Search Results (12)

Motorbike taxis are widely used in Yaoundé and Douala, despite their association with heightened accident risks and relatively high fares. This research combines qualitative methods, including 38 semi-structured interviews and direct field observations, with a quantitative survey of 280 motorbike taxi passengers (customers). It employs a dynamic risk approach to analyse both the factors motivating individuals to choose motorbike taxis and the strategies adopted by drivers and passengers to mitigate and prevent accidents. The findings reveal that speed, cost-effectiveness, and the limited accessibility of certain neighbourhoods to other transport options are key factors driving regular motorbike taxi use. Moreover, strategies for managing accident risks include regulating passenger positions based on gender, perceived age, or physical stature; invoking deities for protection; and passengers’ verbal interactions with drivers to ensure safer behaviour. This research also explores how overloading, a collectively tolerated deviance, is managed to avoid or minimize the impact of accidents. By addressing both risk acceptance and prevention strategies, this study provides new insights into passengers’ social perceptions, which are often overlooked in motorbike taxi research. It expands the understanding of motorbike taxi use in urban Global South transport contexts, particularly in terms of users’ risk management behaviours. Full article

Recent studies revealed that the high production of reactive oxidative species due to exposure to fine or ultrafine particles are involved in many chronic respiratory disorders. However, the poor standard of clinical data in sub-Saharan countries makes the assessment of our knowledge on the health impacts of air pollution in urban cities very difficult. Objective: The aim of this study was to evaluate the distribution of respiratory disorders associated with exposure to fine and ultrafine air particles through the changes of some oxidative stress biomarkers among motorbike drivers from two cities of Cameroon. Methods: A cross-sectional survey using a standardized questionnaire was conducted in 2019 on 191 motorcycle drivers (MDs) working in Douala and Dschang. Then, the activities of superoxide dismutase (SOD) and the level of malondialdehyde (MDA) were measured using colorimetric methods. The data of participants, after being clustered in Microsoft Excel, were analyzed and statistically compared using SPSS 20 software. Results: The motorbike drivers recruited from both cities were from 21 to 40 years old, with a mean age of 29.93 (±0.82). The distribution of respiratory disorders, such as a runny nose, cold, dry cough, chest discomfort, and breathlessness, was significantly increased among MDs in Douala. According to the results of biological assays, SOD and MDA were significantly greater among the MDs recruited in Douala compared to those of Dschang. The change in these oxidative stress markers was significantly positively correlated with the mobilization of monocytes and negatively correlated with neutrophils, showing the onset and progression of subjacent inflammatory reactions, and it seemed to be significantly influenced by the location MDs lived in. Conclusions: Through this study, we have confirmed the evidence supporting that the onset and progression of oxidative stress is caused by the long-term exposure to fine or ultrafine air particles among working people living in urban cities. Further studies should be conducted to provide evidence for the cellular damage and dysfunction related to the chronic exposure to fine particulate matter (PM) in the air among working people in the metropolitan sub-Saharan Africa context. Full article

Shared mobility is one of the smart city applications in which traditional individually owned vehicles are transformed into shared and distributed ownership. Ensuring the safety of both drivers and riders is a fundamental requirement in shared mobility. This work aims to design and implement an adequate framework for shared mobility within the context of a smart city. The characteristics of shared mobility are identified, leading to the proposal of an effective solution for real-time data collection, tracking, and automated decisions focusing on safety. Driver and rider safety is considered by identifying dangerous driving behaviors and the prompt response to accidents. Furthermore, a trip log is recorded to identify the reasons behind the accident. A prototype implementation is presented to validate the proposed framework for a delivery service using motorbikes. The results demonstrate the scalability of the proposed design and the integration of the overall system to enhance the rider’s safety using machine learning techniques. The machine learning approach identifies dangerous driving behaviors with an accuracy of 91.59% using the decision tree approach when compared against the support vector machine and K-nearest neighbor approaches. Full article

Accidents pose significant obstacles to economic progress and quality of life, especially in developing countries. Thailand faces such challenges and this research seeks to assess the frequency and most common causes of road accidents that lead to fatalities. This study employed the Apriori algorithm to examine the interrelationships among factors contributing to accidents in order to inform policymaking for reducing accident rates, minimizing economic and human losses, and enhancing the effectiveness of the healthcare system. By analyzing road accident data from 2015 to 2020 in Thailand (167,820 accidents causing THB 1.13 billion in damages), this article specifically focuses on the drivers responsible for fatal highway accidents. The findings reveal several interconnected variables that heighten the likelihood of fatalities, such as male gender, exceeding speed limits, riding a motorbike, traveling on straight roads, encountering dry surface conditions, and clear weather. An association rule analysis underscores the increased risk of injury or death in traffic accidents. Full article

