A major hurdle in freight demand modeling has always been a lack of adequate data on freight movements for different industry sectors for planning applications. Several data sources are available for freight planning purposes in the United States. Of these, the most commonly adopted sources include Freight Analysis Framework (FAF), Transearch (TS), American Trucking Research Institute (ATRI) truck GPS data, and Department of Transportation (DOT) weigh-in-motion (WIM) data. Of these, the two most commonly adopted commodity flow data sources are FAF and TS. We developed a fused database from FAF and TS to realize transportation network flows at a fine spatial resolution while accommodating the production and consumption behavioral trends (provided by TS). Towards this end, we formulated and estimated a joint econometric model framework embedded within a network flow approach and grounded in maximum likelihood technique to estimate county level commodity flows. Subsequently, we developed additional algorithms to disaggregate county levels flows to the statewide traffic analysis zone resolution. The second part of the project was focused on generating truck OD flows by different weight categories, including empty truck flows. The estimated empty flows (where truck load is less than a threshold) were disaggregated into finer granularity to get better understanding about the patterns associated with empty flows.
Freight plays a vital role in the national, state, and local economy. But its specific contribution is difficult to quantify, and the mechanism of impact of freight transportation investments on economic production is not clear. Traditional cost-benefit analysis focuses on the cost and benefits of building specific facilities at the project level. This is insufficient and underestimates the true benefits of freight as it does not take into account the multiplier effects that freight brings to the economy as a whole.
Florida Freight Transportation Economic Impact Kit (FTEIK)
The Florida Freight Transportation Economic Impact Kit (FTEIK) was developed based on the research study titled “Economic Analysis Framework for Freight Transportation Based on Florida Statewide Multi-Modal Freight Model”. Users of this kit are encouraged to review the final report before proceeding to use this kit.
FTEIK is an economic analysis kit based on regional economic input-output model and Freight Supply-chain Intermodal Model (FreightSIM). The core of FTEIK lies in the combination of a freight demand model with a multi-sectoral economic model. Outputs from FreightSIM are converted into money values which are the inputs for the regional IO model to derive sectoral impacts for the studied economy. The kit also allows users to customize values for converting transportation outputs at the project level. The output from FreightSIM is vehicle miles traveled (VHT). It is assumed that freight transportation investment (e.g. new highway investment) would in the long run lead to more efficient freight traffic with less vehicle hour traveled on the highway links in the study area.
Demand forecasting models and simulation models have been developed, calibrated, and used in isolation of each other. However, the advancement of transportation system technologies and strategies, the increase in the availability of data, and the uncertainty of traveler behavioral responses to new strategies have increased consideration of integrating different modeling tools. This project investigated the ability of combinations of tools to assess congestion impacts and advanced strategies that address such impacts. As a result, the project has developed a multi-resolution modeling framework for use in support of agency analyses and modeling of congestion impacts and advanced strategies. As examples, this project applies the multi-resolution modeling framework to (1) managed lanes with consideration of travel time reliability and heterogeneous traveler attitudes towards paying tolls, (2) work zones and associated diversion, and (3) active traffic management on arterial streets. The project investigated associated activities, including estimating origin-destination demand matrices using data from multiple sources such as automatic vehicle identification data and turning movement counts and assessing link-level variation of connected vehicle market penetration.
In this study, Florida International University researchers developed a demand estimation framework to assess managed lanes (ML) strategies by utilizing dynamic traffic assignment (DTA) instead of the traditional static traffic assignment (STA). Effective planning for ML strategies requires the accurate assessments of traffic flow conditions provided by advanced models, such as DTA coupled with mesoscopic or microscopic modeling. The researchers found that existing ML modeling frameworks varied greatly in level of detail and complication. This offered them a selection of approaches, for example, in choosing the right procedures for supply and demand calibration or convergence. This project demonstrated successful integration of high-quality, high-volume data with advanced modeling software. It showed the advantages of dynamic over static modeling in understanding ML performance and management. Better simulations mean better planning and management, and ultimately more efficient and cost-effective transportation in the crowded corridors where ML plays a key role.
In Florida, transportation planning often uses the Florida Standard Urban Transportation Model Structure (FSUTMS) to provide a consistency of data and approach. Currently, demand forecasting in FSUTMS uses static traffic assignment, in which properties of transportation networks, such as travel times and flow rates, are constant over time and drivers are described homogeneously. Dynamic traffic assignment (DTA) could greatly advance FSUTMS by allowing scenarios in which transportation measures vary with time and drivers are treated as individuals, permitting new levels of detail and precision, thus supporting better demand and performance forecasting.
