スペクトログラムを用いた車いす走行時の路面推定

A technique has been developed for predicting road traffic noise for environmental assessment, taking into account traffic volume as well as road surface conditions. The ASJ model (ASJ Prediction Model for Road Traffic Noise, 1999), which is based on the sound power level of the noise emitted by the interaction between the road surface and tires, employs regression models for two road surface types: dense-graded asphalt (DGA) and permeable asphalt (PA). However, these models are not applicable to other types of road surfaces. Accordingly, this paper introduces a parameter estimation procedure for ASJ-based noise prediction models, utilizing a harmony search (HS) algorithm. Traffic noise measurement data for four different vehicle types were used in the algorithm to determine the regression parameters for several road surface types. The parameters of the traffic noise prediction models were evaluated using another measurement set, and good agreement was observed between the predicted and measured sound power levels.

A road surface recognition sensor has been realized by using an optical spatial filter. The sensor transforms a road surface image into spatial frequency spectra. Frequency distribution depends on the road surface type. Ground velocity is shown by a filtered moving image. Experimental results show that five road surface types were recognized in cold districts. Both road surface condition and vehicle velocity can be detected by a noncontact sensor; therefore, the sensor is useful for active safety systems.

Road surface adhesion condition identification has major impact on playing the various active safety automotive electronic control devices’ performance as much as possible, the vehicle model with 7 degrees of freedom is established with the application of magic formula tire model and engine model in this paper. By comparing the real-time vehicle dynamic parameters with 6 kinds of typical road characteristics when a vehicle accelerates or brakes, the current road surface type is determined. A road surface identification simulation is carried out when an automobile accelerates on uniform road and brakes on split road by using MATLAB/Simulink software. Finally, a bench test is done to prove the validity of this approach. It is showed by the simulation results that the actual road surface type and the identified road surface type are in good agreement. It can be used for active safety automotive electronic control devices’ road surface identification.

The most significant development in modern braking is the Anti-lock Braking System (ABS) The intervention of ABS, however, is not always advantageous. Major limitations arise when ABS is applied on loose unsealed surfaces such as gravel and sand. In the event of wheel 'lock up' on unsealed surfaces, the loose material forms a wedge in front of the tyre which contributes an additional braking force and results in shorter stopping distances. This paper considers the problem of distinguishing amongst various types of road surface by means of the Power Spectral Density (PSD) of fluctuations in the spectra of the intensity of laser light reflected from the road surface. To measure these fluctuations, a test rig was constructed consisting of a visible light laser diode coupled with a light-to-voltage sensor. By mounting the sensor at a height comparable to a vehicle under body and passing it over simulated road surfaces, it is possible to generate power spectral density charts for specific surfaces. It was discovered that each of the road surfaces tested had characteristic power spectral density charts, which could be used to discriminate between surfaces.

The noise has long been recognized as a stress factor for humans. A high level of noise has many negative influences on health such as: difficulty to concentrate, stress, health issues for the ears, and also has a damaging effect on the cardiovascular and endocrine systems. Road traffic is main source of total noise, especially in conurbations. In order to effectively reduce noise emissions, surface courses can be installed as road surfaces that generate particularly little noise in interaction with the vehicle tire. This paper delas with different types of noise-reducing road surfaces.

Developments in adaptive systems for maintenance and repair of automotive vehicles set the task of monitoring the conditions of their operation. One of the main factors determining these conditions is the type of road surface.The article describes the results of identification of the type (and condition) of the road surface obtained by theoretical and experimental methods based on the analysis of vertical accelerations recorded on the vehicle body.The purpose of research was to provide a possibility of continuous monitoring of the type of road surface on which a vehicle is driving, with the subsequent application of the obtained data to correct maintenance intervals. The results of experiments have shown the dependence of the vertical acceleration of the body on the micro-profile of the road surface. The described experimentally obtained profiles of vertical accelerations refer to different types of road surface in different conditions. For quantitative assessment, it is proposed to calculate the average level of accelerations as an integral average over a certain time interval.The results of the experiments have allowed to substantiate the empirical dependence of the average level of accelerations on speed of a vehicle. Based on this dependence, a method is proposed for recalculating the current values of the average levels of accelerations obtained at different speeds into values adjusted to the base speed to ensure the possibility of their comparison.It is shown that based on the values of average acceleration levels obtained through operation monitoring regarding a previously known type of road surface, it is possible to determine its condition. A short algorithm is formulated for practical implementation and assessment of road conditions of traffic flows. As for hardware, it is proposed not to equip a vehicle with additional sensors but to use operational standard accelerometers as part of in-vehicle emergency call systems, e.g., ERA-GLONASS equipment units.

