Inerter is a recent advancement in vehicle suspension that have been shown to be capable of improving vehicle ride comfort, however its ride improvement is less encouraging in the parallel layout. This study investigated the possibility of further ride improvement brought by vehicle suspensions with a switchable parallel inerter instead. In this theoretical study, the inerter was assumed to be on-off switchable based on semi-active force cancellation strategy. A two-degree-of-freedom quarter vehicle model was used to evaluate several cases of suspension system, which included ordinary passive suspension as reference, a system with switchable inerter and a system with both switchable damper and inerter. The model was solved mathematically with random road profile and step profile as ground excitations. Results showed that the use of switchable inerter in parallel to spring and damper in the different test cases was capable of reducing vertical sprung mass acceleration by a healthy 12 %, which is far superior to a mere 2 % achieved by a passive parallel inerter, as well as comparable to that achieved by a switchable damper. When both sprung mass acceleration and dynamic tire load were considered, comparison made on the Pareto fronts indicated that the switchable capability of an inerter managed to further improve the Pareto optimal sets over those obtained for cases with passive inerter.

Heavy vehicles make up a relatively small percentage of traffic volume on Malaysian roads compared to other vehicle types. However, heavy vehicles have been reported to be involved in 30,000–40,000 accidents yearly and caused significantly more fatalities. Rollover accidents may also incur cargo damages and cause environmental or human disasters for vehicles that carry hazardous cargos if these contents are spilled. Thus, in this paper, a comprehensive study was conducted to investigate the effects of vehicle and road conditions on rollover of commercial heavy vehicles during cornering at curved road sections. Vehicle conditions include the heavy vehicle class (based on the axle number and vehicle type), speed and gross vehicle weight, while road conditions include the cornering radius and coefficient of friction values. In order to reduce the risks involved in usage of actual heavy vehicles in crash experiments, a simulation approach using a multi-body vehicle dynamic software was applied in this study, where the verified virtual heavy vehicle model was simulated and the output results were extracted and analyzed. The results showed that a maximum of 40% and a minimum of 23% from the total number of simulations resulted in an unsafe condition (indicated as failed) during the simulations. From the unsafe conditions, two types of rollover accidents could be identified, which were un-tripped and tripped rollovers. The heavy vehicle speed was also found to have a strong correlation to the lateral acceleration (to cause a rollover), followed by gross vehicle weight, coefficient of friction and cornering radius, respectively.

Objective: The increasing number of road crashes and fatalities on Malaysian federal roads is a cause for concern. The main causes of road crashes and fatalities on federal roads have been attributed to the speeding behavior among drivers. As such, this research investigates the possible predictors from sociodemographic characteristics and attitudes in predicting speeding behavior among drivers on Malaysia federal roads. Methods: A face-to-face survey was conducted via purposive sampling on 300 drivers at rest areas at 6 crash hotspots on Malaysian federal roads. A set of questions related to speeding behavior was developed. The questionnaire consisted of 10 questions related to sociodemographic characteristics of the participants, 37 questions regarding attitudes toward speeding, and 1 question regarding speeding behavior. Subsequently, the sociodemographics were analyzed using binary logistic regression and the attitude variable was analyzed using factor analysis and binary logistic regression. Results: The findings from the sociodemographic data revealed that male gender, single/separated status, broad driving experience, crash experience, and leisure/vacation trip purposes emerged as significant predictors for speeding behavior. Additionally, the results of factor analysis for the attitude variable revealed 3 components: Affective, behavioral, and cognitive. These 3 components are originally derived from the ABC model of attitude (affective, behavioral, and cognitive) that was adapted in this study. Furthermore, the findings from binary logistic regression appeared consistent with the model assumption, signifying that behavioral aspects significantly influenced speeding behavior among drivers. Nevertheless, affective and cognitive components were insignificant predictors. Furthermore, strong agreement with speeding countermeasures was observed among the participants. Conclusion: In conclusion, sociodemographic characteristics and attitude have been proven to affect speeding behavior among drivers on Malaysian federal roads. The findings have important implications in designing driver risk profiles on federal roads to develop suitable countermeasures based on the 4E approach (engagement, education, enforcement, and engineering) to enhance road safety.

Intelligent Control for Self-erecting Inverted Pendulum Via Adaptive Neuro-fuzzy Inference System

Motorcyclists are considered a very vulnerable group of road users as far as road accidents are concerned. Safety of this form of transportation is an important issue. This paper details the study on the changes in riding behaviour, such as changes in speed as well as brake force and throttle force applied, when motorcyclists ride over a curve section road using an instrumented motorcycle. In this study, an instrumented motorcycle equipped with various types of sensors, on-board cameras and data loggers, was developed in order to collect the riding data on the study site. Results from the statistical analysis showed that riding characteristics such as changes in speed, brake force and throttle force applied, are influenced by the distance from the curve entry, riding experience and travel mileage of the riders. A structural equation modeling was used to study the impact of these variables towards the change of riding behaviour in curve entry section. Three regression equations are formed to study the relationship between three dependent variables, which are speed, throttle force and brake force applied with the independent variables. In short, riders tend decelerate to achieve a comfortable speed, by applying higher brake intensity and release throttle force, when they ride closer to the curve section.