Effects of Alternative Traffic Input Levels on Interstate Pavement Performance in New Mexico

Traffic is one of the key inputs in pavement design. The pavement Mechanistic-Empirical (ME) design allows three different types of input level of traffic data based on the availability of the data. They are: site specific data (Level 1), regional data (Level 2), and the national data (Level 3). Level 1 inputs (e.g., load magnitude, configuration, and frequency) are generated from Weigh-in-Motion (WIM) station installed in each site. However, it is not always practically possible to install WIM station due to high cost of WIMs. Therefore, often time the designers have to rely on the Level 2 or Level 3 traffic data. But it is not known yet how good the national data or the regional data compared to New Mexico\u27s site specific data in predicting interstate pavement performances. To this end, this study examines the effects of different levels of traffic inputs on predicted pavement distresses in New Mexico. Two major interstate highways were considered in this study: Interstate-40 (I-40) and Interstate-25 (I-25). Site-specific inputs were developed using installed WIM stations at the pavement sites. WIM data was analyzed using an advanced and updated software developed by the UNM researchers. Traffic data were simulated through the ME design software for predicting pavement performances. Results show that axle load spectra (ALS) and lane distribution have a great influence on predicted interstate pavement performance. Vehicle class distribution (VCD), directional distribution, and standard deviation of lateral wander have a moderate impact on pavement performance. Monthly adjustment factor, axles per vehicle, axle spacing, and operational speed have very little effect on the predicted pavement performance. On the other hand, predicted pavement performance is insensitive to hourly distribution and wheelbase distribution. Hence, regional traffic data were developed from ten site specific data using both arithmetic average and clustering methods. Since, ALS and VCD are two inputs which affect the predicted distresses significantly, these two values were considered for this case. Finally, using the regional inputs, the national inputs, and the site-specific inputs of VCD and ALS, pavement ME predicted performances were determined. Results show that predicted performance by the cluster data are much closer to those by the site-specific data. Performance generated by the ME default values are significantly different from those generated by the site-specific or cluster values. When comparing performance by the ME design default to those by the statewide average data, the ME design default VCD produces less error than the ALS. Therefore, this study recommends using clustered data or site-specific WIM data instead of ME default or statewide average value. In addition, a guideline was successfully established to select appropriate axle load spectra inputs based on vehicle class data

Effects of Alternative Traffic Input Levels on Interstate Pavement Performance in New Mexico

Traffic is one of the key inputs in pavement design. The pavement Mechanistic-Empirical (ME) design allows three different types of input level of traffic data based on the availability of the data. They are: site specific data (Level 1), regional data (Level 2), and the national data (Level 3). Level 1 inputs (e.g., load magnitude, configuration, and frequency) are generated from Weigh-in-Motion (WIM) station installed in each site. However, it is not always practically possible to install WIM station due to high cost of WIMs. Therefore, often time the designers have to rely on the Level 2 or Level 3 traffic data. But it is not known yet how good the national data or the regional data compared to New Mexico’s site specific data in predicting interstate pavement performances. To this end, this study examines the effects of different levels of traffic inputs on predicted pavement distresses in New Mexico. Two major interstate highways were considered in this study: Interstate-40 (I-40) and Interstate-25 (I-25). Site-specific inputs were developed using installed WIM stations at the pavement sites. WIM data was analyzed using an advanced and updated software developed by the UNM researchers. Traffic data were simulated through the ME design software for predicting pavement performances. Results show that axle load spectra (ALS) and lane distribution have a great influence on predicted interstate pavement performance. Vehicle class distribution (VCD), directional distribution, and standard deviation of lateral wander have a moderate impact on pavement performance. Monthly adjustment factor, axles per vehicle, axle spacing, and operational speed have very little effect on the predicted pavement performance. On the other hand, predicted pavement performance is insensitive to hourly distribution and wheelbase distribution. Hence, regional traffic data were developed from ten site specific data using both arithmetic average and clustering methods. Since, ALS and VCD are two inputs which affect the predicted distresses significantly, these two values were considered for this case. Finally, using the regional inputs, the national inputs, and the site-specific inputs of VCD and ALS, pavement ME predicted performances were determined. Results show that predicted performance by the cluster data are much closer to those by the site-specific data. Performance generated by the ME default values are significantly different from those generated by the site-specific or cluster values. When comparing performance by the ME design default to those by the statewide average data, the ME design default VCD produces less error than the ALS. Therefore, this study recommends using clustered data or site-specific WIM data instead of ME default or statewide average value. In addition, a guideline was successfully established to select appropriate axle load spectra inputs based on vehicle class data.New Mexico Department of Transportation (NMDOT)Civil EngineeringMastersUniversity of New Mexico. Dept. of Civil EngineeringTarefder, RafiqulStormont, JohnNg, Tang-Ta

Prevention of shoulder-surfing attacks using shifting condition using digraph substitution rules

Graphical passwords are implemented as an alternative scheme to replace alphanumeric passwords to help users to memorize their password. However, most of the graphical password systems are vulnerable to shoulder-surfing attack due to the usage of the visual interface. In this research, a method that uses shifting condition with digraph substitution rules is proposed to address shoulder-surfing attack problem. The proposed algorithm uses both password images and decoy images throughout the user authentication procedure to confuse adversaries from obtaining the password images via direct observation or watching from a recorded session. The pass-images generated by this suggested algorithm are random and can only be generated if the algorithm is fully understood. As a result, adversaries will have no clue to obtain the right password images to log in. A user study was undertaken to assess the proposed method's effectiveness to avoid shoulder-surfing attacks. The results of the user study indicate that the proposed approach can withstand shoulder-surfing attacks (both direct observation and video recording method).The proposed method was tested and the results showed that it is able to resist shoulder-surfing and frequency of occurrence analysis attacks. Moreover, the experience gained in this research can be pervaded the gap on the realm of knowledge of the graphical password