Evaluating the effectiveness of signal timing optimization based on microscopic simulation evaluation

The optimization of the timing parameters of traffic signals provides for efficient operation of traffic along a signalized transportation system. Optimization tools with macroscopic simulation models have been used to determine optimal timing plans. These plans have been, in some cases, evaluated and fine tuned using microscopic simulation tools. A number of studies show inconsistencies between optimization tool results based on macroscopic simulation and the results obtained from microscopic simulation. No attempts have been made to determine the reason behind these inconsistencies. This research investigates whether adjusting the parameters of macroscopic simulation models to correspond to the calibrated microscopic simulation model parameters can reduce said inconsistencies. The adjusted parameters include platoon dispersion model parameters, saturation flow rates, and cruise speeds. The results from this work show that adjusting cruise speeds and saturation flow rates can have significant impacts on improving the optimization/macroscopic simulation results as assessed by microscopic simulation models.

Developing performance measures of mangrove wetlands using simulation models of hydrology, nutrient biogeochemistry, and community dynamics

The goal of mangrove restoration projects should be to improve community structure and ecosystem function of degraded coastal landscapes. This requires the ability to forecast how mangrove structure and function will respond to prescribed changes in site conditions including hydrology, topography, and geophysical energies. There are global, regional, and local factors that can explain gradients of regulators (e.g., salinity, sulfides), resources (nutrients, light, water), and hydroperiod (frequency, duration of flooding) that collectively account for stressors that result in diverse patterns of mangrove properties across a variety of environmental settings. Simulation models of hydrology, nutrient biogeochemistry, and vegetation dynamics have been developed to forecast patterns in mangroves in the Florida Coastal Everglades. These models provide insight to mangrove response to specific restoration alternatives, testing causal mechanisms of system degradation. We propose that these models can also assist in selecting performance measures for monitoring programs that evaluate project effectiveness. This selection process in turn improves model development and calibration for forecasting mangrove response to restoration alternatives. Hydrologic performance measures include soil regulators, particularly soil salinity, surface topography of mangrove landscape, and hydroperiod, including both the frequency and duration of flooding. Estuarine performance measures should include salinity of the bay, tidal amplitude, and conditions of fresh water discharge (included in the salinity value). The most important performance measures from the mangrove biogeochemistry model should include soil resources (bulk density, total nitrogen, and phosphorus) and soil accretion. Mangrove ecology performance measures should include forest dimension analysis (transects and/or plots), sapling recruitment, leaf area index, and faunal relationships. Estuarine ecology performance measures should include the habitat function of mangroves, which can be evaluated with growth rate of key species, habitat suitability analysis, isotope abundance of indicator species, and bird census. The list of performance measures can be modified according to the model output that is used to define the scientific goals during the restoration planning process that reflect specific goals of the project.

Improving Analytical Travel Time Estimation for Transportation Planning Models

This dissertation aimed to improve travel time estimation for the purpose of transportation planning by developing a travel time estimation method that incorporates the effects of signal timing plans, which were difficult to consider in planning models. For this purpose, an analytical model has been developed. The model parameters were calibrated based on data from CORSIM microscopic simulation, with signal timing plans optimized using the TRANSYT-7F software. Independent variables in the model are link length, free-flow speed, and traffic volumes from the competing turning movements. The developed model has three advantages compared to traditional link-based or node-based models. First, the model considers the influence of signal timing plans for a variety of traffic volume combinations without requiring signal timing information as input. Second, the model describes the non-uniform spatial distribution of delay along a link, this being able to estimate the impacts of queues at different upstream locations of an intersection and attribute delays to a subject link and upstream link. Third, the model shows promise of improving the accuracy of travel time prediction. The mean absolute percentage error (MAPE) of the model is 13% for a set of field data from Minnesota Department of Transportation (MDOT); this is close to the MAPE of uniform delay in the HCM 2000 method (11%). The HCM is the industrial accepted analytical model in the existing literature, but it requires signal timing information as input for calculating delays. The developed model also outperforms the HCM 2000 method for a set of Miami-Dade County data that represent congested traffic conditions, with a MAPE of 29%, compared to 31% of the HCM 2000 method. The advantages of the proposed model make it feasible for application to a large network without the burden of signal timing input, while improving the accuracy of travel time estimation. An assignment model with the developed travel time estimation method has been implemented in a South Florida planning model, which improved assignment results.

