In order to improve the operational efficiency of a shared electric vehicle (SEV) system, this paper conducts integrated planning research from the aspects of locating and sizing SEV outlets, locating and sizing charging facilities, and operation scheduling by considering the carbon emission reduction and its benefits. Firstly, a multi-objective locating model was constructed with the optimization objectives of minimum outlet construction cost and user travel time cost and minimum carbon emissions from outlet construction and vehicle routing. Secondly, a charging demand simulation predictive model framework was designed. Finally, an SEV scheduling model with maximal operating income and minimal scheduling costs as optimization objectives was constructed considering the benefits of carbon emission reduction. By applying these models to the Chicago Sketch network and using a genetic algorithm to solve the models, it is concluded that the optimal outlet location solution considering carbon emission reduction will increase the outlet construction cost and user travel time cost. When the user loss rate is 40%, the initial number of vehicles in each outlet can make the operator’s daily income reach the maximum. The number of charging piles in each outlet is optimally configured according to 55% of the highest charging demand. Scheduling can make the distribution of charging demand more uniform and reduces the maximum charging demand by about 36.8% and the total number of charging piles by about 24.0%, at the same time, increases the average time utilization rate of charging piles at all outlets by about 15.6% and the user satisfaction rate by about 8.6%. Although scheduling increases the scheduling cost by about 3.8%, it can increase the total daily revenue of operators by 6.5%. Carbon reduction benefits can increase the total daily revenue of operators by 0.7%.

Given the severe global traffic safety challenges—including threats to human lives and socioeconomic impacts—this study analyzes visual behavior to promote sustainable transportation, improve road safety, and reduce resource waste and pollution caused by accidents. Four typical road sections, namely, turning, straight ahead, uphill, and downhill, were selected, and the eye movement data of 23 drivers in different driving stages were collected by aSee Glasses eye-tracking device to analyze the visual gaze characteristics of the drivers and their transfer patterns in each road section. Using Markov chain theory, the probability of staying at each gaze point and the transfer probability distribution between gaze points were investigated. The results of the study showed that drivers’ visual behaviors in different road sections showed significant differences: drivers in the turning section had the largest percentage of fixation on the near front, with a fixation duration and frequency of 29.99% and 28.80%, respectively; the straight ahead section, on the other hand, mainly focused on the right side of the road, with 31.57% of fixation duration and 19.45% of frequency of fixation; on the uphill section, drivers’ fixation duration on the left and right roads was more balanced, with 24.36% of fixation duration on the left side of the road and 25.51% on the right side of the road; drivers on the downhill section looked more frequently at the distance ahead, with a total fixation frequency of 23.20%, while paying higher attention to the right side of the road environment, with a fixation duration of 27.09%. In terms of visual fixation, the fixation shift in the turning road section was mainly concentrated between the near and distant parts of the road ahead and frequently turned to the left and right sides; the straight road section mainly showed a shift between the distant parts of the road ahead and the dashboard; the uphill road section was concentrated on the shift between the near parts of the road ahead and the two sides of the road, while the downhill road section mainly occurred between the distant parts of the road ahead and the rearview mirror. Although drivers’ fixations on the front of the road were most concentrated under the four road sections, with an overall fixation stability probability exceeding 67%, there were significant differences in fixation smoothness between different road sections. Through this study, this paper not only reveals the laws of drivers’ visual behavior under different driving environments but also provides theoretical support for behavior-based traffic safety improvement strategies.

Drivers’ visual characteristics have an important impact on traffic safety, but existing studies are mostly limited to single-scene analyses and lack a systematic study on the dynamic changes in drivers’ eye tracking characteristics on different road sections. In this study, 23 drivers were recruited to wear the aSee Glasses eye tracking device and driving tests were conducted on four typical road sections, namely, straight ahead, turning, climbing, and downhill. The average fixation duration, pupil diameter, and the saccade amplitude of the eye tracking were collected, one-way analysis of variance (ANOVA) was used to explore the differences between the different road sections, and a mathematical model of changes in the visual characteristics over time was constructed, based on the fitting of the data. Computerized fitting models of changes over time were also constructed using the Origin 2021 software. The results show that different road sections had significant effects on drivers’ visual tasks: the longest average fixation duration was found in the straight road section, the largest pupil diameter was found in the curved road section, and the highest saccade amplitude was found in the downhill road section, reflecting the influence of the complexity of the driving task on the cognitive load. The fitted model further reveals the dynamic change law of eye tracking indicators over time, providing a quantitative basis for modeling driving behavior and visual tasks. This study provides a theoretical basis and practical reference for the optimal design of advanced driver assistance systems, traffic safety management, and road planning.

Smart home is an important symbol of smart grid electricity. Combing with the properties of household appliances, smart home appliance scheduling is based on the user-side and has different schedules in several periods for diverse equipment. Firstly, in this paper different household appliances are scheduled according to various prices in different periods, so that the daily electricity charge is limited to a certain range; also, numerous consumption plans can be developed in electricity charge. Then, based on the consumption plan, the simulated annealing algorithm is used to find out the solution and meanwhile computer simulations are performed. At last, the simulation results are analyzed. The results of the experiment show the accuracy of the model which can be very good for home appliances scheduling scheme, for the reason users could achieve the purpose of reducing the cost of electricity in the way of rational distribution of electricity.