A modal share scenario evaluation framework including electric vehicles

Abstract

When urban residents predominantly rely on private vehicles for their daily commutes, the urban ecosystem experiences substantial negative repercussions, including increased road congestion and significant negative environmental impact. This reality underscores the importance of evaluating and adapting modal share as a key objective in the planning of transportation systems. To provide accurate data-driven decision-making support, a modeling framework is proposed and utilized to analyze modal share change objectives, based on a real-world transportation demand dataset and the long-term mobility strategy from Budapest, Hungary. With the Modal Share Evaluation (MSE) algorithm supported by the Daily Activity Chain Optimization (DACO) framework, several variables, such as travel time, distance, and CO2 emissions, can be estimated for several future hypothetical scenarios. The data for the baseline scenario are derived from a historical dataset on travel patterns from the city, and respective estimations are made for future scenarios while considering five transport modes. The results indicate that a different prioritization of the adoption of transport modes for Budapest may result in various effects on the travelers' activity chains. The proposed MSE algorithm aims to be the first step towards a reproducible disaggregate-level framework that supports decision-makers in estimating how modal share change objectives affect travelers and the urban environment, which will help in creating future transportation policies.