Opportunities to reduce road traffic injury: new insights from the study of urban areas

Abstract

Over the past four decades considerable efforts have been taken to mitigate the growing burden of road injury. With increasing urbanisation along with global mobility that demands not only safe but equitable, efficient and clean (reduced carbon footprint) transport, the responses to dealing with the burgeoning road traffic injury in low- and middle-income countries has become increasingly complex. In this paper, we apply unique methods to identify important strategies that could be implemented to reduce road traffic injury in the Asia-Pacific region; a region comprising large middle-income countries (China and India) that are currently in the throes of rapid motorisation. Using a convolutional neural network approach, we clustered countries containing a total of 1632 cities from around the world into groups based on urban characteristics related to road and public transport infrastructure. We then analysed 20 countries (containing 689 cities) from the Asia-Pacific region and assessed the global burden of disease attributed to road traffic injury and these various urban characteristics. This study demonstrates the utility of employing image recognition methods to discover new insights that afford urban and transport planning opportunities to mitigate road traffic injury at a regional and global scale.     

Hybrid and electric low-noise cars cause an increase in traffic accidents involving vulnerable road users in urban areas

Due to resource scarcity, the number of low-noise and electric cars is expected to increase rapidly. The frequent use of these cars will lead to a significant reduction of traffic related noise and pollution. On the other hand, due to the adaption and conditioning of vulnerable road users the number of traffic accidents involving pedestrians and bicyclists is postulated to increase as well. Children, older people with reduced eyesight and the blind are especially reliant on a combination of acoustic and visual warning signals with approaching or accelerating vehicles. This is even more evident in urban areas where the engine sound is the dominating sound up to 30 kph (kilometres per hour). Above this, tyre–road interaction is the main cause of traffic noise. With the missing typical engine sound a new sound design is necessary to prevent traffic accidents in urban areas. Drivers should not be able to switch the sound generator off.     

Using daily store-level data to understand price promotion effects in a semiparametric regression model

Though it has been widely reported in the marketing literature that temporary price discounts generate substantial short-term sales increase, the shape of the deal effect curve constitutes a key research topic, for which there are still limited empirical results. To address this issue, a semiparametric regression approach is used to model the complex nature of this phenomenon. Our model is developed at the brand level using daily store-level scanner-data, which allows the study of several nonreported promotional effects, such as the influence of the day of the week both in promotional and nonpromotional periods. The results show that the weekend is the most effective in increasing promotional sales and that asymmetric and neighborhood effects hold. However, 9-ending promotional prices are not impactful.     

Using animated agents in learner‐controlled training: the effects of design control

Animated agents have the potential to increase engagement and learning during online training by acting as personalized tutors. However, little is known about the conditions that make these agents most effective. In this study, 183 e‐learners completed a Microsoft Excel training course. Approximately half were assigned an agent with predetermined features. The others were allowed to choose their agent's appearance, personality, feedback style or all of the above features. Offering multiple choices increased learning. Unexpectedly, choice of feedback style alone decreased self‐efficacy. Choosing the agent's appearance increased self‐efficacy and the number of training modules completed. Overall, this study expands the learner control literature, identifying a new form of learner control that has some beneficial effects on knowledge acquisition.