Income-contingent user preferences in policy evaluation: application and discussion based on multi-agent transport simulations

Abstract

Standard economic policy evaluation allows the realization of projects if the aggregated economic benefit outweighs their costs. The use of one single aggregated welfare measure for evaluating and ranking projects has often been criticized for many reasons. A major issue is that differentiated effects on individuals or subgroups of the population are not taken into consideration. This leads to the need for transport planning tools that provide additional information for politicians and decision makers. The microscopic multi-agent simulation approach presented in this paper is capable of helping to design better solutions in such situations. In particular, it is shown that the inclusion of individual income in utility calculations allows a better understanding of problems linked to public acceptance. First, individual income-contingent utility functions are estimated based on survey data in order to describe human mobility behavior. Subsequently, using the MATSim framework, the implementation is tested in a test scenario. Furthermore, and going beyond Franklin (2006), it is shown that the approach works in a large-scale real world example. Based on a hypothetical speed increase of public transit, effects on the welfare distribution of the population are discussed. It is shown that the identification of winners and losers seems to be quite robust. However, results indicate that a conversion or aggregation of individual utility changes for welfare analysis is highly dependent on the functional form of the utility functions as well as on the choice of the aggregation procedure.

Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System

“…Our findings also emphasize the impact of the user's preferences and neighbor's influence on personal decisions. Several prior studies have examined the relationship between user preferences and decision-making based on multi-agent models [50,51]. They also seek to take in the advantage of this granularity in order to explore the impact of user preferences on the modal split of shared autonomous vehicles in the area of transport.…”

Congestion pricing in a world of self-driving vehicles: An analysis of different strategies in alternative future scenarios

“…Activities often have higher importance in the daily schedule than trips that simply represent connections among them. Since MATSim represents traffic behavior at a highly disaggregated level by modeling individual agents (with different socio-demographic characteristics), it is possible to investigate the effects of transport policies on travel behavior and traffic in more detail than in traditional 4-steps models (Kickhöfer et al, 2011). The overall process ( Figure 1) can be summarized in the following stages:…”

Initial Analysis of Urban Air Mobility’s Transport Performance in Sioux Falls

“…Agents try to maximize their score by changing their travel behavior, their activity order, or the activities' starting times until an optimal allocation is reached, as described by Kai et al [10]. MATSim has been used for a broad range of analyses, such as for the simulation of autonomous cars [11], policy evaluation [12], land-use analyses [13], and traffic signal analyses [14]. The most closely related to the application of MATSim for UAM, is an analysis of autonomous taxis by Hörl [15].…”