Data-driven estimation of vehicles approaching an intersection in a partially connected environment

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu


In spite of several envisioned advantages of Connected Vehicles (CV) for urban trafficcontrol, considerable investment and time are needed before all vehicles are equipped with connection technology. Recently, some methods have been proposed to compensate for thelack of data in the case of low penetration rate of CVs, aiming, for example, at estimating thequeue length at a signalized intersection by combination of available data from CVs and fixedsensors. However, queue length estimation methods might be useful in intersection controlbut they are not practical for under-saturated traffic condition. Moreover, the queue length innext signal cycles cannot be predicted using those methods. In this work, we propose machine-
learning-based models for estimating the number and characteristic of vehicles in the vicinityof intersections by utilising only information from CVs in a partially connected environment.
In contrast to previously proposed methods, our method is based on the idea of estimating thenumber of non-connected vehicles between each pair of CVs, for the cases of moving vehicles,stopping vehicles, and vehicles queuing. The models are built employing high-resolution dataproduced by a traffic simulation software. For evaluation, we test the models for different CV penetration rates by implementing statistical tests on prediction results. The early findings show the developed models can estimate the number of unequipped vehicles with acceptable
Otsikko3rd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2020): Booklet of abstracts
TilaHyväksytty/In press - 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaSymposium on Management of Future Motorway and Urban Traffic Systems - Luxembourg, Luxemburg
Kesto: 6 heinäk. 20208 heinäk. 2020
Konferenssinumero: 3


ConferenceSymposium on Management of Future Motorway and Urban Traffic Systems


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