More Like this

1. Interactive Bike Lane Planning using Sharing Bikes' Trajectories

   https://doi.org/10.1109/TKDE.2019.2907091  

   He, Tianfu ; Bao, Jie ; Ruan, Sijie ; Li, Ruiyuan ; Li, Yanhua ; He, Hui ; Zheng, Yu ( March 2019 , IEEE Transactions on Knowledge and Data Engineering)

   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 to promote the cycling life style, as well-planned bike lanes can reduce traffic congestions and safety risks. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider one or more 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 the large-scale real world bike trajectory data collected from Mobike, a station-less bike sharing system. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. To improve the efficiency of the bike lane planning system for the urban planner, we propose a novel trajectory indexing structure and deploy the system based on a parallel computing framework (Storm) to improve the system’s efficiency. Finally, extensive experiments and case studies are provided to demonstrate the system efficiency and effectiveness. 

   more » « less
2. Robust Tensor Recovery with Fiber Outliers for Traffic Events

   https://doi.org/10.1145/3417337  

   Hu, Yue ; Work, Daniel B. ( January 2021 , ACM Transactions on Knowledge Discovery from Data)

   Event detection is gaining increasing attention in smart cities research. Large-scale mobility data serves as an important tool to uncover the dynamics of urban transportation systems, and more often than not the dataset is incomplete. In this article, we develop a method to detect extreme events in large traffic datasets, and to impute missing data during regular conditions. Specifically, we propose a robust tensor recovery problem to recover low-rank tensors under fiber-sparse corruptions with partial observations, and use it to identify events, and impute missing data under typical conditions. Our approach is scalable to large urban areas, taking full advantage of the spatio-temporal correlations in traffic patterns. We develop an efficient algorithm to solve the tensor recovery problem based on the alternating direction method of multipliers (ADMM) framework. Compared with existing l 1 norm regularized tensor decomposition methods, our algorithm can exactly recover the values of uncorrupted fibers of a low-rank tensor and find the positions of corrupted fibers under mild conditions. Numerical experiments illustrate that our algorithm can achieve exact recovery and outlier detection even with missing data rates as high as 40% under 5% gross corruption, depending on the tensor size and the Tucker rank of the low rank tensor. Finally, we apply our method on a real traffic dataset corresponding to downtown Nashville, TN and successfully detect the events like severe car crashes, construction lane closures, and other large events that cause significant traffic disruptions. 

   more » « less
3. Minimizing Multimodular Functions and Allocating Capacity in Bike-Sharing Systems

   https://doi.org/10.1287/opre.2022.2320  

   Freund, Daniel ; Henderson, Shane G. ; Shmoys, David B. ( September 2022 , Operations Research)

   The growing popularity of bike-sharing systems around the world has motivated recent attention to models and algorithms for their effective operation. Most of this literature focuses on their daily operation for managing asymmetric demand. In this work, we consider the more strategic question of how to (re)allocate dock-capacity in such systems. We develop mathematical formulations for variations of this problem (either for service performance over the course of one day or for a long-run-average) and exhibit discrete convex properties in associated optimization problems. This allows us to design a polynomial-time allocation algorithm to compute an optimal solution for this problem, which can also handle practically motivated constraints, such as a limit on the number of docks moved in the system. We apply our algorithm to data sets from Boston, New York City, and Chicago to investigate how different dock allocations can yield better service in these systems. Recommendations based on our analysis have led to changes in the system design in Chicago and New York City. Beyond optimizing for improved quality of service through better allocations, our results also provide a metric to compare the impact of strategically reallocating docks and the daily rebalancing of bikes. 

   more » « less
4. 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. 

   more » « less
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. 

   more » « less