행동심리를 활용한 스쿨존 도로환경 서비스디자인 개발

Digital Transformation (DT) requires governments to swiftly adapt to meet society’s growing demands. In Brazil, the Startup Gov.BR program aims to speed up DT through the deployment of temporary multidisciplinary teams. This paper introduces a Service Design (SD) approach derived from experiences in supporting this governmental program, leveraging collaborative methodologies like co-design and co-creation to facilitate the redesign and creation of digital services. This work highlights the facilitators and hinderers of adopting the proposed SD approach. The findings underscore the critical role of SD adoption in aligning with citizen needs, thereby improving public services and promoting DT that delivers public value for stakeholders.

In this study, statistical features of traffic accidents in Northern Cyprus (TRNC) especially for the last decade were examined. Such that, risk factors (causes) of traffic accident and their demographic reflections were identified by evaluating official statistics. The main findings of this study are these: Fast driving as the most important cause of traffic accidents; Straight road as the chief location of severe traffic accidents; Inverse relationship between the severe traffic accidents and driver's licenses period (experience); and Inverse relationship between the severe traffic accidents and the age of accused persons.

The high impact of traffic accidents makes it imperative to formulate public policies to reduce their occurrence. In this task, knowing the cause of accidents is of paramount importance. The use of data mining and big data adapts to the complexity of the phenomenon under study. In order to classify some possible causes of traffic accidents, we built a data model to describe the behavior and dynamic of the participant agents in the traffic accident event in Colombia. This paper presents the application of MLP and Naive Bayes algorithms to identify the possible immediate cause and the rules decision algorithm PART for the root cause of traffic accidents. Models have been tested aiming to obtain the goodness of fit by increasing metrics like Recall, Precision, ROC and Kappa index, and minimize the RMSE.

Risky driving is a leading cause of traffic accidents all over the world. Major factors contributing to risky driving include driver distraction (e.g. smart-phone usage) and an onset of driver drowsiness. As a result, improving road-safety requires modeling and detecting in-vehicle conditions indicative of risky driver behavior. Previous works towards driver behavior modeling and classification possess limitations such as increased expenditure and/or additional external infrastructure requirements. In this paper, we propose a novel driver behavior detection framework that at its core makes use of in-vehicle Wi-Fi signals. The main premise of the proposed framework is that risky behavior induces specific differentiable patterns in received WiFi signals - patterns that are then exploited to build an efficient classification system. More specifically, we classify drowsy and inattentive driving into four main gestures that reflect unsafe driving. These gestures include (a) Yawning, (b) Head Jerks, (c) Sideways motion, and (d) Smart-phone usage. Channel State Information (CSI) is used to determine the driver's gesture which is further used for behavior modeling. A number of low computational features are used to classify among different gestures. Experiments are performed in a real in-vehicle environment using software-defined radios. Using data collected from these experiments, a simple ensemble classifier is implemented which is shown to achieve an average accuracy of 88.64%.

When any distracted driving of the driver is concerned, use of smartphones and navigation systems while driving are major causes for traffic accidents. However, in a situation where navigation systems are commonly installed and smartphones are commonly used within vehicles, it is better to implement technologies that control the use of smartphones in high-risk situations and allow brief periods of use otherwise, rather than ban the use of smartphones altogether while driving. This study introduces the DWMS (Driver Workload Management System) and proposes service that controls the use of smartphones according to driver's workloads. The DWMS periodically monitors the driver's behaviors and smartphone usages in order to calculate driving workload. When the calculated workload exceeds the threshold value, the driver is deemed to be overburdened and all incoming calls and SMS (Short Message Service) is blocked. The service will then make a notification when this status is relieved. In addition, at the end of driving, the DWMS will provide a summary of the information on how the driver drives, workload, and behaviors taken as a text message to the driver's smartphone. The result of this study can increase both driving safety and convenience for drivers.

Adapting ISA system warnings to enhance user acceptance

Fatigue driving is one of the primary causes of traffic accidents, posing a serious threat not only to the driver’s life and property but also to the safety of surrounding vehicles and pedestrians. Therefore, accurate and efficient detection of driver fatigue is essential for preventing traffic incidents. This study proposes a novel driver fatigue detection framework that integrates AlphaPose* with a Long Short-Term Memory (LSTM) network. To enhance the performance of AlphaPose, the original human detector is replaced with an optimized YOLOv11n model, which incorporates a Hybrid Pooling Fusion Block (HPFB) to improve feature representation and meet the requirements of keypoint estimation. Furthermore, a multi-view data processing strategy based on the Driving Monitoring Dataset (DMD) is introduced to capture driver behavior from frontal, lateral, and hand views. To effectively model the temporal dynamics of driver behavior, a fatigue behavior monitoring network is designed using LSTM. Comparative experiments demonstrate that the proposed AlphaPose*-LSTM-based system achieves superior performance in fatigue detection tasks compared to existing state-of-the-art approaches.

