Route planning is the key technical difficulty of logistics distribution. In this paper, a mathematical model is established for road network selection and real-time path planning. The improved genetic algorithm is used to select the road network of rail transit. Based on the traditional ant colony algorithm, the time-varying factor and line risk factor are introduced to improve the algorithm in line with real-time path planning. The experimental results show that the improved algorithm can change the route more quickly and accurately according to the real-time traffic changes to effectively improve the logistics transportation efficiency for complex routes and traffic conditions, and save the transportation cost.
The causes of track irregularity are determined by many factors. It is very difficult to evaluate and quantitatively analyze the causes of the natural environment. On this basis, the track irregularity is regarded as a grey system, and the GM (1,1) model is used to model and evaluate it. However, the traditional grey model with equal time intervals is used to evaluate the deterioration trend of track irregularity, which can not fully reflect the changing trend of track irregularity in a single maintenance cycle. Therefore, to reduce the impact of various factors and improve the accuracy of the assessment results, this paper combines the actual characteristics of the trend assessment of track irregularity based on the grey model theory, which optimizes and improves the traditional grey model from four directions: time interval, calculation of background value, determination of initial value, and weight of detection data. The evaluation accuracy of the model in this paper is more than 1. 64% higher than that of other comparison methods, which can reduce the solution error of the model, improve the sensitivity of the model, and make the model accurately and timely predict the deterioration of track irregularity.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.