Research on City Area Traffic Generation Analysis Based on Multi-source Big Data

Abstract

City area traffic demand analysis is an important foundation for comprehensive traffic planning. In the context of the rapid development of big data technology, how to organically combine multi-source data, improve model accuracy, improve work efficiency and reduce work cost is a key issue in the construction of traffic generation analysis model. This paper integrates relevant data such as resident travel OD data, population migration data, and poi data. On the basis of the traditional traffic generation model, add the geographical advantage correction factor to establish a city area traffic generation analysis model based on multi-source big data. And take Heyuan City, Guangdong Province as an example for case analysis to demonstrate the validity and practicability of the model. The results show that the average absolute errors of traffic production and attraction in all traffic communities are 16.29% and 14.98% respectively. This paper can provide a new method to improve the accuracy of city area traffic generation analysis modeling. CCS CONCEPTS • Applied computing • Operations research • Transportation