Data-Driven Optimization for Smart Urban Mobility

Topic Information

Dear Colleagues,

In the 21st century, rapid urbanization and smart city initiatives have contributed to sustainable and efficient mobility becoming a pressing priority. Data-driven optimization now plays a central role in addressing congestion, pollution, and accessibility challenges. By integrating big data analytics, IoT, and artificial intelligence, cities are reshaping transportation planning and real-time management, enabling smarter decisions and improved service delivery. Yet, these advances also raise issues of equity, implementation, and public acceptance. This Topic welcomes the submission of contributions regarding AI and data analytics in transport planning, smart mobility case studies, consumer behavior insights, sustainability impacts, and innovative public–private collaborations. Through interdisciplinary research and practice, we aim to explore strategies that ensure urban transport systems are not only intelligent, but also inclusive, resilient, and sustainable.

Prof. Dr. Zhijie Dong
Dr. Weike Lu
Dr. Tianqi Gu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Energies energies	3.2	7.3	2008	16.8 Days	CHF 2600	Submit
Future Transportation futuretransp	1.7	3.8	2021	21.7 Days	CHF 1200	Submit
Smart Cities smartcities	5.5	14.7	2018	25.2 Days	CHF 2000	Submit
Sustainability sustainability	3.3	7.7	2009	17.9 Days	CHF 2400	Submit

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Published Papers (2 papers)

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All Electronics Energies Future Transportation Smart Cities Sustainability

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34 pages, 9182 KB  

Open AccessArticle

A Reputation-Aware Adaptive Incentive Mechanism for Federated Learning-Based Smart Transportation

by Abir Raza, Elarbi Badidi and Omar El Harrouss

Smart Cities 2026, 9(2), 27; https://doi.org/10.3390/smartcities9020027 - 4 Feb 2026

Abstract

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving distributed intelligence in modern urban transportation systems, where vehicles collaboratively train global models without sharing raw data. However, the dynamic nature of vehicular environments introduces critical challenges, including unstable participation, data heterogeneity, [...] Read more.

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving distributed intelligence in modern urban transportation systems, where vehicles collaboratively train global models without sharing raw data. However, the dynamic nature of vehicular environments introduces critical challenges, including unstable participation, data heterogeneity, and the potential for malicious behavior. Conventional FL frameworks lack effective trust management and adaptive incentive mechanisms capable of maintaining fairness and reliability under these fluctuating conditions. This paper presents a reputation-aware federated learning framework that integrates multi-dimensional reputation evaluation, dynamic incentive control, and malicious client detection through an adaptive feedback mechanism. Each vehicular client is assessed based on data quality, stability, and behavioral consistency, producing a reputation score that directly influences client selection and reward allocation. The proposed feedback controller self-tunes the incentive weights in real time, ensuring equitable participation and sustained convergence performance. In parallel, a penalty module leverages statistical anomaly detection to identify, isolate, and penalize untrustworthy clients without compromising benign contributors. Extensive simulations conducted on real-world datasets demonstrate that the proposed framework achieves higher model accuracy and greater robustness against poisoning and gradient manipulation attacks compared to existing baseline methods. The results confirm the potential of our trust-regulated incentive mechanism to enable reliable federated learning in smart cities transportation systems. Full article

(This article belongs to the Topic Data-Driven Optimization for Smart Urban Mobility)

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23 pages, 893 KB  

Open AccessArticle

Dynamic Graph Information Bottleneck for Traffic Prediction

by Jing Pang, Minzhe Wu, Bingxue Xie, Yanqiu Bi and Zhongbin Luo

Electronics 2026, 15(3), 623; https://doi.org/10.3390/electronics15030623 - 1 Feb 2026

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Abstract

Traffic forecasting in large-scale urban networks must operate reliably under imperfect sensing conditions, where measurements may contain noise or missing values. Most existing spatio-temporal graph neural networks focus primarily on modeling spatial–temporal dependencies, while paying limited attention to the propagation of irrelevant or [...] Read more.

Traffic forecasting in large-scale urban networks must operate reliably under imperfect sensing conditions, where measurements may contain noise or missing values. Most existing spatio-temporal graph neural networks focus primarily on modeling spatial–temporal dependencies, while paying limited attention to the propagation of irrelevant or unstable information through dynamic graph structures. In this work, we propose a Dynamic Graph Information Bottleneck (DGIB) framework that enhances prediction stability by introducing task-aware representation compression into dynamic graph learning. Instead of relying solely on architectural complexity, DGIB explicitly regulates the information flow within spatio-temporal embeddings through a variational bottleneck objective. The model adaptively constructs time-evolving adjacency matrices, extracts spatial features via graph convolutions, captures temporal dependencies using recurrent modeling, and constrains the latent representation to retain only predictive content relevant to future traffic states. By jointly optimizing topology adaptation and information-theoretic regularization in an end-to-end manner, the proposed framework mitigates the amplification of noisy or redundant signals in dynamic graphs. Experiments on multiple benchmark traffic datasets demonstrate that DGIB achieves competitive forecasting accuracy while maintaining strong robustness under noisy and incomplete data scenarios. Full article

(This article belongs to the Topic Data-Driven Optimization for Smart Urban Mobility)

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