Analysis of bus ride comfort using smartphone sensor data

10.32604/cmc.2019.05664Computers, Materials and Continua602455-46

Investigating Tafheet as a Unique Driving Style Behaviour

Road safety has become a major concern due to the increased rate of deaths caused by road accidents. For this purpose, intelligent transportation systems are being developed to reduce the number of fatalities on the road. A plethora of work has been undertaken on the detection of different styles of behaviour such as fatigue and drunken behaviour of the drivers; however, owing to complexity of human behaviour, a lot has yet to be explored in this field to assess different styles of the abnormal behaviour to make roads safer for travelling. This research focuses on detection of a very complex driver’s behaviours: ‘tafheet’, reckless and aggressive by proposing and building a driver’s behaviour detection model in the context-aware system in the VANET environment. Tafheet behaviour is very complex behaviour shown by young drivers in the Middle East, Japan and the USA. It is characterised by driving at dangerously high speeds (beyond those commonly known in aggressive behaviour) coupled with the drifting and angular movements of the wheels of the vehicle, which is similarly aggressive and reckless driving behaviour. Thus, the dynamic Bayesian Network (DBN) framework was applied to perform reasoning relating to the uncertainty associated with driver’s behaviour and to deduce the possible combinations of the driver’s behaviour based on the information gathered by the system about the foregoing factors. Based on the concept of context-awareness, a novel Tafheet driver’s behaviour detection architecture had been built in this thesis, which had been separated into three phases: sensing phase, processing and thinking phase and the acting phase. The proposed system elaborated the interactions of various components of the architecture with each other in order to detect the required outcomes from it. The implementation of this proposed system was executed using GeNIe 2.0 software, resulting in the construction of DBN model. The DBN model was evaluated by using experimental set of data in order to substantiate its functionality and accuracy in terms of detection of tafheet, reckless and aggressive behaviours in the real time manner. It was shown that the proposed system was able to detect the selected abnormal behaviours of the driver based on the contextual data collected. The novelty of this system was that it could detect the reckless, aggressive and tafheet behaviour in sequential manner, based on the intensity of the driver’s behaviour itself. In contrast to previous detection model, this research work suggested the On Board Unit architecture for the arrangement of sensors and data processing and decision making of the proposed system, which can be used to pre-infer the complex behaviour like tafheet. Thus it has the potential to prevent the road accidents from happening due to tafheet behaviour

Investigation of bus passenger discomfort and driver fatigue: An electroencephalography (EEG) approach

Efforts to improve urban bus transport systems’ comfort and increase user satisfaction have been made for many years across the globe. Increasing bus users and reducing car users has an economic benefit. Whenever the urban bus share is larger than 25%, there are journey time savings due to lower congestion levels on the network. A driver’s loss of alertness due to fatigue has been recognised to be one of the major factors responsible for road accidents/crashes for many decades. Comfort and fatigue are psychophysiological phenomena. Objective measures of human psychological and physiological factors must be defined, investigated, and evaluated in order to have an indepth understanding of the cause-effect mechanisms regulating psychophysiological factors. Electroencephalography (EEG) developed as bio-sensor equipment to interpret and collate bioelectrical signals was used to gather the time-series quantitative data of urban bus passengers and HGV drivers. This study’s EEG data application was designed to link the brain activity dynamics to dynamic experimental design variables or tasks by correlating increased or decreased measured brain activity by using a baseline for comparisons. Two experiments were conducted in this study. The first sought to understand the influence of driving time and rest breaks on a driver’s psychophysiological response. Therefore, the EEG data was collected, categorised and grouped based on two hours of driving before a 30 minute break, two hours of driving after a 30 minute break and four hours of driving with no break. The Samn-Perelli seven-point scale of fatigue assessment was used to evaluate the influence of the duration of driving time on a driver’s performance decrements. The second experiment investigated bus passenger discomfort by examining experimental design stage-related changes in EEG measured by using a control experiment for comparison. Consequently, datasets in two stages were collected for each subject (passenger), including the stationary laboratory (control) and dynamic onboard bus environment experiments. A subjective evaluation of the average ride comfort on each stage of the experiments was conducted by using the recommended assessment scale of the International Standard ISO 2631-1. The ERP EEG oscillations were evaluated by decomposing the EEG signals into magnitudes and phase information, and then characterising their changes relative to the experimentally designed phases and variables. A two-way analysis of variance (ANOVA) was conducted to test the model’s predictor under different experimental conditions for passenger discomfort and driving fatigue experiments. Efforts to improve urban bus transport systems’ comfort and increase user satisfaction have been made for many years across the globe. Increasing bus users and reducing car users has an economic benefit. Whenever the urban bus share is larger than 25%, there are journey time savings due to lower congestion levels on the network. A driver’s loss of alertness due to fatigue has been recognised to be one of the major factors responsible for road accidents/crashes for many decades. Comfort and fatigue are psychophysiological phenomena. Objective measures of human psychological and physiological factors must be defined, investigated, and evaluated in order to have an indepth understanding of the cause-effect mechanisms regulating psychophysiological factors. Electroencephalography (EEG) developed as bio-sensor equipment to interpret and collate bioelectrical signals was used to gather the time-series quantitative data of urban bus passengers and HGV drivers. This study’s EEG data application was designed to link the brain activity dynamics to dynamic experimental design variables or tasks by correlating increased or decreased measured brain activity by using a baseline for comparisons. Two experiments were conducted in this study. The first sought to understand the influence of driving time and rest breaks on a driver’s psychophysiological response. Therefore, the EEG data was collected, categorised and grouped based on two hours of driving before a 30 minute break, two hours of driving after a 30 minute break and four hours of driving with no break. The Samn-Perelli seven-point scale of fatigue assessment was used to evaluate the influence of the duration of driving time on a driver’s performance decrements. The second experiment investigated bus passenger discomfort by examining experimental design stage-related changes in EEG measured by using a control experiment for comparison. Consequently, datasets in two stages were collected for each subject (passenger), including the stationary laboratory (control) and dynamic onboard bus environment experiments. A subjective evaluation of the average ride comfort on each stage of the experiments was conducted by using the recommended assessment scale of the International Standard ISO 2631-1. The ERP EEG oscillations were evaluated by decomposing the EEG signals into magnitudes and phase information, and then characterising their changes relative to the experimentally designed phases and variables. A two-way analysis of variance (ANOVA) was conducted to test the model’s predictor under different experimental conditions for passenger discomfort and driving fatigue experiments.The variability in the driver’s psychophysiological responses to the duration of driving occurs systematically. The effects appear to be progressive and aligned such that the driving performance was worst during the last 60 minutes of driving for four hours without a break, but better during the first 30 minutes. Data analysis also showed that a pronounced psychophysiological response exists relative to the influence of the road roughness characteristics, the passenger’s postures, and the bus type. Further analysis of passenger discomfort showed that passengers are more strained while in a standing posture than in a seated posture, irrespective of the bus type and the degree of the road’s roughness. The results indicated that passenger comfort deteriorates as the road roughness coefficient increases. Furthermore, the results demonstrated that female passengers express more discomfort/dissatisfaction than males under the same experimental conditions. Therefore, female passengers are more sensitive than males to a deviation from optimal comfort conditions.This study provides opportunities for future research applications of EEG in transport research studies. It also provides a platform for evaluating different Intelligent Transport System (ITS) technologies, particularly passenger’s reactions in autonomous vehicles