Acceptability of Pavement Roughness on Urban Highways by Driving Public

2003 ◽  
Vol 1860 (1) ◽  
pp. 187-193 ◽  
Author(s):  
Kevan Shafizadeh ◽  
Fred Mannering
Keyword(s):  
Empirical Support ◽  
Past Research ◽  
Ride Quality ◽  
International Roughness Index ◽  
Pavement Roughness ◽  
Road Roughness ◽  
Roughness Index ◽  
National Highway ◽  
User Acceptability ◽  
Highway System

The driving public’s attitude toward acceptable levels of road roughness is explored using empirical data collected on urban highways. Individual driver acceptability levels are matched with international roughness index (IRI) levels to examine the existence of potential user acceptability thresholds. In particular, the observed trends are compared with the federal IRI guideline of 170 in./mi (2.7 m/km) for acceptable ride quality recommended by FHWA in its 1998 strategic plan for the National Highway System. The research reported on appears to provide empirical support for the federal IRI guidelines that are already in existence. This study also found that IRI levels provided a very good indication of driver acceptability, which agrees with past research based on antiquated present serviceability ratings.

scholarly journals Impact of Pavement Roughness on Traffic Safety under Heterogeneous Traffic Conditions

2020 ◽  
Vol 17 (1) ◽  
pp. 13-19
Author(s):  
M.O. Popoola ◽  
O.A. Apampa ◽  
O. Adekitan
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
Heterogeneous Traffic ◽  
Ride Quality ◽  
International Roughness Index ◽  
Traffic Crash ◽  
Traffic Conditions ◽  
Pavement Roughness ◽  
Roughness Index ◽  
Transportation Agencies

H ighway safety is a major priority for public use and for transportation agencies. Pavement roughness indirectly influence drivers' concentration, vehicle operation, and road traffic accidents, and it directly affect ride quality. This study focuses on analyzing the influence of pavement roughness on traffic safety using traffic, pavement and accident data on dual and single carriageway operated under heterogeneous traffic conditions in South-west, Nigeria. Traffic crash data between 2012 and 2015 was obtained from the Federal Road Safety Commission (FRSC) and International Roughness Index (IRI) data from the Pavement Evaluation Unit of the Federal Ministry of Works, Kaduna. Crash road segments represented 63 percent of the total length of roads. IRI values for crash and non-crash segments was a close difference of 0.3,This indicates that roughness is not the only factors affecting occurrence of traffic crashes but a combination with other factors such as human error, geometric characteristics and vehicle conditions. Crash severity was categorized into Fatal, serious and minor injury crashes. In all cases, the total crash rate increases with increase in IRI value up to a critical IRI value of 4.4 and 6.15 for Sagamu-Ore road and Ilesha-Akure-Owo road respectively, wherein the crash rate dropped. The conclusion is key in improving safety concerns, if transportation agencies keep their road network below these critical pavement conditions, the crash rate would largely decrease. The study concluded that ride quality does not directly affect traffic crash rate. Keywords: Pavement conditions, traffic safety, International Roughness Index, crash rate, carriageway.

Calibrating Road Roughness Meters in Developing Countries

1996 ◽  
Vol 1536 (1) ◽  
pp. 73-81 ◽  
Author(s):  
Christopher R. Bennett
Keyword(s):  
Developing Countries ◽  
Feasibility Studies ◽  
Calibration Data ◽  
International Roughness Index ◽  
Pavement Roughness ◽  
Road Roughness ◽  
Calibration Experiment ◽  
Roughness Index ◽  
Road Performance ◽  
Vehicle Chassis

Pavement roughness is an important characteristic monitored by many road agencies. It is used as an indicator of road performance and also for feasibility studies. In developing countries, the most common method of recording road roughness is the use of response-type roughness meters. These are instruments that measure the displacement of the vehicle chassis relative to its axle. Because vehicles respond differently to roughness, it is necessary to calibrate them against a standard roughness measure. Calibrating roughness meters against the international roughness index, profiling test sections, undertaking a calibration experiment, and analyzing the calibration data are addressed.

scholarly journals Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US Roads

2020 ◽  
Vol 12 (9) ◽  
pp. 1507 ◽  
Author(s):  
Franz J. Meyer ◽  
Olaniyi A. Ajadi ◽  
Edward J. Hoppe
Keyword(s):  
High Resolution ◽  
Remote Sensing Data ◽  
Ride Quality ◽  
International Roughness Index ◽  
Pavement Roughness ◽  
X Band ◽  
The Us ◽  
Network Scale ◽  
Sar Data ◽  
Satellite Radar

