INTRODUCING MULTIDIMENSIONAL RASCH MODEL IN MEASURING TRAFFIC POLICE OFFICERS' BEHAVIOR
スポンサーリンク
概要
- 論文の詳細を見る
This study applied a multidimensional item response model to measure police officers' ability on conducting red-light running enforcement. The ability to conduct red-light running enforcement was found to consist of five component constructs, including ability on mental demands (AMD), on stopping and chasing vehicle (ASC), on psychological demands (APD), on physical demands (APHD), and under abnormal environment (AAE). The multidimensional Rasch models was then applied to assess the perceived enforcement ability of officers and item/task difficulties of conducting traffic law enforcement. The results showed that most participant police offices performed well on AMD, ASC, and APD constructs, but half of them were weak on APHD and AAE constructs. Young officers were more appropriate to conduct red-light running enforcement than the other officers. Finally, some management strategies are suggested to improve performance when conducting red-light running enforcement.
- Eastern Asia Society for Transportation Studiesの論文
著者
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CHANG Hsin-Li
Department of Transportation Technology And Management National Chiao Tung University
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SHIH Chang-Ku
Department of Global Marketing and Logistic, Mingdao University
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CHENG Mei-Hsing
Department of International Trade, National Taichung University of the Science and Technology
関連論文
- EXPLORING THE MODE CHOICE IN DAILY TRAVEL BEHAVIOR OF THE ELDERLY IN TAIWAN
- HOW DO THE TRAFFIC POLICE PERCEIVE THEIR ABILITY FOR RED LIGHT RUNNING ENFORCEMENT? - AN APPLICATION OF THE RASCH MEASURE:- AN APPLICATION OF THE RASCH MEASURE
- THE IMPACT OF TRAFFIC INFORMATION ON DRIVERS' ROUTE CHOICE-USING COMPETENCE SETS ANALYSIS
- Using Rasch Modeling to Explore the Motorcyclist's Behavioral Intention in Red-light Running
- INTRODUCING MULTIDIMENSIONAL RASCH MODEL IN MEASURING TRAFFIC POLICE OFFICERS' BEHAVIOR
- POTENTIAL FALLACIES IN MEASURING THE SAFETY EFFECTS OF ROAD ACCIDENT PREVENTION POLICES WITH DIFFERENT OBSERVATIONAL PERIODS
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