Xu L.-X.,Beihang University |
Xu L.-X.,Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security |
Wang Y.-P.,Beihang University |
Wang Y.-P.,Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security |
And 2 more authors.
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | Year: 2016
To simplify the way of expressing road network state and maximize the value of network information, this paper constructs a model of extracting feature parameter from massive historical traffic data to express road network running state. In this model, the flow, speed and density data of road network in urban areas are selected, considering the nonlinearity and correlation of traffic data, the feature of urban road network data is extracted based on adaptive neighborhood selection of local sensitive discriminant analysis algorithm (ANS-LSDA). Examples demonstrate the effectiveness of the model, results show that feature parameter obtained in this paper can effectively describe the road network 24 h periodicity, directly reflect the phenomenon of morning and evening peak as well as the difference between weekday and weekend. Compared to kernel principal component analysis (KPCA), the feature parameter of ANS-LSDA model has better divisibility, which can express macro road network running state and provide basis for traffic managers in decision making. Copyright © 2016 by Science Press. Source
Wang Y.,Beihang University |
Wang Y.,Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security |
Duan X.,Beihang University |
Duan X.,Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security |
And 8 more authors.
International Journal of Distributed Sensor Networks | Year: 2014
In vehicular ad hoc networks (VANETs) safety applications, vehicular position is fundamental information to achieve collision avoidance and fleet management. Now, position information is comprehensively provided by global positioning system (GPS). However, in the dense urban, due to multipath effect and signal occlusion, GPS-based positioning method potentially fails to provide accurate position information. For this reason, an assistant approach has been presented in this paper by using three-dimensional radio frequency, such as time of arrival (TOA) and direction of arrival (DOA). With the goal of providing an efficient and reliable estimation of vehicular position in general traffic scenarios, we propose a hybrid TOA/DOA positioning method based on Bayesian compressive sensing (BCS), which benefits from the realization of vehicle-to-roadside wireless interaction with the dedicated short range communication. The effectiveness of the proposed approach is proved through extensive experiments in several scenarios where different signal configurations and the noise conditions are taken into account. Moreover, some comparative experiments are also performed to confirm the strength of our proposed approach. © 2014 Yunpeng Wang et al. Source