Behavior Modeling Systems Research Articles

Multidimensional Analysis of Willingness to Share Rides in a Future of Autonomous Vehicles

A sustainable transportation future is one in which people eschew personal car ownership in favor of using autonomous vehicle (AV)-based ridehailing services in a shared mode. However, the traveling public has historically shown a disinclination toward sharing rides and carpooling with strangers. In a future of AV-based ridehailing services, it will be necessary for people to embrace both AVs as well as true ridesharing to fully realize the benefits of automated and shared mobility technologies. This study investigated the factors influencing willingness to use AV-based ridehailing services in the future in a shared mode (i.e., with strangers). This was done through the estimation of a behavioral model system on a comprehensive survey data set that included rich information about attitudes, perceptions, and preferences pertaining to the adoption of AVs and shared mobility modes. The model results showed that current ridehailing experiences strongly influenced the likelihood of being willing to ride AV-based services in a shared mode. Campaigns that provide opportunities for individuals to experience such services firsthand would potentially go a long way to enabling a shared mobility future at scale. In addition, several attitudinal variables were found to strongly influence the adoption of future mobility services; these findings provide insights on the likely early adopters of shared autonomous mobility services and the types of educational awareness campaigns that may effect change in the prospects of such services.

Multi-scale driver behavior modeling based on deep spatial-temporal representation for intelligent vehicles

The mutual understanding between driver and vehicle is critical to the realization of intelligent vehicles and customized interaction interface. In this study, a unified driver behavior modeling system toward multi-scale behavior recognition is proposed to enhance the driver behavior reasoning ability for intelligent vehicles. Specifically, the driver behavior recognition system is designed to simultaneously recognize the driver's physical and mental states based on a deep encoder-decoder framework. The model jointly learns to recognize three driver behaviors with different time scales: mirror checking and facial expression state, and two mental behaviors, including intention and emotion. The encoder module is designed based on a deep convolutional neural network (CNN) to capture spatial information from the input video stream. Then, several decoders for different driver states estimation are proposed with fully-connected (FC) and long short-term memory (LSTM) based recurrent neural networks (RNN). Two naturalistic datasets are used in this study to investigate the model performance, which is a local highway dataset, namely, CranData, and one public dataset from Brain4Cars. Based on the spatial–temporal representation of driver physical behavior, it shows that the observed physical behaviors can be used to model the latent mental behaviors through the proposed end-to-end learning process. The testing results on these two datasets show state-of-the-art results on mirror checking behavior, intention, and emotion recognition. With the proposed system, intelligent vehicles can gain a holistic understanding of the driver's physical and phycological behaviors to better collaborate and interact with the human driver, and the driver behavior reasoning system helps to reduce the conflicts between the human and vehicle automation.

Factors Driving Users’ Engagement in Patient Social Network Systems

Participatory medicine and e-health help to promote health literacy among non-medical professionals. Users of e-health systems actively participate in a patient social network system (PSNS) to share health information and experiences with other users with similar health conditions. Users’ activities provide valuable healthcare resources to develop effective participatory medicine between patients, caregivers, and medical professionals. This study aims to investigate the factors of patients’ engagement in a PSNS by integrating and modifying an existing behavioral model and information system model (i.e., affective events theory (AET) and self-determination theory (SDT)). The AET is used to model the structure, the affective aspects of the driven behavior, and actual affective manifestation. The SDT is used to model interest and its relations with behavior. The data analysis and model testing are based on structural equation modeling, using responses from 428 users. The results indicate that interest and empathy promote users’ engagement in a PSNS. The findings from this study suggest recommendations to further promote users’ participation in a PSNS from the sociotechnical perspective, which include sensitizing and constructive engagement features. Furthermore, the data generated from a user’s participation in a PSNS could contribute to the study of clinical manifestations of disease, especially an emerging disease.

