Floating Car Data for Road Roughness: An Innovative Approach to Optimize Road Surface Monitoring and Maintenance

Commercial floating car data application in Sub-Saharan African transport planning contexts: A critical review and research agenda

Traditional traffic monitoring systems are mostly based on road side equipment (RSE) measuring traffic conditions throughout the day. With more and more GPS‐enabled connected devices, floating car data (FCD) has become an interesting source of traffic information, requiring only a fraction of the RSE infrastructure investment. While FCD is commonly used to derive historic travel times on individual roads and to evaluate other traffic data and algorithms, it could also be used in traffic management systems directly. However, as live systems only capture a small percentage of all traffic, its use in live operating systems needs to be examined. Here, the authors investigate the potential of FCD to be used as input data for live automated traffic management systems. The FCD in this study is collected by a live country‐wide FCD system in the Netherlands covering 6–8% of all vehicles. The (anonymised) data is first compared to available road side measurements to show the current quality of FCD. It is then used in a dynamic speed management system and compared to the installed system on the studied highway. Results indicate the FCD set‐up can approximate the installed system, showing the feasibility of a live system.

On the Initiation of World Neurosurgery

Motorway speed pattern identification from floating vehicle data for freight applications

This paper attempts to integrate data from models, traditional surveys and big data in a situation of limited information. The goal is to increase the capacity of transport planners to analyze, forecast, and plan passenger mobility. (Big) data are a precious source of information and substantial effort is necessary to filter, integrate, and convert big data into travel demand estimates. Moreover, data analytics approaches without demand models are limited because they allow: (a) the analysis of historical and/or real-time transport system configurations, and (b) the forecasting of transport system configurations in ordinary conditions. Without the support of travel demand models, the mere use of (big) data does not allow the forecasting of mobility patterns. The paper attempts to support traditional methods of transport systems engineering with new data sources from ICTs. By combining traditional data and floating car data (FCD), the proposed framework allows the estimation of travel demand models (e.g., trip generation and destination). The proposed method can be applied in a specific case of an area where FCD are available, and other sources of information are not available. The results of an application of the proposed framework in a sub-regional area (Calabria, southern Italy) are presented.

High spatial density ground thermal measurements in a warming permafrost region, Beiluhe Basin, Qinghai-Tibet Plateau

In Sweden today, friction measurements are performed manually, often using methods generating spot-wise measurements. Because the low numbers of measurements provided by these methods are insufficient to follow up on the friction requirements set by the Swedish Transport Administration, the Administration has initiated the Digital Winter project. In Digital Winter, floating car data (FCD) are utilised for road friction estimation. The focus in this investigation is on coverage, and on whether the FCD detects harsh weather conditions with decreasing road friction. Two different methods—one continuous and one slip-based—are implemented in this investigation. Furthermore, different approaches on how to build the vehicle fleet to collect the FCD have been applied using different combinations of commercial and private vehicles. The results showed that both methods detect low-friction events, and for roads with high annual average daily traffic (AADT), the data collection using slip-based methods and larger fleets gives more data points than for smaller fleets using continuous methods, and the reverse is true for lower AADT. The results showed differences between the two fleets in terms of coverage for the weekly and daily distributions, but overall, the method of using FCD for road friction estimation seems promising for the follow-up of winter road maintenance.

The importance of the road network in Indonesia as a vital infrastructure that connects various regions has made road maintenance a top priority in development planning. However, various challenges such as ineffective handling methods, limited experts, and minimal equipment have caused road management to not be optimal. Therefore, innovations are needed in road condition measurement, one of which is through the development of an ultrasonic sensor-based surface roughness measuring instrument as a prototype of International Roughness Index (IRI) measurement to support more accurate road maintenance evaluation and planning. The purpose of this research is to measure road roughness through IRI and pavement modulus values to improve road condition assessment.This study employs the International Roughness Index (IRI) to assess the functional condition of roads and the Pavement Modulus to evaluate the structural strength of the pavement. The IRI is measured through road surface roughness surveys using a roughness meter, with the results used to classify the severity of road damage. The IRI calculation is based on a quarter-car simulation model that utilizes vehicle dynamic parameters in response to road surface profiles, following the mathematical approach developed by Sayers, Gillespie, and Paterson (1986). The research results show that the prototype Ultrasonic Surface Roughness Meter was able to measure IRI values ranging from 4 to 8 at three different locations. These measurements fall within the "Good–Fair" classification, indicating relatively mild surface roughness. Based on these findings, the Directorate General of Highways recommends light rehabilitation and periodic maintenance, and the prototype device has the potential to serve as an effective, low-cost alternative for road condition monitoring, especially in areas with limited access to conventional IRI measurement tools.

