The CODeff prediction performances of the three models in the paper were compared. ● The CODeff prediction effects of different models in other studies were discussed. The mining-beneficiation wastewater treatment is highly complex and nonlinear. Various factors like influent quality, flow rate, pH and chemical dose, tend to restrict the effluent effectiveness of mining-beneficiation wastewater treatment. Chemical oxygen demand (COD) is a crucial indicator to measure the quality of mining-beneficiation wastewater. Predicting COD concentration accurately of mining-beneficiation wastewater after treatment is essential for achieving stable and compliant discharge. This reduces environmental risk and significantly improves the discharge quality of wastewater. This paper presents a novel AI algorithm PSO-SVR, to predict water quality. Hyperparameter optimization of our proposed model PSO-SVR, uses particle swarm optimization to improve support vector regression for COD prediction. The generalization capacity tested on out-of-distribution (OOD) data for our PSO-SVR model is strong, with the following performance metrics of root means square error (RMSE) is 1.51, mean absolute error (MAE) is 1.26, and the coefficient of determination (R2) is 0.85. We compare the performance of PSO-SVR model with back propagation neural network (BPNN) and radial basis function neural network (RBFNN) and shows it edges over in terms of the performance metrics of RMSE, MAE and R2, and is the best model for COD prediction of mining-beneficiation wastewater. This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures. Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment. In addition, PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning. 相似文献
● Efficient carbon methanation and nitrogen removal was achieved in AnMBR-PN/A system.● AOB outcompeted NOB in PN section by limiting aeration and shortening SRT.● The moderate residual organic matter of PN section triggered PD in anammox unit.● AnAOB located at the bottom of UASB played an important role in nitrogen removal. An AnMBR-PN/A system was developed for mainstream sewage treatment. To verify the efficient methanation and subsequent chemolitrophic nitrogen removal, a long-term experiment and analysis of microbial activity were carried out. AnMBR performance was less affected by the change of hydraulic retention time (HRT), which could provide a stable influent for subsequent PN/A units. The COD removal efficiency of AnMBR was > 93% during the experiment, 85.5% of COD could be recovered in form of CH4. With the HRT of PN/A being shortened from 10 to 6 h, nitrogen removal efficiency (NRE) of PN/A increased from 60.5% to 80.4%, but decreased to 68.8% when the HRTPN/A further decreased to 4 h. Microbial analysis revealed that the highest specific ammonia oxidation activity (SAOA) and the ratio of SAOA to specific nitrate oxidation activity (SNOA) provide stable NO2−-N/NH4+-N for anammox, and anammox bacteria (mainly identified as Candidatus Brocadia) enriched at the bottom of Anammox-UASB might play an important role in nitrogen removal. In addition, the decrease of COD in Anammox-UASB indicated partial denitrification occurred, which jointly promoted nitrogen removal with anammox. 相似文献