Alexander Paz

Estimation of performance indices for the planning of sustainable transportation systems

In the context of sustainable transportation systems, previous studies have either focused only on the transportation system or have not used a methodology that enables the treatment of incomplete, vague, and qualitative information associated with the available data. This study proposes a system of systems (SOS) and a fuzzy logic modeling approach. The SOS includes the Transportation, Activity, and Environment systems. The fuzzy logic modeling approach enables the treatment of the vagueness associated with some of the relevant data. Performance Indices (PIs) are computed for each system using a number of performance measures. The PIs illustrate the aggregated performance of each system as well as the interactions among them. The proposed methodology also enables the estimation of a Composite Sustainability Index to summarize the aggregated performance of the overall SOS. Existing data was used to analyze sustainability in the entire United States. The results showed that the Transportation and Activity systems follow a positive trend, with similar periods of growth and contractions; in contrast, the environmental system follows a reverse pattern. The results are intuitive and are associated with a series of historic events, such as depressions in the economy as well as policy changes and regulations.

Fuzzy Control Model Optimization for Behavior-Consistent Traffic Routing Under Information Provision

This paper presents an H-infinity filtering approach to optimize a fuzzy control model used to determine behavior-consistent (BC) information-based control strategies to improve the performance of congested dynamic traffic networks. By adjusting the associated membership function parameters to better respond to nonlinearities and modeling errors, the approach is able to enhance the computational performance of the fuzzy control model. Computational efficiency is an important aspect in this problem context, because the information strategies are required in subreal time to be real-time deployable. Experiments are performed to evaluate the effectiveness of the approach. The results indicate that the optimized fuzzy control model contributes in determining the BC information-based control strategies in significantly less computational time than when the default controller is used. Hence, the proposed H-infinity approach contributes to the development of an efficient and robust information-based control approach.

Calibration of CORSIM models considering all model parameters simultaneously

This study proposes a calibration methodology for microscopic traffic flow simulation models that has the capability to simultaneously consider all model parameters and also to calibrate such time-dependent aspects of the model as link counts. The Simultaneous Perturbation Stochastic Approximation algorithm provides the optimization engine that determines the calibrated set of model parameters. In this study, experiments were conducted using two different CORSIM models; the results illustrate the effectiveness of the proposed calibration methodology. Current research focuses on expanding the proposed methodology to enable the simultaneous calibration of link counts, speeds, and associated bottlenecks.

Holistic Calibration of Microscopic Traffic Flow Models: Methodology and Real World Application Studies

This study proposes and applies a methodology to calibrate microscopic traffic flow simulation models. The proposed methodology has the capability to calibrate simultaneously all the calibration parameters as well as demand patterns for any type of network. Parameters considered include global and local as well as driver behaviour and vehicle performance parameters. Demand patterns, in terms of turning volumes, are included in the calibration framework. Multiple performance measures involving link counts and speeds are used to formulate and solve the proposed calibration problem. In addition, multiple time periods were considered. A Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is used to search for the vector of the model’s parameters that minimizes the difference between actual and simulated network states. (Punzo V, Ciuffo B, Montanino M Transp Res Rec J Transp Res Board 2315(1):11–24 2012, Punzo et al. [1]) commented on the uncertainties present in many calibration methodologies. The motivation to consider simultaneously all model parameters is to reduce that uncertainties to a minimum, by leaving to the experience of the engineers as little parameter tuning as possible. The effects of changing the values of the parameters are taken into consideration to adjust them slightly and simultaneously. This results in a small number of evaluations of the objective function. Three networks were calibrated with excellent results. The first network was an arterial network with link counts and speeds used as performance measurements for calibration. The second network included a combination of freeway ramps and arterials, with link counts used as performance measurements. The third network was an arterial network, with time-dependent link counts and speed used as performance measurements. The experimental results illustrate the effectiveness and validity of this proposed methodology. The same set of calibration parameters was used in all experiments.

Assessment of Economic Impacts of Vehicle Miles Traveled Fee for Passenger Vehicles in Nevada

This study evaluated the effectiveness and equity of a fee for vehicle miles traveled (VMT) for passenger vehicles in Nevada. In the evaluation of the fees effectiveness, the collection capabilities, as well as the fees impact on the number of miles users drove, were considered. Equity was evaluated by considering the impact of the VMT fee on various population groups on the basis of socioeconomics, demographics, household type, location, and ownership of fuel-efficient vehicles. To estimate the impacts on various VMT fees, a linear regression model was developed with the use of data from the 2009 National Household Travel Survey, which provided a mechanism to estimate VMT in Nevada as a function of the cost to drive, among other characteristics. The effectiveness and the equity of two alternative VMT fees were compared with the existing fuel tax system. These fees were calculated on the basis of the average fuel efficiency of vehicles in Nevada and the historical revenue from the state fuel tax. In general, a VMT fee of 3.3 cents per mile seemed to be more effective than both the existing fuel tax and a VMT fee of 2.91 cents per mile. Although the 3.3 cents per mile fee had a slightly greater impact on various population groups, its equitable distribution of the tax burden among 71.1% of households created a small average cost increase of just 0.37% per household. Thus, a 3.3 cents per mile fee would provide the necessary revenue without significantly affecting Nevada households.

