Education influence in traffic safety: A case study in Vietnam

It is well known that traffic accidents are of high importance to the public health spectrum around the world. Moreover, in developing countries such as Vietnam, the mortality rate from road traffic accidents is rather high in comparison with other Southeast Asian countries. Not only do the majority of the people killed and seriously injured significantly affect the quality of life of the citizens, but traffic accidents also negatively impact a nation's economic and social development. Statistics show that far more people are injured or die in traffic accidents than are afflicted by any of the most serious diseases. The very high occurrence of traffic accidents in Vietnam has become one of the country's major social issues. The importance of human factors in transport policy discussion is growing. There is a realization that policy options that appear beneficial in principle have to be checked for their feasibility of implementation. Understanding and describing driver behavior become a challenge when one tries to identify driver errors in determining accident/conflict causal factors and countermeasures.In recent years, having understood the serious effects of traffic accidents on society at large, scientific researchers, traffic engineers and policy makers in Vietnam have developed many projects and conducted research in the field of traffic safety. The human factor is also considered to be the central element in the whole system. The final goal is to organize a traffic environment that is convenient and safe for road users.This article explains the application of the risk analysis approach in evaluating influences of education and enforcement in traffic safety.     

Driving analytics using smartphones: Algorithms,comparisons and challenges

The present work investigates the use of smartphones as an alternative to gather data for driving behavior analysis. The proposed approach incorporates i. a device reorientation algorithm, which leverages gyroscope, accelerometer and GPS information, to correct the raw accelerometer data, and ii. a machine-learning framework based on rough set theory to identify rules and detect critical patterns solely based on the corrected accelerometer data. To evaluate the proposed framework, a series of driving experiments are conducted in both controlled and “free-driving” conditions. In all experiments, the smartphone can be freely positioned inside the subject vehicle. Findings indicate that the smartphone-based algorithms may accurately detect four distinct patterns (braking, acceleration, left cornering and right cornering) with an average accuracy comparable to other popular detection approaches based on data collected using a fixed position device.     

Eco-driving key factors that influence fuel consumption in heavy-truck fleets: A Colombian case

This research identifies key variables that influence fuel consumption that might be improved through eco-driving training programs under three circumstances that have been scarcely studied before: (a) heavy- and medium-duty truck fleets, (b) long-distance freight transport, and (c) the Latin American region. Based on statistical analyses that include multivariate regression of operational variables on fuel consumption, the impacts of an eco-driving training campaign were measured by comparing ex ante and ex post data. Operational variables are grouped into driving errors, trip conditions, driver behavior, driver profile, and vehicle attributes.The methodology is applied in a freight fleet with nationwide transport operations located in Colombia, where the steepness of its roads plays an important role in fuel consumption. The fleet, composed of 18 trucks, is equipped with state-of-the-art real-time data logger systems. During four months, 517 trips traveling a total distance of 292,512 km and carrying a total of 10,034 tons were analyzed.The results show a baseline average fuel consumption (FC) of 1.716 liters per ton-100 km. A different logistics performance indicator, which measures FC in liters per ton transported each 100 km, shows an average of 3.115. After the eco-driving campaign, reductions of 6.8% and 5.5% were obtained. Drivers’ experience, driving errors, average speed, and weight-capacity ratio, among others, were found to be highly relevant to FC. In particular, driving errors such as acceleration, braking and speed excesses are the most sensitive to eco-driving training, showing reductions of up to 96% on the average number of events per trip.     

Interface design considerations for an in-vehicle eco-driving assistance system

This high-fidelity driving simulator study used a paired comparison design to investigate the effectiveness of 12 potential eco-driving interfaces. Previous work has demonstrated fuel economy improvements through the provision of in-vehicle eco-driving guidance using a visual or haptic interface. This study uses an eco-driving assistance system that advises the driver of the most fuel efficient accelerator pedal angle, in real time. Assistance was provided to drivers through a visual dashboard display, a multimodal visual dashboard and auditory tone combination, or a haptic accelerator pedal. The style of advice delivery was varied within each modality. The effectiveness of the eco-driving guidance was assessed via subjective feedback, and objectively through the pedal angle error between system-requested and participant-selected accelerator pedal angle. Comparisons amongst the six haptic systems suggest that drivers are guided best by a force feedback system, where a driver experiences a step change in force applied against their foot when they accelerate inefficiently. Subjective impressions also identified this system as more effective than a stiffness feedback system involving a more gradual change in pedal feedback. For interfaces with a visual component, drivers produced smaller pedal errors with an in-vehicle visual display containing second order information on the required rate of change of pedal angle, in addition to current fuel economy information. This was supported by subjective feedback. The presence of complementary audio alerts improved eco-driving performance and reduced visual distraction from the roadway. The results of this study can inform the further development of an in-vehicle assistance system that supports ‘green’ driving.     

Interface design of eco-driving support systems – Truck drivers’ preferences and behavioural compliance

The aim of the study was to investigate the perceived usefulness of various types of in-vehicle feedback and advice on fuel efficient driving. Twenty-four professional truck drivers participated in a driving simulator study. Two eco-driving support systems were included in the experiment: one that provided continuous information and one that provided intermittent information. After the simulator session, the participants were interviewed about their experiences of the various constituents of the systems. In general, the participants had a positive attitude towards eco-driving support systems and behavioural data indicated that they tended to comply with the advice given. However, different drivers had very different preferences with respect to what type of information they found useful. The majority of the participants preferred simple and clear information. The eco-driving constituents that were rated as most useful were advice on gas pedal pressure, speed guidance, feedback on manoeuvres, fuel consumption information and simple statistics. It is concluded that customisable user interfaces are recommended for eco-driving support systems for trucks.