For more than 30 years, pedestrian safety studies have considered pedestrian–vehicle collision patterns and pedestrian and driver behavior at marked and unmarked crosswalks at uncontrolled crossings. Recent research in this area, conducted by the University of California, Berkeley, Traffic Safety Center on behalf of the California Department of Transportation (Caltrans), was designed to fill key gaps in the literature by analyzing driver and pedestrian behavior and knowledge of right-of-way laws for marked and unmarked crosswalks. The Caltrans study, as with most previous crosswalk studies, focused on urban and suburban areas (in this case, the San Francisco, California, Bay Area), where driver and pedestrian characteristics do not change significantly from day to day. After this study came the recognition that similar research was needed in rural and recreational locations, where the population frequently changes. As such, this paper summarizes results from field observations of driver and pedestrian behavior at marked and unmarked crosswalks at uncontrolled crossings during the summer in the Tahoe Basin of California. This study, also funded by Caltrans, concludes that the behavior trends identified in the study of urban and suburban areas in the Bay Area are largely similar in a rural and recreational context. This finding is significant for Caltrans, a statewide agency that is seeking to provide a consistent crosswalk installation and treatment policy for its facilities across California. Other regional and state agencies may similarly benefit from the findings of the study.

A FATAL MOTOR-CAR ACCIDENT AND CANNABIS USE: Investigation by Radioimmunoassay

Every year, traffic congestion and traffic accidents have been rapidly increasing in proportion to increasing number of vehicles. Although the roadway design and signal system have been improved to relieve traffic congestion, traffic casualties and property damage do not decrease. The traffic accident is a serious issue of society because vehicle is a primary means of transportation. This paper develops a real-time traffic accident detection system (RTADS): This system helps us to cope with accidents and discover the causes of traffic accident by detecting the accident. We gathered video data recorded at several intersections and used them to detect accidents at different intersections which have different traffic flow and intersection design. However, because the data gathered from intersections have incompleteness, uncertainty and complicated causal dependency between them, we construct probability-based networks which calculate based on the probability for correct accident detection. This system instantly sends the detected result to managers using accident alarm system. RTADS features real time accident detection and analysis of the cause of accidents. In performance evaluation, the proposed system achieved a detection rate of 97% with a correct detection rate of 92% and a false alarm rate of 0.77%.

Modularized design-oriented systematic inventive thinking approach supporting collaborative service innovations

Innovative service design is crucial in the ever-changing healthcare environment in order for organizations to hold competitive advantage. The concept of service design thinking refers to the marketing and design of services that improve the customer experience and the interactions between customers and service providers. Innovative thinking in service design can help organizations gain competitive advantage. The challenges with service design thinking in healthcare and innovative ways of thinking about service design in healthcare are introduced, as well as ways to build and sustain competitive advantage in healthcare.

Over the past 20 years there has been an increase in post-secondary visual communication education in Australia while the growth of the industry itself has been low, meaning an increasing number of graduates compete for a limited pool of jobs . The use of visualisation in human centered and service design approaches provides alternative employment opportunities for these graduates. This paper presents a case study of a visual communication honors research project that is indicative of those opportunities and the potential benefits of having highly skilled visual practitioners involved in human centered and service design processes. Furthermore, we argue that a consideration of the aesthetics of the visualisation methods used in this context is essential and that visual aesthetics should be a significant part of the service design skillset.

Background:In Thailand, it has the second highest road traffic fatality rate in the world at 36.2 deaths per 100,000 populations. The pedestrian crash zones are based on the road and physical environment, vehicle and driver behavior and pedestrian behavior, especially the area around the school. Therefore, this paper would like to improve safety by identifying crash area through Geographic Information Systems (GIS).Objective:The objective of this paper is to identify pedestrian crash zones of primary schools and secondary schools in Bangkok, Thailand through the spatial analysis and GIS tool.Method:The research methodology was the data collection from pedestrian-vehicle crashes in 2016 at 12 schools of 1,218 locations in Bangkok. The data analysis used GIS for geocoding the crash locations. The spatial patterns and pedestrian crash zone map were applied by Moran’s I statistic and the Kernel Density Estimation (KDE).Results:The Moran’s index showed that the accident locations within school zone were a clustered pattern considering on Moran’s Index which approached +1 and the z-scores greater than 2.58. The KDE showed that the pedestrian crash zones were different depending on the physical environment; however, the most significant areas were at urban areas, crowded areas, and intersections of arterial roads and local roads about 508 meters from the school center.Conclusion:This research could be concluded that the spatial patterns and pedestrian crash zone map will assist the transportation planners and traffic police for identifying crash locations and specific vulnerable road users, especially pedestrians and bicycle users.

In February 2018, after three years of design and development work, the British Broadcasting Corporation (BBC) Weather launched its redesigned service across multiple platforms. The project involved new ways of cross-disciplinary communication design working across broadcast and digital services. This research examines these innovations and considers the transcorporeality of our relationship with weather forecasting. BBC Weather developed an iconography in the mid-1970s that has been integral to its brand identity and which has survived the changes in television graphics technology from magnetised acrylic symbols to digital systems. Satellite imagery, advanced computer weather modelling, mobile interaction, and weather on the move in realtime, have become integral to presenting different layers of visual sophistication and information that require translation and editing to communicate the weather across multiple platforms and formats. An ethnographic study of the leading participants in the design project mapped out the creative process and highlighted reflexive points where design practice was modified and adapted by the interdisciplinarity of the team. Their approach to design anthropology and service design approaches are revealed in the context of Design Thinking, and how the domestication of digital services is linked to a relationship to the weather for a UK and Irish audience.