Development of operational performance models for truck-lane restrictions on freeway corridors

Highways are generally designed to serve a mixed traffic flow that consists of passenger cars, trucks, buses, recreational vehicles, etc. The fact that the impacts of these different vehicle types are not uniform creates problems in highway operations and safety. A common approach to reducing the impacts of truck traffic on freeways has been to restrict trucks to certain lane(s) to minimize the interaction between trucks and other vehicles and to compensate for their differences in operational characteristics. ^ The performance of different truck lane restriction alternatives differs under different traffic and geometric conditions. Thus, a good estimate of the operational performance of different truck lane restriction alternatives under prevailing conditions is needed to help make informed decisions on truck lane restriction alternatives. This study develops operational performance models that can be applied to help identify the most operationally efficient truck lane restriction alternative on a freeway under prevailing conditions. The operational performance measures examined in this study include average speed, throughput, speed difference, and lane changes. Prevailing conditions include number of lanes, interchange density, free-flow speeds, volumes, truck percentages, and ramp volumes. ^ Recognizing the difficulty of collecting sufficient data for an empirical modeling procedure that involves a high number of variables, the simulation approach was used to estimate the performance values for various truck lane restriction alternatives under various scenarios. Both the CORSIM and VISSIM simulation models were examined for their ability to model truck lane restrictions. Due to a major problem found in the CORSIM model for truck lane modeling, the VISSIM model was adopted as the simulator for this study. ^ The VISSIM model was calibrated mainly to replicate the capacity given in the 2000 Highway Capacity Manual (HCM) for various free-flow speeds under the ideal basic freeway section conditions. Non-linear regression models for average speed, throughput, average number of lane changes, and speed difference between the lane groups were developed. Based on the performance models developed, a simple decision procedure was recommended to select the desired truck lane restriction alternative for prevailing conditions. ^

Design strategies for an artificial neural network based algorithm for automatic incident detection on major arterial streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our nation’s highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

A computer simulation model for single -lane roundabouts

During the past three decades, the use of roundabouts has increased throughout the world due to their greater benefits in comparison with intersections controlled by traditional means. Roundabouts are often chosen because they are widely associated with low accident rates, lower construction and operating costs, and reasonable capacities and delay. ^ In the planning and design of roundabouts, special attention should be given to the movement of pedestrians and bicycles. As a result, there are several guidelines for the design of pedestrian and bicycle treatments at roundabouts that increase the safety of both pedestrians and bicyclists at existing and proposed roundabout locations. Different design guidelines have differing criteria for handling pedestrians and bicyclists at roundabout locations. Although all of the investigated guidelines provide better safety (depending on the traffic conditions at a specific location), their effects on the performance of the roundabout have not been examined yet. ^ Existing roundabout analysis software packages provide estimates of capacity and performance characteristics. This includes characteristics such as delay, queue lengths, stop rates, effects of heavy vehicles, crash frequencies, and geometric delays, as well as fuel consumption, pollutant emissions and operating costs for roundabouts. None of these software packages, however, are capable of determining the effects of various pedestrian crossing locations, nor the effect of different bicycle treatments on the performance of roundabouts. ^ The objective of this research is to develop simulation models capable of determining the effect of various pedestrian and bicycle treatments at single-lane roundabouts. To achieve this, four models were developed. The first model simulates a single-lane roundabout without bicycle and pedestrian traffic. The second model simulates a single-lane roundabout with a pedestrian crossing and mixed flow bicyclists. The third model simulates a single-lane roundabout with a combined pedestrian and bicycle crossing, while the fourth model simulates a single-lane roundabout with a pedestrian crossing and a bicycle lane at the outer perimeter of the roundabout for the bicycles. Traffic data was collected at a modern roundabout in Boca Raton, Florida. ^ The results of this effort show that installing a pedestrian crossing on the roundabout approach will have a negative impact on the entry flow, while the downstream approach will benefit from the newly created gaps by pedestrians. Also, it was concluded that a bicycle lane configuration is more beneficial for all users of the roundabout instead of the mixed flow or combined crossing. Installing the pedestrian crossing at one-car length is more beneficial for pedestrians than two- and three-car lengths. Finally, it was concluded that the effect of the pedestrian crossing on the vehicle queues diminishes as the distance between the crossing and the roundabout increases. ^