Adaptive traffic control for large scale dynamic traffic assignment applications

Dynamic traffic assignment (DTA) applications require traffic signal control data that are typically difficult to obtain and cumbersome to code in the required format. In addition, the evaluation of future scenarios requires future traffic signal settings consistent with the forecast demand. These signal settings are not known a priori and are costly to estimate. Intuitively, future signal timings need to be reasonably optimized so as to represent what the traffic management agency will do. In the literature, integration between traffic control and DTA models has been formulated as a bi-level or single-level optimization problem with system or user optimal constraints. Most existing solution procedures require certain nested structure with an inner-loop algorithm solving the problem of user-equilibrium or system-optimal assignment and the outer-loop algorithm searching for the optimal signal-timing settings. Most of these solution approaches remain only research tools without practical use because of computational intractability. This research proposes an efficient solution algorithm to the problem. An adaptive traffic signal control model is embedded in a simulation-based DTA model. For each inbound approach at an intersection of interest, the adaptive model uses upstream information and a dynamic rolling-horizon approach to project traffic flow conditions for a dynamic but short (projection) period. The adaptive model provides the signal settings during the entire process of traffic flow simulation and for every iteration of the solution algorithm. Thus, during the entire solution process, the experienced travel times and resulting traffic assignment flows are based on the adaptive (demand-responsive) signal settings, allowing the DTA flows and the adaptive signal settings to be generated simultaneously in a single-loop algorithmic structure. Simulation experiments illustrate the capabilities of the proposed approach.

Development of a Visualization System for Safety Analyst

The AASHTO software Safety Analyst is a state-of-the-art tool with significant capabilities and advanced analytical methods for comprehensive analysis and management of highway safety. However, this tool does not provide visualization capabilities. To address that limitation, this study proposes a visualization system for Safety Analyst that provides graphical displays, including location and color-coded information for each module. In addition, the system generates charts, which have various degrees of resolution and aggregation; tables; and a report summarizing safety performance measures. The system can use Google Maps or ESRI ArcGIS to generate the graphical displays. The advantage of using Google Maps is its simplicity; in contrast, the ArcGIS display provides additional modeling and computing capabilities of the GIS framework. All displays are very intuitive and can be customized according to user needs. Because the user can see the locations of every specific site, the displays facilitate analysis as well as the decision-making process. The visualization system interacts with Safety Analyst so that the user can access all tools and data throughout the entire modeling and analysis process.

Estimation of optimal pavement performance models for highways

A mathematical program is proposed to determine an optimum number of pavement clusters, memberships of the pavement samples to clusters, and associated significant explanatory variables. Simulated annealing and all subsets regression was used to solve the mathematical program. Potential multicollinearity issues were exam-ined and addressed. All possible combinations of the explanatory variables were explored to select the best model specification. Six-cluster models were determined to be the optimum solution for the dataset used in this research. The resultant models were applied to the test data set to examine the prediction accuracy. Nor-malized root-mean-square error was calculated for each of the resultant models. The associated models were robust with small prediction errors.

Multi-objective Memetic Algorithm Based on NSGA-II and Simulated Annealing for Calibrating CORSIM Micro-Simulation Models of Vehicular Traffic Flow

This paper proposes a multi-objective memetic algorithm based on NSGA-II and Simulated Annealing (SA), NSGA-II-SA, for calibration of microscopic vehicular traffic flow simulation models. The NSGA-II algorithm performs a scan in the search space and obtains the Pareto front which is optimized locally with SA. The best solution of the obtained front is selected. Two CORSIM models were calibrated with the proposed NSGA-II-SA whose performance is compared with two alternative state-of-the-art algorithms, a single-objective genetic algorithm which uses simulated annealing (GASA) and a simultaneous perturbation stochastic approximation algorithm (SPSA). The results illustrate the superiority of the NSGA-II-SA algorithm in terms of both runtime and convergence.

Development of a Comprehensive Database System for Safety Analyst

This study addressed barriers associated with the use of Safety Analyst, a state-of-the-art tool that has been developed to assist during the entire Traffic Safety Management process but that is not widely used due to a number of challenges as described in this paper. As part of this study, a comprehensive database system and tools to provide data to multiple traffic safety applications, with a focus on Safety Analyst, were developed. A number of data management tools were developed to extract, collect, transform, integrate, and load the data. The system includes consistency-checking capabilities to ensure the adequate insertion and update of data into the database. This system focused on data from roadways, ramps, intersections, and traffic characteristics for Safety Analyst. To test the proposed system and tools, data from Clark County, which is the largest county in Nevada and includes the cities of Las Vegas, Henderson, Boulder City, and North Las Vegas, was used. The database and Safety Analyst together help identify the sites with the potential for safety improvements. Specifically, this study examined the results from two case studies. The first case study, which identified sites having a potential for safety improvements with respect to fatal and all injury crashes, included all roadway elements and used default and calibrated Safety Performance Functions (SPFs). The second case study identified sites having a potential for safety improvements with respect to fatal and all injury crashes, specifically regarding intersections; it used default and calibrated SPFs as well. Conclusions were developed for the calibration of safety performance functions and the classification of site subtypes. Guidelines were provided about the selection of a particular network screening type or performance measure for network screening.