Search Results (1,220)

Personal Rapid Transit (PRT) and Group Rapid Transit (GRT) are classes of fully automated public transport systems, where passengers can travel in small vehicles on an interconnected, grade-separated network of guideways, non-stop, from origin to destination. PRT and GRT are considered sustainable as they are low-emission and able to attract car drivers. The parameterized cost modeling framework developed in this paper has the advantage that profitability of different PRT/GRT systems can be rapidly verified in a transparent way and in function of a variety of relevant system parameters. This framework may contribute to a more transparent, rapid, and low-cost evaluation of PRT/GRT schemes for planning and decision-making purposes. The main innovation is the introduction of the “peak hour network saturation” S: the number of vehicles in circulation during peak hour divided by the maximum number of vehicles running at line speed with minimum time headways. It is an index that aggregates the main uncertainties in the planning process, namely the demand level relative to the supply level. Furthermore, a maximum S can be estimated for a PRT/GRT project, even without a detailed demand estimation. The profit per trip is analytically derived based on S and a series of more certain parameters, such as fares, capital and maintenance costs, daily demand curve, empty vehicle share, and physical properties of the system. To demonstrate the ability of the framework to analyze profitability in function of various parameters, we apply the methods to a single vehicle PRT, a platooned PRT, and a mixed PRT/GRT. The results show that PRT services with trip length proportional fares could be profitable already for $S>0.25$. The PRT capacity, profitability, and robustness to tripled infrastructure costs can be increased by vehicle platooning or GRT service during peak hours. Full article

Amylose content significantly influences the technological, quality, and nutritional properties of starchy foods. This study developed a rapid, non-destructive method to quantify amylose content in starch using mid-infrared (MIR) spectroscopy combined with chemometric techniques. Manually prepared starch mixtures with varying amylose levels were scanned to obtain MIR spectra, which were preprocessed using smoothing and z-score normalization to reduce operational variability. Three variable selection methods, including bootstrap soft shrinkage (BOSS), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE), were applied to select the useful spectra. A partial least square (PLS) model was then constructed to correlate selected spectral data with amylose content. The results revealed that the number and position of selected variables differed across different optimization methods, which influenced the model’s performance. It is worth noting that the optimized PLS model significantly reduced the root mean squared error of cross-validation (RMSECV) and improved prediction accuracy in 50 runs. In particular, the CARS-PLS model showed superior performance, achieving a correlation coefficient (R_p) of 0.964 and a root mean squared error of prediction (RMSEP) of 4.59, a 60% improvement over the original PLS model, which had an RMSEP of 11.56. These results highlight MIR spectroscopy’s potential, combined with optimized chemometric models, for accurate amylose quantification in food quality control. Full article

(1) Background: The number of casualties due to car accidents in Europe is decreasing. However, there are still very large differences in the levels of road safety between countries, even within the European Union. Therefore, it is vital to conduct reliable international analyses to compare the effectiveness of actions taken to prevent road accidents. Information on the number of accidents, injuries, and fatalities can be found in various databases (e.g., Eurostat or OECD). In this paper, it is clearly shown that data on car accidents and the resulting injuries are not comparable between different countries, and any conclusions drawn using these data as their basis will be erroneous. (2) Methods: The indicators of the number of car accidents, injured people, and fatalities in relation to the number of inhabitants were determined, then their distribution and mutual correlations were examined for a group of selected European countries. (3) Results: There is no correlation between the indicators of the number of car accidents and injuries and the indicator of fatalities. An assessment of road safety based on these indicators would result in inconsistent and ambiguous conclusions. (4) Conclusions: It has been empirically shown that data on the number of car accidents and injured people from different countries are not comparable. These conclusions were verified by providing examples of the definitions of an injured person used in different countries. This paper clearly indicates that any international comparisons can only be made based on data regarding the number of road accident fatalities. Full article

It is evident that a novel engineering solution is required in order to elevate a greater number of polluting cars into a higher category. There appears to be a paucity of direct interest in upgrading Euro 1, 2, 3, 4, and 5 vehicles to the highest possible level, primarily through software modifications of the parameters determining the performance of the internal combustion engine (ICE). The potential for advancement in this area is evidenced by the presence of systems that enhance environmental efficiency, even in Euro 2 vehicles. These include exhaust gas recirculation, catalytic converter, lambda sensor, electronic control fuel injection, and ignition timing. It is precisely these vehicles that are subject to optimization, a process which would allow the maximum service life of otherwise more reliable but older vehicles to be exploited. Full article

