KEYWORDS: Support vector machines, Education and training, Data modeling, Machine learning, Transportation, Data analysis, Principal component analysis, Overfitting, Computer programming, Standards development
The issue of student poverty is not only a core concern in the field of education but also an important topic in socioeconomic development. Identifying and assisting impoverished students is crucial for achieving educational equity and social justice. However, the determination and classification of the poverty levels among students face numerous challenges due to various influencing factors. Therefore, this paper employs a k-means improved linearly separable support vector machine model to classify the levels of poverty. Experimental results show that the classification model performs well, with an accuracy of 97.419%.
KEYWORDS: Transportation, Roads, Global Positioning System, Mining, Data acquisition, Data analysis, Data processing, Data mining, Data fusion, Visualization
With the increase of urban residents' population, the urban public transportation system faces new challenges. As an important transportation mode to satisfy residents' spatial and temporal needs, the mining of the operation characteristics of taxis is of great significance to understand the urban travel pattern and reduce the cost of the transportation system. Based on the taxi trajectory data in Chengdu, this study fuses electronic maps and GPS positioning data, firstly, the GPS data are trajectorized, then the trajectory data are matched with road network maps for OD maps, and finally, the operating time and distance characteristics of taxis are explored. It is found that taxi trips tend to be short duration and short distance trips, especially trips within 15 minutes and 3 kilometers. The taxi operation characteristics mining in this paper provides valuable references for urban transportation planning and taxi operation management.
Trajectory data contains rich information about urban residents' taxi trips, and processing and analyzing the trajectory data to explore the spatial and temporal characteristics of residents' travel behaviors as well as to explore the hotspots of urban taxis is an important basis for improving the efficiency of taxi operation and management level. Firstly, the order information of taxis is identified, and the length and distance distribution characteristics of the orders are mined for each time period of the day. Then the density noise applied spatial clustering algorithm based on grid optimization is proposed to mine the hotspots of urban taxi passengers, and compared with the traditional DBSCAN algorithm, K-distance DBSCAN algorithm and grid density clustering algorithm. The experimental results show that the grid-based DBSCAN algorithm has the highest accuracy and lowest time complexity, and the Davies Bouldin Score (DBI) is reduced by about 35% on average compared with the other three algorithms. The method proposed in this paper has reference value in passenger hotspot mining based on massive taxi trajectory data.
KEYWORDS: Roads, Mining, Statistical analysis, Data mining, Transportation, Analytical research, Data modeling, System integration, Sustainability, Industry
In order to explore the characteristics of choosing passenger-seeking roads in the taxi passenger-seeking process and improve the operational efficiency of taxis, this paper proposes a method of mining the passenger-seeking road laws of high-occupancy taxis based on cross-tabulation analysis. The representative city of Chengdu is selected as the research area, and the stable taxi operation data in Chengdu is used as the research basis. Using the cross-tabulation analysis method, the correlation analysis between the number of passengers and the factors of road hotspot and road class is carried out in the form of Sankey diagrams, and it is found that the number of passengers is strongly correlated with the attraction of hotspot. In contrast, the correlation with the attraction of road class is weaker. We summarise the road selection rules for high occupancy taxis, such as "pay attention to road sections with many road hotspots," "pay attention to bus stop areas," and "pay attention to roads around primary roads," which are of strong practicality and reference value. It has strong practicality and reference value.
This paper proposes an evaluation method for the level of bus and slow traffic system connection around rail stations, considering the necessity, feasibility, accuracy and other attributes of the indicators, and determines seven constraint-guaranteed indicators and five quality-improved indicators, which can reflect the level of bus-slow traffic connection in a more comprehensive way; and gives the calculation method of each indicator, so that we can get the quantitative results of the calculation; and then determines the weight value of each indicator. Then, according to the entropy weighting method and the hierarchical analysis method improved by spherical fuzzy set, the weighting value of each index is determined. Finally, the fuzzy comprehensive evaluation method is used to get the evaluation level of corresponding stations. Finally, the targeted evaluation of different types of rail stations is realised, which can provide the basis for the subsequent adjustment of bus lines and the construction of connecting facilities.
In order to quickly mine hotspot areas from taxi trajectory data, so as to help taxi drivers improve the efficiency of passenger search and economic income, this paper proposes a grid-optimised DBSCAN algorithm. Firstly, the study area is divided into a number of grids of the same size, then the trajectory data is mapped to the grid by a function, and finally clusters are generated based on the width-first search, which reduces the search range of the boundary points of each point, and forms clustered hotspots based on the clustering of high-density grid cells. In this paper, we use taxi trajectory data in Chengdu city to conduct experiments and compare with three clustering algorithms, DBSCAN, GSCAN and K-DBSCAN, and the results show that the algorithm in this paper has a smaller time complexity, and the algorithm's DBI index is reduced by an average of 35.25%, and the average running time is reduced by 19.14%.
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