Influence of the Spatial Distribution of Jobs in Intervening Opportunities Models

Abstract

This article analyzes the influence of spatial distribution of jobs, which represents intervening opportunities, in aggregated and disaggregated trip distribution models that generated eight origin–destination matrices. The observed trip matrix was obtained from the Urban Transport Master Plan (PDTU) of the Rio de Janeiro Metropolitan Region in Brazil. The estimated matrices were calculated using the gravity, intervening opportunities and Naïve Bayes models, organized at two aggregation levels. These models’ performance was evaluated considering the aggregation level, the model approach adopted (aggregated or disaggregated), and the spatial distribution of the intervening opportunities. The results indicated that the intervening opportunities exert a positive influence on all models, under any level of zone size (subdistricts or neighborhoods). In the aggregate models, the variable “jobs” insertion improved the models’ performance when applied at a higher aggregation level (subdistricts). The best-case scenario showed a coefficient of determination (R²) equal to 0.8705, obtained in a disaggregated model with intervening opportunities applied in subdistricts.

Keywords

planning and analysis transportation demand forecasting trip generation modeling transportation planning analysis and application performance models

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