The traditional four-step demand modeling approach seen in many metropolitan travel demand models is also the most dominant methodology for statewide demand analysis. Most statewide models that have adopted the traditional four-step method have the following characteristics in their passenger and freight analysis modules. Their passenger travel models usually are constructed from existing metropolitan planning organization (MPO) models and national personal/household travel surveys (e.g. Massachusetts and Missouri). In some cases (e.g. New Jersey), the statewide model is entirely built from the MPO models (three MPO models in New Jersey). Methods for the freight analysis modules range from simple growth factor methods (e.g. Kentucky and Oklahoma, see CSI 2008), to non-commodity- based approaches using quick response techniques (e.g. Massachusetts and New Jersey, see FHWA 2007), and to commodity-based approaches based on national and commercial freight databases (e.g. Freight Analysis Framework, TRANSSEARCH).
2.3.1 Model Structure
A basic four-step model is defined by its four sequential stages: trip generation, trip distribution, modal split, and traffic assignment, as shown in Figure 30. The demand modeling process is aggregate and trip- based with limited analysis of individual travel behavior. Trip generation is usually analyzed with household-level cross-classification methods based on large-scale surveys that forecast trip production totals for all Traffic Analysis Zones (TAZ) by trip purpose. Zonal-level multiple regression models are often formulated to predict trip attractions. Trip distribution is estimated with gravity and multinomial logit models. For the modal split stage, the basic four-step models either adopt fixed modal shares from observed datasets or employ multinomial logit mode choice analysis. Traffic assignment is typically based on a single-class static user equilibrium assignment algorithm. Time-of-day choices and peak spreading are not considered in basic four step models. Instead, the 24-hour demand matrix is converted to several time-of-day matrices (e.g. AM peak, PM peak, mid-day, and other off-peak) based on observed demand shares in different time periods. Statewide models that fall into the category of traditional four step models include Indiana, Maryland, Massachusetts, Michigan, New Jersey, Tennessee, Virginia, Wisconsin, and others.
The New Jersey Statewide Model is an interesting case. Three MPO models in the State of New Jersey and two cross-border regional travel demand models have been directly combined into a cohesive statewide model. The three MPO models include the North Jersey Regional Transportation Model, Delaware Valley Regional Planning Commission Model, and the South Jersey Transportation Planning Organization Model. The cross-border models are the Port Authority of NY/NJ Interstate Network Model and the Delaware New Castle County Model. All of them are basic four-step models. If most of the areas/population in a state are within metropolitan planning boundaries (e.g. New Jersey, Maryland), this approach of developing statewide models based on existing MPO models can be quite effective and save model development cost and time.
MD-11-SP009B4S Project Final Report UMD Transportation Systems Research Lab 50 Figure 30. The Basic Four-Step Model Structure without Feedbacks
One challenge in developing statewide models from several existing MPO models is to ensure consistency among all component MPO models, especially along MPO model boundaries. In the development of the New Jersey Statewide Model, a technique known as “trip table weaving” has been used extensively to address this issue. For trips in each MPO model with at least one end in external zones (i.e. external-external, external-internal, internal-external) with their origin-destination tables at different time scales (daily, peak periods, and time-of-day) are “woven” between relevant MPO models. After trips crossing MPO model boundaries are distributed from one model to its connecting model, the total number of external trips is then balanced to ensure consistency. For freight trips, a gravity model representing inter-county commodity flows is used to construct truck trip tables.
In statewide models based on the traditional four-step methods, the freight modules usually employ relatively simple methodologies that rely on available freight demand data sources. For instance, Louisiana, Oklahoma, and Kentucky all employ the Origin-Destination Factoring Method wherein growth factors based on economic, employment, and other growth indicators are applied to existing freight OD data, such as the TRANSEARCH database. Other states with more closely spaced or contiguous urban areas such as New Jersey and Massachusetts (Cambridge Systematics, Inc. 2008) have employed four-step truck models without considering the modal split step (i.e. only truck freight trips are considered). All truck trips are usually classified into light, medium, and heavy truck trips, and then assigned to highway networks simultaneously with passenger trips.
