Travel reliability is the most essential determinant for operating the transit system and improving its service level. In this study, an optimization model for the electric transit route network design problem is proposed, under the precondition that the locations of charging depots are predetermined. Objectives are to pursue maximum travel reliability and meanwhile control the total cost within a certain range. Constraints about the bus route and operation are also considered. A Reinforcement Learning Genetic Algorithm is developed to solve the proposed model. Two case studies including the classic Mandl's road network and a large road network in the context of Zhengzhou city are conducted to demonstrate the effectiveness of the proposed model and the solution algorithm. Results suggest that the proposed methodology is helpful for improving the travel reliability of the transit network with minimal cost increase.
BackgroundThe study explored sources of meaning in older adults and the action path among family care, meaning in life, quality of life, and depression.Materials and methodsWe investigated 627 older adults using the Sources of Meaning in Life Scale for the Elderly (SMSE), the Family Care Index (APGAR), the Center for Epidemiological Studies Depression Scale-10 (CES-D-10), and the EuroqOL-5 Dimensions (EQ-5D).ResultsScores categorized 454 older adults with good family function, 99 with moderate, and 47 with severe family dysfunction; 110 older adults had depression. The structural equation model showed that family care affected the quality of life and depression by influencing meaning, and depression had a significant negative effect on the quality of life (P < 0.05). The model was a good fit for the data (χ2/df = 3.300, SRMR = 0.0291, GFI = 0.975, IFI = 0.971, TLI = 0.952, CFI = 0.971, RMSEA = 0.062).ConclusionMeaning in life is an intermediary factor that affects depression and quality of life in older adults. Family care had a significant positive impact on SMSE and a negative influence on depression. The SMSE effectively clarifies the sources of meaning in life and can be used to improve meaning and promote mental health in older adults.
In order to reduce the impact of highway passenger transportation hubs on urban internal traffic operations and improve the efficiency of external travel, a method to determine the location of such hubs based on POI data and the Dijkstra algorithm is proposed. Firstly, the process of connecting the starting point of travel in the city to the urban road node and the highway exit is analyzed. Taking the minimum total time cost as the objective function, the mathematical model of the location method of a highway passenger transportation hub is established. Then, an algorithm for solving the mathematical model is proposed. In the solution method, the concept of “equivalent distance” is introduced into the Dijkstra algorithm to calculate the shortest path for passengers traveling on the urban road network. Weighted random numbers are generated to simulate the choice of highway exits when intercity coaches carrying passengers leave the city. Finally, Zhengzhou City is taken as an example for application analysis. The calculation is carried out using a script written in Python. The calculation results of the proposed method are compared with the results of the
P
-median model and the current locations of hubs. The results show that the proposed method consumes less time than the
P
-median method. The total time cost is also lower, which can reduce the time for passengers to leave the city. The location of the highway passenger transportation hub is closer to the periphery of the city and highway nodes, which conforms to the engineering practice and development trend of hub location so as to reduce its impact on urban internal traffic.
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