Study on flow demand forecasting of arterial highways under sparse road network conditions: a case study of Shanshan-Kumish project

Qiang Lin; Jiali Han; Xiaomin Dai

doi:10.1117/12.3054636

20 December 2024 Study on flow demand forecasting of arterial highways under sparse road network conditions: a case study of Shanshan-Kumish project

Qiang Lin, Jiali Han, Xiaomin Dai

Author Affiliations +

Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 134214M (2024) https://doi.org/10.1117/12.3054636
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China

Abstract

In this study of arterial highway traffic demand under sparse road network conditions, the four-phase method was selected for traffic flow prediction, combined with the traffic volume data predicted by the Trans CAD software. In the context of a limited coverage of arterial road networks, the lack of convenient arterial road networks between some areas leads to a lack of traffic accessibility. Consequently, a rational highway traffic planning strategy is required to meet the demands of today's society. The findings of this study indicate that there is an uneven spatial distribution of traffic flow and concentration of flow on arterial road sections in the sparse road network scenario. The characteristics can then be used to inform the construction level of road sections and improve the scientific nature of technical solutions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiang Lin, Jiali Han, and Xiaomin Dai "Study on flow demand forecasting of arterial highways under sparse road network conditions: a case study of Shanshan-Kumish project", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 134214M (20 December 2024); https://doi.org/10.1117/12.3054636

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE