Citation

Zahiri M, Chen XM. Transp. Res. Rec. 2018; 2672(31): 110-119.

Affiliation

College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China Corresponding Author: Address correspondence to Xiqun (Michael) Chen: chenxiqun@zju.edu.cn

Copyright

(Copyright © 2018, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198118758638

PMID

unavailable

Abstract

Traffic volume is a fundamental measurement in traffic analyses. In mixed traffic, vehicles vary in size, length, headway, spacing, and acceleration/deceleration. Therefore, if we can categorize the vehicles in mixed traffic in greater detail, the estimated passenger car equivalent (PCE) number will be more accurate. Practical and appropriate methods that convert different vehicles into the equivalent number of passenger cars need to be employed to determine PCE factors for heterogeneous traffic. Following economic growth and increased use of motor vehicles in developing countries, the purchase of sports utility vehicles (SUVs) continues to grow, though the government encourages people to buy small cars because of the limited road capacity, as well as air pollution problems. In this research, we categorize passenger cars into three subsets: small cars (hatchback cars without a trunk), SUVs, and standard cars (taxis and typical family cars). A field investigation shows that the penetration rates of these passenger cars are 12%, 23%, and 55%, respectively (10% are other vehicles) in Hangzhou, China. We also measure the PCE value for small cars and SUVs using the mean time headway method. Because different countries have different weather conditions, we continue to measure the PCE values for sunny days and moderate rainy days. The results show that PCE values for small cars and SUVs are 0.87 and 1.26 on sunny days, and 0.87 and 1.31 on rainy days, respectively. By using the PCE with high precision, urban managers can accomplish the analysis of urban traffic with greater accuracy.

Language: en