The traveling public judges the quality of a road mostly by its roughness and/or ride quality. Hence, mapping, monitoring, and maintaining adequate pavement smoothness is of high importance to State Departments of Transportation in the US. Current methods rely mostly on in situ measurements and are, therefore, time consuming and costly when applied at the network scale. This paper studies the applicability of satellite radar remote sensing data, specifically, high-resolution Synthetic Aperture Radar (SAR) data acquired at X-band, to the network-wide mapping of pavement roughness of roads in the US. Based on a comparison of high-resolution X-band Cosmo-SkyMed images with road roughness data in the form of International Roughness Index (IRI) measurements, we found that X-band radar brightness generally increases when pavement roughness worsens. Based on these findings, we developed and inverted a model to distinguish well maintained road segments from segments in need of repair. Over test sites in Augusta County, VA, we found that our classification scheme reaches an overall accuracy of 92.6%. This study illustrates the capacity of X-band SAR for pavement roughness mapping and suggests that incorporating SAR into DOT operations could be beneficial.

Customer Usage Based on Pseudo Damage

2017 ◽  
Author(s):  
Craig T. Altmann ◽  
John B. Ferris
Keyword(s):  
Performance Monitoring ◽  
International Roughness Index ◽  
The Road ◽  
Road Profile ◽  
Road Roughness ◽  
Federal Highway ◽  
Roughness Index ◽  
Average Annual Daily Traffic ◽  
Road Type ◽  
Early Design Stages

Modeling customer usage in vehicle applications is critical in performing durability simulations and analysis in early design stages. Currently, customer usage is typically based on road roughness (some measure of accumulated suspension travel), but vehicle damage does not vary linearly with the road roughness. Presently, a method for calculating a pseudo damage measure is developed based on the roughness of the road profile, specifically the International Roughness Index (IRI). The IRI and pseudo damage are combined to create a new measure referred to as the road roughness-insensitive pseudo damage. The road roughness-insensitive pseudo damage measure is tested using a weighted distribution of IRI values corresponding to the principal arterial (highways and freeways) road type from the Federal Highway Administration (FHWA) Highway Performance Monitoring System (HPMS) dataset. The weighted IRI distribution is determined using the number of unique IRI occurrences in the functional road type dataset and the Average Annual Daily Traffic (AADT) provided in the FHWA HPMS data.

scholarly journals Airport pavement roughness evaluation based on cockpit and center of gravity vertical accelerations

TRANSPORTES ◽  
2020 ◽  
Vol 28 (1) ◽  
pp. 147-159
Author(s):  
Jorge Braulio Cossío Durán ◽  
José Leomar Fernandes Júnior
Keyword(s):  
Center Of Gravity ◽  
International Roughness Index ◽  
Airport Pavement ◽  
Pavement Roughness ◽  
Roughness Index ◽  
Roughness Evaluation ◽  
Vertical Accelerations

Pavimentos irregulares são geralmente responsáveis por acelerações verticais (VA) que afetam as aeronaves, aumentam a distância de parada e dificultam a leitura dos instrumentos de navegação na cabine dos pilotos. O International Roughness Index (IRI) e o Boeing Bump Index (BBI) são utilizados atualmente para quantificar a irregularidade longitudinal dos pavimentos aeroportuários e identificar seções que demandam atividades de manutenção e reabilitação (M&R). Contudo, tais índices baseiam-se apenas nas respostas dinâmicas de um automóvel a 80 km/h às irregularidades longitudinais dos pavimentos rodoviários, bem como nas características físicas das irregularidades (comprimento e altura), respectivamente, ignorando o efeito das VA nas aeronaves. Ainda, limites críticos atuais, sugeridos por Sayers & Karamihas e ANAC para IRI (2,0 e 2,5 m/km, respectivamente) e pela FAA para BBI (1,0) podem subestimar a condição real do pavimento. Este artigo avalia o efeito da irregularidade longitudinal nas acelerações na cabine dos pilotos (VACP) e no centro de gravidade (VACG). O software ProFAA permitiu calcular os índices e simular as VA em 4 aeronaves representativas atravessando 20 pistas de pouso e decolagem em 10 velocidades de taxiamento variando de 37 a 370 km/h. Comparações estatísticas e análises de regressão foram realizadas. Principais resultados mostram que VACP é 50% maior do que VACG e que ultrapassa o limite critico de 0,40 g quando o IRI e BBI estão acima de 3,7 m/km e 0,20, respectivamente. Um estudo de caso é também apresentado para comparar esses limites e sugere que a tomada de decisão baseada em IRI e VA pode trazer diferenças significativas na quantidade de atividades de M&R.

