Low Road Research Articles

Numerical investigation of tire shape parameters on the tire acoustic cavity resonance noise

Tire acoustic cavity resonance (TACR) noise is one kind of low-frequency and narrowband noise that particularly annoys the passengers, especially beyond endurance in the electric vehicles. This paper demonstrates a numerical investigation on the TACR noise via the acoustic-structural coupling finite element model. According to the simulated models, shape parameters of inner tire cavity have been studied in detail, including the curvature radius of tire crown region ρ2, transition region ρ3, tire shoulder ρ4, and tire sidewall ρ5, together with the half-section height H of the inner tire cavity. By the orthogonal test and range analysis, it is found that the curvature radius of the tire shoulder ρ4, tire sidewall ρ5, and half-section height H have a more obvious influence on the TACR noise, rather than the curvature radius of tire crown region ρ2 and transition region ρ3. Meanwhile, it also illustrates that increasing the curvature radius at the tire shoulder ρ4 and reducing the half-section height H have a reduction effect on the TACR noise. This study can contribute to the design of low road noise tire.

Changes in motor traffic in London’s Low Traffic Neighbourhoods and boundary roads

This paper meta-analyses traffic data extracted from monitoring reports for 46 Low Traffic Neighbourhood schemes in 11 London boroughs introduced between May 2020 and May 2021. Schemes are controversial with still limited academic evidence on impacts. The analysis covers internal and boundary roads, looking at actual changes in motor traffic, and at what changes might have been expected based on background trends in London’s three ‘functional zones’ (Central, Inner, and Outer). All metrics show substantial relative declines in motor traffic on internal roads. For instance, a mean ‘pre-LTN’ traffic volume of 1,780 dropped to 930 ‘post-LTN’, against a small projected decline to 1,745 if background trends were followed. By contrast, the schemes are only marginally associated with change in traffic volume on boundary roads. While there are inevitable data limitations associated with the use of routine monitoring data, this research provides some support for the position that LTNs can form an effective part of wider strategies to reduce motor traffic and its associated disbenefits. Monitoring data should be publicly shared regionally and nationally to aid analysis.

Detection, localisation and quantification of stiffness loss in a bridge using indirect drive-by measurements

Drive-by health monitoring uses the measurements gathered on a vehicle while traversing a bridge to assess its condition. To date, drive-by monitoring has been mostly proposed as a pre-screening tool to detect damage under favourable conditions of low vehicle speeds and low road roughness. A major shortcoming is the potential degradation of the road profile, which is often indistinguishable from bridge damage. In this paper, the influence of vehicle dynamics, speed and road roughness is removed by applying cross-entropy optimisation to the response of individual crossings of the vehicle at different speeds. The proposed model-based algorithm locates and quantifies damage while remaining unaffected by changes in the road profile. In addition to providing the distribution of bending stiffness in the underlying bridge, the algorithm isolates the bridge deflections accurately making the reconstruction of the actual road profile on the bridge possible. The latter can be a useful feature for ensuring traffic safety as well as preventing a major dynamic amplification of the bridge response. The algorithm is successfully tested for a quarter-car driving on a 15 m simply supported beam bridge model at highway speeds of 30 m/s over a class ‘B’ road with a roughness coefficient of 64 × 10−6 m3/cycle.

Exploring bus tracking data to characterize urban traffic congestion

Quantification of traffic dynamics is a valuable tool for city planning and management. Metrics such as the vehicle average speed, travel time, delays, and count of stops, can be used to characterize mobility and traffic congestion in an area. However, effective study of mobility data is often hindered by the difficulty of gathering mobility data in a practical, inexpensive, and prompt way.In this work, we explore the use of city buses as mobility probes, using the existing smart city infrastructure deployed in Aveiro, Portugal. We propose a method for traffic congestion detection considering the low vehicle speed, low traffic flow and road occupancy close to its capacity. Three degrees of congestion are identified using the k-means approach; DBSCAN is used to characterize the typical level of congestion in a road. Using four-weeks of mobility data, it was possible to assess the congestion along the day and for the different days of the week; some road segments proved to be consistently prone to congestion. We also studied parameters of driving safety, considering speed and acceleration.In this work, we show that knowledge discovery can be applied to mobility data being collected by tracking buses, exploring data that is often collected for other purposes also to characterize traffic congestion. These methods can inform decision makers and are easily ported to other cities.