An accelerated growth in the volume of freight shipped on American highways has led to a significant increase in truck traffic, influencing traffic operations, safety, and the state of repair of highway infrastructure. Traffic congestion in turn has impeded the speed and reliability of freight movement on the highway system. As freight movement continues to grow within and between urban areas, appropriate planning and decision making processes are necessary to mitigate the above-mentioned impacts. However, a main challenge in establishing these processes is the lack of adequate data on freight movements such as detailed origin-destination (OD) data, truck travel times, freight tonnage distribution by OD pairs, transported commodity by OD pairs, and details about truck trip stops and paths. As traditional data sources on freight movement are either inadequate or no longer available, new sources of data must be investigated.
In this research project a number of theoretical volume-delay functions have been proposed with some gaining wide practical applications. The major practical volume-delay functions (VDFs) include Bureau of Public Roads (PBR) function, the Davidson function, the Conical function, and the Akcelik function. The predictive accuracy of these models is heavily dependent on accurately specifying the free flow speed and practical capacity of the highway under study. In addition, properly calibrated parameters of these models are of paramount importance in ensuring realistic results.
This research investigated the issue of facilitating network information exchange among models and more specifically concentrated on two primary objectives: (a) identify solutions to the model information exchange problem, focusing on the network, and (b) assess the feasibility of the implementation of the proposed solution and provide recommendation for its practical implementation.
Special generators are introduced in the sequential four-step modeling procedure to represent certain types of facilities whose trip generation characteristics are not fully captured by the standard trip generation module. They are also used in the traffic impact analysis to represent new developments. The objectives of this research project are twofold: 1) to analyze qualitatively trip generation characteristics of special generators and provide recommendations on how to improve the modeling of special generators in the Florida Standard Urban Transportation Model Structure (FSUTMS); 2) to examine the advantages and disadvantages of two modeling approaches, i.e., the link distribution percentage method and the special generator method, for performing traffic impact analyses for proposed developments.
FSUTMS training is a major activity of the Systems Planning Office of FDOT. The training aims to establish and maintain quality assurance for consistent statewide modeling standards and provide up-to-date information on recent model enhancements. FSUTMS training workshops have been conducted mainly with a lead instructor in a computer laboratory setting at selected locations. The costs of conducting and attending these workshop are sizable for both FDOT and the participants. This inevitably creates a limiting effect on the workshop availability and participation. The objective of this project is to develop an online alternative of the live version of the FSUTMS Comprehensive Modeling Workshop.
Intelligent Transportation Systems (ITS) planning requires that use of tools to assess the performance of ITS deployment alternatives relative to each ot her and to other types of transportation system improvement alternatives. This research project investigates the development of tools and procedures to perform sketch-planning evaluation of the costs and benefits of ITS alternatives within the Florida Standard Urban Transportation Model Structure (FSUTMS) modeling environment.
This research report describes a standalone master network system that was developed to work with Cube Voyager- the modeling engine of the Florida Standard Urban Transportation Model Structure. Stored in ESRI's Personal Geodatabase format, the master network database structure was designed to minimize data storage space, extract efficient networks with consolidated links, support open and unlimited data attributes, and implement both forward and backward network propagations.
Over the past several years, there has been a growing interest in the development of disaggregate (individual- or household-level) travel-demand models. In the case of Florida, this is evident from their efforts to incorporate socio-demographic variables within the FSUTMS structure via "lifestyle" trip production models. However, the lack of a systematic procedure to forecast the household characteristics required by such disaggregate travel-demand models has been recognized as an important impediment to furthering these efforts for state-wide adoption. In this context, the broad focus of this research is to contribute towards the devleopment of methodology for comprehensively forecasting all traveler characteristcs required as inputs to travel-demand forecasting models.
Transportation models are used to forecast travel behavior and patterns, and thus to aid decision makers in developing transportation plans and making investments. Quantity and quality of data are critical to effective modeling. However, data accessibility has been a major and persisting challenge facing transportation modelers. Indeed, it is not unusual for data preparation to take up over 70% of the total modeling effort.
Advances in computer hardware and software have led to an increasing level of sophistication in travel demand forecasting. Many researchers have taken advantage of the improved computing power to refine travel demand models. Traditional travel demand models rarely consider intersection delays when estimating travel time. This is because modeling intersection delays is challenging due to the complexity in roadway geometry, signal plans, and vehicle movements.
Freight transportation is both multimodal and intermodal in nature, involving highways, railways, waterways, air transportaiton, terminals, and intermodal transfers. Multimodal and intermodal orientation holds major promise in significantly improving freight transportation efficiency. Wise investment in the development of multimodal and intermodal infrastructure can effectively remove major bottlenecks on freight networks, expand shipping alternatives, reduce congestion and environmental impact, and improve safety and efficiency of the entire transportation system.