scholarly journals Research on the International Roughness Index Threshold of Road Rehabilitation in Metropolitan Areas: A Case Study in Taipei City

2020 ◽  
Vol 12 (24) ◽  
pp. 10536
Author(s):  
Shong-Loong Chen ◽  
Chih-Hsien Lin ◽  
Chao-Wei Tang ◽  
Liang-Pin Chu ◽  
Chiu-Kuei Cheng
Keyword(s):  
Threshold Value ◽  
City Government ◽  
International Roughness Index ◽  
The Road ◽  
Standard Scale ◽  
Road Roughness ◽  
Roughness Index ◽  
Taipei City ◽  
Road Surface Roughness ◽  
Asphalt Concrete Pavement

The International Roughness Index (IRI) is the standard scale for evaluating road roughness in many countries in the world. The Taipei City government actively promotes a Road Smoothing Project and plans to complete the rehabilitation of the main and minor roads within its jurisdiction. This study aims to detect the road surface roughness in Taipei City and recommend appropriate IRI thresholds for road rehabilitation. A total of 171 asphalt concrete pavement sections in Taipei City with a total length of 803.49 km were analyzed and compared by IRI. The longitudinal profile of the detected road sections was measured using an inertial profiler. The statistical analysis showed that the IRI value prior to road leveling was mainly distributed between 5 and 8 m/km, while the IRI value after road leveling was mainly distributed between 3 and 4.5 m/km. This confirms that the implementation of the Road Smoothing Project has a significant effect on improving road smoothness. Moreover, based on the analysis results, it is recommended that the IRI threshold value for road rehabilitation in Taipei City be set at 4.50 m/km.

Full-Car Roughness Index as Summary Roughness Statistic

2005 ◽  
Vol 1905 (1) ◽  
pp. 148-156 ◽  
Author(s):  
Renato A. C. Capuruço ◽  
Tarek Hegazy ◽  
Susan L. Tighe ◽  
Sameh Zaghloul
Keyword(s):  
Mechanical Response ◽  
Pavement Management ◽  
Ride Quality ◽  
Management Decision ◽  
International Roughness Index ◽  
Front Suspension ◽  
Car Model ◽  
Roughness Index ◽  
Wheel Model ◽  
Management Decision Making

The international roughness index (IRI) and the half-car roughness index (HRI) are the two commonly used roughness indices for pavement management, decision making, prioritization, budgeting, and planning. This work presents a new statistic, termed the full-car roughness index (FRI), for calculation of roughness from longitudinal pavement profiles. FRI is calculated from a single, equivalent profile that is a composite of four corner profiles based on both civil and mechanical engineering principles. More specifically, the full-car (four-wheel) model combines the rear and front suspension systems through an interdependent relation of motion with the longitudinal axle. To validate this model, the FRI values for different pavement sections are determined for sampling roughness measurements from several states and provinces. Then, the behavior of FRI is compared with that of IRI and HRI. The methodology of assessment uses a Monte Carlo simulation for calibration and validation of the index. Correlations derived from this sensitivity analysis on the basis of regression analysis arrive at a conversion chart to propose conversion values from these indices to FRIs. Overall, this paper suggests that the mechanical response of the proposed full-car model is more representative of the characteristics of a real vehicle than the response of a quarter- or half-car model. The results also indicate that FRI is less sensitive to the governing factors that account for the quarter-car simulation and thus provides an index that is unique, insightful, and more effective in the characterization of ride quality.

Control Research on Asphalt Pavement Roughness in Anchorage Zone

2014 ◽  
Vol 1030-1032 ◽  
pp. 754-757
Author(s):  
Zheng Wang ◽  
Wei Zhang ◽  
Jia Jia Cheng ◽  
Meng Chen ◽  
Wen Jing Liu
Keyword(s):  
Asphalt Pavement ◽  
International Roughness Index ◽  
Retaining Structure ◽  
Pavement Roughness ◽  
Roughness Index ◽  
Data Collection Instrument ◽  
Control Research ◽  
Main Factors ◽  
The Stability ◽  
Stability Of Structure

The effect of pavement roughness on the roadbed and retaining structure underground was studied. Three different groups of International Roughness Index (IRI) were analyzed based on 6221 data collection instrument in this paper. Results show that different pavement roughness has different effect on retaining structure. Additionally, the vibration RMS increases with IRI when it is in the range of normal driving for the car, but amplitude of the IRI is larger. Finally, the main factors which influence the stability of structure and some corresponding improving measures are presented.

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