In recent decades, environmental requirements for reducing the toxic components emitted from vehicle exhausts have decreased drastically. Technologies for after-treatment of diesel vehicle emissions are being improved continuously in order to meet increasingly stringent regulations. Passenger cars are a significant source of air pollution, especially in urban areas. The EU has decided to phase out internal combustion engines. Stricter Real Driving Emissions (RDE) testing procedures have also been introduced, aiming to assess the emissions of nitrogen oxides (NO_x) and particle number (PN). The present work investigates the interaction between performance and smoke emissions of a diesel vehicle on a pre-established route in an urban environment with an everyday (normal) driving style. The results showed that when the vehicle is technically sound and meets its technical specifications, smoke emissions are within normal limits. Full article

In the present study, a quantitative analysis of culturable microflora of car cabin filters was accomplished, with a special focus on bacteria resistant to some antibiotics. The occurrence of antibiotic-resistant bacteria was considered in the filters with activated carbon and filters with antibacterial properties. The minimum inhibitory concentration was evaluated for selected bacterial strains isolated from the filters. It was found that cabin filters after long-time operation are not only heavily contaminated with bacteria and fungi but also constitute a habitat for numerous antibiotic-resistant bacteria. The numbers of culturable bacteria resistant to penicillin, nitrofurantoin, rifampicin, doxycycline, or gentamicin reached 10²–10³ CFU/g of filter material. No relationship was observed between car brand or filter type and the abundance of antibiotics-resistant bacteria. The lower bacterial content of antibacterial filters was not accompanied by a proportionally lower content of resistant microorganisms which may indicate that the present techniques are not sufficient to limit their growth effectively. Pseudomonas sp. isolates from the filter material were not sensitive even in relation to high concentrations of some antibiotics, which confirms their significant resistance potential and may be important in the context of the spread of drug resistance in the vehicles indoors. Full article

Due to the increasing number of vehicles and the aging population, the vulnerability to sudden medical emergencies among drivers is a growing problem. Events such as heart attack, stroke, and loss of consciousness can occur without warning and endanger everyone on the road. Modern vehicles, equipped with electronic systems, can support real-time driver’s health monitoring through early detection technologies. The existing Driver Monitoring Systems (DMS) in our cars assess behavioral states such as drowsiness and distraction. In the future, DMS will include biometric sensors to monitor vital signs such as heart rate and respiration. By finding predictors of sudden illnesses (SI), such a system will provide valuable time for the driver to react before the strike of a medical event. In this paper, we present our vision for DMS operation with physiological monitoring capabilities. A brief overview of sensor’s types and their locations in the vehicle interior used in the research studies for monitoring the corresponding physiological parameters is presented. A comparative analysis of the advantages and disadvantages of the sensing methods used for physiological monitoring of the driver in real driving scenarios is made. Full article

This study compared the oxidation resistance of three commercial oils used in electric car transmissions. The tests were carried out on a stand equipped with a gear train in accordance with ASTM D5704. The changes in physicochemical and dielectric parameters as well as the degree of degradation were assessed by means of the FTIR spectral analysis method. Significant changes in physicochemical parameters were noticeable, including an increase in the acid number as well as an increase in kinematic viscosity at 40 °C and a decrease at 100 °C. The test results show that the oil dedicated to hybrid vehicles degraded the least, while the other oils, dedicated to electric vehicles, lost their lubricating properties to a significant extent. In addition, attention was paid to the abrasion generated during the operation of the gearbox, which has a fairly considerable impact on the change in the dielectric properties of the oils tested. In the future, more detailed research should be carried out on the effects of varying temperatures and of an electromagnetic field on the degradation of gear oils dedicated to EVs and to determine how their dielectric properties change. Full article

This paper explores the potential of public webcams as a source of data for transport research. Eight different open-source object detection models were tested on three publicly accessible webcams located in the city of Brunswick, Germany. Fifteen images at different lighting conditions (bright light, dusk, and night) were selected from each webcam and manually labelled with regard to the following six categories: cars, persons, bicycles, trucks, trams, and buses. The manual counts in these six categories were then compared to the number of counts found by the object detection models. The results show that public webcams constitute a useful source of data for transport research. In bright light conditions, applying out-of-the-box object detection models can yield reliable counts of cars or persons in public squares, streets, and junctions. However, the detection of cars and persons was not reliably accurate at dusk or night. Thus, different object detection models might have to be used to generate accurate counts in different lighting conditions. Furthermore, the object detection models worked less well for identifying trams, buses, bicycles, and trucks. Hence fine-tuning and adapting the models to the specific webcams might be needed to achieve satisfactory results for these four types of traffic participants. Full article

