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Bayesian Network based speed estimation in case of C2X data at very low equipment rates

Junghans, Marek and Leich, Andreas and Härri, Jerome (2016) Bayesian Network based speed estimation in case of C2X data at very low equipment rates. In: 9th Triennal Symposium on Transportation Analysis. 9th Triennial Symposium on Transportation Analysis, 13.-17. Juni 2016, Oranjestad, Aruba.

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The estimation of the traffic state, particularly speed and vehicle counts, is important for modern traffic light control (TLC) algorithms. Recent developments in the field of so called self-organizing TLC focus on using cooperative traffic data transmitted via Car-to-Infrastructure (C2I) communication. In this context, vehicles periodically exchange their GPS position and speed via so called Cooperative Awareness Messages (CAM) in the EU (a.k.a. Basic Safety Messages (BMS) in the US) transmitted on a vehicular-specific Extension to the WiFi ad-hoc mode IEEE802.11p. Equipped with such technology, TLC may intercept CAM/BSM and use the contained data to estimate its local traffic state. This technology is yet emerging and car manufacturers are not expecting a sufficient penetration at the time future self-organizing TLC systems will be deployed. In this paper, a novel approach proposes to complement missing C2I technology with Bluetooth technology (BT), which is already widely available in all modern vehicles as well as smartphones. BT data has notably been tested for traffic data acquisition for years, but neither provide the same type nor the same quality of C2I cooperative data. Accordingly, we propose to rely on a stochastic data fusion engine based on Bayesian Networks (BN) to enrich and integrate traffic state data from a low C2I penetration rate (less than 1%) and moderate BT Penetration rate (30%). One of the challenges addressed is to be able to extrapolate mobility from detection processes, and then extract the estimated BT speed likelihood. Using BN, we will propose to link such likelihood to different a-priori knowledge of mobility states. Another challenge will be to integrate the BT and C2I speed likelihood, considering both processes have different properties. We show the robustness of our approach assuming a BN being able to reach already 2 m/s speed RMSE and complete the traffic state estimation by 35% by fusing 1% C2I with 30% BT.

Item URL in elib:https://elib.dlr.de/102972/
Document Type:Conference or Workshop Item (Speech)
Title:Bayesian Network based speed estimation in case of C2X data at very low equipment rates
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Junghans, Marekmarek.junghans (at) dlr.deUNSPECIFIED
Leich, Andreasandreas.leich (at) dlr.deUNSPECIFIED
Härri, Jeromejerome.haerri (at) eurecom.frUNSPECIFIED
Date:13 June 2016
Journal or Publication Title:9th Triennal Symposium on Transportation Analysis
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Keywords:C2X, Bayesian Networks, data Fusion, traffic state estimation
Event Title:9th Triennial Symposium on Transportation Analysis
Event Location:Oranjestad, Aruba
Event Type:international Conference
Event Dates:13.-17. Juni 2016
Organizer:Technische Universität Eindhoven, TU Delft, Erasmus University
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - I.MoVe (old)
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Transportation Systems
Deposited By: Junghans, Marek
Deposited On:21 Jun 2016 12:28
Last Modified:20 Jun 2021 15:47

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