How do density, employment and transit affect the prevalence of COVID-19 pandemic? A study of 3,141 counties across the United States

Previous research has explored the effect of the built environment on the spread of the coronavirus disease (COVID-19) pandemic. This study extends the existing literature by examining the relationship between pandemic prevalence and density, employment, and transit factors at the county level. Using multilinear spatial-lag regressions and time series clustering analyses on the Smart Location Database encompassing 3141 counties in the United States, our findings reveal the following: (1) Density, employment, and transit variables yield heterogeneous effects to infection rate, death rate, and mortality rate. (2) Pedestrian-oriented road density is positively correlated to the prevalence of COVID-19, every 0.011 miles/acre increase is associated with 1% increase in the infection rate. (3) A consistent negative correlation is observed between jobs per household and infection rate, while a decrease in unemployment rate leads to an increase in the death rate. (4) The results from time series analysis suggest that areas characterized by low auto-oriented intersection density but high pedestrian-oriented road density are more susceptible to the impacts of pandemics. This highlights the need to prioritize pandemic prevention efforts in the suburban and rural areas with low population density, as emphasized in existing literature emphasized.

Swin-ResUNet+: An edge enhancement module for road extraction from remote sensing images

Road extraction from remote sensing images is very important in navigation, urban planning, traffic management and other fields. Deep learning methods have achieved great success in computer vision tasks. Therefore, road extraction from remote sensing images using deep learning methods can significantly improve the road extraction accuracy. However, these methods generally have problems such as low road extraction accuracy, slow training speed, high computational complexity, and poor road topology connectivity. In order to solve the above issues, we propose a Swin-ResUNet+ structure and use the new paradigm Swin-Transformer to extract roads in remote sensing images. Specifically, we construct an Edge Enhancement module based on residual connection and add this module to each stage of the encoder, which can obtain the edge information in remote sensing images. Based on the Edge Enhancement module, we propose a Swin-ResUNet+ structure in order to better capture the topology of roads. On the Massachusetts road dataset, our model has the least computational cost with only less than one percent accuracy decrease. On the DeepGlobe2018 road dataset, our model not only has the least computational complexity but also achieves the highest values of mIOU, mDC, mPA and F1-score. In a word, Swin-ResUNet+ obtains a much better trade-off between accuracy and efficiency than previous CNN-based and Transformer-based methods.

Modelling crash severity outcomes for low speed urban roads using back propagation – Artificial neural network (BP – ANN) – A case study in Indian context

This work analyses influence of road, weather and crash-specific factors on crash severity outcomes for low-speed urban midblock sections and intersections, for day and night time, using Backpropagation–Artificial Neural Network (BP–ANN). Five-year crash data (2015–2019) from 82Km urban road network of Patna, India was used for the study. The road factors include pavement width, distress condition, marking; shoulder type, condition; road section type as mid-block, intersection and intersection control. Weather factors include season of crash, fog or rain at crash time. Crash factor include collision partner, type and crash time. The most appropriate BP–ANN model architecture was estimated using Misclassification-Rate. It was observed that midblock segments witness higher severities during daytime, whereas intersections witness higher severities during night. Controlled intersections are safer compared to un-controlled intersections. Pavement distress greatly increase the chance of higher severities. Narrow roads record greater severities during day due to lack of surveillance.

