半自動運転時の権限移譲を支援する適応型マルチモーダル・インタフェースのデザインのためのドライバの心的負荷の分析

付記する学位プログラム名: デザイン学大学院連携プログラム京都大学新制・課程博士博士(工学)甲第24607号工博第5113号新制||工||1978(附属図書館)京都大学大学院工学研究科機械理工学専攻(主査)教授 椹木 哲夫, 教授 小森 雅晴, 教授 泉井 一浩学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA

Understanding Attitudes towards Proenvironmental Travel: An Empirical Study from Tangshan City in China

Understanding people’s attitudes towards proenvironmental travel will help to encourage people to adopt proenvironmental travel behavior. Revealed preference theory assumes that the consumption preference of consumers can be revealed by their consumption behavior. In order to investigate the influences on citizens’ travel decision and analyze the difficulties of promoting proenvironmental travel behavior in medium-sized cities in China, based on revealed preference theory, this paper uses the RP survey method and disaggregate model to analyze how individual characteristics, situational factors, and trip features influence the travel mode choice. The field investigation was conducted in Tangshan City to obtain the RP data. An MNL model was built to deal with the travel mode choice. SPSS software was used to calibrate the model parameters. The goodness-of-fit tests and the predicted outcome demonstrate the validation of the parameter setting. The results show that gender, occupation, trip purpose, and distance have an obvious influence on the travel mode choice. In particular, the male gender, high income, and business travel show a high correlation with carbon-intensive travel, while the female gender and a medium income scored higher in terms of proenvironmental travel modes, such as walking, cycling, and public transport

Comparing eye-tracking metrics of mental workload caused by NDRTs in semi-autonomous driving

The objective of this study was to verify the effectiveness of eye-tacking metrics in indicating driver’s mental workload in semi-autonomous driving when the driver is engaged in different non-driving related tasks (NDRTs). A driving simulator was developed for three scenarios (high-, medium-, and low-mental workload presented by SAE (Society of Automotive Engineers) Levels 0, 1, and 2) and three uni-modality secondary tasks. Thirty-six individuals participated in the driving simulation experiment. NASA-TLX (Task Load Index), secondary task performance, and eye-tracking metrics were used as indicators of mental workload. The subjective rating using the NASA-TLX showed a main effect of autonomous level on mental workload in both visual and auditory tasks. Correlation-matrix calculation and principal-component extraction indicated that pupil diameter change, number of saccades, saccade duration, fixation duration, and 3D gaze entropy were effective indicators of a driver’s mental workload in the visual and auditory multi-tasking situations of semi-autonomous driving. The accuracy of predicting the mental-workload level using the K-Nearest Neighbor (KNN) classifier was 88.9% with bootstrapped data. These results can be used to develop an adaptive multi-modal interface that issues efficient and safe takeover requests

Optimization of Hybrid Hub-and-Spoke Network Operation for Less-Than-Truckload Freight Transportation considering Incremental Quantity Discount

This paper presents a mixed integer linear programming model (MILP) for optimizing the hybrid hub-and-spoke network operation for a less-than-truckload transportation service. The model aims to minimize the total operation costs (transportation cost and transfer cost), given the determined demand matrix, truck load capacity, and uncapacitated road transportation. The model also incorporates an incremental quantity discount function to solve the reversal of the total cost and the total demand. The model is applied to a real case of a Chinese transportation company engaged in nationwide freight transportation. The numerical example shows that, with uncapacitated road transportation, the total costs and the total vehicle trips of the hybrid hub-and-spoke network operation are, respectively, 8.0% and 15.3% less than those of the pure hub-and-spoke network operation, and the assumed capacity constraints in an extension model result in more target costs on the hybrid hub-and-spoke network. The two models can be used to support the decision making in network operations by transportation and logistics companies

Adaptive multi-modal interface model concerning mental workload in take-over request during semi-autonomous driving

With the development of automated driving technologies, human factors involved in automated driving are gaining increasing attention for a balanced implementation of the convenience brought by the technology and safety risk in commercial vehicle models. One influential human factor is mental workload. In the take-over request (TOR) from autonomous to manual driving at level 3 of International Society of Automotive Engineers' (SAE) Levels of Driving Automation, the time window for the driver to have full comprehension of the driving environment is extremely short, which means the driver is under high mental workload. To support the driver during a TOR, we propose an adaptive multi-modal interface model concerning mental workload. In this study, we evaluated the reliability of only part of the proposed model in a driving-simulator experiment as well as using the experimental data from a previous study

Modeling of a Small Transportation Company’s Start-up with Limited Data during Economic Recession

This paper presents a modeling method for analyzing a small transportation company’s start-up and growth during a global economic crisis which had an impact on China which is designed to help the owners make better investment and operating decisions with limited data. Since there is limited data, simple regression model and binary regression model failed to generate satisfactory results, so an additive periodic time series model was built to forecast business orders and income. Since the transportation market is segmented by business type and transportation distance, a polynomial model and logistic curve model were constructed to forecast the growth trend of each segmented transportation market, and the seasonal influence function was fitted by seasonal ratio method. Although both of the models produced satisfactory results and showed very nearly the same of goodness-of-fit in the sample, the logistic model presented better forecasting performance out of the sample therefore closer to the reality. Additionally, by checking the development trajectory of the case company’s business and the financial crisis in 2008, the modeling and analysis suggest that the sample company is affected by national macroeconomic factors such as GDP and import & export, and this effect comes with a time lag of one to two years

Proportion of contextual effects in the treatment of fibromyalgia - a meta-analysis of randomised controlled trials

Objectives: To examine the proportion of the total treatment effect that is attributable to contextual effects in randomised controlled trials (RCTs) of treatments for fibromyalgia. Methods: A systematic literature search was undertaken in Medline, Web of Science, EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine in September 2015. The proportion of contextualeffect (PCE) was calculated by dividing the improvement in the placebo arm by the improvement in the treatment arm. The measure was log-transformed for each trial and the random effects model was used to pool data. The primary outcome was pain. Secondary outcomes were fibromyalgia impact questionnaire (FIQ) total and fatigue. Heterogeneity was quantified using I2. Publication bias was assessed using a funnel plot and Egger's test. Subgroup analysis was undertaken to explore heterogeneity and potential determinants of the PCE. Results: 51 eligible trials (9599 participants) were identified. The PCE was 0.60 (95% CI0·56 to 0·64) for pain, 0·57 (95% CI 0·53 to 0·61) for FIQ total, and 0·63 (95% CI 0·59 to 0·68) for fatigue. The I2 was 99.4% for pain, 99.2% for FIQ total, and 97.6% for fatigue. Conclusion: More than half of the treatment effect in fibromyalgia RCTs results from non-specific contextual factors. Reporting the total treatment effect and the proportion of contextual effect in trials may help to better translate research evidence into clinical practice