Multimodal traffic assignment from privacy-protected OD data

Guoyang Qin; Shidi Deng; Qi Luo; Jian Sun

doi:10.1016/j.commtr.2025.100223

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Multimodal traffic assignment from privacy-protected OD data

Journal article

Open access

Peer reviewed

Multimodal traffic assignment from privacy-protected OD data

Guoyang Qin, Shidi Deng, Qi Luo and Jian Sun

Communications in transportation research, Vol.5, 100223

12/2025

DOI: 10.1016/j.commtr.2025.100223

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Abstract

The (static) traffic assignment (TA) problem, which computes network equilibrium flows from origin–destination (OD) demand under flow conservation, is central to transportation modeling. As multimodal transportation systems (MTSs) grow, sharing detailed OD data – such as trip counts, timestamps, and routes – raises serious privacy concerns. Differential privacy (DP) has emerged as the leading standard for releasing such data, offering adjustable protection beyond traditional anonymization. However, current methods mostly apply extrinsic DP by adding noise to aggregate OD matrices before release, without fully addressing its effects on traffic modeling. This reveals TA’s unpreparedness for privacy-protected data and calls for redesigned methods that operate reliably under such constraints. To fill this gap, we propose the privacy-preserving traffic assignment (PPTA) framework, which embeds DP intrinsically within the TA process. Instead of externally perturbing aggregate demand, PPTA injects structured noise at the individual trip level. This preserves privacy while ensuring equilibrium feasibility through chance-constrained optimization, unifying privacy protection and traffic assignment. The framework supports various discrete choice models and noise types, using a moment-based approximation to boost computational efficiency. Our results show PPTA attains a privacy-utility balance beyond extrinsic methods, enabling robust, privacy-aware multimodal routing, network design, and pricing.

Differential privacy

Multimodal mobility

Stochastic convex optimization

Stochastic equilibrium

Details

Title: Subtitle: Multimodal traffic assignment from privacy-protected OD data
Creators: Guoyang Qin - Tongji University
Shidi Deng - Ludwig-Maximilians-Universität München
Qi Luo - Tippie College of Business, University of Iowa, Iowa City, IA, 52242, USA
Jian Sun - Tongji University
Resource Type: Journal article
Publication Details: Communications in transportation research, Vol.5, 100223
DOI: 10.1016/j.commtr.2025.100223
ISSN: 2772-4247
eISSN: 2772-4247
Publisher: Elsevier Ltd
Grant note: National Natural Science Foundation of China: 52125208 Young Scientists Fund of the National Natural Science Foundation of China: 52302413 State Key Lab of Intelligent Transportation Systems: 2024-A002 National Science Foundation: CMMI-2308750 Clemson UniversityCNRS
The research was funded by the National Natural Science Foundation of China (No. 52125208) , the Young Scientists Fund of the National Natural Science Foundation of China (No. 52302413) , the State Key Lab of Intelligent Transportation Systems (No. 2024-A002) , and the National Science Foundation (No. CMMI-2308750) . We thank the partnership between SMSS, Clemson University, and CNRS, Universite Clermont-Auvergne for creating this platform for collaboration.
Language: English
Date published: 12/2025
Academic Unit: Business Analytics
Record Identifier: 9985091804702771

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