МЕТОД АДАПТАЦІЇ ПОВЕДІНКИ АГЕНТІВ В ІНТЕЛЕКТУАЛЬНІЙ НАВЧАЛЬНІЙ СИСТЕМІ ПІДГОТОВКИ ДИСПЕТЧЕРІВ УПРАВЛІННЯ ПОВІТРЯНИМ РУХОМ

В рамках агентно-орієнтованого підходу з метою підвищення варіативності середовища підготовки диспетчерів управління повітряним рухом у статті удосконалено метод адаптації поведінки агентів навчального середовища інтелектуальної навчальної системи, що ґрунтується на знаннях про результати взаємодії агентів. На етапі внесення змін до параметрів підсистеми планування при настоюванні систем моделювання поведінки агентів запропоновано використовувати механізм самонастроювання. Для управління процесом самонастроювання поведінки інтелектуальних агентів в статті розроблено інструментарій адаптації системи планування поведінки агента. Застосування моделей поведінки агентів із методами планування дозволить збільшити показник успішності реалізації очікуваної поведінки агентів при побудові інтелектуальних навчальних систем. Запропонований у статті підхід адаптації поведінки агентів навчального середовища інтелектуальної навчальної системи дозволить підвищити його варіативність. Як наслідок, така інтелектуальна навчальна система дозволить вивести на якісно новий рівень підготовку диспетчерів управління повітряним

FirebehavioR: An R Package for Fire Behavior and Danger Analysis

Wildland fire and ecological researchers use empirical and semi-empirical modeling systems to assess fire behavior and danger. This technical note describes the firebehavioR package, a porting of two fire behavior modeling systems, Crown Fire Initiation and Spread and a Rothermel-based framework, to the R programming language. We also highlight supporting data objects and functions to predict inputs required for fire behavior estimation. Last, this package contains functions for fifteen indices to express fire danger using weather and/or fuels observations. Specific advantages of predicting fire behavior using R, a free-and-open-source programming language, include freedom to adapt calculations to suit users’ needs, transparency of source code, and reduction of workflow inefficiencies, thereby aiding in sophisticated fire behavior analyses.

Understanding the Influence of Wireless Communications and Wi‐Fi Access on Customer Loyalty: A Behavioral Model System

New technologies offer new possibilities to better understand complex consumer behavior points of sale. The data obtained using wireless communications and Wi‐Fi services available in restaurants and catering companies make it possible to acquire in‐depth knowledge on consumer behavior complexity. In the present study, the PLS‐SEM analysis was used to analyze the impact of free wireless communications and Wi‐Fi access on customer loyalty. Our results demonstrate that client satisfaction with wireless communications and Wi‐Fi access networks services has a direct impact on customer loyalty. Therefore, wireless communications and Wi‐Fi networks and technologies available at the points of sale should be updated in order to better meet customers’ expectations.

The rabbit as a behavioral model system for magnetic resonance imaging

BackgroundfMRI requires that subjects not move during image acquisition. This has been achieved by instructing people not to move, or by anesthetizing experimental animal subjects to induce immobility. We have demonstrated that a surgically implanted headbolt onto the skull of a rabbit allows their brain to be imaged comfortably while the animal is awake. This article provides a detailed method for the preparation. New methodWe took advantage of the rabbit's tolerance for restraint to image the brain while holding the head at the standard stereotaxic angle. Visual stimulation was produced by flashing green LEDs and whisker stimulation was done by powering a small coil of wire attached to a fiber band. Blinking was recorded with an infrared emitter/detector directed at the eye with fiber-optic cabling. ResultsResults indicate that a single daily session of habituation is sufficient to produce adequate immobility on subsequent days to avoid movement artifacts. Results include high resolution images in the stereotaxic plane of the rabbit. Comparison with existing method(s)We see no degradation or distortion of MR signal, and the headbolt provides a means for rapid realignment of the head in the magnet from day to day, and across subjects. The use of rabbits instead of rodents allows much shorter periods of habituation, and the rabbit allows behavior to be observed during the day while the animal is in its normal wake cycle. ConclusionsThe natural tolerance of the rabbit for restraint makes it a valuable subject for MRI studies of the brain.

