Data driven design: An investigation into the fit between the individuality of people and the uniformity of mass manufactured items

<p>Due to the economic advantage of mass manufacturing technology humans have designed a world of products built for the average body size and shape. This conformity of diverse body shapes to fixed 3 dimensional forms raises the question for this research; how can 3D scanning and additive manufacturing (AM) create a personal fit between an individual’s body and a product? This question challenges a tool driven standardised approach to manufacture by exploring the interface between a person and a mass produced product, in this case a motorcycle rider and a motorcycle. By taking advantage of digital data and the tool-less build process of 3D printing, every object produced can be different, tailoring it to the customer’s individual aesthetic or physical fit. This investigation into the space between the motorcycle and the human has produced a custom 3D printed seat designed for and inspired by the unique physicality of the individual rider. The following methods are employed. 3D scanning is used to obtain the geometry of the human form and motorcycle, 3D modelling and 3D printing to generate and evaluate ideas and concepts, and a pressure measurement system to evaluate the riders comfort and fit. This new relationship between body and object, rarely seen in mass produced products, questions the way we design and make products with consideration towards digital personalisation and manufacturing efficiency.</p>

Data driven design: An investigation into the fit between the individuality of people and the uniformity of mass manufactured items

<p>Due to the economic advantage of mass manufacturing technology humans have designed a world of products built for the average body size and shape. This conformity of diverse body shapes to fixed 3 dimensional forms raises the question for this research; how can 3D scanning and additive manufacturing (AM) create a personal fit between an individual’s body and a product? This question challenges a tool driven standardised approach to manufacture by exploring the interface between a person and a mass produced product, in this case a motorcycle rider and a motorcycle. By taking advantage of digital data and the tool-less build process of 3D printing, every object produced can be different, tailoring it to the customer’s individual aesthetic or physical fit. This investigation into the space between the motorcycle and the human has produced a custom 3D printed seat designed for and inspired by the unique physicality of the individual rider. The following methods are employed. 3D scanning is used to obtain the geometry of the human form and motorcycle, 3D modelling and 3D printing to generate and evaluate ideas and concepts, and a pressure measurement system to evaluate the riders comfort and fit. This new relationship between body and object, rarely seen in mass produced products, questions the way we design and make products with consideration towards digital personalisation and manufacturing efficiency.</p>

Risky motorcycle riding behaviour among young riders in Manipal, India

Background Motorcycles are one of the most commonly used transportation modes in low and middle-income countries. In India, motorized two-wheelers comprise 70% of the total vehicle population, and motorcycle users are considered the most vulnerable road users. It is essential to understand the risky riding behaviour and associated factors among the motorcyclists to develop evidence-based traffic safety programs targeting motorcycle riders. The purpose of the current study was two-fold. First, it aimed to determine the appropriate structure of a modified version of the MRBQ among young riders in Manipal, India. Second, it assessed to what extent MRBQ factors were associated with self-reported crash involvement and violations. Methods The motorcycle rider behaviour questionnaire (MRBQ) is a 43-item scale that assesses five aspects of risky motorcycle rider behaviour, i.e., violations, control errors, traffic errors, stunts, and protective equipment. The MRBQ, along with measures of socio-demographic variables and the number of motorcycle crashes, was filled out by 300 young motorcycle riders who were in the age group of 18–25 years and had been riding for at least the past three years (93% males, 92.3% students). Results Five factors emerged out of the MRBQ after an exploratory factor analysis: traffic errors, control errors, stunts, protective equipment, and violations. Cronbach’s alpha for these factors ranged from .66 to .82. Reports of performing stunts and committing violations were positively associated with self-reported near-crash experiences over the past three months. Riders reporting stunts, violations and using a motorcycle of 125-200 cc reported having received more fines in the last three months. These findings were confirmed in both univariate and multivariate binary logistic regression models. Conclusion The study assessed the factor structure of a modified version MRBQ and the extracted factors associations with self-reported crash involvement. The factor structure revealed in the current study is consistent with MRBQ factor structures found in other countries. However, the support for a relationship between MRBQ factors and self-reported crashes was less significant. The findings suggest that if replicated by future studies, local policymakers are advised to focus on the five MRBQ factors while planning future interventions to achieve a reduction in the number of road crashes among motorcyclists.

