Advanced methodologies in dynamic traffic assignment modeling of managed lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. ^ Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. ^ Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. ^ With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.^

BIKE – Bicycle Knowledge Explorer (Palm Beach)

This route planner, funded by the Palm Beach Metropolitan Planning Organization (MPO), is a joint effort by Florida International University GIS Center and University of Florida Geomatics Program at Fort Lauderdale Research and Education Center. It is designed as a planning tool for bicyclists. Assistance was received from the Palm Beach County Bicycle, Greenways, Pedestrian Advisory Committee.

BIKE – Bicycle Knowledge Explorer [Miami-Dade]

This route planner, funded by the Miami-Dade Metropolitan Planning Organization(MPO), is a joint effort by Florida International University GIS Center and University of Florida Geomatics Program at Fort Lauderdale Research and Education Center. It is designed as a planning tool for bicyclists. Assistance was received from the Miami-Dade County Bicycle/Pedestrian Advisory Committee and various cyclists and transportation professionals.

BIKE – Bicycle Knowledge Explorer [Broward County]

This route planner, funded by the Broward Metropolitan Planning Organization (MPO), is a joint effort by Florida International University GIS Center and University of Florida Geomatics Program at Fort Lauderdale Research and Education Center. It is designed as a planning tool for bicyclists. Assistance was received from the Broward County Bicycle/Pedestrian Advisory Committee and various cyclists and transportation professionals.

Advanced Methodologies in Dynamic Traffic Assignment Modeling of Managed Lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.

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. ^

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

A state of war: Florida from 1939 to 1945

World War II profoundly impacted Florida. The military geography of the State is essential to an understanding the war. The geostrategic concerns of place and space determined that Florida would become a statewide military base. Florida's attributes of place such as climate and topography determined its use as a military academy hosting over two million soldiers, nearly 15 percent of the GI Army, the largest force the US ever raised. One-in-eight Floridians went into uniform. Equally, Florida's space on the planet made it central for both defensive and offensive strategies. The Second World War was a war of movement, and Florida was a major jump off point for US force projection world-wide, especially of air power. Florida's demography facilitated its use as a base camp for the assembly and engagement of this military power. In 1940, less than two percent of the US population lived in Florida, a quiet, barely populated backwater of the United States. But owing to its critical place and space, over the next few years it became a 65,000 square mile training ground, supply dump, and embarkation site vital to the US war effort. Because of its place astride some of the most important sea lanes in the Atlantic World, Florida was the scene of one of the few Western Hemisphere battles of the war. The militarization of Florida began long before Pearl Harbor. The pre-war buildup conformed to the US strategy of the war. The strategy of theUS was then (and remains today) one of forward defense: harden the frontier, then take the battle to the enemy, rather than fight them in North America. The policy of "Europe First," focused the main US war effort on the defeat of Hitler's Germany, evaluated to be the most dangerous enemy. In Florida were established the military forces requiring the longest time to develop, and most needed to defeat the Axis. Those were a naval aviation force for sea-borne hostilities, a heavy bombing force for reducing enemy industrial states, and an aerial logistics train for overseas supply of expeditionary campaigns. The unique Florida coastline made possible the seaborne invasion training demanded for US victory. The civilian population was employed assembling mass-produced first-generation container ships, while Floridahosted casualties, Prisoners-of-War, and transient personnel moving between the Atlantic and Pacific. By the end of hostilities and the lifting of Unlimited Emergency, officially on December 31, 1946, Floridahad become a transportation nexus. Florida accommodated a return of demobilized soldiers, a migration of displaced persons, and evolved into a modern veterans' colonia. It was instrumental in fashioning the modern US military, while remaining a center of the active National Defense establishment. Those are the themes of this work.

Optimization of waiting time at toll plazas

Toll plazas have several toll payment types such as manual, automatic coin machines, electronic and mixed lanes. In places with high traffic flow, the presence of toll plaza causes a lot of traffic congestion; this creates a bottleneck for the traffic flow, unless the correct mix of payment types is in operation. The objective of this research is to determine the optimal lane configuration for the mix of the methods of payment so that the waiting time in the queue at the toll plaza is minimized. A queuing model representing the toll plaza system and a nonlinear integer program have been developed to determine the optimal mix. The numerical results show that the waiting time can be decreased at the toll plaza by changing the lane configuration. For the case study developed an improvement in the waiting time as high as 96.37 percent was noticed during the morning peak hour.

A State of War: Florida from 1939 to 1945

World War II profoundly impacted Florida. The military geography of the State is essential to an understanding the war. The geostrategic concerns of place and space determined that Florida would become a statewide military base. Florida’s attributes of place such as climate and topography determined its use as a military academy hosting over two million soldiers, nearly 15 percent of the GI Army, the largest force theUS ever raised. One-in-eight Floridians went into uniform. Equally,Florida’s space on the planet made it central for both defensive and offensive strategies. The Second World War was a war of movement, and Florida was a major jump off point forUSforce projection world-wide, especially of air power. Florida’s demography facilitated its use as a base camp for the assembly and engagement of this military power. In 1940, less than two percent of the US population lived in Florida, a quiet, barely populated backwater of the United States.[1] But owing to its critical place and space, over the next few years it became a 65,000 square mile training ground, supply dump, and embarkation site vital to the US war effort. Because of its place astride some of the most important sea lanes in the Atlantic World,Florida was the scene of one of the few Western Hemisphere battles of the war. The militarization ofFloridabegan long before Pearl Harbor. The pre-war buildup conformed to theUSstrategy of the war. The strategy of theUS was then (and remains today) one of forward defense: harden the frontier, then take the battle to the enemy, rather than fight them inNorth America. The policy of “Europe First,” focused the main US war effort on the defeat of Hitler’sGermany, evaluated to be the most dangerous enemy. In Florida were established the military forces requiring the longest time to develop, and most needed to defeat the Axis. Those were a naval aviation force for sea-borne hostilities, a heavy bombing force for reducing enemy industrial states, and an aerial logistics train for overseas supply of expeditionary campaigns. The unique Florida coastline made possible the seaborne invasion training demanded for USvictory. The civilian population was employed assembling mass-produced first-generation container ships, while Floridahosted casualties, Prisoners-of-War, and transient personnel moving between the Atlantic and Pacific. By the end of hostilities and the lifting of Unlimited Emergency, officially on December 31, 1946, Floridahad become a transportation nexus. Florida accommodated a return of demobilized soldiers, a migration of displaced persons, and evolved into a modern veterans’ colonia. It was instrumental in fashioning the modern US military, while remaining a center of the active National Defense establishment. Those are the themes of this work. [1] US Census of Florida 1940. Table 4 – Race, By Nativity and Sex, For the State. 14.