Automatic passenger counter (APC) systems have been implemented in various public transit systems to obtain bus occupancy along with other information such as location, travel time, etc. Such information has great potential as input data for a variety of applications including performance evaluation, operations management, and service planning. In this study, a dynamic model for predicting bus-arrival times is developed using data collected by a real-world APC system. The model consists of two major elements: the first one is an artificial neural network model for predicting bus travel time between time points for a trip occurring at given time-of-day, day-ofweek, and weather condition; the second one is a Kalman filter-based dynamic algorithm to adjust the arrival-time prediction using up-to-the-minute bus location information. Test runs show that this model is quite powerful in modeling variations in bus-arrival times along the service route.

No abstract

No abstract

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.

No abstract