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This paper discusses service times based on vehicle class at barrier-operated toll booths. Service times were measured using four vehicle classifications: car (C), medium goods vehicle (MGV), trucks & bus (TB), and articulated truck (AT) with two payment options: electronic (ETC) and manual toll collection (MTC). Each toll booth has both payment systems and resulting in mixed toll booth utilization in terms of payment and vehicles. The collected data was utilized to estimate logistic regression, and non-parametric statistical tests were performed. Findings indicate, when vehicle size increases, so does service time for even ETC. Furthermore, logistic regression used to compute threshold service times shifting from ETC to MTC for C, MGV, TB, and AT, which were 5.92, 7.51, 10.00, and 12.80 s, respectively. The transition time for a vehicle switch from ETC to MTC based on the logistic curve is denoted by threshold service time. Below the threshold, ETC is more favorable option and affected by barrier and vehicle class. Overall, the results indicate that operators can benefit from more dynamic operational conditions facilitated by threshold service times, enabling adaptive and efficient toll booth operations. Additionally, these thresholds can serve as valuable measure of service quality for managing freeway exits.

Understanding the timing and drivers of migration can be beneficial for improving response efforts aimed at reducing invasive species densities. Efforts by management agencies to remove grass carp (Ctenopharyngodon idella), an invasive species to the Laurentian Great Lakes, have been ongoing in Lake Erie tributaries since 2018. To bolster efforts, deployment of a non-physical barrier has been proposed downstream of a known grass carp spawning location near Brady's Island (BI) in the Sandusky River, OH, USA to limit recruitment. However, knowledge of grass carp migratory timing, the environmental variables that cue carp migration, and the potential effects the barrier might impose on native fish [e.g., walleye (Sander vitreus)] movements would help inform barrier deployment and scheduling. We used detection data from grass carp (n = 29) and walleye (n = 84) tagged with acoustic transmitters to address four objectives: (1) quantify interannual variation (years = 2015–2021) of grass carp migration timing to BI; (2) evaluate timing of different grass carp movement modalities (residents and migrants); (3) assess overlap in migration timing with native walleye, and (4) evaluate environmental cues of grass carp migration to BI. Median grass carp arrival at BI occurred within a three-week period (148–165 Julian days), suggesting that deploying a barrier immediately prior to this time frame may be effective for deterring grass carp spawning. Temperature, photoperiod, and discharge influenced grass carp migration timing given that most arrival events occurred at daylengths > 14.5 h, temperatures exceeding 18 °C, and low discharge events (< 3,000 cubic feet second−1 [CFS]). Minimal interannual variability in migration timing existed for grass carp and walleye over a six-year period. However, the median departure time of walleye was more than 45 days before the median arrival time of grass carp, suggesting a spawning barrier may minimally affect walleye spawning. No differences in arrival timing at BI were observed between grass carp migratory contingents, indicating that if a barrier were deployed in the spring, it would likely affect all grass carp spatial contingents. This work highlights management implications of barrier control efforts of aquatic invasive species and provides insight into the environmental cues that grass carp use for upstream migration. [ABSTRACT FROM AUTHOR]

With the massive amount of data generated from mobile devices and the increase of computing power of edge devices, the paradigm of Federated Learning has attracted great momentum. In federated learning, distributed and heterogeneous nodes collaborate to learn model parameters. However, while providing benefits such as privacy by design and reduced latency, the heterogeneous network present challenges to the synchronisation methods, or barrier control methods, used in training, regarding system progress and model convergence etc. The design of these barrier mechanisms is critical for the performance and scalability of federated learning systems. We propose a new barrier control technique called Probabilistic Synchronous Parallel (PSP). In contrast to existing mechanisms, it introduces a sampling primitive that composes with existing barrier control mechanisms to produce a family of mechanisms with improved convergence speed and scalability. Our proposal is supported with a convergence analysis of PSP-based SGD algorithm. In practice, we also propose heuristic techniques that further improve the efficiency of PSP. We evaluate the performance of proposed methods using the federated learning specific FEMNSIT dataset. The evaluation results show that PSP can effectively achieve good balance between system efficiency and model accuracy, mitigating the challenge of heterogeneity in federated learning. [ABSTRACT FROM AUTHOR]

Lane keeping control needs to achieve smooth steering operation while ensuring safety-no lane departures and maintain small lateral displacement. In this paper, we solve this challenge by developing a preview lane keeping control supervised by a safeguard controller. The preview control utilizes both tracking errors and future lane curvatures to generate the optimal steering commands. The safeguard controller is then designed to guarantee bounded tracking errors. It supervises the preview control and intervenes if and only if the tracking error is approaching the safety boundary. Both algorithms have analytical control laws and thus require little on-line computations. The integrated system is compared with a model predictive control (MPC) design in terms of both tracking performance and computing efficiency. We implemented the proposed controls on a self-driving vehicle platform and tested on open roads and in the Mcity test facility.

The paper proposes a sensors platform to control a barrier that is installed for vehicles entrance. This platform is automatized by image-based license plate recognition of the vehicle. However, in situations where standardized license plates are not used, such image-based recognition becomes non-trivial and challenging due to the variations in license plate background, fonts and deformations. The proposed method first detects the approaching vehicle via ultrasonic sensors and, at the same time, captures its image via a camera installed along with the barrier. From this image, the license plate is automatically extracted and further processed to segment the license plate characters. Finally, these characters are recognized with the help of a standard optical character recognition (OCR) pipeline. The evaluation of the proposed system shows an accuracy of 98% for license plates extraction, 96% for character segmentation and 93% for character recognition.

The paper proposes a sensors platform to control a barrier that is installed for vehicles entrance. This platform is automatized by image-based license plate recognition of the vehicle. However, in situations where standardized license plates are not used, such image-based recognition becomes non-trivial and challenging due to the variations in license plate background, fonts and deformations. The proposed method first detects the approaching vehicle via ultrasonic sensors and, at the same time, captures its image via a camera installed along with the barrier. From this image, the license plate is automatically extracted and further processed to segment the license plate characters. Finally, these characters are recognized with the help of a standard optical character recognition (OCR) pipeline. The evaluation of the proposed system shows an accuracy of 98% for license plates extraction, 96% for character segmentation and 93% for character recognition. [ABSTRACT FROM AUTHOR]