Pavement performance prediction using adaptive logic networks

Pavement performance is one of the most important components of the pavement management system. Prediction of the future performance of a pavement section is important in programming maintenance and rehabilitation needs. Models for predicting pavement performance have been developed on the basis of traffic and age. The purpose of this research is to extend the use of a relatively new approach to performance prediction in pavement performance modeling using adaptive logic networks (ALN). Adaptive logic networks have recently emerged as an effective alternative to artificial neural networks for machine learning tasks. ^ The ALN predictive methodology is applicable to a wide variety of contexts including prediction of roughness based indices, composite rating indices and/or individual pavement distresses. The ALN program requires key information about a pavement section, including the current distress indexes, pavement age, climate region, traffic and other variables to predict yearly performance values into the future. ^ This research investigates the effect of different learning rates of the ALN in pavement performance modeling. It can be used at both the network and project level for predicting the long term performance of a road network. Results indicate that the ALN approach is well suited for pavement performance prediction modeling and shows a significant improvement over the results obtained from other artificial intelligence approaches. ^

An automatic tuning strategy for local fuzzy logic ramp metering algorithm using Particle Swarm Optimization (PSO)

Freeway systems are becoming more congested each day. One contribution to freeway traffic congestion comprises platoons of on-ramp traffic merging into freeway mainlines. As a relatively low-cost countermeasure to the problem, ramp meters are being deployed in both directions of an 11-mile section of I-95 in Miami-Dade County, Florida. The local Fuzzy Logic (FL) ramp metering algorithm implemented in Seattle, Washington, has been selected for deployment. The FL ramp metering algorithm is powered by the Fuzzy Logic Controller (FLC). The FLC depends on a series of parameters that can significantly alter the behavior of the controller, thus affecting the performance of ramp meters. However, the most suitable values for these parameters are often difficult to determine, as they vary with current traffic conditions. Thus, for optimum performance, the parameter values must be fine-tuned. This research presents a new method of fine tuning the FLC parameters using Particle Swarm Optimization (PSO). PSO attempts to optimize several important parameters of the FLC. The objective function of the optimization model incorporates the METANET macroscopic traffic flow model to minimize delay time, subject to the constraints of reasonable ranges of ramp metering rates and FLC parameters. To further improve the performance, a short-term traffic forecasting module using a discrete Kalman filter was incorporated to predict the downstream freeway mainline occupancy. This helps to detect the presence of downstream bottlenecks. The CORSIM microscopic simulation model was selected as the platform to evaluate the performance of the proposed PSO tuning strategy. The ramp-metering algorithm incorporating the tuning strategy was implemented using CORSIM's run-time extension (RTE) and was tested on the aforementioned I-95 corridor. The performance of the FLC with PSO tuning was compared with the performance of the existing FLC without PSO tuning. The results show that the FLC with PSO tuning outperforms the existing FL metering, fixed-time metering, and existing conditions without metering in terms of total travel time savings, average speed, and system-wide throughput.

Ecological indicators for assessing and communicating seagrass status and trends in Florida Bay

A suite of seagrass indicator metrics is developed to evaluate four essential measures of seagrass community status for Florida Bay. The measures are based on several years of monitoring data using the Braun-Blanquet Cover Abundance (BBCA) scale to derive information about seagrass spatial extent, abundance, species diversity and presence of target species. As ecosystem restoration proceeds in south Florida, additional freshwater will be discharged to Florida Bay as a means to restore the bay's hydrology and salinity regime. Primary hypotheses about restoring ecological function of the keystone seagrass community are based on the premise that hydrologic restoration will increase environmental variability and reduce hypersalinity. This will create greater niche space and permit multiple seagrass species to co-exist while maintaining good environmental conditions for Thalassia testudinum, the dominant climax seagrass species. Greater species diversity is considered beneficial to habitat for desired higher trophic level species such as forage fish and shrimp. It is also important to maintenance of a viable seagrass community that will avoid die-off events observed in the past. Indicator metrics are assigned values at the basin spatial scale and are aggregated to five larger zones. Three index metrics are derived by combining the four indicators through logic gates at the zone spatial scale and aggregated to derive a single bay-wide system status score standardized on the System-wide Indicator protocol. The indicators will provide a way to assess progress toward restoration goals or reveal areas of concern. Reporting for each indicator, index and overall system status score is presented in a red–yellow–green format that summarizes information in a readily accessible form for mangers, policy-makers and stakeholders in planning and implementing an adaptive management strategy.