Social acceptance toward a childcare support robot system: web-based cultural differences investigation and a field study in Japan

This paper investigates people’s social acceptance of a childcare support robot system and compares it with existing childcare technologies (anesthesia during labor and baby food, i.e. processed food and formula milk) through web-based questionnaires between Japan and USA and a field study in Japan. We investigated social acceptance through four scales: intention to use, safety and trustworthiness, negative attitudes, and decreasing workload. For this paper, our participants included 400 people (200 from each country) in Japan and USA located through a web-based survey who answered questionnaires about the four scales to investigate their social acceptance of childcare support technologies. Our web-based survey results indicate that our system’s concept was evaluated lower than current childcare support technologies in both Japan and USA. We also conducted a field trial with 30 additional people in Japan and through their actual experiences investigated their evaluations of the prototype of our childcare support robot system.     

高铁客运服务质量综合评价模型研究

高速铁路由于准时、快捷等因素已经成为人们出行的重要选择方式。与有线普通铁路线相比较,高铁所吸引的旅客群体主要为高中端旅客,他们在旅行需求和心理存在较大的差异。以西成高铁为例,在分析旅客旅行服务需求的基础上,建立影响西成高铁客运服务质量的主要因素,对西成高铁服务质量进行分析和评价。根据结果,对沿途主要铁路站的服务进行了排序,并提出相应的改进对策和建议。     

Passenger flow prediction in bus transportation system using deep learning

The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.

     

Towards proximity-based passenger sensing on public transport buses

While substantial research on intelligent transportation systems has focused on the development of novel wireless communication technologies and protocols, relatively little work has sought to fully exploit proximity-based wireless technologies that passengers actually carry with them today. This paper presents the real-world deployment of a system that exploits public transit bus passengers’ Bluetooth-capable devices to capture and reconstruct micro- and macro-passenger behavior. We present supporting evidence that approximately 12 % of passengers already carry Bluetooth-enabled devices and that the data collected on these passengers captures with almost 80 % accuracy the daily fluctuation of actual passengers flows. The paper makes three contributions in terms of understanding passenger behavior: We verify that the length of passenger trips is exponentially bounded, the frequency of passenger trips follows a power law distribution, and the microstructure of the network of passenger movements is polycentric.     

基于时间序列的机场短时段值机客流量预测

在机场向数字化运营的转型过程中,为了实现航站楼内高效的运作以及资源的合理分配,从而对短时段的值机客流量的预测提出了更高的要求。通过对机场历史数据的统计和分析,结合航班的DOW特性,分析值机客流量的相关影响因素,以每小时的值机客流量为研究对象,构建基于时间序列的动态回归ARIMAX模型。实验结果表明,上述模型相对于传统的预测模型,预测精度更高,拟合效果更精确,有效地预测了航站楼内短时段的值机旅客人数,为航站楼内资源的动态分配和优化提供了不可或缺的决策支持。     

Online-review analysis based large-scale group decision-making for determining passenger demands and evaluating passenger satisfaction: Case study of high-speed rail system in China

High-speed rail (HSR) has become an essential mode of public transportation in China and is likely to remain so for the foreseeable future. To promote the development of the HSR industry, a high level of passenger satisfaction must be ensured, which means that passenger satisfaction must be assured. Focusing on HSR in-cabin factors that affect the travel experience of HSR passengers, this study aims to determine passenger demands (PDs) and to evaluate passenger satisfaction by using a combination of online review analysis and large-scale group decision-making (LSGDM). By using web crawler technology, online reviews related to HSR were harvested from a microblogging platform to extract PD data and information. The six PDs that reflect the most frequent concerns of passengers were identified by analyzing the online reviews. The level of satisfaction of passengers with respect to these PDs was analyzed based on the online responses from 100 HSR passengers and by adopting the interval-valued two-tuple linguistic representation model. The final degrees of satisfaction and rankings of the PDs were then determined by using the LSGDM approach with the k-means clustering method and a consensus-reaching process. This research thus constructs an index system of HSR passenger satisfaction evaluation based on online-review analysis and evaluates the process by using LSGDM approaches. The conclusions provide insights into the improvements desired by HSR passengers for in-cabin services and to improve passenger satisfaction.     