The Florida Standard Urban Transportation Modeling Structure (FSUTMS) is a computerized model package developed by the Florida Department of Transportation (FDOT) for planning and analysis of transportation systems. It has been used by all 26 Metropolitan Planning Organizations, FDOT Districts and other planning agencies in Florida. Currently FSUTMS models daily travel demand and then produces estimates of peak volumes through a simple post-processing routine. However, there are pressing needs to address planning issues and answer questions that are time-of-day (TOD) related. The daily-basis modeling framework is not competent for those tasks.
Over the past few decades, the transportation modeling field has seen rapid and significant development in the application and implementation of new state-of-the-art information technologies that make it possible to simulate travel demand in urban areas in a graphical environment.
Model validation is an important step in travel demand modeling. It serves to ensure that the calibrated model produces outputs that are consistent with the observed data. In the Florida Standard Urban Transportation Model Structure (FSUTMS), various consistency checks for the validation of the distribution and assignment models are suggested for highway networks.
Travel demand models are for the purpose of estimating future travel demand given changes in transportation infrastructure and socioeconomics/demographics. Given that the methodologies in a travel demand model are a controlled factor, the effect of input data on the accuracy of the traffic volumes projected for a short term horizon has not yet been thoroughly investigated.
Current FSUTMS model uses a set of highway-transit speed curves based on facility type and area type to model the relationship between highway speed and transit speed. The drawbacks of this method include that there is considerable vagueness in the definition of area types and that transit boarding and alighting activities not considered in estimating transit travel time.
In the current practice at the Florida Department of Transportation (FDOT), seasonal factors (SFs) are used in the calculation of annual average daily traffic (AADT) at portable traffic monitoring sites (PTMS). The permanent traffic monitoring sites (TTMSs) are first manually classified into different groups (known as seasonal categories) based on similarities in traffic characteristics of roads and on engineering judgment.
This project aims to address the well-known problem of inconsistent travel impedances that exists within FSUTMS' four-step traditional demand model by designing and implementing an automated feedback loop in FSUTMS. The direct method and Method of Successive Averages (MSA) method are used for implementing feedback.
The standard highway assignment model in the Florida Standard Urban Transportation Model Structure (FSUTMS) is based on the equilibrium trip assignment method. The method involves running several iterations of all-or-nothing capacity-restraint assignment with an adjustment for travel time to reflect delays encountered in the associated iteration.
While it is generally agreed that transportation and land use interact with each other, the feedback mechanism in their relationship has not been well defined at a level of detail that adequately supports travel demand modeling. Most studies to date have been at metropolitan level, thus unable to account for interactions spatially and temporally at smaller geographic scales.
As part of the project, a survey of Florida Metropolitan Planning Organizations (MPOs) was conducted in 2001. The survey results were summarized in this report.
This CBT (computer-based training) was developed for the Florida Department of Transportation (FDOT) Systems Planning Office for the FSUTMS/TRANPLAN "Basic Workshop". FSUTMS (Florida Standard Urban Transportation Modeling Structure) is the standard model used by Florida's urban areas for travel demand forecasting.
Advanced Traveler Information Systems (ATIS) provide real-time information to motorists regarding traffic accidents, roadway maintenance and construction, heavy congestion, emergencies, and other traffic delays. Information is transmitted via the Internet, television, rest area kiosks, in-vehicle displays, radio, cellular telephone, and changeable message signs.
Over the past few decades, there has been a growing interest in planning for the safe, efficient, and smooth movement of goods and freight across all modes of transportation. This is because of the growing realization that freight transportation and economic development are inextricably linked to one another. Planning for the safe and efficient movement of freight is directly tied to the economic development of an area, because businesses often locate in areas where just-in-time logistics practices can be implemented effectively and reliably.
Travel demand models in the State of Florida generally model daily travel demand and produce estimates of daily link volumes that are then converted to peak hour volumes through the application of appropriate conversion factors. Current FSUTMS models in the state operate on a daily basis and then produce peak hour estimates through a simple post-processing routine.
This document provides a high-level overview of the features of the TRANSIMS software package that were used in the Portland Case Study. As TRANSIMS-DOT was not completed during the period covered by this project, the project team had to perform the case study in collaboration with Los Alamos National Laboratory and Portland Metro using the Portland area databases.
Every year, the Florida Department of Transportation (FDOT) and many local and regional agencies conduct a large number of transportation planning studies. These studies are critical to the development of the transportation system in the state because they are useful in the identification of needs and prioritization of transportation improvement projects. Transportation planning studies, such as the long range transportation plan updates undertaken periodically by metropolitan planning organizations in each urbanized area, provide information that guide billions of dollars in transportation investment.
In recent years, urban policymakers, faced with the growing and complex problems of air pollution and traffic congestion have begun to ask for more sophisticated decision-making tools, including models to forecast travel demand and its effect under various circumstances. Discrete choice models have played an important role in transportation modeling for the last 25 years. They are used chiefly to provide a detailed representation of the complex aspects of transportation demand, based on strong theoretical justifications.