Method for Identifying Truck Traffic Site Clustering Using Weigh-in-Motion (WIM) Data

• Main Authors: Dan Liu, Zhenghong Deng, Wang Yinhai, Evangelos I. Kaisar
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Truck traffic monitoring site; <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-means clustering; weigh-in-motion
• Online Access: https://ieeexplore.ieee.org/document/9146642/

The increasingly growing truck traffic volume data while limited truck weigh-in-motion weight data has posed great challenges for transport agencies to access the freight tonnage of all the truck traffic sites. By mapping a group of traffic sites with similar traffic patterns to a weigh-in-motion site, the clustered truck traffic data is expected to be smaller than the sum of all data from all traffic sites, and the cluster can be fully utilized in a period of time by transport agencies to evaluate the freight tonnage. This study developed a novel and implementable approach of integrating two complementary data, Weigh-in-Motion (WIM) weigh data and Telemetric Traffic Monitoring Sites (TTMSs) volume data, to produce truck traffic sites clustering. An improved k-means clustering with three attributes is fitted to the TTMS, which are the distances to the WIM sites (WIMSs), truck volumes in TTMS, and vehicle class distribution. The aforementioned methodology was tested in a case study in Florida using WIM data in 2012 and 2017. The proposed model might shed light on the statewide performance evaluation of freight traffic with low computing cost.