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A Hybrid Submicroscopic-Microscopic Traffic Flow Simulation Framework. / Mullakkal-Babu, Freddy; Wang, Meng; van Arem, Bart; Shyrokau, Barys; Happee, Riender.

In: IEEE Transactions on Intelligent Transportation Systems, 06.05.2020.

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@article{92c4f25e137c4e4a9407708e0a588c55,
title = "A Hybrid Submicroscopic-Microscopic Traffic Flow Simulation Framework",
abstract = "Current lane-based microscopic traffic simulators combine car-following and lane changing logic to describe the (often discrete) lateral vehicle motion on multi-lane road segments. However, the simulated lateral trajectories are physically unplausible and inside-lane behavior such as lane-keeping and curve negotiation cannot be modelled. In this work, we integrate lateral vehicle dynamics and yaw motion into a traffic simulation framework, aiming to describe lateral motion and vehicle interactions with more precision. The resulting framework consists of two coupled layers, an upper tactical level that plans maneuvers such as lane-changing; and a lower operational layer with a control module (steering and acceleration control) that operates in a closed loop with the bicycle model of vehicle dynamics. The feedback mechanism between the layers allows for dynamic trajectory re-planning. Unlike the microscopic traffic models, the proposed framework accounts for lateral vehicle dynamics and yaw motion; provides additional variables such as vehicle heading and front wheel steering angle; and is hence termed as submicroscopic. Case study results demonstrate the power of the framework to include lateral maneuvers such as curve negotiation, corrective steering, lane change abortion and fragmented lane changing. The framework was operationalized to model multi-lane traffic flow consisting of human-driven vehicles. At the macroscopic level, the traffic flow simulation can reproduce phenomena such as capacity drop. Thus the framework preserves the properties of the component models and at the same time describe the continuous 2-D planar movement of vehicles.",
keywords = "Traffic model, submicroscopic, microscopic, hybrid, multilane",
author = "Freddy Mullakkal-Babu and Meng Wang and {van Arem}, Bart and Barys Shyrokau and Riender Happee",
note = "Accepted Author Manuscript",
year = "2020",
month = may,
day = "6",
doi = "10.1109/TITS.2020.2990376",
language = "English",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "IEEE",

}

RIS

TY - JOUR

T1 - A Hybrid Submicroscopic-Microscopic Traffic Flow Simulation Framework

AU - Mullakkal-Babu, Freddy

AU - Wang, Meng

AU - van Arem, Bart

AU - Shyrokau, Barys

AU - Happee, Riender

N1 - Accepted Author Manuscript

PY - 2020/5/6

Y1 - 2020/5/6

N2 - Current lane-based microscopic traffic simulators combine car-following and lane changing logic to describe the (often discrete) lateral vehicle motion on multi-lane road segments. However, the simulated lateral trajectories are physically unplausible and inside-lane behavior such as lane-keeping and curve negotiation cannot be modelled. In this work, we integrate lateral vehicle dynamics and yaw motion into a traffic simulation framework, aiming to describe lateral motion and vehicle interactions with more precision. The resulting framework consists of two coupled layers, an upper tactical level that plans maneuvers such as lane-changing; and a lower operational layer with a control module (steering and acceleration control) that operates in a closed loop with the bicycle model of vehicle dynamics. The feedback mechanism between the layers allows for dynamic trajectory re-planning. Unlike the microscopic traffic models, the proposed framework accounts for lateral vehicle dynamics and yaw motion; provides additional variables such as vehicle heading and front wheel steering angle; and is hence termed as submicroscopic. Case study results demonstrate the power of the framework to include lateral maneuvers such as curve negotiation, corrective steering, lane change abortion and fragmented lane changing. The framework was operationalized to model multi-lane traffic flow consisting of human-driven vehicles. At the macroscopic level, the traffic flow simulation can reproduce phenomena such as capacity drop. Thus the framework preserves the properties of the component models and at the same time describe the continuous 2-D planar movement of vehicles.

AB - Current lane-based microscopic traffic simulators combine car-following and lane changing logic to describe the (often discrete) lateral vehicle motion on multi-lane road segments. However, the simulated lateral trajectories are physically unplausible and inside-lane behavior such as lane-keeping and curve negotiation cannot be modelled. In this work, we integrate lateral vehicle dynamics and yaw motion into a traffic simulation framework, aiming to describe lateral motion and vehicle interactions with more precision. The resulting framework consists of two coupled layers, an upper tactical level that plans maneuvers such as lane-changing; and a lower operational layer with a control module (steering and acceleration control) that operates in a closed loop with the bicycle model of vehicle dynamics. The feedback mechanism between the layers allows for dynamic trajectory re-planning. Unlike the microscopic traffic models, the proposed framework accounts for lateral vehicle dynamics and yaw motion; provides additional variables such as vehicle heading and front wheel steering angle; and is hence termed as submicroscopic. Case study results demonstrate the power of the framework to include lateral maneuvers such as curve negotiation, corrective steering, lane change abortion and fragmented lane changing. The framework was operationalized to model multi-lane traffic flow consisting of human-driven vehicles. At the macroscopic level, the traffic flow simulation can reproduce phenomena such as capacity drop. Thus the framework preserves the properties of the component models and at the same time describe the continuous 2-D planar movement of vehicles.

KW - Traffic model

KW - submicroscopic

KW - microscopic

KW - hybrid

KW - multilane

U2 - 10.1109/TITS.2020.2990376

DO - 10.1109/TITS.2020.2990376

M3 - Article

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

ER -

ID: 72825281