A system for underground road condition monitoring

Poor road conditions in underground mine tunnels can lead to decreased production efficiency and increased wear on production vehicles. A prototype system for road condition monitoring is presented in this paper to counteract this. The system consists of three components i.e. localization, road monitoring, and scheduling. The localization of vehicles is performed using a Rao-Blackwellized extended particle filter, combining vehicle mounted sensors with signal strengths of Wi Fi access points. Two methods for road monitoring are described: a Kalman filter used together with a model of the vehicle suspension system, and a relative condition measure based on the power spectral density. Lastly, a method for taking automatic action on an ill-conditioned road segment is proposed in the form of a rescheduling algorithm.The scheduling algorithm is based on the large neighborhood search and is used to integrate road service activities in the short-term production schedule while minimizing introduced production disturbances.The system is demonstrated on experimental data collected in a Swedish underground mine.

New Adaptive Approach for Road Condition Identification in ASR Control System

A novel tire-road adaptive model in longitude direction to formulate the dynamic characteristic between tire and road is proposed in this paper, based on this model, a new adaptive approach of road condition identification is presented to identify the model＇s parameters on-line in order to improve the performance of anti-slip regulation system（ASR）. The optimal slip is determined by using the drive wheel＇s slip and longitude traction force in ASR before the slipping of the drive wheel. Co-simulation is done based on the model for JETTA GTX building with ADAMS/CAR and Matlab, and results show that the adaptive model accords with Pacejka model very well. This adaptive model has simpler form, less number of parameters and higher adaptability than usual, and the new identification approach has a small amounts of operation, which is very suitful for ASR.

Assessing Gravel Roads Condition to Examine Trends and Behaviors of Gravel Roads in Rural Areas in Kyela District

Gravel roads are an economical choice where traffic volume is low. Many gravel roads were constructed in rural areas in the Kyela district and they were well-served for many years. Besides the gravel roads, the main challenge faced is the issue of unpaved road condition deterioration by the behaviors of road materials. The study selected three gravel rural roads with a total of 31.24 kilometers as constructed roads to gravel standard. The aim of this study was to assess gravel roads condition to examine trends and behaviors of gravel roads in rural areas. A descriptive analysis was conducted to determine trends and behaviors of gravel roads. The methods used to collect data were observation and physical measurement. The results show that there was more than one road distress identified and assessed on each study road. It was noted that all the study roads had excellent to poor distress conditions associated with poor road materials and lack of maintenance. This study recommended that road authorities improve the current condition by avoiding the use of poor road materials in order to reduce surface deterioration associated.

Real-world fuel efficiency and exhaust emissions of light-duty diesel vehicles and their correlation with road conditions

The real-world fuel efficiency and exhaust emission profiles of CO, HC and NOx for light-duty diesel vehicles were investigated. Using a portable emissions measurement system, 16 diesel taxies were tested on different roads in Macao and the data were normalized with the vehicle specific power bin method. The 11 Toyota Corolla diesel taxies have very good fuel economy of （5.9 ± 0.6） L/100 km, while other five diesel taxies showed relatively high values at （8.5 ± 1.7） L/100 km due to the variation in transmission systems and emission control strategies. Compared to similar Corolla gasoline models, the diesel cars confirmed an advantage of ca. 20% higher fuel efficiency. HC and CO emissions of all the 16 taxies are quite low, with the average at （0.05 ± 0.02） g/km and （0.38 ± 0.15） g/km, respectively. The average NOx emission factor of the 11 Corolla taxies is （0.56 ± 0.17） g/km, about three times higher than their gasoline counterparts. Two of the three Hyundai Sonata taxies, configured with exhaust gas recirculation （EGR） ＋ diesel oxidation catalyst （DOC） emission control strategies, indicated significantly higher NO2 emissions and NO2/NOx ratios than other diesel taxies and consequently trigger a concern of possibly adverse impacts on ozone pollution in urban areas with this technology combination. A clear and similar pattern for fuel consumption and for each of the three gaseous pollutant emissions with various road conditions was identified. To save energy and mitigate CO2 emissions as well as other gaseous pollutant emissions in urban area, traffic planning also needs improvement.

Highway icing time prediction with deep learning approaches based on data from road sensors

In harsh climates,highway icing poses a hazard to traffic safety and increases road maintenance costs.It is of great significance to predict when the highway icing may occur and take a preventive plan.However,there are few studies on highway icing time prediction due to the scarcity and complexity of data.In this study,variables of icing temperature,friction,ice percentage,road surface temperature,water film height,saline concentration,and road condition were collected by road sensors distributed on a highway in China.A large-scale time series highway surface information dataset called HighwayIce is formed.Furthermore,a deep learning approach called IceAlarm,composed of long short-term memory neural network(LSTM),multilayer perceptron(MLP),and residual connection,has been developed to predict when the highway will ice.The LSTM is used to process dynamic variables,the MLP is used to process static variables,and the fully-connected layers with residual connections are used to make a deep fusion.The experimental results show that the average mean absolute error before icing using the IceAlarm model is about 6min and outperforms all baseline models.The HighwayIce dataset and IceAlarm model can help improve the prediction accuracy and efficiency of forecasting real-world road icing time,therefore reducing the impact of icy road conditions on traffic.