Variable Speed Limit Strategies to Reduce the Impacts of Traffic Flow Breakdown at Recurrent Freeway Bottlenecks

Variable Speed Limit (VSL) strategies identify and disseminate dynamic speed limits that are determined to be appropriate based on prevailing traffic conditions, road surface conditions, and weather conditions. This dissertation develops and evaluates a shockwave-based VSL system that uses a heuristic switching logic-based controller with specified thresholds of prevailing traffic flow conditions. The system aims to improve operations and mobility at critical bottlenecks. Before traffic breakdown occurrence, the proposed VSL’s goal is to prevent or postpone breakdown by decreasing the inflow and achieving uniform distribution in speed and flow. After breakdown occurrence, the VSL system aims to dampen traffic congestion by reducing the inflow traffic to the congested area and increasing the bottleneck capacity by deactivating the VSL at the head of the congested area. The shockwave-based VSL system pushes the VSL location upstream as the congested area propagates upstream. In addition to testing the system using infrastructure detector-based data, this dissertation investigates the use of Connected Vehicle trajectory data as input to the shockwave-based VSL system performance. Since the field Connected Vehicle data are not available, as part of this research, Vehicle-to-Infrastructure communication is modeled in the microscopic simulation to obtain individual vehicle trajectories. In this system, wavelet transform is used to analyze aggregated individual vehicles’ speed data to determine the locations of congestion. The currently recommended calibration procedures of simulation models are generally based on the capacity, volume and system-performance values and do not specifically examine traffic breakdown characteristics. However, since the proposed VSL strategies are countermeasures to the impacts of breakdown conditions, considering breakdown characteristics in the calibration procedure is important to have a reliable assessment. Several enhancements were proposed in this study to account for the breakdown characteristics at bottleneck locations in the calibration process. In this dissertation, performance of shockwave-based VSL is compared to VSL systems with different fixed VSL message sign locations utilizing the calibrated microscopic model. The results show that shockwave-based VSL outperforms fixed-location VSL systems, and it can considerably decrease the maximum back of queue and duration of breakdown while increasing the average speed during breakdown.

Design Strategies for an Artificial Neural Network Based Algorithm for Automatic Incident Detection on Major Arterial Streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our national highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

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.

Business process quantitative analysis and optimization for service industries

The total time a customer spends in the business process system, called the customer cycle-time, is a major contributor to overall customer satisfaction. Business process analysts and designers are frequently asked to design process solutions with optimal performance. Simulation models have been very popular to quantitatively evaluate the business processes; however, simulation is time-consuming and it also requires extensive modeling experiences to develop simulation models. Moreover, simulation models neither provide recommendations nor yield optimal solutions for business process design. A queueing network model is a good analytical approach toward business process analysis and design, and can provide a useful abstraction of a business process. However, the existing queueing network models were developed based on telephone systems or applied to manufacturing processes in which machine servers dominate the system. In a business process, the servers are usually people. The characteristics of human servers should be taken into account by the queueing model, i.e. specialization and coordination. ^ The research described in this dissertation develops an open queueing network model to do a quick analysis of business processes. Additionally, optimization models are developed to provide optimal business process designs. The queueing network model extends and improves upon existing multi-class open-queueing network models (MOQN) so that the customer flow in the human-server oriented processes can be modeled. The optimization models help business process designers to find the optimal design of a business process with consideration of specialization and coordination. ^ The main findings of the research are, first, parallelization can reduce the cycle-time for those customer classes that require more than one parallel activity; however, the coordination time due to the parallelization overwhelms the savings from parallelization under the high utilization servers since the waiting time significantly increases, thus the cycle-time increases. Third, the level of industrial technology employed by a company and coordination time to mange the tasks have strongest impact on the business process design; as the level of industrial technology employed by the company is high; more division is required to improve the cycle-time; as the coordination time required is high; consolidation is required to improve the cycle-time. ^

Analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing

Parallel processing is prevalent in many manufacturing and service systems. Many manufactured products are built and assembled from several components fabricated in parallel lines. An example of this manufacturing system configuration is observed at a manufacturing facility equipped to assemble and test web servers. Characteristics of a typical web server assembly line are: multiple products, job circulation, and paralleling processing. The primary objective of this research was to develop analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing. The analytical formulations extend previous queueing models used in assembly manufacturing systems in that they can handle serial and different configurations of paralleling processing with multiple product classes, and job circulation due to random part failures. In addition, appropriate correction terms via regression analysis were added to the approximations in order to minimize the gap in the error between the analytical approximation and the simulation models. Markovian and general type manufacturing systems, with multiple product classes, job circulation due to failures, and fork and join systems to model parallel processing were studied. In the Markovian and general case, the approximations without correction terms performed quite well for one and two product problem instances. However, it was observed that the flow time error increased as the number of products and net traffic intensity increased. Therefore, correction terms for single and fork-join stations were developed via regression analysis to deal with more than two products. The numerical comparisons showed that the approximations perform remarkably well when the corrections factors were used in the approximations. In general, the average flow time error was reduced from 38.19% to 5.59% in the Markovian case, and from 26.39% to 7.23% in the general case. All the equations stated in the analytical formulations were implemented as a set of Matlab scripts. By using this set, operations managers of web server assembly lines, manufacturing or other service systems with similar characteristics can estimate different system performance measures, and make judicious decisions - especially setting delivery due dates, capacity planning, and bottleneck mitigation, among others.

Frameworks for Systemic and Structural Analysis of Financial Innovations in Infrastructure

Financial innovations have emerged globally to close the gap between the rising global demand for infrastructures and the availability of financing sources offered by traditional financing mechanisms such as fuel taxation, tax-exempt bonds, and federal and state funds. The key to sustainable innovative financing mechanisms is effective policymaking. This paper discusses the theoretical framework of a research study whose objective is to structurally and systemically assess financial innovations in global infrastructures. The research aims to create analysis frameworks, taxonomies and constructs, and simulation models pertaining to the dynamics of the innovation process to be used in policy analysis. Structural assessment of innovative financing focuses on the typologies and loci of innovations and evaluates the performance of different types of innovative financing mechanisms. Systemic analysis of innovative financing explores the determinants of the innovation process using the System of Innovation approach. The final deliverables of the research include propositions pertaining to the constituents of System of Innovation for infrastructure finance which include the players, institutions, activities, and networks. These static constructs are used to develop a hybrid Agent-Based/System Dynamics simulation model to derive propositions regarding the emergent dynamics of the system. The initial outcomes of the research study are presented in this paper and include: (a) an archetype for mapping innovative financing mechanisms, (b) a System of Systems-based analysis framework to identify the dimensions of Systems of Innovation analyses, and (c) initial observations regarding the players, institutions, activities, and networks of the System of Innovation in the context of the U.S. transportation infrastructure financing.

Analytical Approximations to Predict Performance Measures of Manufacturing Systems with Job Failures and Parallel Processing

Parallel processing is prevalent in many manufacturing and service systems. Many manufactured products are built and assembled from several components fabricated in parallel lines. An example of this manufacturing system configuration is observed at a manufacturing facility equipped to assemble and test web servers. Characteristics of a typical web server assembly line are: multiple products, job circulation, and paralleling processing. The primary objective of this research was to develop analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing. The analytical formulations extend previous queueing models used in assembly manufacturing systems in that they can handle serial and different configurations of paralleling processing with multiple product classes, and job circulation due to random part failures. In addition, appropriate correction terms via regression analysis were added to the approximations in order to minimize the gap in the error between the analytical approximation and the simulation models. Markovian and general type manufacturing systems, with multiple product classes, job circulation due to failures, and fork and join systems to model parallel processing were studied. In the Markovian and general case, the approximations without correction terms performed quite well for one and two product problem instances. However, it was observed that the flow time error increased as the number of products and net traffic intensity increased. Therefore, correction terms for single and fork-join stations were developed via regression analysis to deal with more than two products. The numerical comparisons showed that the approximations perform remarkably well when the corrections factors were used in the approximations. In general, the average flow time error was reduced from 38.19% to 5.59% in the Markovian case, and from 26.39% to 7.23% in the general case. All the equations stated in the analytical formulations were implemented as a set of Matlab scripts. By using this set, operations managers of web server assembly lines, manufacturing or other service systems with similar characteristics can estimate different system performance measures, and make judicious decisions - especially setting delivery due dates, capacity planning, and bottleneck mitigation, among others.