During last decades, owing to the increase in a number of vehicles, the rate of accident occurrence grows significantly. Efforts must be made to provide efficient tools to prioritize segments requiring safety improvement and identify influential factors on accidents. This objective of the research was to determine the safety oriented threshold of International Roughness Index (IRI) to recognize Accident-Prone Segments (APSs) using new segmentation method. The modified Floating Fixed-length Segmentation (FFLS) was performed based upon the determined safety oriented IRI threshold with respect to the available literature. Floating fixed-length patterns with lengths of 100, 200 and 500 meters were moved over an entire length of a selected highway to detect segments with IRI values higher than the threshold. To diminish the lack of heterogeneity in characteristics of segments, it was proposed to analyze adjacent road segments with a similar pattern of IRI variation, as a unit. Owing to the limitation in road maintenance and rehabilitation costs for safety improvement, the entire APSs cannot be treated. Therefore, prioritization and selection of APSs were followed by imposing constraints upon the preservation of different percentages of the highway. Results indicated that the assumed safety oriented threshold of IRI and the modified segmentation method led to correct recognition of segments with high IRI associated with low level of safety. Application of the proposed method using 200-meter floating segment resulted in the shortest length of APSs for safety improvement. The outcomes lead to preserving the most deteriorated segments considering budget constraints. Furthermore, the validation supported the outcomes in which most of the segments were selected from sections with PCI values of 30 or 19. The latter supports the results achieved by the determined IRI threshold and segmentation method. Therefore, considering safety issues as well as maintenance operations would result in optimal use of available budget.

Flexible pavements are considered an essential element of transportation infrastructure. So, evaluations of flexible pavement performance are necessary for the proper management of transportation infrastructure. Pavement condition index (PCI) and international roughness index (IRI) are common indices applied to evaluate pavement surface conditions. However, the pavement condition surveys to calculate PCI are costly and time-consuming as compared to IRI. This article focuses on developing regression models that predict PCI from IRI. Eighty-three flexible pavement sections, with section length equal to 250 m, were selected in Al-Diwaniyah, Iraq, to develop PCI-IRI relationships. In terms of the quantity and severity of each observed distress, the pavement condition surveys were conducted by actually walking through all the sections. Using these data, PCI was calculated utilizing Micro PAVER software. Dynatest Road Surface Profiler (RSP) was used to collect IRI data of all the sections. Using the SPSS software, linear and nonlinear regressions have been used for developing two models between PCI and IRI based on the collected data. These models have the coefficients of determination (R2) equal to 0.715 and 0.722 for linear and quadratic models. Finally, the results indicate the linear and quadratic models are acceptable to predict PCI from IRI directly.

In order to best prioritize road maintenance, the level of deterioration must be known for all roads in the network. International Roughness Index (IRI) is a key standardized and widely used parameter to quantify road roughness and to aid officials in making road maintenance decisions. A low IRI value indicates smooth road and a high value indicates road has distresses such as severe cracking, potholes, and rutting, etc. IRI measurement using laser profilometers with accelerometers mounted on the front or rear bumper of a vehicle along the wheel path is commonly used at a driving speed of above 40 mph. IRI values measured through laser are questionable below this speed and will not work when road is wet. This paper discusses an alternative method to estimate IRI using a dynamic tire pressure sensor (DTPS) inside the tire as vehicle is moving. This method is working at a speed of above 15 MPH suitable for both interstate highways as well as urban roadways which required a driving speed of below 40 MPH. It works at all weather conditions since the measurement is the tire pressure change inside the tire through road and tire interaction. A feasibility and repeatability test on highway and airport through a certification test which was administered by Massachusetts Department of Transportation is therefore reported. The accuracy is equivalent to laser based IRI values.

