01698233

Component

Vehicle detectors or traffic sensors play an indispensable role in modern traffic management systems. However, traffic management agencies are not able to comprehensively deploy a large number of sensors in practice due to annual budget constraint. It drives the need to address the problem of optimal number of deployed sensors and their installation locations under a budget constraint in a given network. This problem and its variants is called the network sensor location problem (NSLP). With the advent of internet of things (IoT), application of some advanced sensors for traffic data collection becomes feasible. For the NSLP, this study introduces a node-based smart virtual sensor which is able to detect vehicle trajectories in a given network via two-way communications. By using the collected traffic flow information by the smart virtual sensor, this study seeks to identify the smallest subset of nodes for the NSLP by using the vertex cover method. Further, two nonlinear least squares (NLS) models are developed for the estimation of path flows and origin and destination (O-D) demands in a given network. The numerical analysis results based on different network complexities indicate that the node-based vertex cover method can effectively identify the smallest subset of sensor-equipped nodes for the NSLP. In addition, given the link flows and partial path trajectory information collected by the smart virtual sensors installed at some strategic nodes/locations, the developed NLS models are able to obtain accurate path flow and O-D demand estimates.

This paper was sponsored by TRB committee ADB30 Standing Committee on Transportation Network Modeling.

19-01612

English

15p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; Photos; References; Tables

Data collection; Graph theory; Highway traffic control; Intelligent agents; Location; Network nodes; Optimization; Origin and destination; Sensors; Traffic data; Trajectory; Vehicle detectors; Virtual reality

Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting

Transportation Research Board Annual Meeting 2019 Paper #19-01612

TRIS, TRB, ATRI

Dec 7 2018 9:49AM