Stochastic approach for modeling the effects of intersection geometry on turning vehicle paths

Alhajyaseen, Wael; Asano, Miho; Nakamura, Hideki; Tan, Dang Minh

doi:10.1016/j.trc.2012.09.006

Cited by 60 publications

(23 citation statements)

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“…In general, significant characteristics concerning the intersection layout and the turning vehicle were highlighted [26][27][28][29]. As an example, Alhajyaseen et al [30] underlined that the path of the turning vehicle is strongly related to the intersection's angle, the vehicle's type, and speed. However, it is agreed that the turning maneuver of vehicles is a further complex phenomenon whose variability extends to be related to highly dynamic factors [31,32].…”

Section: Modeling Of Vehicle Turning Trajectorymentioning

confidence: 99%

“…Accordingly, the longitudinal and lateral movements are simultaneously simulated, and therefore, the characteristics of the turning paths are reliably reflected [42]. However, these approaches produce the path of the turning vehicles only without the speed and acceleration profiles [30], which are usually estimated using other independent models. In this context, a microscopic simulation model that generates vehicles' turning trajectories was developed by Tan et al [43] using different models for path (Euler spiral-based approximation method) and speed profiles.…”

Section: Modeling Of Vehicle Turning Trajectorymentioning

confidence: 99%

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Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks

2020

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This paper aims at developing a convolutional neural network (CNN)-based tool that can automatically detect the left-turning vehicles (right-hand traffic rule) at signalized intersections and extract their trajectories from a recorded video. The proposed tool uses a region-based CNN trained over a limited number of video frames to detect moving vehicles. Kalman filters are then used to track the detected vehicles and extract their trajectories. The proposed tool achieved an acceptable accuracy level when verified against the manually extracted trajectories, with an average error of 16.5 cm. Furthermore, the trajectories extracted using the proposed vehicle tracking method were used to demonstrate the applicability of the minimum-jerk principle to reproduce variations in the vehicles’ paths. The effort presented in this paper can be regarded as a way forward toward maximizing the potential use of deep learning in traffic safety applications.

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Section: Modeling Of Vehicle Turning Trajectorymentioning

confidence: 99%

Section: Modeling Of Vehicle Turning Trajectorymentioning

confidence: 99%

Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks

2020

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“…A related body of literature that has taken shape in recent years addresses the AV motion-planning problem (i.e., programming an AV's trajectory as it performs driving tasks); compare [32] for a detailed review of this literature, with Alhajyaseen et al [33], Gu et al [18], and Wolfermann et al [34] having studied the trajectories of turning vehicles at intersections. Studies in this category have focused on the task of controlling the trajectory of a single AV and do not consider the capacity impacts arising from the interactions among multiple AVs' driving behaviors (e.g., consequences on saturation flow rates).…”

Section: Introductionmentioning

confidence: 99%

Protected Turning Movements of Noncooperative Automated Vehicles: Geometrics, Trajectories, and Saturation Flow

Liu

Lai

Kong

et al. 2018

Journal of Advanced Transportation

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This study is the first to quantify throughput (saturation flow) of noncooperative automated vehicles when performing turning maneuvers, which are critical bottlenecks in arterial road networks. We first develop a constrained optimization problem based on AVs' kinematic behavior during a protected signal phase which considers both ABS-enabled and wheels-locked braking, as well as avoiding encroaching into oncoming traffic or past the edge-of-receiving-lane. We analyze noncooperative ("defensive") behavior, in keeping with the Assured Clear Distance Ahead legal standard to which human drivers are held and AVs will likely also be for the foreseeable future. We demonstrate that, under plausible behavioral parameters, AVs appear likely to have positive impacts on throughput of turning traffic streams at intersections, in the range of +0.2% (under the most conservative circumstances) to +43% for a typical turning maneuver. We demonstrate that the primary mechanism of impact of turning radius is its effect on speed, which is likely to be constrained by passenger comfort. We show heterogeneous per-lane throughput in the case of "double turn lanes." Finally, we demonstrate limited sensitivity to crash-risk criterion, with a 4% difference arising from a change from 1 in 10,000 to 1 in 100,000,000. The paper concludes with a brief discussion of policy implications and future research needs.

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“…Present studies have demonstrated three most significant characteristics of turning flow. First and foremost, trajectories of turning traffic are of great variation (Alhajyaseen et al., ). Furthermore, turning traffic flow shares the priority when interacting with other flows (Troutbeck and Kako, ).…”

Section: Introductionmentioning

confidence: 99%

Two‐Dimensional Simulation of Turning Behavior in Potential Conflict Area of Mixed‐Flow Intersections

Xie

Xiao

et al. 2017

Computer aided Civil Eng

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The potential conflict area of intersection is the space where conflicting traffic flows pass through in the same signal phase. At this area, turning vehicles interact with most traffic flows, which introduce complex features including variation of trajectories and sharedpriority phenomenon. The traditional one-dimensional simulation oversimplifies these features with lane-based assumption. This study integrates the modified social force model with behavior decision and movement constraints to reproduce the two-dimensional turning process. The method is framed into a three-layered mathematical model. First, the decision layer dynamically makes decision for turning patterns. Then the operation layer uses the modified social force model to initially generate vehicle movements. Finally, the constraint layer modifies the vehicular motion with vehicle dynamics constraints, boundary of intersection and the collision avoidance rule. The proposed model is validated using trajectories of left-turn vehicles at a real-world mixed-flow intersection with nonprotected signal phases, resulting in a more realistic simulation than previous methods. The distributions of decision points and travel time in simulation are compared with the empirical data in statistics. Moreover, the spatial distribution of simulated trajectories is also satisfactory. C 2017 Computer-Aided Civil and Infrastructure Engineering.

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Stochastic approach for modeling the effects of intersection geometry on turning vehicle paths

Cited by 60 publications

References 0 publications

Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks

Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks

Protected Turning Movements of Noncooperative Automated Vehicles: Geometrics, Trajectories, and Saturation Flow

Two‐Dimensional Simulation of Turning Behavior in Potential Conflict Area of Mixed‐Flow Intersections

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