In order to best prioritize road maintenance, the level of deterioration must be known for all roads in a city’s network. Pavement Condition Index (PCI) and International Roughness Index (IRI) are two standard methods for obtaining this information. However, IRI is substantially easier to measure. Significant time and money could be saved if a method were developed to estimate PCI from IRI. This research introduces a new method to estimate IRI and correlate IRI with PCI. A vehicle-mounted dynamic tire pressure sensor (DTPS) system is used. The DTPS measures the signals generated from the tire/road interaction while driving. The tire/road interaction excites surface waves that travel through the road. DTPS, which is mounted on the tire’s valve stem, measures tire/road interaction by analyzing the pressure change inside the tire due to the road vibration, road geometry and tire wall vibration. The road conditions are sensible to sensors in a similar way to human beings in a car. When driving on a smooth road, tire pressure stays almost constant and there are minimal changes in the DTPS data. When driving on a rough road, DTPS data changes drastically. IRI is estimated from the reconstructed road profile using DTPS data. In order to correlate IRI with PCI, field tests were conducted on roads with known PCI values in the city of Brockton, MA. Results show a high correlation between the estimated IRI values and the known PCI values, which suggests that DTPS-based IRI can provide accurate predictions of PCI.

The United States is facing grave infrastructure challenges related to repairing aging roads with limited resource allocation. Every year, large sums of money are invested into repairs, but these repairs are still inadequate. In order to best prioritize road maintenance investment, city officials should know the grade of all roads prior to any action of repair. Two major parameters are widely used to assess road surface conditions: International Roughness Index (IRI) and Pavement Condition Index (PCI). IRI is a standardized and widely used parameter to quantify road roughness and riders' comfort level for major highways. A low IRI value indicates a smooth road and a high value indicates that the road has distresses, such as potholes or deep depressions. One limitation of the current IRI measurement is that the laser profilometers used with accelerometers are incapable of operating on a wet road surface. Another is IRI's inaccuracy due to the speed effect and complicated road conditions (potholes, manholes, etc.). PCI has been used widely on urban roads. A high PCI value indicates a good road and a low value indicates a poor road. The limitations of current PCI measurement include the high cost, difficulty of manually gathering measurements without traffic interruptions , and low efficiency on data processing due to large amounts of pictures. To overcome these limitations, this dissertation develops two new sensor systems for IRI measurement: a directional microphone and dynamic tire pressure sensor (DTPS) with an axle accelerometer. These sensors are all mounted on a moving vehicle. This research develops that IRI measurement use a directional microphone and a probabilistic method to analyze the probability density function (PDF) of acoustic data collected while driving. Acoustic response of tire/road interactions was measured. Weibull distribution of the acoustic data was applied to the IRI estimation on Superpave, Stone Matrix Asphalt (SMA), and Open Grade Friction Coarse (OGFC) roads with IRI values less than 2 m/km. IRI measurement using DTPS with an axle accelerometer analyzes the tire pressure change inside the tire with an axle accelerometer. Speed effect has been minimized in the derivation. Therefore, it is suitable for both urban roads and state/interstate highways. Field road tests have been conducted to validate the accuracy for the cities of Brockton and Boston, MA, and for part of interstate highway I-95 MA, state highway US-1 and US-128. A certification test was also completed at New Bedford Regional Airport administered by the Massachusetts Department of Transportation. Results showed that the higher the IRI values, the lower the PCI values. It is possible to use IRI to assess road conditions for both urban roads and highways and indirectly infer PCI values of urban roads. The advantages of this method are that it works under all weather conditions since the sensor is inside the tire, and it does not interrupt the traffic. The speed effect, which was encountered in the method which used laser profilometers with accelerometers, is considerably minimized in the DTPS approach since this approach does not require the systematic integration of acceleration data. Therefore, it works for both highways and urban roads. Meanwhile, a miniature fixture was designed to further the simplification of the mounting process for easy installation. An energy harvesting system was also designed and tested at a speed of up to 52 km/h in the lab in order to power the DTPS sensing system. Not only can this energy harvester be used to power the DTPS system, but it can also potentially be used as an independent energy harvester to recharge car batteries and to power vehicle based sensors, including their wireless transmitters and vehicle computer chips. This new development will enable continuous, network-wide assessments of roadway conditions to effectively and efficiently make the right repair, at the right time, in the right place.

