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1. Planning Bike Lanes based on Sharing-Bikes' Trajectories

   https://doi.org/10.1145/3097983.3098056  

   Bao, Jie; He, Tianfu; Ruan, Sijie; Li, Yanhua; Zheng, Yu (January 2017, the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining)

   Cycling as a green transportation mode has been promoted by many governments all over the world. As a result, constructing effective bike lanes has become a crucial task for governments promoting the cycling life style, as well-planned bike paths can reduce traffic congestion and decrease safety risks for both cyclists and motor vehicle drivers. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider key realistic government constraints: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization. In this paper, we propose a data-driven approach to develop bike lane construction plans based on large-scale real world bike trajectory data. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of the number of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. Finally, we deploy our system on Microsoft Azure, providing extensive experiments and case studies to demonstrate the effectiveness of our approach. 

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2. The Traffic Calming Effect of Delineated Bicycle Lanes

   https://doi.org/10.1016/j.urbmob.2024.100071  

   Younes, Hannah; Andrews, Clinton; Noland, Robert B; Xia, Jiahao; Wen, Song; Zhang, Wenwen; Metaxas, Dimitri; Von_Hagen, Leigh Ann; Gong, Jie (June 2024, Journal of Urban Mobility)

   We analyze the effect of a bicycle lane on traffic speeds. Computer vision techniques are used to detect and classify the speed and trajectory of over 9,000 motor-vehicles at an intersection that was part of a pilot demonstration in which a bicycle lane was temporarily implemented. After controlling for direction, hourly traffic flow, and the behavior of the vehicle (i.e., free-flowing or stopped at a red light), we found that the effect of the delineator-protected bicycle lane (marked with traffic cones and plastic delineators) was associated with a 28 % reduction in average maximum speeds and a 21 % decrease in average speeds for vehicles turning right. For those going straight, a smaller reduction of up to 8 % was observed. Traffic moving perpendicular to the bicycle lane experienced no decrease in speeds. Painted-only bike lanes were also associated with a small speed reduction of 11–15 %, but solely for vehicles turning right. These findings suggest an important secondary benefit of bicycle lanes: by having a traffic calming effect, delineated bicycle lanes may decrease the risk and severity of crashes for pedestrians and other road users. 

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3. A Novel Learning Framework for Sampling-Based Motion Planning in Autonomous Driving

   https://doi.org/10.1609/aaai.v34i01.5473  

   Zhang, Yifan; Zhang, Jinghuai; Zhang, Jindi; Wang, Jianping; Lu, Kejie; Hong, Jeff (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   Sampling-based motion planning (SBMP) is a major trajectory planning approach in autonomous driving given its high efficiency in practice. As the core of SBMP schemes, sampling strategy holds the key to whether a smooth and collision-free trajectory can be found in real-time. Although some bias sampling strategies have been explored in the literature to accelerate SBMP, the trajectory generated under existing bias sampling strategies may lead to sharp lane changing. To address this issue, we propose a new learning framework for SBMP. Specifically, we develop a novel automatic labeling scheme and a 2-Stage prediction model to improve the accuracy in predicting the intention of surrounding vehicles. We then develop an imitation learning scheme to generate sample points based on the experience of human drivers. Using the prediction results, we design a new bias sampling strategy to accelerate the SBMP algorithm by strategically selecting necessary sample points that can generate a smooth and collision-free trajectory and avoid sharp lane changing. Data-driven experiments show that the proposed sampling strategy outperforms existing sampling strategies, in terms of the computing time, traveling time, and smoothness of the trajectory. The results also show that our scheme is even better than human drivers. 

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4. Full Vehicle Trajectory Planning Model for Urban Traffic Control Based on Imitation Learning

   https://doi.org/10.1177/03611981221077263  

   Ying, Jun; Feng, Yiheng (February 2022, Transportation Research Record: Journal of the Transportation Research Board)

   Connected and automated vehicles (CAVs) extend urban traffic control from temporal to spatiotemporal by enabling the control of CAV trajectories. Most of the existing studies on CAV trajectory planning only consider longitudinal behaviors (i.e., in-lane driving), or assume that the lane changing can be done instantaneously. The resultant CAV trajectories are not realistic and cannot be executed at the vehicle level. The aim of this paper is to propose a full trajectory planning model that considers both in-lane driving and lane changing maneuvers. The trajectory generation problem is modeled as an optimization problem and the cost function considers multiple driving features including safety, efficiency, and comfort. Ten features are selected in the cost function to capture both in-lane driving and lane changing behaviors. One major challenge in generating a trajectory that reflects certain driving policies is to balance the weights of different features in the cost function. To address this challenge, it is proposed to optimize the weights of the cost function by imitation learning. Maximum entropy inverse reinforcement learning is applied to obtain the optimal weight for each feature and then CAV trajectories are generated with the learned weights. Experiments using the Next Generation Simulation (NGSIM) dataset show that the generated trajectory is very close to the original trajectory with regard to the Euclidean distance displacement, with a mean average error of less than 1 m. Meanwhile, the generated trajectories can maintain safety gaps with surrounding vehicles and have comparable fuel consumption. 

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5. Longitudinal-Scanline-Based Arterial Traffic Video Analytics with Coordinate Transformation Assisted by 3D Infrastructure Data

   https://doi.org/10.1177/0361198120971257  

   Zhang, Terry Tianya; Guo, Mengyang; Jin, Peter J.; Ge, Yi; Gong, Jie (March 2021, Transportation Research Record: Journal of the Transportation Research Board)

   null (Ed.)

   High-resolution vehicle trajectory data can be used to generate a wide range of performance measures and facilitate many smart mobility applications for traffic operations and management. In this paper, a Longitudinal Scanline LiDAR-Camera model is explored for trajectory extraction at urban arterial intersections. The proposed model can efficiently detect vehicle trajectories under the complex, noisy conditions (e.g., hanging cables, lane markings, crossing traffic) typical of an arterial intersection environment. Traces within video footage are then converted into trajectories in world coordinates by matching a video image with a 3D LiDAR (Light Detection and Ranging) model through key infrastructure points. Using 3D LiDAR data will significantly improve the camera calibration process for real-world trajectory extraction. The pan-tilt-zoom effects of the traffic camera can be handled automatically by a proposed motion estimation algorithm. The results demonstrate the potential of integrating longitudinal-scanline-based vehicle trajectory detection and the 3D LiDAR point cloud to provide lane-by-lane high-resolution trajectory data. The resulting system has the potential to become a low-cost but reliable measure for future smart mobility systems. 

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