Development of Integrated Weigh-in-motion System and Analysis of Traffic Flow Characteristics considering Vehicle Weight

• Material Type: 博士論文
• Title: Development of Integrated Weigh-in-motion System and Analysis of Traffic Flow Characteristics considering Vehicle Weight
• Author/Editor: Ahmad, Saifizul Abdullah
• Author Heading: Ahmad, Saifizul Abdullah
• Publication Date: 2011-11
• Publication Date (W3CDTF): 2011-11
• Alternative Title: 統合型走行時車両重量計測装置の開発と車両重量を考慮した交通流特性の分析
• Pages: 1-76
• Degree grantor/type: 徳島大学
• Date Granted: 2011-11-10
• Dissertation Number: 乙第2067号
• Degree Type: 博士（工学）
• Text Language Code: eng
• Subject Heading: Weigh-in-motion; Traffic-flow; Free-flow-speed; Car-following-characteristics
• Target Audience: 一般
• Note (General): This study attempts to explore empirically how gross vehicle weight (GVW) will affect traffic flow characteristics in both free-flow and vehicle following situations. The success of the study is highly dependent on the empirical data provided by the traffic datacollection system. Currently, many kinds of systems or devices are available which measure or monitor or enforce traffic dedicatedly. The use of these dedicated or nonintegrated systems or devices in traffic data-collection or monitoring and traffic enforcement is useful in certain application, especially when only dedicated parameter is considered. However, these devices may have limited measurement capabilities and unsuitable for this study, where the devices are incapable of simultaneously measuring all essential traffic and vehicle parameters such as speed, headway as well as weight in real-time. The use of these dedicated devices for this study will result in data inconsistency and incomprehensive measurable traffic parameters. Thus, a comprehensive and continuous traffic data-collection system based on weigh-inmotion technology has been developed and installed at one of the federal roads in Malaysia for study purposes. The developed system is capable of simultaneously and continuously measuring large sample and all essential traffic and vehicle parameters in real-time. It also uses the minimum number of sensors necessary to provide the maximum number of various traffic and vehicle parameters. Statistical analysis was then performed to the collected data to quantify that the gross vehicle weight can have a significant effect in traffic flow characteristics in both free flow and following situations. The results lead to explore the driver behavior in controlling the vehicle from two different perspective: driver’s visual input and vehicle dynamics capability. The first empirical analysis results showed that statistically for each type of heavy vehicle, there was a significant relationship between free flow speed of a heavy vehicle and its GVW. Specifically, the results suggest that the mean and variance of free flow speed decrease with an increase GVW by the amount unrelated to size and shape for all GVW range. Then, based on the 85th percentile principle, this study proposed a new concept for setting differential speed limit for heavy vehicle by incorporating GVW where a different speed limit is imposed to the heavy vehicle according to its GVW. The second empirical analysis results showed that how GVW of following vehicle and size of leading vehicle will affect the driver behavior in controlling their speed under different compositions of leader-follower pairs in a car-following situation. The main findings of this study are when we incorporate the vehicle dynamic’s capability in a carfollowing situation, the GVW of following vehicle and the size of leading vehicle were significant sources of variation in following vehicle speed and relative speed, and their interaction influence the driver behavior in controlling the speed.The third study investigated empirically how different composition of leader-follower pairs will affect time headway with the focus on following vehicle (FV) GVW and leading vehicle (LV) size. The results from statistical analysis were quite revealing in terms of quantifying that the GVW of FV and the class of LV were a significant source of variation in time headway. Based on these empirical results, the study suggests a preferred minimum headway model from driver perspective incorporating heavy vehicle size and GVW. The proposed model also suggests the selection of the optimum value of preferred minimum headway based on percentile value if human reaction time, public support and traffic flow interference are to be considered. Based on these empirical analyses, it can be concluded that, the main findings of this study are when we incorporate the vehicle dynamics capability in a traffic flow study, the gross vehicle weight should be considered as one of the variable of interests to obtain more rational results.
• Format (IMT): application/pdf
• Source: http://repo.lib.tokushima-u.ac.jp/files/public/8/83497/20170929150109586488/LID201111221001.pdf （fulltext）; http://repo.lib.tokushima-u.ac.jp/files/public/8/83497/20170929150109586488/LID201111221001.pdf
• Access Restrictions: インターネット公開
• Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• Original Data Provider (Database): 徳島大学 : 徳島大学機関リポジトリ