There are a large number of unstructured scenes and special targets in the construction machinery application scene, which brings greater interference to the environment sensing system for Construction Machinery Autonomous Operation Application. The conventional mature sensing scheme in passenger cars is not fully applicable to construction machinery. By taking the environmental characteristics and operating conditions of construction machinery into consideration, a set of environmental sensing algorithms based on LiDAR for construction machinery scenarios is studied. Real-time target detection of the environment, trajectory tracking, and prediction for dynamic targets are achieved. Decision instructions are provided for upstream detection information for the subsequent behavioral decision-making, motion planning, and other modules. To test the effectiveness of the information exchange between the proposed algorithm and the overall machine interface, the early warning and emergency braking for autonomous operation is implemented. Experiments are carried out through an excavator test platform. The superiority of the optimized detection model is verified through real-time target detection tests at different speeds and under different states. Information exchange between the environmental sensing and the machine interface based on safety warning and braking is achieved. Full article

This study is titled “Success Factors in Transport Interventions: A Mixed-Method Systematic Review (1990–2022)”. The purpose of the systematic review is to (1) identify effective interventions for transitioning individuals from private car reliance to sustainable transport, (2) summarise psychosocial theories shaping transportation choices and identify enablers and barriers influencing sustainable mode adoption, and (3) determine the success factors for interventions promoting sustainable transport choices. The last search was conducted on 18 November 2022. Five databases (Scopus, Web of Science, MEDLINE, APA PsycInfo, and ProQuest) were searched using customised Boolean search strings. The identified papers were included or excluded based on the following criteria: (a) reported a modal shift from car users or cars to less CO₂-emitting modes of transport, (b) covered the adoption of low-carbon transport alternatives, (c) comprised interventions to promote sustainable transport, (d) assessed or measured the effectiveness of interventions, or (e) proposed behavioural models related to mode choice and/or psychosocial barriers or drivers for car/no-car use. The identified papers eligible for inclusion were critically appraised using Sirriyeh’s Quality Assessment Tool for Studies with Diverse Designs. Inter-rater reliability was assessed using Cohen’s Kappa to evaluate the risk of bias throughout the review process, and low-quality studies identified by the quality assessment were excluded to prevent sample bias. Qualitative data were extracted in a contextually relevant manner, preserving context and meaning to avoid the author’s bias of misinterpretation. Data were extracted using a form derived from the Joanna Briggs Institute. Data transformation and synthesis followed the recommendations of the Joanna Briggs Institution for mixed-method systematic reviews using a convergent integrated approach. Of the 7999 studies, 4 qualitative, 2 mixed-method, and 30 quantitative studies successfully passed all three screening cycles and were included in the review. Many of these studies focused on modelling individuals’ mode choice decisions from a psychological perspective. In contrast, case studies explored various transport interventions to enhance sustainability in densely populated areas. Nevertheless, the current systematic reviews do not show how individuals’ inner dispositions, such as acceptance, intention, or attitude, have evolved from before to after the implementation of schemes. Of the 11 integrated findings, 9 concerned enablers and barriers to an individual’s sustainable mode choice behaviour. In addition, two integrated findings emerged based on the effectiveness of the interventions. Although numerous interventions target public acceptance of sustainable transport, this systematic review reveals a critical knowledge gap regarding their longitudinal impact on individuals and effectiveness in influencing behavioural change. However, the study may be affected by language bias as it only included peer-reviewed articles published in English. Due to methodological heterogeneity across the studies, a meta-analysis was not feasible. Further high-quality research is needed to strengthen the evidence. This systematic review is self-funded and has been registered on the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY; Registration Number INPLASY202420011). Full article

In this paper, an assessment of the impact of the electrification of the vehicle fleet in Bosnia and Herzegovina on the total electrical energy consumption is made, for different scenarios of increasing the number of electric vehicles. Based on a statistical analysis of the structure and number of vehicles in Bosnia and Herzegovina in the period from 2010 to 2024, an estimate of the total number of passenger cars, as well as the number of electric vehicles for the period up to 2050, is made. It is estimated that in 2050 the number of electric passenger cars will be around 300,000. For one representative electric passenger car, averaged annual electrical energy consumption is calculated. Based on the calculation and for the estimated number of electric vehicles in use, the total annual consumption of electrical energy for the segment of passenger cars is defined, for different scenarios of increasing the number of electric vehicles. Following the estimated increase in the number of passenger electric cars, an exponential increase in electrical energy consumption is estimated, reaching the annual amount of 635 GWh in 2050, which is 10 times higher than the total electrical energy consumption of the transport sector in 2024. In this way, for the period up to 2050, the additional amount of electrical energy that the electrical power grid should provide, due to the electrification of the vehicle fleet, is estimated. Full article