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2.3.2 Data
In general, the development of statewide models based on traditional four-step techniques does not require dedicated data collection efforts. Since it is usually very costly to collect comprehensive travel behavior data at the statewide level, national data sources are commonly used with oversampling at the state level (e.g. Massachusetts). The available national datasets include the decennial Census Transportation Planning Package (CTPP), the American Travel Survey (ATS) in 1995, the Nationwide Personal Transportation Survey (NPTS) in 1995, and the National Household Travel Survey (NHTS) in 2001. Indiana is an exception, which conducted its own household travel survey in 1995 and used this dedicated survey data together with the CTPP and the NHTS 2001 data.
Aside from the national level data sources, sub-state level datasets initially collected for regional and metropolitan demand modeling have also been used in statewide model development. For instance, the New Jersey model also relies on the sub-state Regional Travel Household Interview Survey (RT-HIS) and the Comprehensive Total Travel Survey (CTTS) conducted by the Metropolitan Transportation Authority (MTA) in 1989. The RT-HIS data, collected during 1997 and 1998, include 4,541 households with weekday travel information and an additional 275 households with weekend travel information in 14 counties in northern New Jersey. Traffic count data previously collected on various transportation facilities have been routinely used for statewide model calibration and validation. In the development of the trucking freight module for the Virginia statewide model, traffic counts classified by vehicle types were used to develop truck travel demand information with OD matrix estimation methods.
2.3.3 Applications
Statewide transportation models based on the traditional four-step method can be applied to a variety of important transportation planning and policy analyses at the state and sub-state levels, including future travel volume and level-of-service forecasting on multimodal transportation facilities, performance measurement and monitoring, corridor-level transportation planning, congestion management, freight analysis, scenario testing and analysis and environmental impact studies, etc.. Selected application examples are presented below.
Corridor planning has been one of the major application areas of statewide models, and in some cases, the motivation for statewide model development in the first place. For instance, the New Jersey model has been employed to analyze and improve the I-78 corridor (connecting Pennsylvania and New York) traffic conditions. The Virginia model has proven valuable in analyzing trucking commodity flows along the I-81 corridor. The Indiana model was initially developed for the I-69 corridor study as mandated by Congress.
A key motivation for the development of the New Jersey model was to analyze truck freight movement within the state. The New Jersey statewide model has also played a crucial role in the New Jersey Congestion Management System, by evaluating various transportation improvement strategies such as High Occupancy Vehicle lanes, congestion pricing, arterial signal systems, and other intelligent transportation systems (Davis, 1998). These congestion management scenarios are an important component of New Jersey DOT’s five years transportation improvement plan (DeJohn et al. 2007). The Virginia model is capable of tracking interstate travel through the state on significant highway corridors such as I-81. In the I-81 trucking study, the statewide model has been applied to estimate heavy truck percentages at various Interstate highway locations, to differentiate local and through-truck
MD-11-SP009B4S Project Final Report UMD Transportation Systems Research Lab 52 traffic, and to identify truck travel patterns inside Virginia. It has also been applied to estimate automobile traffic in rural areas and to analyze intercity passenger rail.
In Indiana, outputs from the statewide model were used to develop a sub-state demand model for the I- 69 corridor (BLA, 2006). The corridor model was subsequently used to analyze the environmental and economic impact of the highway corridor. Figure 31 illustrates how the corridor travel demand model was developed from the statewide trip tables. The transportation network along the I-69 corridor in the corridor demand model is also more detailed than that in the statewide model. Results from the corridor demand model were used for alternative analysis in the I-69 Evansville to Indianapolis Tier 2 Environmental Impact Statement (EIS) study.
Figure 31. Developing Detailed Corridor Demand Model from the Indiana Statewide Model (Source: BLA, 2006)
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