Delivery depletion: rider viapolitics and the effects of im/mobilities upon platform food delivery couriers

Urban green spaces are an important part of the urban ecosystem and hold significant ecological value. To effectively protect these green spaces, urban managers urgently need to identify them and monitor their changes. Common urban vegetation positioning methods use deep learning segmentation models to process street view data in urban areas, but this is usually inefficient and inaccurate. The main reason is that they are not applicable to the variable climate of urban scenarios, especially performing poorly in adverse weather conditions such as heavy fog that are common in cities. Additionally, these algorithms also have performance limitations such as inaccurate boundary area positioning. To address these challenges, we propose the UGSAM method that utilizes the high-performance multimodal large language model, the Segment Anything Model (i.e., SAM). In the UGSAM, a dual-branch defogging network WRPM is incorporated, which consists of the dense fog network FFA-Net, the light fog network LS-UNet, and the feature fusion network FIM, achieving precise identification of vegetation areas in adverse urban weather conditions. Moreover, we have designed a micro-correction network SCP-Net suitable for specific urban scenarios to further improve the accuracy of urban vegetation positioning. The UGSAM was compared with three classic deep learning algorithms and the SAM. Experimental results show that under adverse weather conditions, the UGSAM performs best in OA (0.8615), mIoU (0.8490), recall (0.9345), and precision (0.9027), surpassing the baseline model FCN (OA improvement 28.19%) and PointNet++ (OA improvement 30.02%). Compared with the SAM, the UGSAM improves the segmentation accuracy by 16.29% under adverse weather conditions and by 1.03% under good weather conditions. This method is expected to play a key role in the analysis of urban green spaces under adverse weather conditions and provide innovative insights for urban development.

BackgroundMotor vehicle crashes are a leading cause of injury mortality. Adverse weather and road conditions have the potential to affect the likelihood of motor vehicle fatalities through several pathways. However, there remains a dearth of assessments associating adverse weather conditions to fatal crashes in the United States. We assessed trends in motor vehicle fatalities associated with adverse weather and present spatial variation in fatality rates by state.MethodsWe analyzed the Fatality Analysis Reporting System (FARS) datasets from 1994 to 2012 produced by the National Highway Traffic Safety Administration (NHTSA) that contains reported weather information for each fatal crash. For each year, we estimated the fatal crashes that were associated with adverse weather conditions. We stratified these fatalities by months to examine seasonal patterns. We calculated state-specific rates using annual vehicle miles traveled data for all fatalities and for those related to adverse weather to examine spatial variations in fatality rates. To investigate the role of adverse weather as an independent risk factor for fatal crashes, we calculated odds ratios for known risk factors (e.g., alcohol and drug use, no restraint use, poor driving records, poor light conditions, highway driving) to be reported along with adverse weather.ResultsTotal and adverse weather-related fatalities decreased over 1994–2012. Adverse weather-related fatalities constituted about 16 % of total fatalities on average over the study period. On average, 65 % of adverse weather-related fatalities happened between November and April, with rain/wet conditions more frequently reported than snow/icy conditions. The spatial distribution of fatalities associated with adverse weather by state was different than the distribution of total fatalities. Involvement of alcohol or drugs, no restraint use, and speeding were less likely to co-occur with fatalities during adverse weather conditions.ConclusionsWhile adverse weather is reported for a large number of motor vehicle fatalities for the US, the type of adverse weather and the rate of associated fatality vary geographically. These fatalities may be addressed and potentially prevented by modifying speed limits during inclement weather, improving road surfacing, ice and snow removal, and providing transit alternatives, but the impact of potential interventions requires further research.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-016-0189-x) contains supplementary material, which is available to authorized users.