Motion sickness in passenger vehicles during test track operations

Abstract

As automation transforms drivers into passengers, the deployment of automated vehicles (AVs) has the potential to greatly increase the incidence of motion sickness. A study was conducted to quantify motion sickness response of front-seat passengers performing ecologically relevant passenger activities during conditions consistent with driving on public roadways. Fifty-two adults with a large range of self-reported levels of motion sickness susceptibility and age participated in data collection on a closed test track in a passenger sedan. Motion sickness ratings increased with task vs. no-task and moderate vs. low acceleration test conditions. Increased motion sickness susceptibility was associated with higher motion sickness ratings. In comparison to older participants (age?>?60), younger participants (age?<?60) experienced increased motion sickness. This is the first in-vehicle study that systematically compared normative passenger activities and acceleration magnitudes typical of normative driving conditions on motion sickness response for a large, diverse sample of passengers, enabling the exploration of the effects of covariates.

Practitioner summary: The data demonstrate that a relatively large range of motion sickness response can be expected to result from passengers performing visual tasks in passenger vehicles. Measurement and modelling efforts should seek to elucidate relationships among the factors contributing to motion sickness for the purpose of informing and prioritising future countermeasures for automated vehicles (AVs).

Abbreviations: AV(S): automated vehicles; BMI: body mass index; BVP: blood volume pulse; EDA: electrodermal activity; FMS: fast motion sickness scale; GPS: global positioning system; IMU: inertial measurement unit; ISO: International Organization for Standardization; MISC: misery scale; MSDV: motion sickness dose value; NDS: naturalistic driving study; SAE: Society of Automotive Engineers International; UMTRI: The University of Michigan Transportation Research Institute

Key Aspect of Research: Motion sickness may be an important barrier to widespread adoption of automated vehicles @UMTRI.     

Dynamic Passenger Assignment for Major Railway Disruptions Considering Information Interventions

Passenger assignment models for major disruptions that require trains to be cancelled/short-turned in railway systems are rarely considered in literature, although these models could make a significant contribution to passenger-oriented disruption timetable design/rescheduling. This paper proposes a dynamic passenger assignment model, where the passengers who start travelling before, during and after the disruption are all considered. The model ensures that on-board passengers are given priority over waiting passengers, and waiting passengers are boarding under the first-come-first-serve rule. Moreover, the model allows information interventions by publishing information about service variations and train congestion at different locations with the aim of distributing passengers wisely to achieve less travel time increase due to the disruption. Discrete event simulation is adopted to implement the model, where loading/unloading procedures are realized and passengers re-plan their paths based on the information they receive. The model tracks individual travels, which helps to evaluate a disruption timetable in a passenger-oriented way.

     

Rail passenger perceptions of risk and safety and priorities for improvement

Recommendation 60 of the Ladbroke Grove rail inquiry stated that “comprehensive market research in regard to safety related measures should be carried out in order to take account of the views of informed passengers” (Cullen 2001, p. 235). In response, the Rail Safety and Standards Board Limited (RSSB) commissioned a survey of railway passengers nationwide, to investigate passenger perceptions of risk and safety at stations, on platforms and on trains, and to research passenger preferences for implementing safety interventions. Following qualitative interviews conducted with rail travellers, two separate questionnaires were designed and piloted. Each survey was administered face-to-face at 15 hub railway stations nationwide, according to a quota sample, and respondents were asked to return completed questionnaires by post. The overall response rate was approximately one-third. The results of the safety and risk survey provide a valuable indicator of passenger perceptions of risk, especially when compared to quantitative assessments of actual risk. The findings suggest that the relationship between perceived and actual risk is not particularly strong. The data from the passenger preferences questionnaire were analysed using conjoint techniques, and the results provide information on passenger preferences and priorities for improvement. The results are likely to be of value to the railway industry in allowing passenger preferences to be taken into account when implementing safety interventions.     