High-throughput sequencing and graph-based cluster analysis facilitate microsatellite development from a highly complex genome.

Despite recent advances in high‐throughput sequencing, difficulties are often encountered when developing microsatellites for species with large and complex genomes. This probably reflects the close association in many species of microsatellites with cryptic repetitive elements. We therefore developed a novel approach for isolating polymorphic microsatellites from the club‐legged grasshopper (Gomphocerus sibiricus), an emerging quantitative genetic and behavioral model system. Whole genome shotgun Illumina MiSeq sequencing was used to generate over three million 300 bp paired‐end reads, of which 67.75% were grouped into 40,548 clusters within RepeatExplorer. Annotations of the top 468 clusters, which represent 60.5% of the reads, revealed homology to satellite DNA and a variety of transposable elements. Evaluating 96 primer pairs in eight wild‐caught individuals, we found that primers mined from singleton reads were six times more likely to amplify a single polymorphic microsatellite locus than primers mined from clusters. Our study provides experimental evidence in support of the notion that microsatellites associated with repetitive elements are less likely to successfully amplify. It also reveals how advances in high‐throughput sequencing and graph‐based repetitive DNA analysis can be leveraged to isolate polymorphic microsatellites from complex genomes.

Lactobacilli with probiotic potential in the prairie vole (Microtus ochrogaster).

BackgroundRecent research suggests integration of the intestinal microbiota in gut-brain communication which could lead to new approaches to treat neurological disorders. The highly social prairie voles are an excellent model system to study the effects of environmental factors on social behavior. For future studies on the role of probiotics in ameliorating disorders with social withdrawal symptoms, we report the characterization of intestinal Lactobacillus isolates with probiotic potential from voles.Methods and results30 bacterial strains were isolated from the vole intestine and found to be distinct but closely related to Lactobacillus johnsonii using 16S rRNA gene sequencing and Random Amplification of Polymorphic DNA fingerprinting. In vitro characterizations including acid and bile tolerance, antimicrobial effects, antibiotic susceptibility, and adherence to intestinal epithelial cells were performed to assess the probiotic potential of selected strains. Since previous studies revealed that mercury ingestion triggers social deficits in voles, mercury resistance of the probiotic candidates was evaluated which could be an important factor in preventing/treating these behavioral changes.ConclusionsThis study demonstrates that lactobacilli with probiotic potential are present in the vole intestine. The Lactobacillus isolates identified in this study will provide a basis for the investigation of probiotic effects in the vole behavioral model system.Electronic supplementary materialThe online version of this article (doi:10.1186/s13099-015-0082-0) contains supplementary material, which is available to authorized users.

Formalization and Verification of Group Behavior Interactions

Group behavior interactions, such as multirobot teamwork and group communications in social networks, are widely seen in both natural, social, and artificial behavior-related applications. Behavior interactions in a group are often associated with varying coupling relationships, for instance, conjunction or disjunction. Such coupling relationships challenge existing behavior representation methods, because they involve multiple behaviors from different actors, constraints on the interactions, and behavior evolution. In addition, the quality of behavior interactions are not checked through verification techniques. In this paper, we propose an ontology-based behavior modeling and checking system (OntoB for short) to explicitly represent and verify complex behavior relationships, aggregations, and constraints. The OntoB system provides both a visual behavior model and an abstract behavior tuple to capture behavioral elements, as well as building blocks. It formalizes various intra-coupled interactions (behaviors conducted by the same actor) via transition systems (TSs), and inter-coupled behavior aggregations (behaviors conducted by different actors) from temporal, inferential, and party-based perspectives. OntoB converts a behavior-oriented application into a TS and temporal logic formulas for further verification and refinement. We demonstrate and evaluate the effectiveness of the OntoB in modeling multirobot behaviors and their interactions in the Robocup soccer competition game. We show, that the OntoB system can effectively model complex behavior interactions, verify and refine the modeling of complex group behavior interactions in a sound manner.

