Analysis of the Correlation of Traffic Congestion Based on Multiple Factors: Taking the Roads in Beijing as an Example Beijing University of Technology

Format:

Book

Conference/Event

Author/Creator:

Feng, Jiarui, author.

Contributor:

Han, Xiran

Conference Name:

2025 5th International Conference on Smart City Engineering and Public Transportation (SCEPT2025) (2025-03-28 : Beijing, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

AbstractIn recent years, traffic issues in China have been emerging continuously, and the traffic congestion problem in Beijing is particularly prominent. We have explored the relationships between factors such as driving duration, road length, weather conditions in Beijing and traffic congestion. By using the Logistic Regression Model to analyze the relationships among driving duration, road length and traffic congestion, we found that both driving duration and road length are negatively correlated with traffic congestion. The model shows high accuracy and recall rate, demonstrating excellent performance. We also employed the Weighted Average Correlation Model to study the relationship between weather conditions and traffic congestion. The results indicate that traffic congestion is more severe in rain, snow, and foggy weather, while it is less serious in sunny and cloudy weather. Subsequently, through the noise level verification, the stability of the model was confirmed. At the same time, we used Shapley value analysis, Bootstrap confidence intervals, and hypothesis testing to examine the impacts of travel time and road length on traffic congestion. Additionally, we employed Cross-validation and Granger causality test to assess the influence of weather conditions on traffic congestion. The results of these analyses all verify the correctness of our conclusions. Finally, based on these results, we put forward suggestions regarding travel arrangements and the setting of traffic facilities. We Suggests guiding the public to rationally choose travel modes based on congestion and weather. Points out that logistic regression and weighted average models have limitations in capturing non-linear relationships and are sensitive to outliers

Notes:

Vendor supplied data

Publisher Number:

2025-99-0074

Access Restriction:

Restricted for use by site license