TY  - JOUR
PY  - 2022//
TI  - An analytical model for quantifying the efficiency of traffic-data collection using instrumented vehicles
JO  - Transportation research part C: emerging technologies
A1  - Cao, Peng
A1  - Xiong, Zhiqiang
A1  - Liu, Xiaobo
SP  - e103558
EP  - e103558
VL  - 136
IS  - 
N2  - Emerging instrumented vehicles (IVs), when equipped with high-precision positioning devices (e.g., DGPS) and ranging sensors (e.g., radar, LIDAR, cameras), are capable of generating high-quality traffic data. This study evaluates the efficiency of such data-collection procedures; this remains largely unknown because variable penetration rates of IVs rarely arise in the real world. We propose an analytical model that establishes a quantitative relationship between the ratio of collected trajectory points to total traffic trajectory points (RCT) and the IV penetration rate, according to stochastic geometry theory. With this, the data-collection efficiency (DCE) of IVs can be effectively evaluated. A simulation approach is developed to generate IV data and thereby validate the proposed analytical model, using a comprehensive set of traffic scenarios; these data consist of eight micro-trajectory datasets from the next-generation simulation (NGSIM) program and four typical sensor IV deployments. The numerical analysis demonstrates that the model perfectly reflects the simulated IV data for all traffic scenarios. In addition, an analytical comparison of the DCEs for fixed sensors, probe vehicles, and IVs reveals that IVs are the most efficient method for collecting traffic data in road networks. This study proposes a theory to predict the percentage of trajectory points that can be collected by a certain percentage of IVs within the entire traffic flow.<p />  <p>Language: en</p>
LA  - en
SN  - 0968-090X
UR  - http://dx.doi.org/10.1016/j.trc.2022.103558
ID  - ref1
ER  -