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The development of urban strategies for the reduction of environmental impacts and decarbonization requires ongoing monitoring from the local scale and further deployment of actions to improve transport demand (user characteristics and modal choice) and supply (infrastructure and services). The analysis of pollution sources and the evaluation of possible scenarios are preliminary to the mitigation of impacts. In particular, the study of geometrical and functional characteristics of infrastructures through micro-simulation allows understanding of which schemes can support the reduction of emissions and guarantee high levels of service (LOS), reducing the problem of vehicular congestion in urban areas. The present work focuses on the small-scale analysis of vehicular traffic emissions at a multi-lane roundabout road intersection and the comparison of geometric schemes (current and design) and use with a turbo roundabout scheme as traffic volumes changes. These volumes have plummeted due to the current COVID-19 pandemic. The results show that the geometric-functional modification of the roundabout intersection from a multi-lane to a turbo-roundabout intersection allows a reduction of up to 30% of the emissions considering the current composition of the traffic fleet in the city of Rzeszow in Poland. The proposed comparative analysis methodology can contribute to the drafting of sustainable urban mobility plans (SUMPs) proposing a set of investments for new road works and considering a number of scenarios with interventions that can be implemented in the medium and long term that can provide the incentive to reduce road congestion and vehicular emissions.

IntroductionSafe driving constantly challenges the driver’s ability to respond to the dynamic traffic scene under space and time constraints. It is of particular importance for older drivers to perform sufficient visual and motor actions with effective coordination due to the fact of age-related cognitive decline. However, few studies have been able to integrate drivers’ visual-motor behaviours with environmental information in a spatial-temporal context and link to the cognitive conditions of individual drivers. Little is known about the mechanisms that underpin the deterioration in visual-motor coordination of older drivers.DevelopmentBased on a review of driving-related cognitive decline in older adults and the context of driver-vehicle-environment interactions, this paper established a conceptual framework to identify the parameters of driver’s visual and motor behaviour, and reveal the cognitive process from visual search to vehicle control in driving. The framework led to a psycho-geoinformatics approach to measure older drivers’ driving behaviours and investigate the underlying cognitive mechanisms. The proposed data collection protocol and the analysis and assessments depicted the psycho-geoinformatics approach on obtaining quantified variables and the key means of analysis, as well as outcome measures.ConclusionsRecordings of the driver and their interactions with the vehicle and environment at a detailed scale give a closer assessment of the driver’s behaviours. Using geoinformatics tools in driving behaviours assessment opens a new era of research with many possible analytical options, which do not have to rely on human observations. Instead, it receives clear indicators of the individual drivers’ interactions with the vehicle and the traffic environment. This approach should make it possible to identify lower-performing older drivers and problematic visual and motor behaviours, and the cognitive predictors of risky driving behaviours. A better targeted regulation and tailored intervention programs for older can be developed by further research.

The measurement of acceleration during vehicle motion can be used to assess the driving styles and behaviours of drivers, to control vehicle traffic, to detect uncontrolled vehicle behaviour, and to prevent accidents. The measurement of acceleration during vehicle motion on an icy road can be used to warn the driver about changing conditions and the related hazards. This paper presents the results of testing the motion parameters of a Ford Transit adapted for passenger transport in critical traffic conditions. It can contribute to the improvement of road safety. Critical traffic conditions are deemed in the paper as sudden braking, rapid acceleration, and circular vehicle motion at maximum speed maintainable in the given conditions. The vehicle’s acceleration and speed were measured during the tests. The tests were carried out with a TAA linear acceleration sensor and a Correvit S-350 Aqua optoelectronic sensor. The same test runs were conducted on a dry surface, a wet (after rain) surface and a surface covered with a thin, invisible ice layer. The objective of the tests was to determine the impact of invisible road icing, the so-called black ice, on the tested vehicle’s braking, acceleration, and circular motion. It was demonstrated that a virtually invisible ice layer covering the road surface has a substantial impact on the tested vehicle’s motion parameters, thereby affecting traffic safety. It substantially extends the braking and acceleration distances and requires the driver to reduce the vehicle’s speed when performing circular motions. A clear wet surface, representing motion after rain, did not substantially affect the analysed parameters. The obtained results can be used in traffic simulations and to analyse the causes of accidents.

The use of sensors in monitoring lateral accelerations in delivery van transport focuses on measuring lateral accelerations on routes with roundabouts and curves to increase road safety. Using microelectromechanical system (MEMS) sensors, it measures the lateral accelerations acting on the vehicle and the load being transported during the test drives to study vehicle dynamics of delivery van for cargo securing, which is essential to the decision of where accelerometer sensors should be placed when monitoring accelerations or performing cargo securing tests. Using an accelerometer and position tracking, accelerations can be detected when traversing curves and roundabouts at selected locations on the vehicle and load. Manual labeling of acceleration events has been used to identify different lateral acceleration events and regression analysis to determine the relationship between lateral accelerations at different sensor positions. The level of acceleration on the roof of the vehicle was found to be like that occurring on a lashed load with limited movements. If we compare the mean values of the lateral accelerations of the individual events between the sensors, the sensor on the side of the vehicle body at the height of the sensor on the load had approximately 5% lower mean values than the sensor on the roof. The sensor on the load measured approximately 5% higher mean values than the sensor on the roof. Hence, the mean lateral accelerations of the individual events for the sensor on the load are 10% higher than for the sensor at the same height on the vehicle body. The values of the mean lateral accelerations of the delivery van from the sensor on the roof of the vehicle are closer to the values of the accelerations of the sensor on the load than to the values of the sensor on the body of the vehicle at the same height.