High levels of tire wear particles in soils along low traffic roads

Traffic pollution has been linked to high levels of metals and organic contaminants in road-side soils, largely due to abrasion of tires, brake pads and the road surface. Although several studies have demonstrated correlations between different pollutants and various traffic variables, they mainly focused on roads with medium to high traffic density (>30,000 vehicles per day). In this study we have focused on investigating tire wear particles and road-related metals (zinc, copper, lead, chromium, nickel, and the metalloid arsenic) in the soils of low traffic roads in rural areas (650–14,250 vehicles per day). Different explanatory factors were investigated, such as traffic density, speed, % heavy vehicles, organic matter content, annual precipitation, soil types and roadside slope profiles. The results show high levels of tire wear particles, from 2000 to 26,400 mg/kg (0.2–2.6 % tire wear in d.w. soil), which is up to five times higher compared to previously reported values in roadside soils of high traffic density areas. A weak but significant correlation was found between tire wear particles, traffic speed and the annual precipitation. No significant relationship was found between tire wear particles metals. The concentrations of metals were comparable to previous studies of high traffic areas of Norway, as well as both urban and rural soils in other countries. For the metals, all factors together explained 45 % of the variation observed, with traffic density (11 %) and organic matter content (10 %) as the most important single variables. The analysis of tire wear particles in soils using Pyrolysis Gas chromatography Mass Spectrometry is challenging, and the results presented demonstrate the need for pretreatment to remove organic matter from the samples before analysis.

Longitudinal Spatial Trends in U.S. Pedestrian Fatalities, 1999–2020

The number of pedestrian deaths from motor vehicle crashes in the U.S. has risen 59% since 2009. Several studies have researched crash characteristics—such as the people, vehicles, and roadways involved—when examining this sharp increase. But where are these fatalities occurring and what spatial characteristics of these locations could be influencing this trend? We looked at pedestrian fatality data from the National Highway Traffic Safety Administration’s Fatality Analysis Reporting System and socioeconomic, demographic, and built environment data from the U.S. Census and the U.S. Environmental Protection Agency’s Smart Location Database for the study period of 1999–2020. After inputting the data into QGIS, we made graphs with 95% confidence intervals to analyze longitudinal trends in crash location characteristics and created heat maps of pedestrian fatality locations in nine large U.S. cities to compare clustering in the beginning of the study period (1999–2002) with clustering at the end (2017–2020). The results of this study indicate that pedestrian fatalities are moving away from downtown areas (with a 63.0% decrease in study cities’ downtowns) and are now happening more in suburbs (a 32.1% increase outside downtowns). Many of these suburbs are post-war suburbs that were built in the 1950s, 1960s, and 1970s and have low population and road densities and low levels of pedestrian commuting. Additionally, these fatalities are happening in neighborhoods with high proportions of minority residents, low educational attainment, and high poverty rates that are more than 60% above the national average.

A phase plane H∞ controller for distributed drive electric vehicles with stability enhancement based on tire road friction coefficient estimation

An H∞ control strategy based on the phase plane method (phase plane H∞ controller) tracking two degrees of freedom (DOF) ideal vehicle trajectory scheme is designed for distributed drive electric vehicles with stability enhancement. Firstly, an Extended Kalman Filter (EKF) tire road friction coefficient estimation method based on Keras neural network is presented to accurately and efficiently identify the tire road friction coefficient, taking into account the huge influence of the tire road friction coefficient on vehicle equilibrium point and stability region. Secondly, the phase plane method is applied to provide a dynamic stability boundary for the switching control strategy of direct yaw moment for different tire road friction coefficients; Furthermore, based on the dynamic stability boundary, the weighted phase stability is applied to achieve more realistic stability evaluation criteria, and the fuzzy control strategy is adopted to carry out the feedback regulator of the target side slip angle and yaw rate on the purpose of limiting its maximum value max ( β) and max ( ωr). Then the torque of the four-wheel was optimized by the quadratic programming method. Finally, the presented method is verified and the results indicate that: (1) the phase plane H∞ control has the advantage in terms of stability and maneuvering performance. More importantly, under a low tire road friction coefficient, the amplitude of the side slip angle is decreased by 26.52% over the H∞; (2) the designed EKF based on Keras neural network parameter correction has a quick and accurate performance in identifying the tire road friction coefficient, and the steady-state error does not exceed 3.25%.

