This paper investigates the longitudinal control of vehicle platoons with a focus on external disturbances, parameter uncertainties, and communication failures. When vehicle platoons encounter vehicle to vehicle wireless communication failures, the preceding vehicle's acceleration is unavailable, which degrades the performance of the system. However, the existing achievements can not be directly used to address the aforementioned three issues. To this end, a generalized extended state observer-based control (GESOBC) law is contrived. First, the parameter uncertainties and the external disturbances are together regarded as a lumped disturbance. Then, a generalized extended state observer is designed to estimate the lumped disturbance and the preceding vehicle's acceleration, separately. Based on the estimation, a composite controller consisting of a state feedback control part and an estimation-based feedforward control part is developed. Furthermore, it is proved that the proposed GESOBC method can guarantee the exponentially bounded stability of the individual vehicle and the input to state string stability of the whole vehicle platoon. Finally, numerical simulations are conducted to demonstrate the effectiveness and feasibility of the proposed method.

Summary: Considerable efforts have been devoted to the development of dynamic origin‐destination (OD) estimation models, which are a key step to realizing self‐adaptive traffic control systems for urban traffic management. However, most of the models proposed to date estimate OD flows based on a single traffic data source, and their performance is limited by the coverage and accuracy of traffic sensors. The inherent difficulty in estimating the dynamic traffic assignment matrix means that dynamic OD estimation remains a challenge for real‐life applications. This paper proposes the use of a Kalman filter for dynamic OD estimation using multi‐source sensor data. The dynamic characteristic of changing OD flow over time is analyzed, and the problem of dynamic OD estimation is converted to a problem of estimating OD structural deviation. The resulting dynamic relationship between traffic volume and OD structural deviation is then used to establish the Kalman filter model. An improved traffic assignment approach is developed and embedded into the measurement equation of the Kalman filter model to enable dynamic updating of the traffic assignment matrix. A dual self‐adaptive mechanism based on the Kalman filter is used to calibrate the model. The proposed method was implemented on a real‐life traffic network in the downtown area of Kunshan City, China. The results show that the proposed method is more accurate than, and outperforms, the traditional link‐volume‐based and turning‐movement‐based methods. Copyright © 2014 John Wiley & Sons, Ltd.