Why Would I Use This in My Home? A Model of Domestic Social Robot Acceptance

Many independent studies in social robotics and human–robot interaction have gained knowledge on various factors that affect people’s perceptions of and behaviors toward robots. However, only a few of those studies aimed to develop models of social robot acceptance integrating a wider range of such factors. With the rise of robotic technologies for everyday environments, such comprehensive research on relevant acceptance factors is increasingly necessary. This article presents a conceptual model of social robot acceptance with a strong theoretical base, which has been tested among the general Dutch population (n = 1,168) using structural equation modeling. The results show a strong role of normative believes that both directly and indirectly affect the anticipated acceptance of social robots for domestic purposes. Moreover, the data show that, at least at this stage of diffusion within society, people seem somewhat reluctant to accept social behaviors from robots. The current findings of our study and their implications serve to push the field of acceptable social robotics forward. For the societal acceptance of social robots, it is vital to include the opinions of future users at an early stage of development. This way future designs can be better adapted to the preferences of potential users.     

Detection of passenger flow on and off buses based on video images and YOLO algorithm

Bus passenger flow information is very important as a reference data for bus company line optimization, schedule scheduling basis, and passenger travel mode arrangement. With the development of image processing technology, it has become a current research trend to count passenger flow with the help of surveillance video of passengers getting on and off the bus. The specific research contents of this paper based on video image detection and statistics of passengers are as follows:(1) Collect head target image samples through a variety of ways, including 3960 positive head target samples and 4150 negative head target samples, which together constitute the head target feature database. (2) Established a head target detection model based on deep learning. First, the labeling of the head target training data set is completed. Then, after 15,000 iterations of model training, the YOLOv3 head target detection network model was obtained, with a recall rate of 92.12% and an accuracy rate of 89.71%. (3) A multi-target matching tracking algorithm based on the combination of Cam-shift and YOLOv3 is proposed. First, the Cam-shift algorithm is used to track the head target. Secondly, the head target tracking data and the YOLOv3 detection data are combined to solve the problem of drift during the tracking of the Cam-shift algorithm through the data association matching method based on the minimum distance, and then combined with the time constraint, a passenger location information judgment rule is proposed. Optimize the error and missed detection in the process of head target detection and tracking, and improve the reliability of passenger trajectory tracking. (4) A statistical algorithm for the detection of passengers getting on and off the bus is proposed. First, the trajectory of passengers in the bus boarding and disembarking area is analyzed, and a process for judging passengers’ boarding and boarding behavior is proposed. At the same time, a passenger position information judgment rule is proposed according to the different situations of whether there are new passengers or missing passengers, so as to optimize the problem of wrong detection and missing detection in the process of head target detection and tracking. (5) Finally, experiments are carried out in actual bus scenes and simulation scenes. The experiment proves that the statistical algorithm for the detection of passengers getting on and off the bus proposed in this paper has good detection, tracking and statistics effects in bus scenes and simulation scenes.

     

Influential Characteristics of Enterprise Information System User Interfaces

ABSTRACT

End-user acceptance is considered as a significant factor influencing the success of enterprise information system (EIS) implementations and operations. This study conceptualizes three aspects of EIS user interfaces (UIs), namely information overload, control familiarity, and UI fit, and proposes a model to understand their effect on two major factors that are considered to influence the end-user acceptance of these systems: EIS end user’s performance expectancy and effort expectancy. We developed a theoretical model and multiitem scales for the proposed EIS UI characteristics and tested the model empirically with data from a survey performed with a sample 98 EIS end users. The results from our test provide evidence for the key role that EIS UI design plays in the end user’s performance and effort expectancy.     