The paper presents the analysis of lateral acceleration based on a vehicle traveling in a circle with a double lane change. The maneuvers of driving in a circle and double lane changes were chosen not by accident. Tests were carried out on a popular brand passenger car on wet and dry surfaces. The vehicle is equipped with high-class electronic equipment that records vehicle motion parameters such as lateral acceleration, longitudinal acceleration, speed and others. In the study, the results of maneuvers were statistically analyzed and tabulated in order to compare and influence the comfort of travellers.

Ensuring the safety and smoothness of road traffic in transports, which by their parameters may cause a special situation in road traffic, is the primary task of the team of workers accompanying overweight and/or oversized vehicles. In order to successfully carry out such an activity, it is necessary not only the appropriate technical equipment but also the knowledge of accompanying staff on the issue of critical points during transport and knowledge of how to prevent the danger in the most appropriate way. For these reasons, there is a need to focus the interest of companies on the course of such transport and to increase the safety of all road users through trained staff to accompany vehicles performing overweight and/or oversized transport.

The measurement of car acceleration in time can be used to assess driving styles and safety behaviours of drivers. The values of lateral acceleration of the car can be an indication of the driver’s aggressive driving style and tendency for risky behaviour. If the lateral acceleration is too high, it may affect the car’s stability and potentially cause it to roll over. The paper outlines the results of the lateral acceleration analysis of a Ford Transit car driving in a circle and in the attempt to change two driving lanes. The tests were conducted in driving practice areas. Measurements were taken for the test vehicle driving in circles with maximum attainable velocity, and changing two driving lanes with pre-specified velocity. The tests were conducted on asphalt and concrete surfaces, in dry, wet and icy conditions. The purpose of the tests was to determine the maximum lateral acceleration of the analysed vehicle. The impact of the surface condition on the lateral acceleration of the test vehicle was also determined. The obtained results can be used as threshold values to assess driving style, and to analyse causes of accidents taking into consideration the condition of the road surface.

The surrounding Taman Mini LRT station boasts a diverse range of land uses, primarily characterized by residential zones, interspersed with dynamic activity hubs such as tourist attractions, shopping malls, and hospitals. The prevailing issue in this area relates to persistent traffic congestion, stemming from the overreliance on private vehicles, inadequate public transportation infrastructure, disjointed intermodal connections, and the lack of seamless integration between transportation modes and activity centers. This study endeavors to comprehensively analyze pedestrian and vehicular movement in the periphery of the Taman Mini LRT station. This analysis is pivotal in assessing the current mobility landscape. Notably, the intensity of activity within a particular land use directly correlates with its ability to draw vehicular traffic, consequently leading to prolonged waiting times for pedestrians at crosswalks. Employing a four-step model, this research methodically defines pedestrian and vehicular flow within the region. This model incorporates output distribution of trips and origin-destination graphs. Additionally, stated preference techniques are employed to discern respondents’ inclinations when making travel decisions. This involves evaluating key factors like travel time, fares, frequency, accessibility level, comfort, and safety – categories that significantly influence their preferences when selecting between LRT and other transportation modes.

Characterization of driving maneuvers or driving styles through motion sensors has become a field of great interest. Before now, this characterization used to be carried out with signals coming from extra equipment installed inside the vehicle, such as On-Board Diagnostic (OBD) devices or sensors in pedals. Nowadays, with the evolution and scope of smartphones, these have become the devices for recording mobile signals in many driving characterization applications. Normally multiple available sensors are used, such as accelerometers, gyroscopes, magnetometers or the Global Positioning System (GPS). However, using sensors such as GPS increase significantly battery consumption and, additionally, many current phones do not include gyroscopes. Therefore, we propose the characterization of driving style through only the use of smartphone accelerometers. We propose a deep neural network (DNN) architecture that combines convolutional and recurrent networks to estimate the vehicle movement direction (VMD), which is the forward movement directional vector captured in a phone’s coordinates. Once VMD is obtained, multiple applications such as characterizing driving styles or detecting dangerous events can be developed. In the development of the proposed DNN architecture, two different methods are compared. The first one is based on the detection and classification of significant acceleration driving forces, while the second one relies on longitudinal and transversal signals derived from the raw accelerometers. The final success rate of VMD estimation for the best method is of 90.07%.

The paper compares the results of the vehicle motion trajectory calculations determined on the basis of the recorded waveforms of the acceleration vector and angular velocity components during the double lane change maneuver, taking into account the errors of acceleration sensors reset (zeroing). The differences in the results of the calculations were determined and possible causes of these differences were indicated. The test results indicate that special attention should be paid to zeroing the acceleration sensors, in particular the a y component.