Comparative Analysis of Human Movement Prediction: Space Syntax and Inverse Reinforcement Learning

01/01/2018
by   Soma Suzuki, et al.
0

Space syntax matrix has been the main approach for human movement prediction in the urban environment. An alternative, relatively new methodology is an agent-based pedestrian model constructed using machine learning techniques. Even though both approaches have been studied intensively, the quantitative comparison between them has not been conducted. In this paper, comparative analysis of space syntax metrics and maximum entropy inverse reinforcement learning (MEIRL) is performed. The experimental result on trajectory data of artificially generated pedestrian agents shows that MEIRL outperforms space syntax matrix. The possibilities for combining two methods are drawn out as conclusions, and the relative challenges with the data collection are highlighted.

READ FULL TEXT
research
12/16/2020

A comparative evaluation of machine learning methods for robot navigation through human crowds

Robot navigation through crowds poses a difficult challenge to AI system...
research
12/21/2017

Multiagent-based Participatory Urban Simulation through Inverse Reinforcement Learning

The multiagent-based participatory simulation features prominently in ur...
research
12/28/2020

Urban volumetrics: spatial complexity and wayfinding, extending space syntax to three dimensional space

Wayfinding behavior and pedestrian movement pattern research relies on o...
research
09/06/2019

Calibrating Wayfinding Decisions in Pedestrian Simulation Models: The Entropy Map

This paper presents entropy maps, an approach to describing and visualis...
research
03/03/2022

Towards Rich, Portable, and Large-Scale Pedestrian Data Collection

Recently, pedestrian behavior research has shifted towards machine learn...
research
11/19/2022

Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning

Inspired by expert evaluation policy for urban perception, we proposed a...
research
01/14/2018

Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis

Cell movement in the early phase of C. elegans development is regulated ...

Please sign up or login with your details

Forgot password? Click here to reset