The International Maritime Organisation (IMO) requirements for the control of greenhouse gas (GHG) emissions of shipping have raised interest in ship manoeuvrability in adverse weather conditions when compliance is accomplished simply by reducing the main engine power. In response, the IMO has adopted the guidelines for determining minimum propulsion power to maintain the manoeuvrability of ships in adverse conditions. In the present paper, a systematic investigation on the manoeuvrability of a ship with different low advance speeds in adverse weather conditions was conducted by means of an unsteady Reynolds-Averaged Navier-Stokes solver. The numerical results demonstrated the contribution of low advance speeds to the course-keeping and turning circle manoeuvre, providing a practical insight into the manoeuvring performance of a ship with minimum propulsion power in adverse weather conditions. For the course-keeping control, the ship experienced more aggressive steering as the propeller revolution decreased in the oblique waves, while it appeared that the difference in the rudder deflection according to the change in the propeller speed in the head, beam, and following waves is negligible. The difficulty of the low speed turning manoeuvre was clearly noted when the direction of the incident wave was opposite to the direction towards which the ship intended to turn. It is believed that this paper can also be impactful in improving the guidelines of minimum powering of ships for safe navigation in adverse weather conditions.

Path-following control problem for maritime autonomous surface ships (MASS) in adverse weather conditions at low speeds

The impact of weather on summer and winter exercise behaviors

Localization-aware logit mimicking for object detection in adverse weather conditions

With the widespread adoption of unmanned aerial vehicles (UAVs) in various applications (e.g., aerial transportation, traffic monitoring), there have been apprehensions regarding the associated risks of employing UAVs in both civilian and military contexts, including concerns about privacy infringement, safety issues, and security threats. Although several methods have been proposed to detect UAVs, a pressing open challenge is posed by varying adverse weather conditions that could degrade the performance of many existing methods. To address these limitations, this work proposes a YOLO-based (You Only Look Once) novel model, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">YOLO-RAW</i> that exhibits improved performance in adverse weather conditions and considers different scales of UAVs. Furthermore, to facilitate a more comprehensive evaluation of the proposed model’s effectiveness in UAV detection, we have curated a complex background dataset and introduced three distinct test sets affected by adverse weather conditions. These three test sets comprise the Rainy Test Set (RTS), the AWGN (Additive White Gaussian Noise) Test Set (ATS), and the Motion Blurred Test Set (MBTS). The comprehensive experiments demonstrate the effectiveness of the proposed YOLO-RAW model over its counterparts in detecting UAVs under adverse conditions. The code and datasets could be found at: https://github.com/AdnanMunir338/YOLO-RAW.

The degree of autonomy in vehicles depends directly on the performance of their sensor systems. The transition to even more autonomously driven cars therefore requires the development of robust sensor systems with different skills. Especially in adverse and changing weather conditions (rain, snow, fog, etc.), conventional sensor systems such as cameras perform unreliably. Moreover, data evaluation has to be performed in real-time, i.e. within a fraction of seconds, in order to safely guide the car through traffic and to avoid a crash with any obstacle. Therefore, we propose to use a so called time-gated-single-pixel-camera, which combines the principles of time gating and compressed sensing. In a single pixel camera, the amount of recorded data can be significantly reduced compared to a conventional camera by exploiting the inherent sparsity of scenes. The lateral information is gained with the help of binary masks in front of a simple photodiode. We optimize the pattern of the masks by including them as trainable parameters within our data evaluation neural network. Additionally, our camera is able to cope with adverse weather conditions due to the underlying time gating principle. The feasibility of our method is demonstrated by simulated and measured data as well.

Identifying crash-prone traffic conditions under different weather on freeways

In response to extreme traffic congestion in metropolitan areas that causes unnecessarily long travel times, high fuel consumption, and excessive greenhouse gas emissions, transportation agencies have implemented various strategies to mitigate traffic congestion. Managed lanes—one of the measures applied worldwide—provide benefits to road users and operating agencies by integrating advanced technologies such as electronic and dynamic tolling systems. However, those agencies already implementing or considering implementing the managed lane strategy are seeking a solution to effectively and properly charge toll rates based on vehicle occupancy and penalize violating vehicles. Vehicle passenger detection systems (VPDSs) have been developed and evaluated worldwide, but limitations still inhibit their full implementation. This study confirms that the performance of the deep learning algorithm, a core VPDS technology, declines under certain adverse weather conditions because of lack of training data sets. The performance of the “you only look once” (YOLOv3) model trained with a normal weather data set decreased by as much as 8.5% when it was tested for adverse weather conditions. In this study, augmented reality (AR) models are developed to enhance the accuracy of vehicle passenger detection (VPDA) by the VPDS by training the algorithm with AR images representing virtual adverse weather conditions. Models trained with AR image sets of various weather categories (fog, rain, and snow) attained VPDA enhanced by up to 7.9%. The final model significantly improves VPDA under adverse weather conditions. The proposed models could be considered for implementation with road weather information systems under adverse weather conditions.