A Neural-Network-Based Methodology for the Evaluation of the Center of Gravity of a Motorcycle Rider

A correct reproduction of a motorcycle rider’s movements during driving is a crucial and the most influential aspect of the entire motorcycle–rider system. The rider performs significant variations in terms of body configuration on the vehicle in order to optimize the management of the motorcycle in all the possible dynamic conditions, comprising cornering and braking phases. The aim of the work is to focus on the development of a technique to estimate the body configurations of a high-performance driver in completely different situations, starting from the publicly available videos, collecting them by means of image acquisition methods, and employing machine learning and deep learning techniques. The technique allows us to determine the calculation of the center of gravity (CoG) of the driver’s body in the video acquired and therefore the CoG of the entire driver–vehicle system, correlating it to commonly available vehicle dynamics data, so that the force distribution can be properly determined. As an additional feature, a specific function correlating the relative displacement of the driver’s CoG towards the vehicle body and the vehicle roll angle has been determined starting from the data acquired and processed with the machine and the deep learning techniques.

Utilizing Beta Regression in Predicting the Underlying Factors of Motorcycle Rider Behavior

Introduction: Motorcyclists have the highest proportion of casualty toll caused by street accidents in Iran, and they endanger themselves and others by those risky behaviors. Health and safety education will not be sufficient without knowing the causes of such behaviors. Since no studies have been carried out based on accurate statistical methods on bounded response variables for motorcyclists' high-risk behaviors in Iran, this study aimed to predict MRBQ by ADHD and the underlying predictors using the Beta Regression as an alternative strategy. Methods: The present sectional study included 311 Motorcyclists randomly selected using a cluster sampling method in Bukan city to evaluating the relationship between the limited response MRBQ with ADHD and its subscales. We used an innovative beta regression method for the analysis and carried out unadjusted and adjusted modeling. Results: Direct and significant relationships were observed between MRBQ score and ADHD score and its subscales, including (DSS score) (coefficients ranged over 0.01 to 0.6, All P<0.05). Additionally, the riding period (coefficients ranged over -0.32 to -0.48, P<0.05), hours of riding (coefficients ranged over: 0.31 to 0.34, P<0.05), using the helmet (coefficients: 0.26 to 0.31, P<0.05), and sub-accident (coefficients ranged over 0.35 to 0.37, P<0.05) also turned out to be significant predictors of MRBQ score. Conclusion: ADHD score and riding parameters can be taken into account when contriving actions on the motorcycle rider behaviors as measured by MRBQ.

Analysis of Traffic Crashes Caused by Motorcyclists Running Red Lights in Guangdong Province of China

Motorcycles are among the primary means of transport in China, and the phenomenon of motorcyclists running red lights is becoming increasingly prevalent. Based on the traffic crash data for 2006–2010 in Guangdong Province, China, fixed- and random-parameter logit models are used to study the characteristics of motorcyclists, vehicles, roads, and environments involved in red light violations and injury severity resulting from motorcyclists’ running red lights in China. Certain factors that affect the probability of motorcyclists running red lights are identified. For instance, while the likelihood of violating red light signals during dark conditions is lower than during light conditions for both car drivers and pedestrians, motorcyclists have significantly increased probability of a red light violation during dark conditions. For the resulting severe casualties in red-light-running crashes, poor visibility is a common risk factor for motorcyclists and car drivers experiencing severe injury. Regarding the relationship between red light violations and the severity of injuries in crashes caused by motorcyclists running red lights, this study indicated that driving direction and time period have inconsistent effects on the probability of red light violations and the severity of injuries. On the one hand, the likelihood of red light violations when a motorcycle rider is turning left/right is higher than when going straight, but this turning factor has a nonsignificant impact on the severity of injuries; on the other hand, reversing, making a U-turn and changing lanes have nonsignificant effects on the probability of motorcyclists’ red light violations in contrast to going straight, but have a very significant impact on the severity of injuries. Moreover, the likelihood of red light violations during the early morning is higher than off-peak hours, but this time factor has a negative impact on the severity of injuries. Measures including road safety educational programs for targeted groups and focused enforcement of traffic policy and regulations are suggested to reduce the number of crashes and the severity of injuries resulting from motorcyclists running red lights.