Lab rearing environment perturbs social traits: a case study withPolisteswasps

Laboratory-based animal rearing is a common tool used to control environmental variation. However, important differences between lab and field environments may affect the biological relevance of results. Moreover, how lab rearing affects social dynamics in groups has received little attention. We investigated the effects of lab rearing on gene expression, physiology, behavior, and colony dynamics in a behavioral model system. The primitively eusocial paper wasp Polistes fuscatus has been studied in both lab and field and is an important system for understanding social evolution and caste (queen vs. worker) development. High nourishment has been implicated as a key factor in the development of prequeen (“gyne”) traits. Because the nutritional environment is altered in the lab, understanding the effects of lab rearing on caste-related traits is critical to properly interpreting experimental results. Lab-reared wasps have access to ad lib food, whereas field colonies are likely food-limited. Our results support the hypothesis that lab rearing (and associated excess nourishment) biased the development of wasp physiology and to some extent, behavior, toward being more gyne-like. Expression levels of 4 caste-related genes, however, were in the opposite direction, toward more worker-like. Lab rearing also affected colony traits: lab-reared colonies dramatically halted nest construction and began producing male eggs 1 week later than field-reared colonies. These results demonstrate the complex effects of lab-rearing on eco-physiological traits of social species, and we discuss some pros and cons of studying ecological model systems in both lab and field settings.

Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDAR

Vegetation structure, including forest canopy height, is an important input variable to fire behavior modeling systems for simulating wildfire behavior. As such, forest canopy height is one of a nationwide suite of products generated by the LANDFIRE program. In the past, LANDFIRE has relied on a combination of field observations and Landsat imagery to develop existing vegetation structure products. The paucity of field data in the remote Alaskan forests has led to a very simple forest canopy height classification for the original LANDFIRE forest height map. To better meet the needs of data users and refine the map legend, LANDFIRE incorporated ICESat Geoscience Laser Altimeter System (GLAS) data into the updating process when developing the LANDFIRE 2010 product. The high latitude of this region enabled dense coverage of discrete GLAS samples, from which forest height was calculated. Different methods for deriving height from the GLAS waveform data were applied, including an attempt to correct for slope. These methods were then evaluated and integrated into the final map according to predefined criteria. The resulting map of forest canopy height includes more height classes than the original map, thereby better depicting the heterogeneity of the landscape, and provides seamless data for fire behavior analysts and other users of LANDFIRE data.

The Effectiveness and Limitations of Fuel Modeling Using the Fire and Fuels Extension to the Forest Vegetation Simulator

Fuel treatment effectiveness is often evaluated with fire behavior modeling systems that use fuel models to generate fire behavior outputs. How surface fuels are assigned, either using one of the 53 stylized fuel models or developing custom fuel models, can affect predicted fire behavior. We collected surface and canopy fuels data before and 1, 2, 5, and 8 years after prescribed fire treatments across 10 National Forests in California. Two new methods of assigning fuel models within the Fire and Fuels Extension to the Forest Vegetation Simulator were evaluated. Field-based values for dead and downed fuel loading were used to create custom fuel models or to assign stylized fuel models. Fire was simulated with two wind scenarios (maximum 1-minute speed and maximum momentary gust speed) to assess the effect of the fuel model method on potential fire behavior. Surface flame lengths and fire type produced from custom fuel models followed the fluctuations and variability of fine fuel loading more closely than stylized fuel models. However, results of 7 out of 10 statistical tests comparing surface flame length between custom and stylized fuel models were not significant (P 0.05), suggesting that both methods used to assign surface fuel loads will predict fairly similar trends in fire behavior. FOR .S CI. ❚❚(❚):000-000.