实时信息下的乘客路径选择行为

智能公交系统伴随着智能交通信息系统的发展而逐渐普及,其目的是向乘客提供各种实时交通信息,以提高出行的便利性和灵活性,最终实现公交出行分担率的提升。针对公交网络的特殊性,提出符合乘客路径选择行为且易于确定的广义路径定义,以成都公交电子站牌信息为背景,设计问卷对乘客路径选择行为及出行意向进行调查。采用定性和定量分析相结合的分析方法,基于随机效用理论,建立包括路径选择方案特性变量和乘客个人社会经济属性特性变量为解释变量的Logit和混合Logit路径选择模型,运用蒙特卡洛模拟和极大似然法完成参数估计。分析结果表明,混合Logit模型能更合理地解释由个体偏好而导致的路径选择行为差异,有助于对复杂公交行为的理解,以便更好地用以指导实践     

Online Banking Information Systems Acceptance: An Empirical Examination of System Characteristics and Web Security

Prior work on the technology acceptance model (TAM) is mainly devoted to the influence of TAM’s core motivational factors and their impact on behavioral intent toward IS acceptance. Relatively little research has focused on what specific system design characteristics motivate individuals toward IS acceptance. This article identified specific systems design factors and examined their impact on TAM’s motivational factors through the TAM. The findings will help designers to design and implement better user-accepted systems.     

SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients

Healthcare practitioners, patient safety leaders, educators and researchers increasingly recognise the value of human factors/ergonomics and make use of the discipline's person-centred models of sociotechnical systems. This paper first reviews one of the most widely used healthcare human factors systems models, the Systems Engineering Initiative for Patient Safety (SEIPS) model, and then introduces an extended model, ‘SEIPS 2.0’. SEIPS 2.0 incorporates three novel concepts into the original model: configuration, engagement and adaptation. The concept of configuration highlights the dynamic, hierarchical and interactive properties of sociotechnical systems, making it possible to depict how health-related performance is shaped at ‘a moment in time’. Engagement conveys that various individuals and teams can perform health-related activities separately and collaboratively. Engaged individuals often include patients, family caregivers and other non-professionals. Adaptation is introduced as a feedback mechanism that explains how dynamic systems evolve in planned and unplanned ways. Key implications and future directions for human factors research in healthcare are discussed.     

Optimal bus scheduling and fleet selection: A programming approach

A set of nonlinear programming models is developed here to determine an optimal vehicle-mix and fleet selection. The institutional framework assumed is as follows: a given number of n vehicles is available for dispatch from some source to serve passengers along fixed routes; the arriving passengers follow a simple queueing process, i.e. if a passenger cannot be served in a given time interval i, he has to wait until the next interval, thus forming a queue. One wishes to choose the number of buses n_i, to assign in interval i (i = 1, 2, …, 9) so as to optimize a criterion function, which includes such components as costs of operating the fleet, gross returns per vehicle per trip and the implicit cost of passengers' waiting time. Stochastic aspects of travel demand are handled through some formulations of stochastic programming. It is shown that under suitable simplifying assumptions, stochastic linear programming methods could provide good approximations to the more general nonlinear programming models.     

Reuse-Conducive Development Environments

Despite its well-recognized benefits, software reuse has not met its expected success due to technical, cognitive, and social difficulties. We have systematically analyzed the reuse problem (especially the cognitive and social difficulties faced by software developers who reuse) from a multidimensional perspective, drawing on our long-term research on information retrieval, human-computer interaction, and knowledge-based systems. Based on this analysis, we propose the concept of reuse-conducive development environments, which encourage and enable software developers to reuse through the smooth integration of reuse repository systems and development environments. We have designed, implemented, and evaluated CodeBroker—a reuse-conducive development environment—that autonomously locates and delivers task-relevant and personalized components into the current software development environment. Empirical evaluations of CodeBroker have shown that the system is effective in promoting reuse by enabling software developers to reuse components unknown to them, reducing the difficulties in locating components, and augmenting the programming capability of software developers.     

