Investigating the Application of a Transportation Energy Consumption Prediction Model for Urban Planning Scenarios in Machine Learning and Shapley Additive Explanations Method

Abstract

Accurate forecasts of future energy usage are an important step towards reaching carbon mitigation commitments for city policymakers. Beyond identifying sources of emission intensity for a region, the forecast mechanism must be capable of compensating for gaps in available data and of accounting for the uncertainties behind the dynamics of an urban system. By considering a range of possible scenarios, the prediction model can identify recurring sources of high energy consumption and fine-tune areas of priority with incoming data. This paper considers the impact of predicted shifts in demographic and economic trends for the region on transportation energy consumption. The transportation energy use model is formulated from the Delaware Valley Regional Planning Commission (DVRPC) open-source Household Travel Survey (HTS). Based on these data inputs, a Machine Learning (ML) algorithm is implemented in the form of an Extreme Gradient Boosting (XGBoost) model to estimate energy consumption with a corresponding SHapley Additive exPlanations (SHAP) analysis of feature contribution. From this, a synthetic population is produced using the ML outputs and marginal sums with data from the Census Bureau’s American Community Survey (ACS) to estimate energy consumption for the region. The results indicate that shifting dominant travel modes and income distribution in accordance with the Enduring Urbanism forecast projections led to a decrease in household transportation energy use. Moreover, additional analysis of the model output demonstrates that changes in energy use depend strongly on geographic area and income group.