Leave the expressway or not? Impact of dynamic information

This study investigates drivers’ diversion decision behavior under expressway variable message signs that provide travel time of both an expressway route and a local street route. Both a conventional cross-sectional logit model and a mixed logit model are developed to model drivers’ response to travel time information. It is based on the data collected from a stated preference survey in Shanghai, China. The mixed logit model captures the heterogeneity in the value of “travel time” and “number of traffic lights” and accounts for correlations among repeated choices of the same respondent. Results show that travel time saving and driving experience serve as positive factors, while the number of traffic lights on the arterial road, expressway use frequency, being a middle-aged driver, and being a driver of an employer-provided car serve as negative factors in diversion. The mixed logit model obviously outperforms the cross-sectional model in dealing with repeated choices and capturing heterogeneity regarding the goodness-of-fit criterion. The significance of standard deviations of random coefficients for travel time and number of traffic lights evidences the existence of heterogeneity in the driver population. The findings of this study have implications for future efforts in driver behavior modeling and advanced traveler information system assessment.

Investigation of driver response to the enhanced urban freeway variable message sign information

Three panel logit models that address correlations among repeated observations from the same Respondent, i.e., a Random Effect Logit Model (REM), a Random Coefficients Logit Model (RCM), and a logit model with both Random Effect and Random Coefficients (RECM), are developed and compared with each other. It is based on the data collected from a Stated Preference (SP) survey that explores expressway users’ diversion decisions under new expressway variable message sign information which provides travel times of an expressway and an alternate arterial road route. Results show that travel time saving and driving experience serve as positive factors, while the number of traffic lights on the arterial road, expressway use frequency, being a mid-age driver and an employer-provided car driver serve as negative factors in diversion. It is found that, all the panel models outperform the cross-sectional model; RCM fits the SP data slightly better than REM; RECM does not substantially improve the goodness-of-fit compared to RCM although the random effect does appear statistically significant; Random coefficients and random effect are basically complementary but somewhat interchangeable in a panel model. The findings of this study have implications for future efforts in driver behavior modeling and VMS systems assessment.

Functional inactivation of orexin 1 receptors in the cerebellum disrupts trace eyeblink conditioning and local theta oscillations in guinea pigs

The cerebellum plays an essential role in motor learning. Recently, orexins, the newfound lateral hypothalamic neuropeptides, have been found to excite Purkinje cells in the cerebellar cortex and neurons in the deep cerebellar nuclei (DCN). However, little is known about their roles in cerebellum-dependent motor learning. Therefore, the present study was designed to investigate the functional significance of hypothalamic orexinergic system during trace eyeblink conditioning, a tractable behavioral model system of cerebellum-dependent motor learning. It was revealed that the orexin 1 receptors (OXR1) were specifically localized on the soma of Purkinje cells and large DCN neurons. Furthermore, interfering with the endogenous orexins’ effects on the cerebellum via the selective OXR1 antagonist SB-334867 disrupted the timing rather than the acquisition of trace conditioned eyeblink responses. In addition to the behavioral effects, the SB-334867 prevented the increase in peak amplitude of cerebellar theta oscillations with learning. These results suggest that the endogenous orexins may modulate motor learning via the activation of cerebellar OXR1.

Developing custom fire behavior fuel models from ecologically complex fuel structures for upper Atlantic Coastal Plain forests

