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Estimating Freight Origin Destination Matrices Using Combined Commodity Flow Survey and Roadside Survey Data

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Published: 2009

Total Pages: 218

ISBN-13:

The paucity of available data was limiting studies of freight flow in Thailand. To overcome this problem, commodity flow survey (CFS) and comprehensive freight transportation by truck using roadside survey (RS) were launched to collect comprehensive freight flow data throughout the kingdom of Thailand. Since these two surveys were pioneering and due to budgetary limitations, the resulting data are still incomplete and must be adjusted. The need to produce a freight origin destination matrix using available data from CFS and RS led to the objectives of this research. This research has two main objectives. The first is to develop a methodology for combining CFS and RS. The second is to develop a method for filling gaps in the origin destination matrix based on the Adaptive Neuro Fuzzy Inference System (ANFIS) approach. The methodology to combine these two data sources was developed which uses the strengths of each method, the CFS distribution pattern and the RS marginal total. The first method is Trip Length Distribution Adjusting (TLDA), which uses adjustments to CFS trip length distribution to meet RS marginal total. The second method is Gravity Model Approach (GMA), which uses CFS friction functions to adjust RS data matrix. The method was calibrated using two difference sources of roadside survey. The results indicated that the adjusted volumes of the two data sources agreed despite being collected at different times and by different authors, and that the differences between the total adjusted volumes were quite small. It can therefore be concluded that the developed method can be used to adjust the data. For the second component, a model using BOX-COX transformation and Adaptive Neuro Fuzzy Inference system (ANFIS) was developed and verified against a convention gravity model. Two types of model, using convention gravity variables and using socio-economic variables, were developed. The results showed that the ANFIS model outperformed both the conventional gravity model and the BOX-COX model. These results proved the performance of the adaptive neuro fuzzy inference system for modeling complex system and its ability to model freight trip distribution.

Assembling and Processing Freight Shipment Data

Author: Peter Gordon

Publisher:

Published: 2001

Total Pages: 150

ISBN-13:

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This research focuses on establishing a systematic non-survey-based method for developing an origin-destination (OD) matrix of freight flows based on secondary data sources. The estimated freight flows and concurrent passenger volumes are loaded onto the regional highway network in the greater Los Angeles area of Southern California. Economic analyses, modeling, and GIS technologies are integrated into building a GIS-based OD matrix for freight flow. In order to load the freight flows onto the regional highway network, a three-step feedback transportation model is developed. It includes trip generation, trip distribution, and traffic assignment. A doubly-constrained gravity model is used to co-distribute and calibrate personal trips and freight trips in the trip distribution step. A version of User-Optimal-Strict On Network Assignment (UO-S-NA) is used to assign all of the vehicle trips to the regional highway network.

Estimating a Freight Mode Choice Model

Author: Nowreen Keya

Publisher:

Published: 2016

Total Pages: 64

ISBN-13:

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This research effort develops a national freight mode choice model employing data from the 2012 Commodity Flow Survey (CFS). While several research efforts have developed mode choice model with multiple modes in the passenger travel context, the literature is sparse in the freight context. The primary reasons being unavailability and/or the high cost associated with the acquisition of mode choice and level of service (LOS) measures--such as travel time and travel cost. The first contribution of the research effort is to develop travel time and cost measures for various modes reported in the CFS. The study considers five modes: hire truck, private truck, air, parcel service and other modes (rail, ship, pipeline, and other miscellaneous single and multiple modes). The LOS estimation is undertaken for a sample of CFS 2012 data that is partitioned into estimation sample and holdout sample. Subsequently, a mixed multinomial logit model is developed using the estimation sample. The exogenous variables considered in the model include LOS measures, freight characteristics, and transportation network and Origin-Destination variables. The model also accounts for unobserved factors that influence the mode choice process. The estimated mode choice model is validated using the holdout sample. Finally, a policy sensitivity analysis is conducted to illustrate the applicability of the proposed model.

Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips

Author: José Holguín-Veras

Publisher:

Published: 2017

Total Pages: 238

ISBN-13: 9780309446198

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Foreword: "NCFRP Research Report 37 provides policy makers with improved establishment-level models that estimate the freight trip generation (FTG), the number of vehicle trips produced and attracted at a given establishment; the freight production (FP), the amount of cargo produced by the establishment; and the service trip attraction (STA), the number of vehicle trips that arrive at the establishment to perform a service activity. These models, estimated with the best data available, provide tools to assess the various facets of the overall freight and service activity (FSA) that takes place in urban and metropolitan areas. The models will allow transportation practicitioners to conduct sound curb-management, proerply size loading and unloading areas, support traffic impact analyses, and improve transportation planning and management efforts."--Page v.