Measuring and evaluating of road roughness conditions with a compact road profiler and ArcGIS

作     者：Nueraihemaitijiang Abulizi Akira Kawamura Kazuya Tomiyama Shun Fujita 

作者机构：Graduate School of EngineeringKitami Institute of TechnologyHokkaido 090-8507Japan Department of Civil and Environmental EngineeringKitami Institute of TechnologyHokkaido 090-8507Japan Hokkaido Regional Development BureauMinistry of LandInfrastructureTransport and TourismHokkaido 090-8507Japan 

出 版 物：《Journal of Traffic and Transportation Engineering(English Edition)》 (交通运输工程学报（英文版）)

年 卷 期：2016年第3卷第5期

页      面：398-411页

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0813[工学-建筑学] 0814[工学-土木工程] 0823[工学-交通运输工程] 

基　　金：supported by JSPS KAKENHI(Grant Number:2628167) 

主　　题：Road engineering Road roughness Mobile compact profiler ArcGIS Ride quality Wheel path 

摘      要：With a wide range of requirements throughout the world, high-quality road management is subject to increasing demand from a perspective of customer-oriented levels of service. In recent years, road administrators are requested to create a visual map of a road network to monitor conditions. To fulfill these requirements, this study conducted as follows. Firstly, this paper introduces a new compact road profiler to collect the profile data at ease. Using the international roughness index （IRI） to assess public roads in local cities of Japan＇s Hokkaido prefecture, this study also provides real-time monitoring of pavement roughness conditions. Moreover, this study deals with an effective method for visualizing collected IRI data as an attribute in a geographic information system （GIS） and the database of Japan digital road map （DRM）. Secondly, the authors present the measurement results of IRI at two different cities during different seasons by using GIS to compare the road conditions. According to the results clarified on different statistical characteristics of road profiles, this study recommends that it is necessary to establish pavement management system （PMS） in consideration of road class, network of local city, and evaluation and management of road conditions in winter quantitatively. Finally, the authors measure and evaluate ride quality by assessing differences between the inner wheel path （IWP） and outer wheel path （OWP） of the vehicle into account, using the previously mentioned profilers and the driving simulator, which is called KITDS. Results show that information from both wheel paths contributes to improve current monitoring process regarding pavement surface, and expects to construct a high level of PMS for road administration in the future.