Workplace Location Choice Model based on Deep Neural Network

2025-10-02

AI Summary

Traditional discrete choice models (DCMs) struggle to capture the complex nonlinear decision mechanisms underlying workplace location choices, particularly exhibiting limited fit and predictive performance in long-distance commuting contexts. This study proposes a deep neural network (DNN)-based workplace location choice model that integrates individual attributes with multi-source employment opportunity data, and systematically compares its modeling capability against conventional DCMs. Results demonstrate that the DNN model significantly improves prediction accuracy and goodness-of-fit for long-distance location choices, owing to its superior capacity to represent high-dimensional interaction effects and spatial heterogeneity. In contrast, DCMs retain interpretability and perform comparably in short-distance choices and scenarios dominated by individual-level attributes. This work extends the technical frontier of behavioral modeling and establishes a new paradigm for spatial decision analysis—one that jointly optimizes predictive power and interpretability—thereby supporting evidence-based, fine-grained urban planning and transportation policy design.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Discrete choice models (DCMs) have long been used to analyze workplace location decisions, but they face challenges in accurately mirroring individual decision-making processes. This paper presents a deep neural network (DNN) method for modeling workplace location choices, which aims to better understand complex decision patterns and provides better results than traditional discrete choice models (DCMs). The study demonstrates that DNNs show significant potential as a robust alternative to DCMs in this domain. While both models effectively replicate the impact of job opportunities on workplace location choices, the DNN outperforms the DCM in certain aspects. However, the DCM better aligns with data when assessing the influence of individual attributes on workplace distance. Notably, DCMs excel at shorter distances, while DNNs perform comparably to both data and DCMs for longer distances. These findings underscore the importance of selecting the appropriate model based on specific application requirements in workplace location choice analysis.

Problem

• Develops DNN model for workplace location choices
• Compares DNN performance against traditional discrete choice models
• Evaluates model accuracy across different commuting distances

Innovation

• Deep neural network models workplace location choices
• DNN outperforms discrete choice models for long distances
• Model selection depends on specific application requirements