This study proposes a method that estimates traffic states using measurements from a vehicle running on the opposite lane in addition to probe vehicle data and examine the sensitivity of the estimates in relation to variabilities of the input data and measurements. A number of studies on traffic state estimation fusing several sensing data have been reported. Most of the studies use data from traffic detectors installed at fixed locations and data from moving objects such as probe vehicles. Traffic detectors provide valuable volume information of all running vehicles which cannot be observed from sample moving objects. However, in local areas in Japan as well as in Asian cities, detector installations are very much limited like one in every 10 to 15 km on a motorway in our country. This study therefore attempts to utilize measurements from a vehicle running on the opposite lane instead of detector measurements, since a vehicle on the opposite lane running backward can in principle measure counts of passing vehicles running forward. In this study, we employ the variational theory to estimate the traffic states utilizing the count measurement from the opposite lane in addition to probe vehicle data on the forward direction and examine the sensitivity of the estimates in relation to variabilities of the input data and measurements. The validation finds that the proposed method can estimate traffic states more accurately than one using only probe vehicle data. Especially, this method has the advantage in quickly responding unexpected incidences such as accidents and vehicle malfunctions.
|ジャーナル||Transportation Research Part C: Emerging Technologies|
|出版ステータス||Published - 2019 7|
ASJC Scopus subject areas
- コンピュータ サイエンスの応用