Dynamic Vehicle OD Flow Estimation for Urban Road Network Using Multi-Source Heterogeneous Data

Song, Shunyao; Hong, Rongrong; Zhang, Weihua; Zhou, De Qun

doi:10.1061/9780784483169.014

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One example of this type of sensor is the Automatic Number Plate Recognition (ANPR) system, which is becoming very useful for conducting real-time traffic and mobility studies on larger-scale urban and inter-urban road networks at a moderate cost (see Álvarez-Bazo et al [40]). The usefulness of the data collected by these sensors has already been tested in studies aimed at the dynamic estimation of O-D matrices and adjustment of dynamic models (Dixon and Rilett [41]; Vaze et al [42]; Robinson and Venter [43]; Liu et al [44]), as well as estimating the evolution of traffic over time based on data collected by different sources (Hadavi et al [45]; Song et al [46]).…”

Section: Dynamic O-d Matrix Estimation From Field Datamentioning

confidence: 99%

Dynamic Route Flow Estimation in Road Networks Using Data from Automatic Number of Plate Recognition Sensors

et al. 2021

View full text Add to dashboard Cite

The traffic flow on road networks is dynamic in nature. Hence, a model for dynamic traffic flow estimation should be a very useful tool for administrations to make decisions aimed at better management of traffic. In fact, these decisions may in turn improve people’s quality of life and help to implement good sustainable policies to reduce the external transportation costs (congestion, accidents, travel time, etc.). Therefore, this paper deals with the problem of estimating dynamic traffic flows in road networks by proposing a model which is continuous in the time variable and that assumes the first-in-first-out (FIFO) hypothesis. In addition, the data used as model inputs come from Automatic Number of Plate Recognition (ANPR) sensors. This powerful data permits not only to directly reconstruct the route followed by each registered vehicle but also to evaluate its travel time, which in turn is also used for the flow estimation. In addition, the fundamental variable of the model is the route flow, which is a great advantage since the rest of the flows can be obtained using the conservation laws. A synthetic network is used to illustrate the proposed method, and then it is applied to the well-known Nguyen-Dupuis and Eastern Massachusetts networks to prove its usefulness and feasibility. The results on all the tested networks are very positive and the estimated flows reproduce the simulated real flows fairly well.

show abstract