This paper is aimed at the problem that the process of students’ developmental assessment is too comprehensive and the process of data collection is too old. It is very important but the performance of existing methods cannot meet the actual needs. So, this paper puts forward the design of distributed collection model of students’ developmental information based on Internet of Things technology, which is based on the hardware design of students’ developmental information distributed collection such as RF RC500F chip, TIMSP430 microprocessor, and interface in Internet of Things environment. Experimental results show that the model has less noise interference and strong data transmission ability, has higher collection accuracy of student development information, and can improve the authenticity and comprehensiveness of student development assessment.
Traffic volume prediction has been an interesting topic for decades during which various prediction models have been proposed. In this paper, Kalman filtering (KF) model is applied to predict traffic volume because of its significance in continuously updating the state variable as new observations. In order to enhance the prediction accuracy, an improved KF model is developed based on the current and historical data. To validate the improved KF model, empirical analysis is conducted. The results show that the improved KF model has higher accuracy than the traditional one and is more reliable and powerful in traffic volume prediction.
With the rapid development of the national economy, the construction of China’s urban transportation is in the stage of rapid development. Both big cities and small-medium cities increasingly appear all kinds of issues such as traffic congestion. Priority to the development of public transport is the most effective way to solve the problem of urban traffic congestion, and urban bus transit network design is the first step in the planning of the public transport system, plays a vital role in the transportation system planning. Bus transit network generally consisted of rail transit, bus rapid transit and conventional bus in big cities, while it composed of conventional bus in small-medium cities. Therefore, this paper made research on the applicability of bus transit network design methods for different sized cities, analyzed the advantages and disadvantages of different methods, and explored a more excellent method for bus transit network design.
Public transit crew scheduling problem is to carry out the operations task with the minimum drivers and operational cost. It is a multi-objective programming problem, which is well-known to be NP-hard. Restrained by the operational constraints and labor agreement, all the feasible sets of shifts were pre-generated in this paper. This work established a penalty function to ensure the validity of the schedule, and a cost function to reduce costs. With the help of Tabu Search, a solution can be found easily. A computational experiment based on the real-world crew scheduling problem in China demonstrates the strength of this method.
This paper addresses the balanced bus crew rostering problem (BBCRP). In this problem, the duty assignment to bus crews in a given time horizon should satisfy that the total workload should be evenly distributed. We firstly formulate the problem as a multi-level balanced assignment problem. Then, a genetic algorithm-based approach is designed to solve the proposed model. Finally, a simple numerical example is given to illustrate the application of the approach. Implementation results show that the proposed approach can obtain good quality solutions in a reasonable time and can be applied to real-life BBCRPs.
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