Evaluation of transfer methods for spatial travel demand models

Karasmaa, Nina

doi:10.1016/j.tra.2006.09.009

Cited by 18 publications

(17 citation statements)

References 5 publications

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“…This paper evaluates four transfer methods which are commonly used to transfer model parameters and/or travel data from one geographical region to another. In the literature, several empirical studies have evaluated different methods used for spatial transferability of model parameters and their predictive performance (Karasmaa 2007, Atherton and Ben-Akiva 1976, Badoe and Miller 1995aand 1995b, Koppelman and Wilmot 1982, Mohammadian and Zhang 2007, Zhang and Mohammadian 2008. The transfer methods evaluated include Naïve Transfer, Joint Context Estimation, Bayesian Updating, and Combined Transfer Estimator.…”

Section: Spatial Transferability Methodsmentioning

confidence: 99%

“…The potential benefits of the spatial transferability of the models cited in the literature include reduction and/or elimination of large data collection and model development efforts in the application region (Karasmaa 2007). Application region refers to a region where data and/or parameters were applied from another region, whereas estimation region refers to a region where data were collected and/or parameters were estimated.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Transferability: Analysis of the Regional Automobile-Specific Household-Level Carbon Dioxide (CO2) Emissions Models

2013

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This paper compared performance of methods for combining model information estimated in one region and applied to another region to improve estimation results. The application is for models developed to estimate household-level automobile-specific CO2 emissions. The results indicated that automobile-specific CO2 emissions models can be transferred from one geographical region to another. The estimates of CO2 emissions can assist agencies such as policy makers, businesses, and transportation planners to track trends and identify opportunities to reduce CO2 emissions and increase efficiency of transportation systems to lessen their impact on global warming, climate change, and air quality standards.

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Section: Spatial Transferability Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Transferability: Analysis of the Regional Automobile-Specific Household-Level Carbon Dioxide (CO2) Emissions Models

2013

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“…Model transferability has been widely studied in the past with reference to the transferability of trip generation models (Agyemang-Duah, Hall 1997), mode choice models ( (Badoe, Miller 1995, Koppelman, Kuah & Wilmot 1985, Koppelman, Wilmot 1982, four-step models (Karasmaa 2007), and gap-acceptance Logit models (Rossi, Meneguzzer & Gastaldi 2013). These authors studied the effectiveness of both full model transfer (direct transfer) and of updating the original model using a small dataset from the application context.…”

Section: Previous Workmentioning

confidence: 99%

Comparing Direct Transferability of Logit and Fuzzy Logic Models of Gap Acceptance at Unsignalized Intersections

Gastaldi

Meneguzzer

Gecchele

et al. 2015

Transportation Research Procedia

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This paper presents a comparative analysis of the performance of Logit models vs. Fuzzy logic models of gap-acceptance behavior in terms of their direct transferability. This refers to situations in which a model estimated in a given experimental context is directly applied in a different context without any updating. Data collected at four priority intersections having different characteristics in terms of geometry, location and type of control (stop vs. yield sign) were used to test the quality of the results obtained when transferring each type of model from an original to an alternative situation. The comparison was carried out based on metrics commonly used in the so-called ROC (Receiver Operating Characteristic) curve analysis. The results show that both models have essentially the same capability in terms of direct transferability, and that their performance in contexts different from those of original development could be more than adequate for application purposes

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“…They found that transferability is limited and that further research must be done to identify the range of contexts in which the model can be applied. Karasmaa (2007) tests the spatial transferability of the simple four-step regional model in two different urban areas in Finland. The research shows that the models are largely transferable between these locations; however, doubts arise as to whether this evidence supports the idea of more universal model transferability or is limited to similar urban structures and cultural contexts.…”

Section: Introductionmentioning

confidence: 99%

Transferability & Forecasting of the Pedestrian Index of the Environment (PIE) for Modeling Applications

Clifton¹,

Orrego-Oñate²,

Singleton³

et al. 2019

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This project focuses on making our measures, models, and methods more transferable to other locations. Specifically, we re-evaluate, compare and test our pedestrian index of the environment (PIE) measure using data resources more commonly available to planning agencies across the country. Next, we test the results of PIE and its input data in models of pedestrian mode choice for stability of estimation results within a region (intraregional) and between regions (interregional). This research is the next logical step in the MoPeD's enhancement and is critical to enabling its utility beyond the Portland region.The results of this project show that population density and pedestrian connectivity had the most consistent and strong relationship to walk mode choice across all of our regions, which echoed the long literature on this topic. However, the other components of the built environment included in PIE had more variability in their ability to explain walk mode choice. Employment density and its subset urban living infrastructure (ULI), intended to capture retail and service access, had less explanatory power and stability in the cities tested. Based upon these findings, we provide several guidelines for the construct of walkability indices, including variables and spatial scales.Our findings raise questions about the relationship between walking and the built environment within a region and thus, the intraregional transferability of one walkability index is suspect. Estimation results suggest that there may be different responses to the built environment in lower-density vs. higher density regimes and that these relationships may be nonlinear. However, smaller sample sizes of travel survey data in high density areas in all of the US cities tested pose limitations to drawing more confident conclusions from these results.The interregional comparisons of PIE and walk mode share between Los Angeles and Portland showed promise for the use of the index in different regions. In these two regions, model results showed a similar walk mode share for the same values of PIE constructed at the block group level. This provides initial support that the PIEbg construct may be transferrable between metropolitan regions, in part, due to population density's prominent role in PIE.

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Evaluation of transfer methods for spatial travel demand models

Cited by 18 publications

References 5 publications

Spatial Transferability: Analysis of the Regional Automobile-Specific Household-Level Carbon Dioxide (CO2) Emissions Models

Spatial Transferability: Analysis of the Regional Automobile-Specific Household-Level Carbon Dioxide (CO2) Emissions Models

Comparing Direct Transferability of Logit and Fuzzy Logic Models of Gap Acceptance at Unsignalized Intersections

Transferability & Forecasting of the Pedestrian Index of the Environment (PIE) for Modeling Applications

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