Оценка воздействия лесозаготовительной деятельности на сохранение биоразнообразия северного региона

In the context of sustainable forestry, biodiversity conservation assessment is a necessary element in asset and service accounting. Logging is an important driver of economic growth in the northern regions of Russia. The past twenty years have seen changes in timber harvesting technologies and forest management principles, which has led to an increase in research interest in identifying ecosystem responses in terms of biodiversity conservation. This study aims to assess changes in biodiversity within the active forest management territory of the Komi Republic, encompassing ten municipal districts and 24 forestry areas. The objectives of the study include evaluating the impact of factors such as population density, road network, and intact area on biodiversity preservation. Statistical analysis methods were utilized to assess the biodiversity status, focusing on the number of rare species threatened by logging. The analysis studied changes in species abundance, territorial distribution across forestry areas, and changes in their conservation status. Using statistical and factor analysis methods, the study identified the impact of forestry and anthropogenic factors on the conservation of species. The novelty of the research consists in identifying the stability degree of ecosystems by analyzing the state of large areas that have experienced long-term anthropogenic impacts on the Komi Republic territory. Most forests within the active forest management zone demonstrated favorable ecosystem conditions, with logging activities causing lichen disappearance and reduced fish numbers only in individual small areas. The anthropogenic impact had minimal influence on the number of rare species due to low population and road network density. Large areas of undisturbed forests contribute to the natural preservation of biodiversity, but their significant reduction in the southern area causes a threat to the existence of rare species. The greatest impact on the change in biodiversity on the territories affected by active forest management is caused by factors such as the level of territorial exploration, the preservation of undisturbed forests, and selective logging. The practical significance of this study lies in the potential application of the proposed algorithm by large Russian forest businesses when conducting their operations.

Brake-by-Wire System Redundancy Concept for the Double Point of Failure Scenario

<div>Brake-by-wire (BbW) systems are one key technology in modern vehicles. Due to their great potential in the areas of energy efficiency and automated driving, they receive more and more attention nowadays. However, increased complexity and reliance on electric and electrical components in BbW systems bring about new challenges. This applies in particular to the fault tolerance of the brake system. Since drivers cannot form a fallback layer of braking functions due to the mechanical decoupling of the brake pedal, known BbW concepts provide a redundant system layer. However, driving is significantly limited in the event of a failure in the BbW system and is only possible under certain restrictions. The reason for that is a further possible failure (double point of failure scenario), which can result in a significant loss of braking performance.</div> <div>To improve the availability level of the braking functions, a principally new redundancy concept for the double point of failure scenario is presented. This allows for a less restricted driving operation when the BbW system is subject to failures. For this purpose, a central electric motor (CEM) and modified electronic parking brake (EPB) actuators are used on the front axle of a vehicle to form a separate redundancy layer. Strategies for deceleration and wheel slip control are developed for the individual actuators as well as for their simultaneous operation. The performance of the different brake modes is evaluated in road tests. The analysis shows that especially the combined operating mode of the CEM and EPB leads to high deceleration levels and robust operation in low road friction conditions.</div>

I focus only when I see your fear-fearful faces are not prioritized by attention when processed outside of awareness.

The defensive reaction to threats consists of two components: non-specific physiological arousal and specific attentional prioritization of the threatening stimulus, both of which are assumed by the so-called "low-road" hypothesis to be induced automatically and unconsciously. Although ample evidence indicates that non-specific arousal can indeed be caused by unconscious threatening stimuli, data regarding the involvement of the attentional selection mechanism remain inconclusive. Therefore, in the present study we used ERPs to compare the potential engagement of attention in the perception of subliminal and supraliminal fearful facial expressions to that of neutral ones. In the conscious condition, fearful faces were preferentially encoded (as indicated by the N170 component) and prioritized by bottom-up (EPN) and spatial attention (N2pc) in an automatic, task-independent manner. Furthermore, consciously perceived fearful expressions engaged cognitive resources (SPCN, P3) when face stimuli were task-relevant. In the unconscious condition, fearful faces were still preferentially encoded (N170), but we found no evidence for any type of attentional prioritization. Therefore, by showing that threatening stimuli engage attention only when perceived consciously, our findings challenge the "low road" hypothesis and point to the limits of unconscious attentional selection.

Visa policies, migration industry and the ethnic entrepreneurship trap: the case of Nepalese restaurant businesses in Japan

Ethnic entrepreneurship can be an alternative economic strategy among immigrants whose labor market opportunities in the host society are hindered by language deficiency, institutional constraints or racial discrimination. However, it can also constrain migrants’ social economic mobility. This is especially likely when, with particular visa policies, ethnic business is appropriated as a channel for labor migration, trapping migrants in a narrow economic niche. Excessive concentration in one type of business leads to low road competition within the community and co-ethnic exploitation. This study uses the case of Nepalese immigrants’ restaurant businesses in Japan to illustrate this pitfall of ethnic entrepreneurship. It highlights the roles of the immigration regime and the migration industry in creating such a condition.