Currently geospatial fire behavior analyses are performed with an array of fire behavior modeling systems such as FARSITE, FlamMap, and the Large Fire Simulation System. These systems currently require standard or customized surface fire behavior fuel models as inputs that are often assigned through remote sensing information. The ability to handle hundreds or thousands of measured surface fuelbeds representing the fine scale variation in fire behavior on the landscape is constrained in terms of creating compatible custom fire behavior fuel models. In this study, we demonstrate an objective method for taking ecologically complex fuelbeds from inventory observations and converting those into a set of custom fuel models that can be mapped to the original landscape. We use an original set of 629 fuel inventory plots measured on an 80,000ha contiguous landscape in the upper Atlantic Coastal Plain of the southeastern United States. From models linking stand conditions to component fuel loads, we impute fuelbeds for over 6000 stands. These imputed fuelbeds were then converted to fire behavior parameters under extreme fuel moisture and wind conditions (97th percentile) using the fuel characteristic classification system (FCCS) to estimate surface fire rate of spread, surface fire flame length, shrub layer reaction intensity (heat load), non-woody layer reaction intensity, woody layer reaction intensity, and litter–lichen–moss layer reaction intensity. We performed hierarchical cluster analysis of the stands based on the values of the fire behavior parameters. The resulting 7 clusters were the basis for the development of 7 custom fire behavior fuel models from the cluster centroids that were calibrated against the FCCS point data for wind and fuel moisture. The latter process resulted in calibration against flame length as it was difficult to obtain a simultaneous calibration against both rate of spread and flame length. The clusters based on FCCS fire behavior parameters represent reasonably identifiable stand conditions, being: (1) pine dominated stands with more litter and down woody debris components than other stands, (2) hardwood and pine stands with no shrubs, (3) hardwood dominated stands with low shrub and high non-woody biomass and high down woody debris, (4) stands with high grass and forb (i.e., non-woody) biomass as well as substantial shrub biomass, (5) stands with both high shrub and litter biomass, (6) pine-mixed hardwood stands with moderate litter biomass and low shrub biomass, and (7) baldcypress-tupelo stands. Models representing these stand clusters generated flame lengths from 0.6 to 2.3m using a 30kmh−1 wind speed and fireline intensities of 100–1500kWm−1 that are typical within the range of experience on this landscape. The fuel models ranked 1<2<7<5<4<3<6 in terms of both flame length and fireline intensity. The method allows for ecologically complex data to be utilized in order to create a landscape representative of measured fuel conditions and to create models that interface with geospatial fire models.

Modeling Mediterranean forest fuels by integrating field data and mapping tools

Fire behavior modeling systems are important in predicting wildfire risk, fire growth, and fire effects. However, simulation software requires a new fuel modeling to include fuel treatments, prescribed fire and the transition to crown fire. The thirteen Rothermel models are insufficient in completely representing Mediterranean ecosystems. In this sense, the new American modeling includes five fuel types, requiring the acquisition of hybrid models made up of the mixture of grass and shrub and the grass or shrub mixed with litter from forest canopy. Respecting meteorological conditions and shrub characteristics, field studies have shown significant differences between American and Mediterranean models. As a consequence, the definition of new Mediterranean models requires the adjustment of specific parameters such as fuel load by category (live and dead) and particle size class (1-, 10- and 100-h time-lag), fuelbed depth and surface area-to-volume ratio. These new parameters were obtained in situ of sample itineraries, prescribed fires, and forest fires. The availability of this new modeling, validated on a field of regional scale, will facilitate preventive planning and management as well as an efficient application of suppression techniques, both ground and aerial operations, required in defending a territory against forest fires.

Adapting the task-taxon-task methodology to model the impact of chemical protective gear

The Task-Taxon-Task method (Anno et al. DNA-TR-95-115, 1996) is a statistical modeling approach to predict performance decrements on behavioral tasks in response to various stressors. We describe the basics of the T3 method and our approach to adapting it to handle more acute stressors, which can require decomposition into task networks via logical or empirical analysis. We provide an illustrative example showing how the method can be used to account for performance decrements in manual tasks associated with wearing protective gloves. This illustration provides a substantive application in which the current T3 method can be augmented to account for performance decrements in a new sub-domain, while additionally providing lessons for extending the method to new stressors, performance domains, and behavior modeling systems.

Interactive design of urban spaces using geometrical and behavioral modeling

The main contribution of our work is in closing the loop between behavioral and geometrical modeling of cities. Editing of urban design variables is performed intuitively and visually using a graphical user interface. Any design variable can be constrained or changed. The design process uses an iterative dynamical system for reaching equilibrium: a state where the demands of behavioral modeling match those of geometrical modeling. 3D models are generated in a few seconds and conform to plausible urban behavior and urban geometry. Our framework includes an interactive agent-based behavioral modeling system as well as adaptive geometry generation algorithms. We demonstrate interactive and incremental design and editing for synthetic urban spaces spanning over 200 square kilometers.