Lane-Free Traffic: History and State of the Art

Sekeran, Maya; Rostami-Shahrbabaki, Majid; Syed, Arslan Ali; Margreiter, Martin; Bogenberger, Klaus

doi:10.1109/itsc55140.2022.9922282

Cited by 11 publications

(5 citation statements)

References 46 publications

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“…In Sekeran et al ( 23 ), an overview of the history of lane-free traffic is provided. Malekzadeh et al ( 24 , 25 ) extended the TrafficFluid concept by mathematical models for real-time internal boundary control by which the total road width is shared among the two directions in dependence of the bi-directional demand.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Modeling Vehicle Flocking in Lane-Free Automated Traffic

Rostami-Shahrbabaki

Weikl

Niels

et al. 2023

Transportation Research Record: Journal of the Transportation R

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In automated lane-free traffic, vehicles can choose any arbitrary lateral location. This enables vehicle flocking where, compared to platooning, the grouping of vehicles is possible with smaller space gaps, not only longitudinally but also laterally. Vehicle flocking can fulfill several purposes, such as increasing the road capacity, saving energy by reducing the aerodynamic drag force, and dampening shockwaves. Within this paper, we develop a control framework for modeling vehicle flocks in automated lane-free traffic. The proposed control algorithm considers two types of agents: [Formula: see text] -agents representing potential flock mates and a [Formula: see text] -agent representing the virtual leader with collective objectives (e.g., slowing down in the case of traffic congestion ahead). Our algorithm is based on energy functions for flock centering and collision avoidance, a consensus algorithm for velocity matching, and navigational feedback exerted by the virtual leader. The virtual leader’s path, which should be followed by the flock, is defined in an upper-level controller. In addition, a feedback algorithm for dynamic road boundary control is implemented. We simulate the proposed approach with very promising results. We show that vehicle flocks are efficiently formed within a few seconds, speeds are successfully aligned, and vehicle arrangements stay stable under different scenarios. In addition, the lateral and longitudinal flock extension changes with different energy functions and changing road boundaries, and vehicle flocks follow the trajectory of the virtual leader. Most importantly, vehicle flocks stay stable in the case of perturbations and the induced shock is dampened efficiently because of slight changes in the vehicles’ lateral locations.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Modeling Vehicle Flocking in Lane-Free Automated Traffic

Rostami-Shahrbabaki

Weikl

Niels

et al. 2023

Transportation Research Record: Journal of the Transportation R

Self Cite

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“…Several research has been conducted on the concept of lane-free traffic. In [18], an exploration of current developments in LFT research and required technologies is provided. The paper offers an overview of existing methods for implementing LFT and delves into the necessary policy considerations for the seamless integration of LFT into the current traffic infrastructure.…”

Section: Related Workmentioning

confidence: 99%

Driving Strategy for Vehicles in Lane-Free Traffic Environment Based on Deep Deterministic Policy Gradient and Artificial Forces

Berahman

Rostmai-Shahrbabaki²,

Bogenberger³

2022

IFAC-PapersOnLine

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“…In SUMO, a driving or bicycle lane is divided into multiple sub-lanes. Within each sub-lane, the simulated road users move along the one-dimensional lane, their movements controlled by dynamics models and their interactions governed by car-following models (Sekeran et al, 2022). The lateral movement between sublanes is controlled using the same or similar models as those used for lane selection and lane change for motor vehicle traffic.…”

Section: Introductionmentioning

confidence: 99%

A movement and interaction model for cyclists and other non-lane-based road users

Kaths

2023

Front. Future Transp.

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Cyclists and other types of road users who do not adhere to lane discipline pose a challenge in microscopic traffic simulation. In most software, the models are adapted to increase the lateral flexibility of road users, either through introducing sub-lanes (SUMO) or introducing a continuous lateral axis (PTV Vissim). These solutions enable the simulation of some behaviors, such as passing within the same driving lane. However, other behaviors exhibited by these flexible road users, including switching between cycling infrastructure, the roadways, and the sidewalk, riding against the given direction of travel, and selecting unexpected pathways to cross at intersections, remain difficult to simulate. This paper presents a modeling approach for cyclists, users of micro-mobility modes, and other non-lane-based road users. This method uses the concept of guidelines, or desire lines, that represent the intended path of non-lane-based road users. Guidelines are the same in form as the center line of road (sub-)lanes. Instead of following these lines precisely, the guideline is used to determine the desired direction for the road user in the next time step, which is used as input into an adapted social force type model. The movement and interaction model is formulated based on the NOMAD model for pedestrian dynamics. The single acceleration vector is divided into a speed component and a direction component that are calibrated and validated using trajectory data from cyclists at four signalized intersections in Munich, Germany. Maximum Likelihood Estimation (MLE) is used to estimate the model parameters and k-fold cross-validation is used to evaluate the modeling approach. The results are discussed and an outlook for future research is presented.

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Lane-Free Traffic: History and State of the Art

Cited by 11 publications

References 46 publications

Modeling Vehicle Flocking in Lane-Free Automated Traffic

Modeling Vehicle Flocking in Lane-Free Automated Traffic

Driving Strategy for Vehicles in Lane-Free Traffic Environment Based on Deep Deterministic Policy Gradient and Artificial Forces

A movement and interaction model for cyclists and other non-lane-based road users

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