An Urban Traffic Monitoring System Based on the ArcGIS Engine

The growing number of vehicles makes traffic jams and accidents significant problems. Making people get to know the real-time road condition can mitigate the effect of congestions greatly, but this is not supported by traditional traffic assistant systems. The intelligent traffic system is born to settle these problems. By making full use of the ArcGIS （Arc Geographic Information System） Engine characteristics, this paper designs and imple- ments an urban traffic monitoring system. The main functions of the system include the real-time road condition information display, layer-control, supervisory control management and the basic operations of a map. With the data collected by monitors deployed in intersections, different road conditions are calculated and shown with dif- ferent colors on the map and users can choose suitable roads to get away from the traffic congestion; meanwhile it can offer a reference for a traffic management department to make decisions on traffic control. The system has been deployed and shows high practicability and reliability in practical use.

Road pavement condition diagnostics using smartphone-based data crowdsourcing in smart cities

The purpose of the paper is to analyse the effectiveness of a solution known as road condition tool(RCT)based on data crowdsourcing from smartphones users in the transport system.The tool developed by the author of the paper,enabling identification and assessment of road pavement defects by analysing the dynamics of vehicle motion in the road network.Transport system users equipped with a smartphone with the RCT mobile application on board record data of linear accelerations,speed,and vehicle location,and then,without any intervention,send them to the RCT server database in an aggregated form.The aggregated data are processed in the combined time and location criterion,and the road pavement condition assessment index is estimated for fixed 10 m long measuring sections.The measuring sections correspond to the sections of roads defined in the pavement management systems(PMS)used by municipal road infrastructure administration bodies.Both the research in question and the results obtained by the method proposed for purposes of the road pavement condition assessment were compared with a set of reference data of the road infrastructure administration body which conducted surveys using highly specialised measuring equipment.The results of this comparison,performed using binary classifiers,confirm the potential RCT solution proposed by the author.This solution makes it possible to global monitor the road infrastructure condition on a continuous basis via numerous users of the transport system,which guarantees that such an assessment is kept up to date.

Road surface condition sensor based on scanning detection of backward power

A method of detecting dry, icy and wet road surface conditions based on scanniag detection of single wavelength backward power is proposed in this letter. The detector is used to receive the backward scattered power which changes with the incidence angle. The relationship between backward power and incidence angle is used to find out the effective angle range and distinguish method. Experiment and simulation show that it is feasible to classifv these three conditions within incidence angle of 5.3 degree.

Learning from the crowd:Road infrastructure monitoring system

The condition of the road infrastructure has severe impacts on the road safety, driving comfort, and on the rolling resistance. Therefore, the road infrastructure must be moni- tored comprehensively and in regular intervals to identify damaged road segments and road hazards. Methods have been developed to comprehensively and automatically digitize the road infrastructure and estimate the road quality, which are based on vehicle sensors and a supervised machine learning classification. Since different types of vehicles have various suspension systems with different response functions, one classifier cannot be taken over to other vehicles. Usually, a high amount of time is needed to acquire training data for each individual vehicle and classifier. To address this problem, the methods to collect training data automatically for new vehicles based on the comparison of trajectories of untrained and trained vehicles have been developed. The results show that the method based on a k-dimensional tree and Euclidean distance performs best and is robust in transferring the information of the road surface from one vehicle to another. Furthermore, this method offers the possibility to merge the output and road infrastructure information from multiple vehicles to enable a more robust and precise prediction of the ground truth.

Effects of rainfall on the weekday traffic flow in major cities of the Beijing-Tianjin-Hebei region, China, in 2021

Rainfall can bring uncertainties to the traffic flow and influence the normal function of urban transportation systems.The impact of precipitation on the urban traffic flow,especially the different impacts among cities and areas within a city,is worth investigating.Here,we analysed the impact of precipitation on the traffic flow in the urban areas of the Beijing-Tianjin-Hebei region by comparing the traffic flow in non-precipitation and rainy weather with different hourly precipitation intensities in 2021.The increase in the travel time index(TTI)is chosen to represent the influence of precipitation on the transportation system.The results show that the maximum of the average TTI increases on the city scale under various rainfall intensities by 3.3%,6.6%and 10.8%in Beijing,Tianjin and Shijiazhuang,respectively.In general,the increase in the TTI contributed by precipitation is the greatest at morning and afternoon peak hours,and the traffic congestion degree increases with the rainfall intensity.However,in the morning peak,afternoon peak and midday hours in Beijing and Tianjin,the influences of the weak rainfall intensity on the traffic flow are generally great,whereas the traffic congestion degree caused by heavy precipitation is relatively low.Particularly,in morning peak hours,the congestion reduction reaches approximately 2%,which may be related to the spatial difference in the impacts of precipitation on the traffic flow and the changes in people's travel intention under different rainfall intensities.The findings can help better understand the relationship between rainfall and urban traffic flow characteristics and also potentially contribute to the development of impact-oriented climate predictions.

Effect of speed hump characteristics on pavement condition

Speed humps are the most common type of traffic calming devices due to their low cost and easy installation. However, in many Egyptian roads, considerable number of these humps is randomly placed without proper engineering studies and justifications. Deteri- oration of pavement condition is observed near these humps. This paper presents a case study applied to collect and analyze visual inspection data for the reason of evaluating the impact of speed humps on pavement condition on intercity rural roads. The paper used 52 speed humps located in an intercity two-lane, two-way road that connects two cities, Tahta and Gerga, in Upper Egypt. The total length of this road is about 34 km. Pavement condition index （PCI）, in road sections, near speed humps in the two directions of travel were calculated from the visual inspection measurements. The characteristics of each speed hump （width, height, and distance from preceding hump） were measured. Using statistical analyses, the correlations between the pavement conditions and hump char- acteristics were examined. Regression analysis models were developed to represent the relationships between pavement conditions and hump characteristics. Generally, the re- sults proved that the pavement conditions are greatly influenced by the presence of speed humps and hump characteristics.