Design of ABS fuzzy sliding mode control system based on pavement recognition

A fuzzy sliding mode variable structure control method based on road surface recognition was proposed to solve the problem that the Anti-lock Braking System (ABS) effect of current ABS algorithm was not ideal on complex road surface. In the road recognition module, real-time estimation of five typical road surfaces using fuzzy logic control. Dynamic calculation of optimal slip ratio for different road surfaces based on identified road conditions. Design of ABS sliding mode variable structure controller with optimal slip ratio and actual slip ratio as input. Aiming at the chattering problem of sliding mode control, a fuzzy controller is designed to reduce chattering. An 8-DOF dynamic simulation model of a four-wheel hub motor is established. The effectiveness of the controller is verified by braking simulation experiments on medium and low adhesion road. By comparing the simulation test with the traditional sliding mode controller under the condition of high adhesion road, the suppression effect of the system chattering is verified, and its excellent control performance is proved.

A Case Study Comparing Active Vs. Passive Enablers for Vehicle Interior Noise Reduction

The implementation of enablers on a luxury sport utility vehicle is used to illustrate the development process for reduction of road noise. The vehicle in this case study was launched into production with two tuned mass dampers for reduction of low frequency road noise content which was amplified by frame modes. Additionally, resonators were integrated into the wheels (rims) to address the dominant cavity resonance frequencies. The results of this successful production implementation are illustrated herein. An RNC (road noise cancellation) system was integrated into the case vehicle to assess its performance relative to the passive enablers listed above. This production representative (embedded software solution) RNC system utilized the vehicle's existing audio system for creation of active noise to cancel noise content which was predicted using accelerometers mounted to the vehicle chassis. A comparison of in-vehicle noise indicated a significant reduction at low frequencies (at all seating locations) when utilizing the active noise control solution. These noise improvements are coupled with a vehicle mass reduction of greater than 4 kg, when compared to the passive enabler solution.

Optimal pavement maintenance and management strategies for flood affected low volume rural roads

Due to the increase in urbanization and industrial development in India, there has been substantial traffic on the existing roads. This impact had increased public demand for good quality roads with better maintenance and high serviceability. Further, catastrophic events like flooding can cause damage to pavements, especially in those with granular base layers and thin asphalt mix surface layers. The degree of damage relies upon the vulnerability of the pavement to the entrance of surface water, the measure of water immersion, and the drainage quality of the pavement. The extensive literature review found limited studies on the post-flood maintenance strategy for low-volume roads by considering flood resilience. In the present scenario of changing climatic conditions, it is necessary to conduct a study on the post-flood maintenance strategy of low-volume rural roads in India. This study focuses on an approach to developing post-flood maintenance and rehabilitation strategies for flood-affected low-volume roads. Ten road stretches adversely affected by the flood were selected to fulfil the aim of the study. The post-flood maintenance strategy was determined based on the Pavement Condition Index (PCI) and deflection-based pavement performance with flood resiliency of pavement materials and budget constraints. The extensive data analysis revealed that the pavement with a cement-treated base or subbase has a longer service life and less Net Present Value (NPV) than all other alternatives. NPV is 30 to 45 for rehabilitation and 52 to 55 for reconstruction, lower than other alternatives considered in the study.

