Citation

Iwata T, Shimizu H, Marumo N. IEEE Trans. Intel. Transp. Syst. 2022; 23(4): 3037-3047.

Copyright

(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2020.3030281

PMID

unavailable

Abstract

We propose a probabilistic model of pedestrian behavior for estimating the population at each road given the observed populations at a limited number of roads and a set of routes. Our proposed model has latent variables called route populations, which represent the pedestrian population who starts each route at each time period, and assumes that pedestrians stochastically walk through roads along the route. The pedestrian depends on the road's congestion. The proposed model incorporates this dependence in a probabilistic framework, where the time length to pass a road is assumed to follow a Gaussian distribution that depends on road congestion. With the reproducing property of Gaussian distribution, we analytically derive the transition probability between roads on each route. The parameters of the proposed method, including the dependence between congestion and speed, congestion for each road, and the latent variables on the route population, are estimated from the given data by minimizing the error between the observed and estimated road populations based on gradient-based optimization methods. In experiments with simulated and real-world data sets, we demonstrate that the proposed model can estimate road populations more accurately than existing methods. We also confirm that it effectively estimates route populations, and the estimated route populations are useful for reproducing real-world population dynamics when used as inputs of a crowd simulator based on a multi-agent system.

Language: en

Keywords

Gaussian distribution; network tomographic model; Pedestrian modeling; Probabilistic logic; Roads; Sociology; Statistics; Task analysis; Tomography; traffic simulator