Search

Weigh-in-motion systems have been widely used by state agencies to collect the traffic data on major state roadways and bridges to support traffic load forecasting, pavement design and analysis, infrastructure investment decision making, and transportation planning. However, the weigh-in-motion system itself poses difficulties in obtaining accurate data due to sensor characteristics that can be sensitive to vehicle speed, weather conditions, and changes in surrounding pavement conditions. This study focuses on developing a systematic methodology to detect weigh-in-motion sensor bias and enhance current practices for weigh-in-motion calibration. A mixture modeling technique using an expectation maximization algorithm was developed to divide the vehicle class 9 gross vehicle weight into three normally distributed components: unloaded, partially loaded, and fully loaded trucks. Then the well-known statistical process control technique cumulative sum control chart analysis was applied to expectation maximization estimates of daily mean gross vehicle weight for fully loaded trucks to identify and estimate shifts in the weigh-in-motion sensor. Special attention was given to the presence of autocorrelation in the data by fitting an autoregressive time-series model and then performing cumulative sum control chart analysis on the fitted residuals. Results from the analysis suggest that the proposed methodology is able to estimate a shift in the weigh-in-motion sensor accurately and also indicate the time point when the system went out of calibration. This methodology can be effectively implemented by state agencies, resulting in more accurate and reliable weigh-in-motion data. [ABSTRACT FROM AUTHOR]

Highway congestion has in recent years become a pervasive problem for urban and suburban areas alike. The concept of Automated Highway Systems is based on the belief that integration of sensing, communication, and control technologies into vehicles and highways can lead to a large improvement in capacity and safety without requiring a significant amount of additional highway right-of-way. A fundamental determinant of Automated Highway Systems capacity is the vehicle-following rule, the rule that governs the behavior of vehicles traveling along a common lane (e.g., the spacing between any two longitudinally adjacent vehicles). Vehicle following affects the longitudinal capacity (achievable flow within a lane), the lateral capacity (achievable flow between lanes) and the conflicting relationship between the longitudinal flow and lateral capacity. The issues are investigated by developing probabilistic models for vehicle I platoon and gap distributions, for vehicles that travel in platoons, in slots, or as free-agents. Mathematical models are also developed to estimate the completion time of a lane change, which can be used as a surrogate for the lateral capacity. Numerical results for the three major vehicle-following rules and their comparison are also provided. [ABSTRACT FROM AUTHOR]