Transportation accidents, are significantly affected by human factors, which account for a substantial proportion of incidents and fatalities. Factors such as fatigue, stress, illness, medication, and substance use impair pilot performance, leading to compromised decision-making, reduced situational awareness, and increased risk-taking behavior (Wingelaar-Jagt et al., 2021). While regulatory guidelines and medical evaluations exist to address these challenges, current measures often rely on self-reporting and subjective assessments that can be prone to bias. Artificial Intelligence (AI) driven facial recognition model has been used in other industries to assess human subjects’ health status (Chan et al., 2024) and cognitive workload (Iarlori et al., 2024). This research aims to develop an AI-driven facial recognition model to objectively assess pilot fitness to fly by analyzing micro expressions, facial symmetry, eye movement, and other biomarkers that reflect fatigue, stress, and impairment. The AI model will be trained using publicly available datasets containing facial images of individuals in varying conditions such as fatigue, drowsiness, stress, sadness, and under the influence of alcohol, drugs, or medication. Data preprocessing will employ facial landmark detection and attention-based image segmentation to isolate key facial regions, including the eyes (tracking movement and redness), mouth (symmetry, dryness, or tremor), and skin tone (color changes indicative of intoxication or stress)(Chan et al., 2024). Model training will leverage deep convolutional neural networks (CNNs), utilizing transfer learning techniques to enhance performance with smaller datasets. There are three tasks in this research. Task 1 focuses on model building using secondary data from publicly available facial image datasets in different conditions. Task 2 involves a laboratory-based experiment with healthy individuals to validate and refine the AI algorithm’s accuracy in detecting cognitive performance changes under stress. Participants will perform cognitive tasks under high-stress conditions, and facial images will be captured to fine-tune the algorithm. Task 3 includes a pilot simulation-based experiment to fine-tune the AI algorithm for aviation-specific applications. Licensed pilots will perform flight simulation tasks under high-workload or stressful conditions, such as emergency scenarios and adverse weather conditions. Data from facial images and simulator metrics like decision-making speed, navigation accuracy, and task prioritization will be analyzed to adapt the AI algorithm for real-time, aviation-specific assessments. The integration of this technology into preflight screening process will provide real-time, non-invasive assessments, complementing existing protocols and enhancing aviation safety by offering early warnings of performance degradation, thereby reducing accident risks and improving operational efficiency. Such AI facial recognition technology can also be utilized in-flight to detect subtle cues informing the pilot of their assessed condition. The authors would like to acknowledge Embry-Riddle Aeronautical University – FIRST Program for the funding provided. The authors would also like to acknowledge the consistent support from College of Aviation - School of Graduate Studies, and College of Engineering - Mechanical Engineering department.

Driving is important for older people to maintain mobility. To reduce age‐related functional decline, older drivers may adjust their driving by avoiding difficult situations. One of these situations is driving in adverse weather conditions such as in the rain, snow and fog which reduce the visual clarity of the road ahead. The upcoming highly automated vehicle (HAV) has the potential of supporting older people. However, only limited work has been done to study older drivers’ interaction with HAV, especially in adverse weather conditions. This study investigates the effect of age and weather on takeover control performance among drivers from HAV. A driving simulation study with 76 drivers has been implemented. The participants took over the vehicle control from HAV under four weather conditions clear weather, rain, snow and fog, where the time and quality of the takeover control are quantified and measured. Results show age did affect the takeover time (TOT) and quality. Moreover, adverse weather conditions, especially snow and fog, lead to a longer TOT and worst takeover quality. The results highlighted that a user‐centred design of human–machine interaction would have the potential to facilitate a safe interaction with HAV under the adverse weather for older drivers.

