摘要
Transportation plays a vital role both in urban economy and usuallives, which is recognized as one of the major functions of a city, along with dwelling, work, and recreation. As an organic system, urban traffic has attracted increased attention during recent years. However, the majority of studies focused on roadways and vehicular technologies, and limited research has been conducted to address the driver characteristics and their impact. A major possible reason is the scarcity of reliable data. In fact, traditional traffic data obtained from cross-sectional detectors as well as video capture devices are not sufficient to fully capture driver behavior. Only in the recent years, with the availability of transportation-related ＂big data＂ and particu- larly the overwhelming information onboard and from road- side facilities, the impacts of driver behavior may be investigated in more detail. In general, driver behavior in microscopic level may include car-following, lane-changing, and gap acceptance models, which are believed largely to affect roadway capacity. Furthermore, safety concerns especially when drivingin an urban environment due to the interaction of different modes, also increase the complexity of quantifying the quality of service of various transportation facilities. These potential complexities in driver behavior challenge academia to develop supporting models and methods of analysis.
Transportation plays a vital role both in urban economy and usuallives, which is recognized as one of the major functions of a city, along with dwelling, work, and recreation. As an organic system, urban traffic has attracted increased attention during recent years. However, the majority of studies focused on roadways and vehicular technologies, and limited research has been conducted to address the driver characteristics and their impact. A major possible reason is the scarcity of reliable data. In fact, traditional traffic data obtained from cross-sectional detectors as well as video capture devices are not sufficient to fully capture driver behavior. Only in the recent years, with the availability of transportation-related ＂big data＂ and particu- larly the overwhelming information onboard and from road- side facilities, the impacts of driver behavior may be investigated in more detail. In general, driver behavior in microscopic level may include car-following, lane-changing, and gap acceptance models, which are believed largely to affect roadway capacity. Furthermore, safety concerns especially when drivingin an urban environment due to the interaction of different modes, also increase the complexity of quantifying the quality of service of various transportation facilities. These potential complexities in driver behavior challenge academia to develop supporting models and methods of analysis.