Sustainable product design has a role in reducing environmental impacts and can provide economic and social benefits for stakeholders. Design is one way to make improvements and provide an overview of alternative solutions to problems. One of the problems of motorcycle users is experiencing the incidence of punctured tires. This research aims to design a tool to help users of automatic and manual motorcycles when a flat tire occurs. The tool design with dimensions of 160 mm x 190 mm x 100 mm uses a 2 mm iron plate as the mainframe, rubber-type wheels on the front and live caster on the back, and stainless steel for bolts and nuts. Testing the tire leak tool uses three motorcycles with automatic and manual types that have an engine capacity of up to 110 cc, and has a wheel size of 62.09 mm for automatic motorbikes and 65.79 mm for manual motorbikes. The test results showed it function well. The tool is stable and safe when used at a speed of 20 km/hour. The tire leak tool can be used on various types of road surfaces and can withstand passenger loads of up to 65 kg. The trial process was carried out 7 times in the environment around the Purwokerto Telkom Institute of Technology campus with up and down road surfaces and various variations of bends. The tool design is easy to install on leaking wheels and is convenient to carry on the go. A tire leak tool can be used as an alternative for motorcyclists when a flat tire occurs, especially automatic and manual motorbikes.

고속도로 이용차량의 증가와 함께 차량의 대형화와 고속화로 인해 고속도로 교통 소음레벨이 높아지고 있으며 저소음 포장노면 및 방음시설 설치 요청도 급격하게 증가하고 있다. 따라서 고속도로 교통소음으로 인한 민원예방과 함께 효율적이고 경제적인 소음저감 대책을 수립하기 위해서는 정확한 소음 예측 기술 마련이 필요하다. 본 연구에서는 시험도로에 포설된 다양한 포장노면에 대해서 CPX(Close Proximity Test) 및 Pass-by 소음 계측 방법을 혼용한 소음 계측 데이터를 이용하였고 차종별 단독 주행 시험을 실시하여 차량 및 노면별 음향파워레벨 산정식이 마련된 데이터를 이용하였다. 아울러, 상기 산정식의 정확성을 검증하기 위하여 고속도로 12개 지점에 대한 총 38회의 소음 계측한 데이터를 이용하여 해당 지점에 대한 소음 예측 모델을 구성하여 측정값과 예측값을 비교 평가하였다. 최종적으로 3차원 GUI 기능을 지원하는 도로교통 소음 예측 프로그램 KRON(Korea Road Noise)을 개발하였다. 이와 더불어 각 포장형태별 및 차종별에 따른 소음특성을 분석하였다. Domestic economic development, industrialization, and urbanization have brought along not only increased highway traffic but also elevated traffic noise levels. Thus, it is necessary to accurately predict the traffic noise levels in order to address the public demand of alleviating the noise levels in urban areas. In this study, the method of evaluating the sound power level of road traffic was investigated in terms of considering the types of road surface and vehicle, based on previous researches. Regarding CPX (Close Proximity Test) and Pass-by test, the measured noise data of Test Road of Korea Highway Corporation were utilized in order to construct the database of sound power levels of various vehicles. Specifically, the 38 noise measurement and analysis in 1/1-octave band frequencies at 12 pre-selected sites were carried out, considering topography and road surface. Finally, the comparison study was conducted between predicted and measured data in terms of traffic noise. The traffic noise prediction was based on the KRON (Korea Road Noise) program, which was developed being equipped wit 3-dimensional GUI. In addition, the traffic noise characteristics were evaluated in terms of vehicle types and pavement surface conditions.

In order to improve the performance of the bridge deck pavement structure, improve the stress state of the bridge deck pavement structure layer, reduce the disease of old concrete bridge asphalt pavement, grasp the stress state of the bridge deck pavement structure layer and material performance, the asphalt pavement material and pavement structure are optimized. Based on the finite element analysis method, the finite element model of asphalt pavement on cement concrete bridge deck is established. The interlaminar shear stress, interlaminar tensile stress, and surface shear stress are selected as the main mechanical control indicators, and the stress variation rule of different thickness and modulus of pavement layer under traffic load is analyzed. The result shows that (1) shear failure is the main failure form of asphalt pavement on bridge deck, and the thickness of asphalt pavement is the main factor affecting the stress distribution inside the pavement, while the modulus of asphalt pavement material has a little effect on the stress distribution. The proper thickness of asphalt pavement can improve the distribution of internal stress, therefore, two kinds of bridge deck pavement structure design schemes, SMA composite structure and AC composite structure are proposed. (2) Based on the indoor test, the high-temperature stability, low-temperature crack resistance, water stability and water permeability of SMA-10, SMA-13 and Ac-13 asphalt mixtures are studied, it is known that all performance indicators of the three asphalt mixtures meet the requirements of the design code, but each has its own advantages and disadvantages in different performance. (3) Finally, according to the results of the performance test and the principle of optimizing the material of the bridge deck pavement, under the condition that the performance can meet the requirements of the project, combined with the requirements of the project cost economy and the safety of the bridge structure, a composite structure is recommended as the old concrete bridge deck pavement structure, AC-13 is selected as the old concrete bridge deck pavement upper and lower layer pavement material.