A Stochastic Process Approach for Frequency-based Transit Assignment with Strict Capacity Constraints

Transit assignment models represent the stochastic nature of waiting times, but usually adopt a deterministic representation route flows and costs. Especially in cities where transit vehicles are small and not operating to timetables, there is a need to represent the variability in flows and costs to enable planners make more informed decisions. Stochastic process (SP) models consider the day-to-day dynamics of the transit demand-supply system, explicitly modelling passengers’ information acquisition and decision processes. A Monte Carlo simulation based SP model that includes strict capacity constraints is presented in this paper. It uses micro-simulation to constrain passenger flows to capacities and obtain realistic cost estimates. Applications of the model and its comparison with the De Cea and Fernandez (Transp Sci, 27:133–147, 1993) model are presented using a small network.     

A fast hybrid computer vision technique for real-time embedded bus passenger flow calculation through camera

Bus passenger flow calculation system is a critical part of the smart public transportation framework. Bus passenger flow information can help to make data statistics report of the passenger at a bus station which can be used by public transport operator to evaluate the quality of the transportation. Statistics report of crowded passengers in the bus station help managers to understand the bus transit operations, can provide the database for the intelligent transportation scheduling, help to provide more and better services for passengers, overall data statistics of passengers has important practical significance to improve public transport environment. This paper presents a passenger counting algorithm based on hybrid machine learning approach. In the first step, an advanced method is used to extract the Histogram of oriented gradients (HOG) feature of passenger’s heads. Classification of head features is done by using support vector machine (SVM) as a classifier for the liner model. Heads are detected successfully after performing all steps. In next step Kanade-Lucas-Tomasi (KLT) is used to reality head tracking, the multiple target tracking is achieved and the head motion trajectory of passenger target is captured stably. At last, the trajectory is analyzed and the automatic counting of bus passenger flow is realized. In the last step, the proposed algorithm is move to embedded system for practical implementation. In this paper, the algorithm intends to use ADSP-BF609 embedded platform for transplantation. The experimental results demonstrate that the statistical accuracy of the proposed algorithm is enhanced successfully; especially during the daytime with the good illustration, the effective counting of the passenger flow is achieved and the inward and outward passenger counting can be realized. In this paper three feature extraction models are used namely local binary patterns, histograms of oriented gradients and binarized statistical image in order to get accurate features. Furthermore, three common classification techniques including naïve bayes classifier, boosted tress and support vector machines are used for fine classification of extracted vectors obtained from different features extractors model. 94.50% accuracy is achieved when support vector machine (SVM) classifies the features extracted using Histogram of oriented gradients (HOG). SVM surpasses the accuracy obtained by Boosted tree namely 81.30% using Histogram of oriented gradients (HOG) features.

     

An observational comparison of the older and younger bus passenger experience in a developing world city

This study was an unobtrusive observational analysis of 333 older and younger bus passengers in Guadalajara, Mexico. A set of data were collected for each observed passenger, as well as more general observations related to driver behaviour, bus design and bus service characteristics. There were significant differences between older and younger passengers in terms of boarding and alighting times, use of handrails, seat location preferences, passenger stability and coping strategies in order to maintain postural stability. The conditions of travel are conducive to a poor passenger experience for the older passengers in particular. Although the problems may be attributed to bus design and driver behaviour typical of that in developing countries, they are also influenced by the wider transport infrastructure, and a lack of a regulatory regime which places drivers under time pressure and in direct competition with each other.

Practitioner Summary: Bus services must cater for all ages of passengers, including the elderly. This unobtrusive observational study investigated the passenger experience in a developing world city. Bus and wider service design were found to compromise the journey experience, with the older users being particularly negatively impacted. Design recommendations are provided.