Mechanical vibrations have a significant impact on ride comfort; the driver is constantly distracted as a result. Volumetric engine inertial unbalances and road profile irregularities create mechanical vibrations. The purpose of this study is to employ optimization algorithms to identify structural elements that contribute to vibration propagation and to provide optimal solutions for reducing structural vibrations induced by engine unbalance and/or road abnormalities in a motorcycle. The powertrain assembly, swing-arm assembly, and vibration-isolating mounts make up the vibration-isolating system. Engine mounts are used to restrict transferred forces to the motorbike frame owing to engine shaking or road irregularities. Two 12-degree-of-freedom (DOF) powertrain motorcycle engine systems (PMS) were modeled and examined for design optimization in this study. The first model was used to compute engine mount parameters by reducing the transmitted load through the mounts while only considering shaking loads, whereas the second model considered both shaking and road bump loads. In both configurations, the frame is infinitely stiff. The mount stiffness, location, and orientation are considered to be the design parameters. The purpose of this study is to employ computational methods to minimize the loads induced by shaking forces. To continue the optimization process, Grey Wolf Optimizer (GWO), a meta-heuristic swarm intelligence optimization algorithm inspired by grey wolves in nature, was utilized. To demonstrate GWO’s superior performance in PMS, other optimization methods such as a Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) were used for comparison. To minimize the engine’s transmitted force, GWO was employed to determine the optimal mounting design parameters. The cost and constraint functions were formulated and optimized, and promising results were obtained and documented. The vibration modes due to shaking and road loads were decoupled for a smooth ride. Full article

Car horns were originally installed in vehicles for safety. However, many urban areas in several countries face noise problems related to the use of car and motorbike horns. To propose measures to suppress the use of horns, relationships between horn use and factors including driver awareness and behavior, traffic environment, and the transportation system should be investigated. The present study therefore conducted surveys to grasp the current circumstances of horn use and traffic at urban intersections in Taiwan. The relationship between horn use and the traffic volume of standard-sized vehicles was found. According to an analysis of horn use during traffic signal cycles, in many cases, horns were honked after entering intersections to turn left. In particular, horns were honked when the driver waited more than 4 s for the car in front to start moving after the green light allowing left turns was turned on. An analysis of noise levels at intersections showed that the maximum noise level value (L_Amax) could be reduced if vehicle horns were not used. Multiple regression analysis also indicated that L_Amax values increased with the frequency of horn use. The equivalent continuous A-weighted sound pressure level (L_Aeq,10min) did not change with driver horn use, and increased with the traffic volume of motorcycles. Full article

Road accidents are a common occurrence throughout the world. The development of efficient electric vehicle (EVs) with high levels of safety is one of today’s biggest challenges. In this article, a novel modular bike helmet based on an RF transmitter and a helmet side based on Arduino Uno, an accelerometer and an LED array that can relay information to approaching vehicles has been proposed. Current motorbike helmets are a form of passive protective gear that only serve the purpose of avoiding fatal damage to the skull. The proposed helmet will add to the current functionality of a helmet by making it smarter, giving it a means of preventing an accident. The proposed helmet will broadcast information about the biker’s movements, such as acceleration and deceleration, to the approaching vehicles. This information has never been broadcasted to approaching vehicle before. Additionally, common turn and stop signals will be broadcasted, allowing the driver of any approaching vehicle to take informed decision that can ensure both their safety and that of the biker. Full article

This study explored the important factors affecting drunk car/motorbike drivers’ willingness to use and pay for alcohol interlocks. Data were obtained through a survey upon choice-based sampling conducted in central Taiwan. Questionnaires were distributed to the participants of drunk driving and road safety education courses from 17 August to 26 October 2020. All drunk drivers whose driver’s licenses are revoked for drunk driving are mandated to participate in this course. Prior to the survey, the researchers explained the questionnaires, instructed the participants to complete the questionnaires, and then collected all the questionnaires. The socioeconomic characteristics of drunk drivers, awareness of alcohol interlocks and drunk driving, drinking patterns and health self-assessment before and after drunk driving ban enforcement, and changes in the number of trips were investigated. This study applied the double-hurdle model for data analysis to estimate the variables affecting drunk car/motorbike drivers. Results indicate that the respondents who were classified by the Alcohol Use Disorders Identification Test as high-risk drinkers before and after drunk driving ban enforcement were more willing to use alcohol interlocks and to pay higher prices. Additionally, the respondents with declined health self-assessments were also more willing to use alcohol interlocks and pay higher prices. This study suggests offering subsidies for alcohol interlocks to families with financial difficulties, in order to increase the alcohol interlock installation rate. Moreover, since the current duration of license suspension and withdrawal is considerably long, drunk drivers avoid using and installing alcohol interlocks by reducing the number of trips. In other words, the willingness to install alcohol interlocks may be increased by reducing the duration of license suspension and withdrawal. Full article

