Heckle and Chide: Results of a Randomized Road Safety Intervention in Kenya

In economies with weak enforcement of traffic regulations, drivers who adopt excessively risky behavior impose externalities on other vehicles, and on their own passengers. In light of the difficulties of correcting inter-vehicle externalities associated with weak third-party enforcement, this paper evaluates an intervention that aims instead to correct the intra-vehicle externality between a driver and his passengers, who face a collective action problem when deciding whether to exert social pressure on the driver if their safety is compromised. We report the results of a field experiment aimed at solving this collective action problem, which empowers passengers to take action. Evocative messages encouraging passengers to speak up were placed inside a random sample of over 1,000 long-distance Kenyan minibuses, or matatus, serving both as a focal point for, and to reduce the cost of, passenger action. Independent insurance claims data were collected for the treatment group and a control group before and after the intervention. Our results indicate that insurance claims fell by a half to two-thirds, from an annual rate of about 10 percent without the intervention, and that claims involving injury or death fell by at least 50 percent. Results of a driver survey eight months into the intervention suggest passenger heckling was a contributing factor to the improvement in safety.Kenya, traffic, driving regulations, matatus, safety

Glance behaviours when using an in-vehicle smart driving aid : a real-world, on-road driving study

In-vehicle information systems (IVIS) are commonplace in modern vehicles, from the initial satellite navigation and in-car infotainment systems, to the more recent driving related Smartphone applications. Investigating how drivers interact with such systems when driving is key to understanding what factors need to be considered in order to minimise distraction and workload issues while maintaining the benefits they provide. This study investigates the glance behaviours of drivers, assessed from video data, when using a smart driving Smartphone application (providing both eco-driving and safety feedback in real-time) in an on-road study over an extended period of time. Findings presented in this paper show that using the in-vehicle smart driving aid during real-world driving resulted in the drivers spending an average of 4.3% of their time looking at the system, at an average of 0.43 s per glance, with no glances of greater than 2 s, and accounting for 11.3% of the total glances made. This allocation of visual resource could be considered to be taken from ‘spare’ glances, defined by this study as to the road, but off-centre. Importantly glances to the mirrors, driving equipment and to the centre of the road did not reduce with the introduction of the IVIS in comparison to a control condition. In conclusion an ergonomically designed in-vehicle smart driving system providing feedback to the driver via an integrated and adaptive interface does not lead to visual distraction, with the task being integrated into normal driving

Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949

Enhanced Accessibility for People with Disabilities Living in Urban Areas

[Excerpt] People with disabilities constitute a significant proportion of the poor in developing countries. If internationally agreed targets on reducing poverty are to be reached, it is critical that specific measures be taken to reduce the societal discrimination and isolation that people with disabilities continue to face. Transport is an important enabler of strategies to fight poverty through enhancing access to education, employment, and social services. This project aims to further the understanding of the mobility and access issues experienced by people with disabilities in developing countries, and to identify specific steps that can be taken to start addressing problems. A major objective of the project is to compile a compendium of guidelines that can be used by government authorities, advocacy groups, and donor/loan agencies to improve the access of people with disabilities to transport and other services in urban areas

Integration of an adaptive infotainment system in a vehicle and validation in real driving scenarios

More services, functionalities, and interfaces are increasingly being incorporated into current vehicles and may overload the driver capacity to perform primary driving tasks adequately. For this reason, a strategy for easing driver interaction with the infotainment system must be defined, and a good balance between road safety and driver experience must also be achieved. An adaptive Human Machine Interface (HMI) that manages the presentation of information and restricts drivers’ interaction in accordance with the driving complexity was designed and evaluated. For this purpose, the driving complexity value employed as a reference was computed by a predictive model, and the adaptive interface was designed following a set of proposed HMI principles. The system was validated performing acceptance and usability tests in real driving scenarios. Results showed the system performs well in real driving scenarios. Also, positive feedbacks were received from participants endorsing the benefits of integrating this kind of system as regards driving experience and road safety.Postprint (published version

Train driver automation strategies to mitigate signals passed at danger on South African railways

A research project report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, in fulfillment of the requirements for the degree of Masters in Engineering. Date 2018/04/18Train derailments or collisions have the potential to result in catastrophic loss of life and/or destruction of property. Ever higher demands for train density (i.e. trains per hour for a given section of track) as well as the catastrophic results when accidents do occur have given rise to the development of railway signalling systems as mitigation measures (Rolt, 2009; Theeg & Vlasenko (2009b). Signals Passed At Danger (SPADs) refers to when a train driver passes a stop signal without authority and is one of the typical causes of such accidents resulting in significant damages reported within Transnet Freight Rail (TFR) in recent years. Studies have shown human train driver error and violation of signals to be a significant cause of SPAD events. This study investigated the application of train driver automation as a mitigation measure against SPADs within the South African railway environment in general and TFR in particular. The study was qualitative in nature, following a model development methodology and used in-depth, semi-structured interviews with railway signalling engineers for data collection. The primary goal was defined to be the development of a train driver function automation method that could be considered the most appropriate within the TFR operational environment. The study determined the most appropriate method to be that of having a human driver with technical supervision. In this arrangement, the human driver could remain in his conventional role of driving the train but with a technical supervision system superimposed that automatically intervenes if a train driver exceeds his movement authority (e.g. Automatic Train Protection or ATP). This approach mitigates many of the costs imposed by human failure associated with SPAD events, yet retains the value of human flexibility which is especially useful under abnormal circumstances.MT 201