Posted Thursday, August 03, 2023
One-page flyer
Posted Friday, February 05, 2021
In this research, we developed and implemented a methodology to evaluate the life-cycle cost and benefit analysis of freight transportation projects based on our previously developed economic impact analysis tool, FreighTEC 1.0 (formerly known as FTEIK). A post-processing tool, FreighTEC 2.0, is developed to assist the project prioritization process for the Florida Department of Transportation (FDOT) based on the freight forecast model -- Freight Supply-chain Intermodal Model (FreightSIM). FreighTEC 2.0 considers costs of the entire life cycle of freight investment projects, including planning, construction, operation, and maintenance, and estimates the direct benefits to the users and economic impacts to the impacted county (i.e. local impacts) and the state as a whole (i.e., state impacts).
Florida's Freight Transportation Economic-impact Calculator (FreighTEC)
FreighTEC 2.0 is a Life-cycle Cost and Benefit Analysis (LCCA) tool that uses a regional economic input-output model and the Freight Supply-chain Intermodal Model (FreightSIM). The core of FreighTEC 2.0 lies in the combination of a freight demand model with a multi-sectoral economic model. Outputs from FreightSIM are converted into monetary values that are the inputs for the regional IO model to derive sectoral impacts for the studied economy. The tool also allows users to customize values for converting transportation outputs at the project level. The outputs from FreightSIM are vehicle miles traveled (VMT) and vehicle hours 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 lower vehicle hours of travel upon highway links in the study area.
Posted Thursday, January 02, 2020
The travel demand models in Florida use different road networks even in geographically overlapping areas. Due to differences in network segmentation and details, currently, it is difficult to share information among models at different scales. Thus, this research examines the issues related to the network structure of travel demand models, with an emphasis on the Statewide model. It aims to identify a more efficient multi-scale network structure that will enable effective information sharing between the Statewide models and the district or local models, while preserving the detailed information provided by finer network segmentation. After considering previous research and practice in and out of Florida on this topic, this research proposes a framework for a planning network database to support efficient travel demand modeling for the state of Florida.
Posted Monday, April 01, 2019
The Florida Standard Urban Transportation Model Structure (FSUTMS) specifies that models use an attribute referred to as UROAD, which is the ratio of the practical to absolute capacity of a model link. This memo demonstrates the role that UROAD plays in the Florida modeling context by examining the impact of different UROAD parameters on total assigned trips, congested trip times, and congested speeds. The UROAD factors currently used in Florida models are listed, along with other associated variable factors, like the alpha and beta parameters and the peak-to-daily capacity ratio attribute.
Posted Monday, April 01, 2019
This document describes the Florida Statewide Model's design and organization, and provides guidance on proper model application methods. It specifies the types of projects for which this model is appropriate, and the recommended steps for performing analysis within a subarea of the state. Finally, the report discusses the emerging modeling topic of risk analysis, quantifying the uncertainties inherent in travel demand forecasting.
Posted Wednesday, May 30, 2018
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.
Posted Tuesday, March 06, 2018
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 (VMT) and vehicle hours 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 hours of travel on the highway links in the study area.
Posted Thursday, January 25, 2018
Agencies is increasingly interested in measuring system performance and the impact of advanced technologies and strategies for existing and future year conditions. This interest increased with the MAP-21 federal legislation emphasis on establishing performance goals focusing on seven areas: safety, infrastructure conditions, congestion reduction, system reliability, freight, environmental sustainability, and project delivery time. The federal legislations require states and metropolitan planning organizations (MPOs) to identify performance measures and associated targets and including these targets in the state and MPO plans. For existing conditions, this estimation can be done based on data collected from multiple sources such as statistics office detectors, traffic management system detectors, incident and crash databases, weather agencies, and other sources of data. For future conditions, there is a need to identify models and methods that can be used to support the estimation of system performance. These models will have to be supported by data from multiple sources.
Posted Monday, January 08, 2018
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.
Posted Sunday, January 07, 2018
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.
Posted Thursday, June 18, 2015
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.
Posted Friday, October 31, 2014