Bridging the gap between micro-economics and micro-mobility: a two-dimensional risk-based microscopic model of pedestrians' and bicyclists' operational behaviors

TL;DR

This paper introduces a novel risk-based microscopic model for pedestrians and bicyclists using Prospect Theory, capturing behavioral and cognitive factors influencing their movement and collision risk in two-dimensional traffic environments.

Contribution

It develops a new microsimulation model based on Prospect Theory to better represent risk-taking behaviors of micro-mobility users, validated with real-world trajectory data.

Findings

Model parameters reflect different risk-taking tendencies.

Simulation reproduces realistic traffic flow patterns.

Calibrated model accurately predicts collision risks.

Abstract

Due to the inherent safety concerns associated with traffic movement in unconstrained two-dimensional settings, it is important that pedestrians' and other modes' movements such as bicyclists are modeled as a risk-taking stochastic dynamic process that may lead to errors and thus contacts and collisions. Among the existing models that may capture risk-taking behaviors are: 1) the social force models (through the interplay of the repulsion and the attraction force parameters); 2) and the discrete-choice models (through the rationality or the bounded rationality paradigm while weighing different alternatives). Given that the social force models may not readily capture the contact/collision dynamics through the Newtonian force framework, decision-making theories are hypothesized as a feasible approach to formulate a new model that can account for cognitive and behavioral dimensions such as uncertainty and risk. However, instead of relying on the bounded rationality theory, in this paper, a generalized Prospect Theory based microsimulation model is proposed. The model relies on the micro-economics Prospect Theory paradigm where pedestrians or bicyclists (i.e., micro-mobility users) evaluate their speed and directional alternatives while considering the possibility of colliding with other obstacles/users. A numerical analysis on the main model parameters is presented. The model is then calibrated and validated using two real-world data sets with trajectories recorded in naturalistic settings. With the calibrated parameters studied, simulation exercises and sensitivity analysis are conducted to recreate bottlenecks and lane formations in different conditions. The findings show that the proposed model's parameters reflect the risk-taking tendencies of different roadway users in mixed right-of-way's environments while showing realistic microscopic and macroscopic traffic flow characteristics.