Estimating Uncertainty of Bus Arrival Times and Passenger Occupancies

Travel time reliability and the availability of seating and boarding space are important indicators of bus service quality and strongly influence users’ satisfaction and attitudes towards bus transit systems. With Automated Vehicle Location (AVL) and Automated Passenger Counter (APC) units becoming common on buses, some agencies have begun to provide real-time bus location and passenger occupancy information as a means to improve perceived transit reliability. Travel time prediction models have also been established based on AVL and APC data. However, existing travel time prediction models fail to provide an indication of the uncertainty associated with these estimates. This can cause a false sense of precision, which can lead to experiences associated with unreliable service. Furthermore, no existing models are available to predict individual bus occupancies at downstream stops to help travelers understand if there will be space available to board. The purpose of this project was to develop modeling frameworks to predict travel times (and associated uncertainties) as well as individual bus passenger occupancies. For travel times, accelerated failure-time survival models were used to predict the entire distribution of travel times expected. The survival models were found to be just as accurate as models developed using traditional linear regression techniques. However, the survival models were found to have smaller variances associated with predictions. For passenger occupancies, linear and count regression models were compared. The linear regression models were found to outperform count regression models, perhaps due to the additive nature of the passenger boarding process. Various modeling frameworks were tested and the best frameworks were identified for predictions at near stops (within five stops downstream) and far stops (further than eight stops). Overall, these results can be integrated into existing real-time transit information systems to improve the quality of information provided to passengers

Experiments of posture estimation on vehicles using wearable acceleration sensors

In this paper, we study methods to estimate drivers' posture in vehicles using acceleration data of wearable sensor and conduct a field test. Recently, sensor technologies have been progressed. Solutions of safety management to analyze vital data acquired from wearable sensor and judge work status are proposed. To prevent huge accidents, demands for safety management of bus and taxi are high. However, acceleration of vehicles is added to wearable sensor in vehicles, and there is no guarantee to estimate drivers' posture accurately. Therefore, in this paper, we study methods to estimate driving posture using acceleration data acquired from T-shirt type wearable sensor hitoe, conduct field tests and implement a sample application.Comment: 4 pages, 4 figures, The 3rd IEEE International Conference on Big Data Security on Cloud (BigDataSecurity 2017), pp.14-17, Beijing, May 201

Developing a valid method to study adaptive behaviours with regard to IEQ in primary schools

Adaptive behaviour impacts the classroom's environment and the student's comfort. Therefore, a deep understanding of students' adaptive behaviour is required. This study aims to develop a valid and reliable method to realize how children in their late middle childhood (9–11) practise adaptive behaviours as a response to the classroom's Indoor Environmental Quality (IEQ). A self-reported questionnaire accompanied with an observation form is designed based on children's ‘here and now’ sensations, their cognitive and linguistic competence. Validity and reliability of the questionnaire were tested by running pilot and field studies in eight primary schools from July 2017 to May 2018. Through transverse sampling, 805 children were observed, and 1390 questionnaires were collected in 31 classrooms. Questions and responses of the designed questionnaire were validated by monitoring answer-process, non-participant observations, cross-checking questions and statistical tests. Validating process improved the wording of the questions and response categories and resulted in a questionnaire with a high and valid response rate. The reliability of the questionnaire was tested by measuring the variability and standard deviations of responses under similar conditions. To conclude, the study introduces a questionnaire and an observation form that should be used together to provide a valid and reliable method for studying adaptive behaviour of primary school children

Toilet assistive system designed for the reduction of accidental falls in the bathroom using admittance controller

This paper suggests an assistive system for the toilet with the objective of measuring human activities and to provide intelligent mechanical assistance to help seating and standing. The project intends to develop a seating assistance as a technical aid in order to reduce accidents and falls in the bathroom. The preferred technique is human-robot physical interaction algorithms known in collaborative robotics (cobot) and adapting it to a personalized assistance technology installed on a smart toilet. First, the design of the mechanical assistance is presented. Then, an admittance controller is designed and implemented in order to help the user in a similar way as a cobot could be used. This technique could be used to assist the user and improve balance with adequate training and an adequate configuration of the admittance controller

Producing power-law distributions and damping word frequencies with two-stage language models

Standard statistical models of language fail to capture one of the most striking properties of natural languages: the power-law distribution in the frequencies of word tokens. We present a framework for developing statisticalmodels that can generically produce power laws, breaking generativemodels into two stages. The first stage, the generator, can be any standard probabilistic model, while the second stage, the adaptor, transforms the word frequencies of this model to provide a closer match to natural language. We show that two commonly used Bayesian models, the Dirichlet-multinomial model and the Dirichlet process, can be viewed as special cases of our framework. We discuss two stochastic processes-the Chinese restaurant process and its two-parameter generalization based on the Pitman-Yor process-that can be used as adaptors in our framework to produce power-law distributions over word frequencies. We show that these adaptors justify common estimation procedures based on logarithmic or inverse-power transformations of empirical frequencies. In addition, taking the Pitman-Yor Chinese restaurant process as an adaptor justifies the appearance of type frequencies in formal analyses of natural language and improves the performance of a model for unsupervised learning of morphology.48 page(s

Using Azure AutoML to Analyze the Effect of Attendance and Seat Choice on University Student Grades

Teachers often claim that class attendance and sitting at the front of a classroom improves student grades. This study employs machine learning on a private University\u27s attendance data to analyze this claim. We perform a correlation analysis in Azure by training regression models. No correlation is found. Next we use the K-means clustering algorithm in Azure. At k=2 clusters, a cluster with perfect attendance shows a higher average grade than a cluster with a late attendance average. Seat choice within the classroom does not prove important to the clustering models

Screw torque traceability control: industrial application

This paper analyses a new screw torque traceability control procedure designed for an operation in a car audio workstation. First the detection score at a Failure Mode and Effects Analysis (FMEA) indicated which process improvement were needed. On the sequence, data was collected to evaluate performance indicators. Finally, specific actions were executed based on the improvement plan. As result, the FMEA detection score improved from 7 to 3, the run rate increased from 12 to 16 products/h and defects caused by improper screwing were eliminated. This research contributes to disseminate a practical application of a screw torque traceability control

Epcast: Controlled Dissemination in Human-based Wireless Networks by means of Epidemic Spreading Models

Epidemics-inspired techniques have received huge attention in recent years from the distributed systems and networking communities. These algorithms and protocols rely on probabilistic message replication and redundancy to ensure reliable communication. Moreover, they have been successfully exploited to support group communication in distributed systems, broadcasting, multicasting and information dissemination in fixed and mobile networks. However, in most of the existing work, the probability of infection is determined heuristically, without relying on any analytical model. This often leads to unnecessarily high transmission overheads. In this paper we show that models of epidemic spreading in complex networks can be applied to the problem of tuning and controlling the dissemination of information in wireless ad hoc networks composed of devices carried by individuals, i.e., human-based networks. The novelty of our idea resides in the evaluation and exploitation of the structure of the underlying human network for the automatic tuning of the dissemination process in order to improve the protocol performance. We evaluate the results using synthetic mobility models and real human contacts traces