Optimisation of lighting quality and energy efficiency of LED luminaires in roadway lighting systems on different road surfaces

Identifying road surface types is crucial for road maintenance and socio-economic development. Crowdsourced street view data, with global coverage and free access, is a valuable source for this task. However, deep learning models typically require large, manually annotated datasets, which are lacking for road surface classification. To address this, three experts initially labeled 200,000 images from the Mapillary crowdsourced street view dataset, categorizing them into “Paved,” “Unpaved,” and “Unknown” road types through a voting mechanism. Five deep learning models were then trained on this expert-labeled dataset to assess the usefulness of the labels for further analysis. The results show that all models achieved accuracy, precision, recall, and F1 scores above 0.90, with the Swin Transformer model reaching the highest accuracy of 0.924. When applied to six 10 × 10 km sub-regions in Africa, the best model exceeded 0.95 in most performance metrics. These findings demonstrate the potential of this dataset to enhance road surface identification and support infrastructure improvement in Africa.

Road information services (RIS) is a major component of the information and communication technologies with the main purpose of RIS-based systems is to monitor road health conditions, weather information and traffic congestion. Considering the road conditions, there are various kinds of road surface types and anomalies with lack of efficient analysis of their behavior on the vehicle sensor measurements. Consequently, there are difficulties in detecting and categorizing the different road types and anomalies. This paper demonstrates road test results for the measurements of inertial sensors mounted in land vehicles while monitoring various road surface types and anomalies. In addition, a wavelet-based feature extraction together with statistical approach for the road types and anomalies are explored in this study. Two road test experiments on two different vehicles performed in Kingston, ON, Canada together with in-depth analysis are discussed in this paper.

The elemental analysis of road dust can be used to determine the contribution of traffic-related metal emissions to the local environment. Previous studies have shown that brake wear, tire wear, and lead wheel weights release significant amounts of metals that lead to decreased biodiversity and degradation of urban streams. Highway pavement has not previously been considered as a source of metal inputs into the environment. To determine the extent that highway pavement contributes to metal loads in the local environment, road dust and pavement samples were collected for both asphalt and concrete pavement types on State Highway 59 in Houston, TX, USA. Samples were analyzed by ICP-MS to quantitate 9 metals: aluminum (Al), iron (Fe), vanadium (V), chromium (Cr), nickel (Ni), cobalt (Co), copper (Cu), zinc (Zn), and lead (Pb). A chemical mass balance receptor model that is commonly used for source apportionment of air pollution was applied to heavy metals in road dust, with brake dust, tire wear, wheel weights, and pavement materials as the emission sources. Pavement is shown to be the primary source of V, Cr, Ni, and Co in road dust, regardless of asphalt or concrete pavement type. Lead concentration in road dust is influenced by pavement type, with concrete pavement contributing approximately 45% of total lead in concrete road dust, while asphalt pavement only contributes 27% of total lead in asphalt road dust. Fly ash is the likely component of concrete pavement responsible for the higher contribution of Pb. An enrichment factor analysis indicates that Cr, Co, and V have low levels of enrichment compared to Cu, Zn, Pb, and Ni, indicating that pavement wear is generally a minor source of environmental metal contamination compared to brake and tire wear. Currently, brake and tire wear are the non-exhaust vehicle emissions of greatest concern; however, alternatives to fly ash should be considered to further reduce Pb and Ni concentrations in the environment.

The present study elucidates the comparison of thermal performance of pavement materials, namely asphalt and concrete. Albedo value of a material is considered to be a crucial parameter to evaluate thermal performance of engineering/natural materials. Experimental investigations reported in the literature suggest, albedo values of concrete pavement to be in the range of 0.25–0.4, whereas the values for asphalt pavement range from 0.05–0.10. The lower albedo value is associated with higher energy absorption and hence higher surface temperature. Based on the albedo values, it has been established that asphalt pavement exhibits higher surface temperature than PCC pavement. Through surface temperature, pavement materials also influence the energy consumption and carbon emissions. The present study highlights the recent advancements towards enhancing the thermal performance of asphalt and concrete pavement. The literature studies confirm that the enhancement in thermal performance of asphalt pavement has been mostly achieved by the application of additives in asphalt and/or utilization of high albedo aggregates. On the contrary, concrete pavement despite being superior in performance as compared to asphalt; fewer studies have attempted to improve the albedo values (viz., better thermal performance) of concrete pavement. The present review summarizes the current state of the art on the role of pavement materials on UHI effects and also suggests future direction towards making cities more sustainable, with lower UHI effects.KeywordsUrban heat islandPavement materialsAlbedoSurface temperatureConcreteAsphalt

Each year, state DOTs make decisions about which paving jobs to let, and whether they should be designed as concrete, asphalt, or let as alternates. There is wide variation in the amount spent on concrete pavement and asphalt pavement across the states and over time. Asphalt and concrete pavement materials can be considered substitutes because they perform a similar function for consumers. We explore the question: Does the presence of competition between pavement material substitutes within a state impact pavement material prices? Using pay item information from 47 state DOTs since the year 2005 from the Oman BidTabs database, we calculate for each material the weighted average unit costs each year, adjust them for year-toyear change, and average those over the study period. We select the percent of total spending on concrete within a state for a given year as the metric to represent presence of competition. Comparing these, we find that the presence of competition has a significant impact on concrete unit costs. A negative linear trend is observable but not significant for asphalt. Therefore, this result suggests that if a state were to sustain a higher presence of competition for multiple years, they could expect to pay a lower unit cost for concrete overall.