Forecasting Statewide Freight Toolkit

Cohen, Harry; Horowitz, Alan J.; Pendyala, Ram M.

doi:10.17226/14133

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…Activity-based, or tour-based, modeling approaches have been shown to provide the most accurate movement predictions (Samimi et al, 2010;Figliozzi, 2007;Kuppam et al, 2014;Gliebe et al, 2007;Hunt & Stefan, 2007;Ruan et al, 2011;Kim et al, 2011;Bradley et al, 2010;Doustmohammadi & Sisiopiku, 2016;Doustmohammadi et al, 2016b). Additionally, the ability to combine modeling techniques, primarily combing tour-based modeling with other types of modeling can meet specific needs (Alho, 2011;Fischer et al, 2005;Jansuwan et al, 2014;Holguín-Veras & Thorson, 2000;Holguín-Veras & Patil, 2008;Daly, 1982;Doustmohammadi et al, 2016a;Doustmohammadi et al, 2016b;Holguín-Veras et al, 2008; (Cambridge, 1996;Cambridge, 2007). The models in QRFM have been shown to be useful in forecasting truck in a medium sized community (Anderson et al, 2013;Cambridge, 2007).…”

Section: Literature Review Of Freight Transportationmentioning

confidence: 99%

Regression Analysis to Create New Truck Trip Generation Equations for Medium Sized Communities

Doustmohammadi¹,

Anderson²,

Doustmohammadi³

2019

CUS

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This paper uses data from a trucking origin/destination study conducted with global positioning system (GPS) technology to develop a truck trip generation model for medium sized urban communities-in this study taken to be communities between 200,000 and 1,000,000 people. The difficulty with developing truck trip generation equations centers on the limitation of data. For passenger transportation, data are collected from household surveys. For truck transportation, if available, data are typically collected from a small collection of shippers/businesses within the urban area and extrapolated to cover the entire study area. Because of the data limitations, truck transportation is typically indirectly modeled or as an afterthought. Increasing truck volumes, coupled with cost saving strategies such as just-in-time delivery systems, require that transportation policymakers analyze infrastructure needs and make investment decisions that explicitly include truck volumes as a component. This paper contains a case study using a medium sized urban area and a GPS collected set of truck origins and destinations to develop a truck specific trip generation equation using standard employment data. The paper presents the models developed and validates the models to the case study community. The paper concludes that the trip generation equations developed can be incorporated into medium sized community travel models to provide a framework for truck planning that can be used to improve resource allocation decisions.

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Section: Literature Review Of Freight Transportationmentioning

confidence: 99%

Regression Analysis to Create New Truck Trip Generation Equations for Medium Sized Communities

Doustmohammadi¹,

Anderson²,

Doustmohammadi³

2019

CUS

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“…Mode share distributions by commodity for Arkansas for shipments made in 2015 are shown in Table 1 ( 1 ). For consistency, the standard classification of transported goods commodity codes used by FAF have been aggregated to the commodity groups adopted by the Arkansas STDM (AR-STDM) ( 13, 14 ). Table 1 shows that the share of chemicals moved by truck in Arkansas is higher than the national average (73.8% statewide versus 64.6% nationwide), while the share moved by water is 0.0% statewide and 8.7% national average.…”

Section: Introductionmentioning

confidence: 99%

Using Data from a State Travel Demand Model to Develop a Multi-Criteria Framework for Transload Facility Location Planning

Asborno

Hernandez

2018

Transportation Research Record: Journal of the Transportation R

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The majority of freight is transported within the U.S. by road. However, the use of alternative modes, such as rail and barge, is associated with lower transportation and infrastructure maintenance costs, release of highway capacity, increased safety, and lower emissions. Thus, there is a latent opportunity for shippers and consumers to benefit from modal shift. In this context, strategically located freight-transfer facilities to improve rail and barge access is key. Moreover, for states with lower commodity tonnages and access to short-line rail and navigable waterways, transload facilities have significant potential to shift freight to underutilized modes. This paper develops a multi-criteria assessment framework to identify strategic locations for transload facilities at the state level. Using a statewide travel demand model (STDM) as the main data source, this framework provides a sketch-planning tool to support decision-making for state Departments of Transportation and economic development agencies. The multi-criteria quantify four measures of facility potential: (a) interaction with the transportation network, (b) amount of freight transported between major freight routes, (c) spatial aggregation, and (d) directionality aggregation. Each criterion is estimated and combined at the county level to produce a multi-criteria score, which defines a county’s potential to support transload movements. Using this score, counties are ranked, and facilities prioritized. The framework is applied to Arkansas and validated using the STDM for base (2010) and forecast (2040) years.

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Geolocation Data as a Research Tool for the Organization of the Settlement System and Mobility Mapping – Case Study of the Spatial Mobility Model in Czechia

Jaroš

2024

Communications in Computer and Information Science

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Geolocation data is a widely used source of the spatial information about the population. Their great potential might be also used for population mobility research to identify spatial interactions forming the hierarchical structure of the settlement system. For this purpose, a model of data acquisition and their preliminary analysis was developed. This model represents an effective tool for mapping the mobility behavior of the population. Using the example of Czechia, primary commuting links are identified, which are subsequently analyzed in detail using GIS tools in both desktop and online environments. Therefore, important commuting centers of different hierarchical levels are defined by the volume and nature of spatial interactions. This approach is used as a source of important expertise for the proposals on subsequent administration reform in Czechia. Nevertheless, the entire model is generally transferable, and the entire method of using the geolocation data for mapping the hierarchy within the settlement system can be replicated in other countries as well.

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Forecasting Statewide Freight Toolkit

Cited by 8 publications

References 2 publications

Regression Analysis to Create New Truck Trip Generation Equations for Medium Sized Communities

Regression Analysis to Create New Truck Trip Generation Equations for Medium Sized Communities

Using Data from a State Travel Demand Model to Develop a Multi-Criteria Framework for Transload Facility Location Planning

Geolocation Data as a Research Tool for the Organization of the Settlement System and Mobility Mapping – Case Study of the Spatial Mobility Model in Czechia

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