Riding Safety Research Articles

Implementation of Data Mining to predict sales of Bogo helmets using the Naïve Bayes algorithm

Consumer needs for safety and comfort in driving are very important, especially for two-wheeled or motorcycle riders. A good helmet is a helmet that is safe and comfortable when worn. Helmet qualifications that meet SNI standards are open helmets and closed helmets. Transactions are carried out online, because it was still in a pandemic situation when it was established. By carrying the tag line "Ride Safety With Your Own Style", trying to educate the younger generation to keep paying attention to safety when driving but also not neglecting fashion. For the type of sale of bogo and retro helmet brands, with a variety of colors and affordable prices. A common problem faced is how to predict or forecast future helmet sales based on pre-recorded data. This prediction is very influential on the decision to determine the number of helmets that must be provided, if you order helmets in sufficient quantities and it turns out that only a few helmet sales are sold and this will cause the stock of helmets to accumulate. The results of predictions for the sale of bogo helmets in Baris True Instances amounted to 16 data, which means Valid with 53% data accuracy. Meanwhile, there are 10 data classified as Instancely Classified Instance which means Invalid, with data accuracy of 46.67%. The amount of accuracy in the Weka application is the same as the amount of accuracy in Excel calculations. From the explanation above, the Naïve Bayes algorithm method is the best solution for predicting important things in a business need and others.

Implications of a Twitter data-centred methodology for assessing commuters’ perceptions of the Delhi metro in India

Owing to the onset of the new media age, the idea of e-public participation has proven to be a great complement to the limitations of the conventional public participation approach. In this respect, location-based social networks (LBSN) data can prove to be a game shift in this digital era to offer an insight into the commuter perception of service delivery. The paper aims to investigate the potential of using Twitter data to assess commuters’ perceptions of the Delhi metro, India, by presenting a comprehensive methodology for extracting, processing, and interpreting the data. The study extracts Twitter data from the official handle of the Delhi metro, performs semantic and sentiment analysis to comprehend commuters’ concerns and assesses commuters’ sentiments on the predicted concerns. The paper outlines that the current depth of Twitter data is more inclined to instantaneous responses to grievances encountered. Moreover, the analysis presents that for the data extraction period, the topics ‘Ride Safety’ and ‘Crowding’ have the lowest scores, while ‘Personnel Attitude’ and ‘Customer Interface’ have the highest scores. Further, the paper highlights insights gleaned from Twitter data in addition to the aspects included in the conventional satisfaction survey. The paper concludes by outlining the opportunities and limitations of LBSN analytics for effective public transportation decision-making in India.

The Influence of the Conditions of Use and the Type of Model Used on the Vertical Dynamic Responses of a Car Suspension

The article presents a study of the influence of vehicle’s conditions of use, such as road class, vehicle speed or its load, on its vertical dynamic responses. In the article only the kinematic excitations were analysed, as these are more common than the dynamic ones. The road profiles were artificially generated according to the ISO 8608 standard, which classifies roads based on power spectral density of excitations which they generate. Ride safety, ride comfort and fatigue strength indicators were computed. Ride safety was defined by the DLC – Dynamic Load Coefficient. Ride comfort was judged taking into consideration the recommendations from the ISO 2631 standard (which contains the information on vibration frequencies and their effect on human body, as well as the allowed exposure times to given vibrations) by calculating root mean square values of sprung mass accelerations for bandwidths defined in the standard. Load spectrums for the fatigue analysis were created using forces generated in a simulation as a basis and further research venues were proposed. Lastly conclusions were drawn from the results, that imply that linear models are sufficient for many standard applications on roads of acceptable quality, however the use of non-linear models is recommended in fatigue strength analysis regardless of conditions of use.

Quality Function Deployment (QFD) on Product Design Development: Group Riding Safety Equipment

Motorcycle communities have various of activities, common one is group’s touring or taking a long journey for several purposes such charity as well for community’s gathering. Some condition found in this kind of situation, groups touring creates situations which makes whole pack member have to be focus on their groups riding. In group’s touring, it’s very common for them to using body gestures in order to communicate in a pack. They create some signals by using their body parts which commonly by hand or feet, to deliver their messages such: accelerate; turning; notifying obstacles; break; etc. This contradiction found very risky and dangerous, improper driving position as well with losing focus on driving will endanger the touring group and its surrounding. Considering the existed contradiction, researchers are trying to formulate the solution. GONDEZ "Going on Road Safely" The Touring Safety Riding Equipment with Integrated Control System. The innovation uses to alternate the contradictive condition, the device help the rider in delivering the messages without creating improper dangerous driving gestures. The research methodology adopted from KAIZEN philosophy, followed by QFD as the methods in order to identify and designing the product.

Research on the Effects of Hydropneumatic Parameters on Tracked Vehicle Ride Safety Based on Cosimulation

Ride safety of a tracked vehicle is the key focus of this research. The factors that affect the ride safety of a vehicle are analyzed and evaluation parameters with their criteria are proposed. A multibody cosimulation approach is used to investigate the effects of hydropneumatic parameters on the ride safety and aid with design optimization and tuning of the suspension system. Based on the cosimulation environment, the vehicle multibody dynamics (MBD) model and the road model are developed using RecurDyn, which is linked to the hydropneumatic suspension model developed in Lab AMESim. Test verification of a single suspension unit is accomplished and the suspension parameters are implemented within the hydropneumatic model. Virtual tests on a G class road at different speeds are conducted. Effects of the accumulator charge pressure, damping diameter, and the track tensioning pressure on the ride safety are analyzed and quantified. This research shows that low accumulator charge pressure, improper damping diameter, and insufficient track tensioning pressure will deteriorate the ride safety. The results provide useful references for the optimal design and control of the parameters of a hydropneumatic suspension.

Wet 'n Wild: When Water Rides Should be Subject to the Highest Duty of Care

Two thrill ride accidents in July 2013—one involving a woman who fell seventy-five feet to her death from a roller coaster, and another involving a log flume that overturned and trapped seven patrons underwater—placed national attention on the safety of amusement park rides. Following the accidents, social media erupted with calls for increased ride safety. At the same time, a Kansas waterpark was completing construction on the world’s “tallest, fastest and ‘most extreme’ water slide ever built”—the Verruckt, which is “German for insane.” A target for “adrenaline junkies who are always looking for the