The condition of the road pavement can be judged from the functional performance by measuring the surface condition of the pavement and the value of surface roughness. Pavement surface condition measurements are usually carried out using the Pavement Condition Index (PCI) survey method. While the measurement of surface roughness values is usually carried out with the Roughness survey method so that the International Roughness Index (IRI) is obtained. This study tries to evaluate the value of a pavement condition by reviewing functional performance to identify the value of the pavement condition. Checking road conditions in this study was carried out using two survey methods, namely the International Roughness Index (IRI) method, the Pavement Condition Index (PCI) method. The results of the examination are analyzed so that the correlation between each method can be known. The results of the study explained that based on the results of observations of the functional condition of road pavements with the Pavement Condition Index (PCI) method using the Hawkeye 2000 tool on the Demak Ring Road section, a PCI value of 65.34 was obtained with the “Fair” category, the functional condition of the road pavement with the International Roughness Index (IRI) method of the Hawkeye 2000 tool on the Demak Ring Road section obtained a IRI value of 6.60 with the category “Medium”, and the correlation of the functional condition of road work using the Pavement Condition Index (PCI) method with the International Roughness Index (IRI) on the Demak Ring Road section has no correlation.

The region between Dome A and South Pole has likely preserved ice older than the current 800-thousand-year limit of continuous ice core records; however, until now this region has been largely unexplored. The Center for Oldest Ice Exploration (COLDEX) is currently performing the second of two planned years of airborne geophysical surveys on the Southern flank of Dome A. These surveys are providing new geological and glaciological constraints that we combine with ice-flow models to help target suitable deep ice core sites with the goal of recovering a continuous ice-core record going back at least 1.5 million years.   Using the new airborne ice penetrating radar data from COLDEX, as well as existing data from the AGAP project, we investigate how local variations in surface conditions may affect the ice record over time. First, we trace englacial layers and date them at the intersection with the South Pole ice core to infer the rate and pattern of past accumulation averaged over different time intervals. Second, we assess the impact of local zones of wind scour that occur on the Southern flank of Dome A (Das et al, 2013), which is at the upstream edge of the COLDEX airborne survey. Local zones of wind scour that lead to ablation or no accumulation, create time-transgressive unconformities that can be mapped from ice penetrating radar data. While the unconformity is initiated due to a relatively local change in surface conditions, the unconformity trace is imaged for many tens of kilometers downstream as it is advected by ice flow. Because the airborne survey flight lines are oriented along flowlines, the unconformities act as particle trajectories.   We use an ice-flow model set up along a flowline to evaluate the surface and flow conditions that develop an unconformity similar to a well-imaged unconformity that is observed in the COLDEX data. The unconformity can be well matched with the simple ice-flow model using a fixed position of the scour zone, indicating that the scour zone has been a persistent feature for the past glacial-interglacial cycle (~100 ka). Consistent with previous work (Das et al, 2013), the scour zones are co-located with subglacial ridges that create steeper surface topography. Thus, the positions of the scour zones are likely independent of the climate state and permanent features on long timescales.   By modeling this unconformity trace we can constrain the modern horizontal velocity to ~1.5 m/yr near the scour zone that is located ~400 km from Dome A. The unconformity disrupts the continuity of all of the dated internal layers, which extend to 94 ka. Running the model back 1.5 Ma, we can evaluate where the climate record is disrupted at different positions along the flowline. The farther downstream a potential drill site is, the more problematic the unconformities become for obtaining a continuous climate record because the unconformity disrupts the continuity at deeper depths and older ages.