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Analyzing Future Freight Challenges in Maryland Using Freight Data Sources and the Maryland Statewide Transportation Model (MSTM) Subrat Mahapatra Maryland Department of Transportation Mark Radovic Gannett Fleming, Inc. Sabyasachee Mishra University of Memphis Presentation Notes: Presented by Subrat Mahapatra, Maryland Department of Transportation. Maryland estimates that freight transportation is expected to more than double by 2025 to an estimated 1.4 billion tons of freight. This leads to several policy questions as to what is the projected increase in demand and how to develop multiple modes to offer choices. The model ran two scenarios: a limited funding scenario and a high funding scenario that assumed a system expansion. Operationally, the model was used to identify hotspots for mobility, reliability, bottlenecks, and safety. Truck trips were generated by industrial, retail, and office employment. The model can be applied to study a variety of scenarios, such as effects from pricing, zoning, infrastructure improvements, changes in the economy, and other external variables. Abstract The state of Maryland is strategically located in the middle of the eastern seaboard and accommodates many major population and manufacturing centers, especially along the I-95 corridor. With numerous major transportation facilities and freight terminals within the state (highway, rail, water, and air), the stateâs own existing manufacturing sector, and the need to get goods to consumers in the state, freight movement is known to be a major and growing component of the trip generation within and through the state. Truck traffic is projected to grow by 61%, and traffic moving through the state is estimated to grow by 52%, by the year 2030. Additionally, an expansion of the Panama Canal would have significant impacts on every major metropolitan region along the East Coast. This has special significance in the state of Maryland, as I-95 is a major backbone for economic growth as well as major commuter route in the Baltimore-Washington region, and the Port of Baltimore is a major freight hub. The corridor is already one of the heaviest traveled corridors in the country, accommodating more than 250,000 vehicles daily and about 15% trucks on many segments. Background The Maryland Statewide Travel Model (MSTM) was developed as a multi-layer model that operates at a national, statewide and urban level. The national model covers North America, the 67
statewide model includes Maryland, Washington DC, Delaware, and selected areas in Pennsylvania, Virginia, and West Virginia, and the urban model serves as a linkage to the existing urban models and is primarily used for comparison purposes. The MSTM includes two truck types: medium trucks and heavy trucks, in addition to commercial vehicles. Long-distance trips from, to, and through Maryland are captured in the national model, whereas short-haul trips are captured in the statewide layer. The MSTM utilizes the third generation of FHWAâs Freight Analysis Framework data (FAF3). This dataset summarizes existing and forecast commodity flows (in tonnage and in dollars) by FAF3 zones. The challenge lies in the coarse resolution of this dataset; some entire states (such as New Mexico, Mississippi, or Idaho) comprise a single FAF3 region. Flows to and from these large regions would appear as if all commodities were produced and consumed in one location in the center of the region. To achieve a finer spatial resolution, truck trips were disaggregated from regional FAF3 zones to flows between counties (based on employment distributions) throughout the entire United States. Subsequently, trips are further disaggregated to the statewide model zone (SMZ) structure in the MSTM model area. The FAF3 data also contain different modes and mode combinations. Additional data are required for the truck model, including the Vehicle Inventory and Use Survey (VIUS). The U.S. Census Bureau publishes the data with survey records of trucks and their usage. County employment by 10 employment types was collected from the Bureau of Labor Statistics, and county-level employment for agriculture was collected from the U.S. Department of Agriculture. The input and output coefficients that were used for flow disaggregation were provided by the Bureau of Economic Analysis. Finally, commodity flows in tons were converted into truck trips using average payload factors, and these truck trips were assigned to traffic network using multi-class time of day assignment and ultimately were validated against truck counts for both medium trucks and heavy trucks. Current Applications The MSTM continues to prove itself as a valuable tool in providing a foundation for data-driven decision making. At the policy level, the MSTM is providing insight to the Governorâs Smart Growth Initiatives, the stateâs Freight Implementation Plan, and the statewide Maryland Transportation Plan. The MSTM has also provided insight regarding evacuation planning and emergency preparedness concerns along major corridors such as the I-95 corridor. The ability to assess truck traffic going through the state on major corridors and how the characteristics of these trips change over time has been invaluable in regards to both short-term and long-range planning. In addition to providing data that support various statewide policies, the MSTM has also been utilized for project-level analysis. An example of this was the evaluation of the CSX Intermodal Container Transfer Facility (ICTF). This facility transfers containers from railcars to trucks and vice-versa. To accommodate the expected growth in freight, a site assessment was performed to evaluate potential locations that would allow accommodations that meet the 68
increased freight demands. Additionally, there are structural limitations on several bridges within Baltimore City that prohibit double-stacked rail cars. Relocating this facility outside of the city would allow double-stacked rail cars to travel along the corridor where the containers would then be transferred to trucks. This would reduce truck traffic on a heavily travelled I-95 corridor and results in creating much needed capacity. The MSTM has also been able to provide insight into characteristics of through truck trips along the I-95 corridor and the anticipated growth in through truck trips along selected corridors over time. SCENARIO ANALYSIS The MSTM design allows truck flows to be assigned to the network jointly with autos in a multi- class assignment. Validation results show a close match with observed travel patterns. MSTM was used to analyze a series of planning scenarios, covering the following two options: (1) Economic growth: Test alternative growth and decline assumptions of the overall economy by 2030. (2) Port growth: Check the impact if there is a capacity increase at the marine port in Baltimore. The model appears to provide reasonable results, as much of the future demand is concentrated on major corridors like I-95, I-270, I-70, I-495, and I-695. Conclusions Although truck trips comprise approximately 2% of total trips in Maryland, they account for 15% of all vehicle miles travelled (VMT). As truck volumes increase, maintaining a safe, efficient, and reliable transportation infrastructure will be critical in sustaining economic growth throughout the region. Developing more advanced tools and forecasting capabilities will help ensure the mobility needs for all users of the system. Accommodating the increased freight movement within and through Maryland is vital to continued economic growth. Developing reliable forecasts will assist in managing this growthâbut these tools can also enable the evaluation of short-term improvements. Having the ability to monitor system performance specifically pertaining to truck traffic will play a critical role in ensuring safe and efficient mobility of goods to, from within, and through the state of Maryland. A tool like the statewide model provides an analysis engine to evaluate and quantify the implications of freight policies, the impact of external variables on the freight industry, and overall transportation system performance. Lack of disaggregate freight flow data and datasets showing the truck tours by commodity classes makes it extremely challenging to model freight from a supply-chain perspective. Nevertheless, using existing datasets to build freight models has provided state DOTs the analysis capabilities to undertake more data- and performance-driven freight planning. 69