Adverse weather condition is one of the inducements that lead to supply uncertainty of an urban transportation system, while travelers’ multiple route choice criteria are the nonignorable reason resulting in demand uncertainty. This paper proposes a novel stochastic traffic network equilibrium model considering impacts of adverse weather conditions on roadway capacity and route choice criteria of two-class mixed roadway travellers on demand modes, in which the two-class route choice criteria root in travelers’ different network information levels (NILs). The actual route travel time (ARTT) and perceived route travel time (PRTT) are considered as the route choice criteria of travelers with perfect information (TPI) and travelers with bounded information (TBI) under adverse weather conditions, respectively. We then formulate the user equilibrium (UE) traffic assignment model in a variational inequality problem and propose a solution algorithm. Numerical examples including a small triangle network and the Sioux Falls network are presented to testify the validity of the model and to clarify the inner mechanism of the two-class UE model under adverse weather conditions. Managerial implications and applications are also proposed based on our findings to improve the operation efficiency of urban roadway network under adverse weather conditions.

Light detection and ranging (LiDAR) can provide continuous and stable pose estimation with the model of normal distribution transform (NDT), which is widely used in autonomous vehicles (AVs), even under adverse weather conditions. However, there are few studies about the influence of inclement weather on LiDAR positioning results. In this article, different weather scenarios (rain, fog, and snow) are composed of synthetic LiDAR datasets based on state-of-the-art weather simulators. Then, the impacts of different adverse weather conditions are quantitatively evaluated in terms of positioning accuracy and uncertainty. Afterward, we perform the first study to qualitatively analyze the relationship between meteorological weather standards and LiDAR positioning performances, which is significant but unexplored. Evaluated results indicate that NDT matching performance will deteriorate in adverse weather conditions, especially when the meteorological level is “Heavy” or “Violent,” threatening the AVs’ positioning security seriously. Therefore, the results of this article provide more basis for the realization of high-precision positioning in adverse weather conditions, to ensure the positioning safety of AVs.

Maneuverability of ships is presently standardized (normed) by rules of classification societies, shipowner requirements, and International Maritime Organization (IMO) standards for ship maneuverability. The latter address turning, initial turning, yaw checking, course keeping, and emergency stopping abilities, evaluated in simple standard maneuvers in calm water, and have practically become an industry standard. However, these standards do not address ship maneuverability in adverse weather conditions; after the introduction of the energy efficiency design index (EEDI), the importance of this issue increased because of concerns that propulsion and steering abilities of ships may become insufficient if EEDI requirements are achieved by simply reducing the installed power. To provide a rational basis for the standardization of maneuverability of ships in adverse weather conditions, relevant criteria, measures, and standards need to be developed, as well as efficient ways to evaluate the criteria in practical design and approval, keeping in mind that, on the one hand, maneuvering in waves is a difficult hydrodynamic problem, presently tackled in research projects in few centers worldwide, and, on the other hand, simple practical regulations are urgently required to be routinely used in design and approval of all new-built ships as a part of EEDI requirements. Obviously, this contradiction requires significant simplifications. The aim of this study was to outline possible approaches to practical assessment, based on a sequence of pragmatic simplifications and leading to practical assessment procedures of various complexity (some of which are implemented in the presently acting 2013 Interim Guidelines for determining minimum propulsion power to maintain the maneuverability of ships in adverse conditions, and in other proposals), validate the proposed simplifications, and summarize remaining difficulties. 1. Introduction Maneuverability of ships is presently standardized (normed) by rules of classification societies, shipowner requirements, and nonmandatory (but gaining increasing acceptance by administrations and classification societies) IMO standards for ship maneuverability (IMO 2002), which address turning, initial turning, yaw checking, course keeping, and emergency stopping abilities, evaluated in simple standard maneuveres in calm water. These standards do not address ship maneuverability in adverse conditions, which requires, on the one hand, evaluation of ship-specific environmental forces and, on the other hand, assessment of the ability of the ship's steering and propulsion systems to overcome these forces.