The aim of this paper is to develop an Information Extension Model (IEM) which uses location data of bus fleets (AVL data) to estimate road traffic conditions and provide input for implementing control strategies. The IEM consists of three sub-models: the Link Traffic Condition Model (LTCM), the AVL Adaptation Model (AVLAM) and the Network Traffic Condition Model (NTCM). The first provides road traffic conditions as a function of mass-transit traffic conditions in the case of shared lanes, the second provides mass-transit traffic conditions as a function of AVL data, and the last provides road traffic conditions over the whole road network as a function of mass-transit traffic conditions. The IEM (and its sub-models) were developed and calibrated in the case of real dimension networks and some tests were performed on a trial network. Numerical results show the effectiveness of the proposed method since it allows a reduction in travel demand estimation errors.
Estimation of urban traffic conditions using an Automatic Vehicle Location (AVL) System
CARTENI', ARMANDO;
2009
Abstract
The aim of this paper is to develop an Information Extension Model (IEM) which uses location data of bus fleets (AVL data) to estimate road traffic conditions and provide input for implementing control strategies. The IEM consists of three sub-models: the Link Traffic Condition Model (LTCM), the AVL Adaptation Model (AVLAM) and the Network Traffic Condition Model (NTCM). The first provides road traffic conditions as a function of mass-transit traffic conditions in the case of shared lanes, the second provides mass-transit traffic conditions as a function of AVL data, and the last provides road traffic conditions over the whole road network as a function of mass-transit traffic conditions. The IEM (and its sub-models) were developed and calibrated in the case of real dimension networks and some tests were performed on a trial network. Numerical results show the effectiveness of the proposed method since it allows a reduction in travel demand estimation errors.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