A new control system implemented with a single-stage DC-DC controller to power an LED headlamp for automotive applications is presented in this work. Daytime running light (DRL), low beam (LB), high beam (HB) and adaptive driving beam (ADB) are typical functions requiring a dedicated LED driver solution to fulfill car maker requirements for front-light applications. Single-stage drivers often exhibit a significant overshoot in LED current during transitions from driving a higher number of LEDs to a lower number. To maintain LED reliability, this current overshoot must remain below the maximum current rating of the LEDs. If the overshoot overcomes this limit, it can cause permanent damage to the LEDs or reduce their lifespan. To preserve LED reliability, a comprehensive system has been proposed to minimize the peak of LED current overshoots, especially during transitions between different operating modes or LED string configurations. A key feature of the proposed system is the implementation of a parallel discharging path to be activated only when the current flowing in the LEDs is higher than a predefined threshold. A prototype incorporating an integrated test chip has been developed to validate this approach. Measurement results and comparison with state-of-the-art solutions available in the market are shown. Furthermore, a critical aspect to be considered is the proper dimensioning of the discharging path. It requires careful considerations about the gate driver capabilities, the discharging resistor values, and the thermal management of the dumping element. For this purpose, an extensive study on how to size the relative components is also presented. Full article

The frequency of road accidents in specific locations is determined by a number of variables, among which an important role is played not only by common determinants such as inappropriate behavior of road users, but also by external factors characterizing a given location. Taking this into account, the main objective of the study was to answer the question of which variables determine that the intensity of car accidents is higher in certain parts of the city of Poznań compared to other locations. The study was based on source data from the police Accident and Collision Records System (SEWiK). For the purposes of the analysis, two variants of the regression method were used: ordinary least squares (OLS) and geographically weighted regression (GWR). The obtained results made it possible to identify variables that increase the likelihood of a traffic accident in specific parts of the city, and the variables that proved to be statistically significant include the size of the built-up area and the number of traffic lights. The results obtained using the GWR technique indicate that the way in which the analyzed features influence road accidents can vary across the city, which may emphasize the complexity of the analyzed phenomenon. The results can be used by relevant entities (transport traffic planners and many others) to create road safety policies. Full article

Autonomous vehicles are a promising solution to traffic congestion, air pollution, accidents, wasted time, and resources. However, remote driver intervention may be necessary in extreme situations to ensure safe roadside parking or complete remote takeover. In these cases, high-quality real-time video streaming is crucial for remote driving. In a preliminary study, we presented a region of interest (ROI) High-Efficiency Video Coding (HEVC) method where the image was segmented into two categories: ROI and background. This involved allocating more bandwidth to the ROI, which yielded an improvement in the visibility of classes essential for driving while transmitting the background at a lower quality. However, migrating the bandwidth to the large ROI portion of the image did not substantially improve the quality of traffic signs and lights. This study proposes a method that categorizes ROIs into three tiers: background, weak ROI, and strong ROI. To evaluate this approach, we utilized a photo-realistic driving scenario database created with the Cognata self-driving car simulation platform. We used semantic segmentation to categorize the compression quality of a Coding Tree Unit (CTU) according to its pixel classes. A background CTU contains only sky, trees, vegetation, or building classes. Essentials for remote driving include classes such as pedestrians, road marks, and cars. Difficult-to-recognize classes, such as traffic signs (especially textual ones) and traffic lights, are categorized as a strong ROI. We applied thresholds to determine whether the number of pixels in a CTU of a particular category was sufficient to classify it as a strong or weak ROI and then allocated bandwidth accordingly. Our results demonstrate that this multi-category ROI compression method significantly enhances the perceptual quality of traffic signs (especially textual ones) and traffic lights by up to 5.5 dB compared to a simpler two-category (background/foreground) partition. This improvement in critical areas is achieved by reducing the fidelity of less critical background elements, while the visual quality of other essential driving-related classes (weak ROI) is at least maintained. Full article