Effects on crash risk of automatic emergency braking systems for pedestrians and bicyclists

Objective The first automatic emergency braking (AEB) system was presented in 2003 and aimed to mitigate or reduce rear-end crashes. Since then, several AEB systems aimed to reduce other collision types have been introduced and studies have shown that they reduce crash risks. The aim with this study was to evaluate crash reductions of cars fitted with AEB systems with pedestrian detection and those with bicyclist detection. Methods The study is based on the Swedish Traffic Accident Data Acquisition that includes road traffic accidents reported by the police and by emergency hospitals. Crashes occurring between 2015 and 2020 and with cars from model years 2015 to 2020 were included. The statistical analysis used odds ratio calculations with an induced exposure approach where the outcomes of sensitive and nonsensitive crashes were studied. The sensitive crashes were hit pedestrians and bicyclists, respectively. The nonsensitive crash type in both comparisons was struck vehicles in rear-end crashes. Evaluations were also made for different light and weather conditions and for high and low speed roads. Results Seven hundred and twelve hit pedestrians and 1,105 hit bicyclists were included, and the nonsensitive crashes consisted of 1,978 vehicles. The overall reduction on crash risk for AEB with pedestrian detection was 8% (±15%; ns) and for AEB with bicyclist detection it was 21% (±17%). When separating for light conditions, no reduction in crash risk for AEB with pedestrian detection nor for AEB with bicyclist detection could be seen in darkness. However, in daylight and twilight conditions, AEB with pedestrian detection reduced pedestrian crash risk by 18% (±19%; ns) and AEB with bicyclist detection reduced bicyclist crash risk by 23% (±19%). No significant reductions could be seen when separating for weather conditions except for a 53% (±31%) reduction for bicyclists in rain, fog, and snowfall. A larger reduction on high-speed roads (50–120 km/h) compared with low-speed roads (10–40 km/h) was also found. Conclusions AEB systems with bicyclist detection were found to reduce the numbers of hit bicyclists, especially in daylight and twilight conditions. In darkness, no reduction for hit pedestrians or bicyclists was found.

Preparation and Properties of Composite Graphene/Carbon Fiber Pouring Conductive Asphalt Concrete.

To solve the problem of snow on steel bridge areas endangering traffic safety and low road traffic efficiency in winter, conductive gussasphait concrete (CGA) was prepared by mixing conductive phase materials (graphene and carbon fiber) into Gussasphalt (GA). First, through high-temperature rutting test, low-temperature bending test, immersion Marshall test, freeze-thaw splitting test and fatigue test, the high-temperature stability, low-temperature crack resistance, water stability and fatigue performance of CGA with different conductive phase materials were systematically studied. Second, the influence of different content of conductive phase materials on the conductivity of CGA was studied through the electrical resistance test, and the microstructure characteristics were analyzed via SEM. Finally, the electrothermal properties of CGA with different conductive phase materials were studied via heating test and simulated ice-snow melting test. The results showed that the addition of graphene/carbon fiber can significantly improve the high-temperature stability, low-temperature crack resistance, water stability and fatigue performance of CGA. The contact resistance between electrode and specimen can be effectively reduced when the graphite distribution is 600 g/m2. The resistivity of 0.3% carbon fiber + 0.5% graphene rutting plate specimen can reach 4.70 Ω·m. Graphene and carbon fiber in asphalt mortar construct a complete conductive network. The heating efficiency of 0.3% carbon fiber + 0.5% graphene rutting plate specimen is 71.4%, and the ice-snow melting efficiency is 28.73%, demonstrating good electrothermal performance and ice-snow melting effect.

Carbon Emission Pattern of Driving Car on Vertical Curves of Highway

Low carbon road design has always been a research hotspot for scholars. One of the primary road variables affecting the carbon emission of automobiles is vertical curve, which serves as the primary geometric alignment of the longitudinal portion of the road. The instantaneous speed and acceleration data of cars on various vertical curve sections are gathered for this study by combining simulation and real vehicle experiment methods. This study then built a database of carbon emissions for cars on various vertical curve sections based on the Motor Vehicle Emission Simulator (MOVES) model, used Statistical Product and Service Solutions (SPSS) to analyze the data, and developed a carbon emission model for cars on various vertical curve sections. Finally, it was found that the average error of the carbon emission model for different vertical curve sections was 8.37%, and the accuracy of carbon emission modelling could meet the requirements. The results show that the main influencing factor of carbon emission of cars on vertical curve sections is the radius of vertical curve, and the carbon emission of cars is inversely proportional to the radius of vertical curve. The smaller the value of vertical curve radius is, the more the engine output increases and the greater the carbon emission level will be. This research will provide reference and theoretical support for low-carbon design of highway longitudinal slope sections.