AbstractTo more accurately describe the evolution process of traffic flow, a novel method was proposed on the basis of the polynomial trend model and the Kalman filtering theory in the paper. The state variables were defined as the deviations of the actual OD flow from the historical values. These deviations were presented as a stochastic evolution process with a sliding trend. Furthermore, the dynamic OD matrix was estimated and predicated by establishing a polynomial trend filtering model. A large number of simulation results prove that the performance of the algorithm proposed in this paper is better than the traditional method, and this algorithm can acquire more accurate estimation and prediction information for OD matrix
The paper proposes a “quasi-dynamic” framework for estimation of origin-destination (o-d) flow from ...
OD flows provide important information for traffic management and planning. The prediction of dynami...
Traffic management applications are supported by dynamic models fed with realistic real-time demand ...
AbstractTo more accurately describe the evolution process of traffic flow, a novel method was propos...
This paper proposes an extended Kalman filter for quasi-dynamic estimation/updating of o-d flows fro...
Summarization: This paper proposes an extended Kalman filter for quasi-dynamic estimation/updating o...
Since OD matrices are not directly observable, indirect procedures have been developed to estimate O...
Dynamic traffic origin-destination estimation has received increasing attention in recent years due ...
[[abstract]]In the present research, a nonlinear Kalman filtering approach, i.e., extended Kalman fi...
Traffic management applications are supported by dynamic models whose input should be realistic real...
[[abstract]]The purpose of this research was to develop a dynamic model for the on-line estimation a...
A variety of methods for dynamic origin-destination (O-D) estimation is available in the transportat...
Time-Dependent Origin-Destination (OD) matrices are a key input to Dynamic Traffic Models. Microscop...
Time-Dependent Origin-Destination (OD) matrices are a key input to Dynamic Traffic Models, microscop...
The purpose of this research is to develop a dynamic model for on-line estimation and prediction of ...
The paper proposes a “quasi-dynamic” framework for estimation of origin-destination (o-d) flow from ...
OD flows provide important information for traffic management and planning. The prediction of dynami...
Traffic management applications are supported by dynamic models fed with realistic real-time demand ...
AbstractTo more accurately describe the evolution process of traffic flow, a novel method was propos...
This paper proposes an extended Kalman filter for quasi-dynamic estimation/updating of o-d flows fro...
Summarization: This paper proposes an extended Kalman filter for quasi-dynamic estimation/updating o...
Since OD matrices are not directly observable, indirect procedures have been developed to estimate O...
Dynamic traffic origin-destination estimation has received increasing attention in recent years due ...
[[abstract]]In the present research, a nonlinear Kalman filtering approach, i.e., extended Kalman fi...
Traffic management applications are supported by dynamic models whose input should be realistic real...
[[abstract]]The purpose of this research was to develop a dynamic model for the on-line estimation a...
A variety of methods for dynamic origin-destination (O-D) estimation is available in the transportat...
Time-Dependent Origin-Destination (OD) matrices are a key input to Dynamic Traffic Models. Microscop...
Time-Dependent Origin-Destination (OD) matrices are a key input to Dynamic Traffic Models, microscop...
The purpose of this research is to develop a dynamic model for on-line estimation and prediction of ...
The paper proposes a “quasi-dynamic” framework for estimation of origin-destination (o-d) flow from ...
OD flows provide important information for traffic management and planning. The prediction of dynami...
Traffic management applications are supported by dynamic models fed with realistic real-time demand ...