With the further development of highly automated vehicles, drivers will engage in non-related tasks while being driven. Still, drivers have to take over control when requested by the car. Here, the question arises, how potentially distracted drivers get back into the control-loop quickly and safely when the car requests a takeover. To investigate effective human–machine interactions, a mobile, versatile, and cost-efficient setup is needed. Here, we describe a virtual reality toolkit for the Unity 3D game engine containing all the necessary code and assets to enable fast adaptations to various human–machine interaction experiments, including closely monitoring the subject. The presented project contains all the needed functionalities for realistic traffic behavior, cars, pedestrians, and a large, open-source, scriptable, and modular VR environment. It covers roughly 25 km², a package of 125 animated pedestrians, and numerous vehicles, including motorbikes, trucks, and cars. It also contains all the needed nature assets to make it both highly dynamic and realistic. The presented repository contains a C++ library made for LoopAR that enables force feedback for gaming steering wheels as a fully supported component. It also includes all necessary scripts for eye-tracking in the used devices. All the main functions are integrated into the graphical user interface of the Unity^® editor or are available as prefab variants to ease the use of the embedded functionalities. This project’s primary purpose is to serve as an open-access, cost-efficient toolkit that enables interested researchers to conduct realistic virtual reality research studies without costly and immobile simulators. To ensure the accessibility and usability of the mentioned toolkit, we performed a user experience report, also included in this paper. Full article

Urban air pollution, despite its dangerous health impact, is poorly studied in sub-Saharan Africa (sSA). Epidemiological data on this silent killer are almost non-existent for cities of Cameroon, which seems to be one of the sSA countries where populations are highly exposed to air pollutants. Objective: The present study was conducted in Douala city, and aimed at determining the association of urban air quality degradation with respiratory and systemic health in active populations exposed to air pollutants on a daily basis. Methods: A cross-sectional study was conducted from 2017 to 2019 in 1182 active people consisting of motorbikes drivers (MD), outdoor urban workers (UW), and fuel station sellers (FSS). A standardized questionnaire was used to document participants’ data. One hundred and twenty-six (126) motorbike drivers were selected to evaluate the relationship between haematological (white blood cells, platelets) and inflammatory (C-reactive protein—CRP) biomarkers, and air pollution; compared with those of a sixty-five (65) motorbike drivers’ control group enrolled in Dschang, another town situated at about 216.3 km from Douala. Results: Among those recruited in urban Douala, some respiratory disorders such as running nostrils, colds, common fever, sore throats, dry cough, wheezing, chest pain, shortness of breath and systemic symptoms such as headaches, eye irritation, conjunctivitis, watery eyes and general tiredness were very common among MD, UW, and FSS. Regarding biological data, blood monocytes, lymphocytes and CRP were found to be significantly increased among selected MD in Douala, compared to control groups in Dschang. Conversely, a more significant decrease in blood neutrophil level was observed among MD in Douala than control groups in Dschang. These changes of haematological markers were significantly associated with place of residence, site of activity, and daily duration. Conclusion: Our results suggest the risk of suffering from respiratory impairments and systemic symptoms with exposure to urban air pollution among active people working near highways in Douala. Full article

This paper proposes a deep-learning model with task-specific bounding box regressors (TSBBRs) and conditional back-propagation mechanisms for detection of objects in motion for advanced driver assistance system (ADAS) applications. The proposed model separates the object detection networks for objects of different sizes and applies the proposed algorithm to achieve better detection results for both larger and tinier objects. For larger objects, a neural network with a larger visual receptive field is used to acquire information from larger areas. For the detection of tinier objects, the network of a smaller receptive field utilizes fine grain features. A conditional back-propagation mechanism yields different types of TSBBRs to perform data-driven learning for the set criterion and learn the representation of different object sizes without degrading each other. The design of dual-path object bounding box regressors can simultaneously detect objects in various kinds of dissimilar scales and aspect ratios. Only a single inference of neural network is needed for each frame to support the detection of multiple types of object, such as bicycles, motorbikes, cars, buses, trucks, and pedestrians, and to locate their exact positions. The proposed model was developed and implemented on different NVIDIA devices such as 1080 Ti, DRIVE-PX2 and Jetson TX-2 with the respective processing performance of 67 frames per second (fps), 19.4 fps, and 8.9 fps for the video input of 448 × 448 resolution, respectively. The proposed model can detect objects as small as 13 × 13 pixels and achieves 86.54% accuracy on a publicly available Pascal Visual Object Class (VOC) car database and 82.4% mean average precision (mAP) on a large collection of common road real scenes database (iVS database). Full article

The paper issues the development, building and testing of a Folding Electric Motorbike, a lightweight, low cost and all-electric two-wheeler vehicle taking full advantage on today’s city infrastructure. The technology offers drivers to combine transportation methods, lowering cost, and greenhouse gas emission. The paper documents innovative studies on how the technology can be used to explode the today’s transportation system and be used to bridge the gap of today’s challenges and future solutions. The optimum components to be used in the small, lightweight vehicles are selected based on the technology’s functional requirements. The selection of motorbike’s drivetrain components is based on the latest available technology, with respect to economic viability. The technologies first two development stages are described. In the first development stage the vehicle’s functional requirements are defined. This is followed by a feasibility study and a realization on technologies capabilities. The feasibility study is performed by developing, building and evaluating an alpha-prototype vehicle. The research indicate that the possibility of developing a powerful , light-weight, low cost and all-electric two-wheeler vehicle taking full advantage on today’s city infrastructure is very prospective. The alpha-prototype was successfully constructed and is considered to be ready for further laboratory testing and test driving before continuations on a fully designed beta-prototype. Full article