Emerging Research in Urban Computing and Intelligent Transport Systems
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 25 July 2025 | Viewed by 44838
Special Issue Editors
Interests: urban computing; smart transportation; data triage in opportunity networks
Interests: federated learning; reinforcement learning; next-generation networking
Interests: city logistics; freight transportation; demand forecasting; intelligent transport system
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue is dedicated to the study of the latest research findings and practical applications of urban computing and intelligent transportation systems (ITS). This Special Issue addresses urban computing and ITS across numerous technical aspects of ITS technology that involve people, vehicles, items, information technology, and physical infrastructures, all of which interact in complex ways. We welcome submissions of original, unpublished, and novel in-depth studies that make significant methodological or applied contributions to the field. Potential topics of interest include, but are not limited to, the following topics:
- Urban computing based on artificial intelligence algorithms;
- Urban computing based on statistical methods;
- Solutions for sustainable transportation/urban development;
- Data integration and analysis;
- Information collection and processing;
- Image processing applications in ITS;
- Autonomous vehicles;
- Traffic flow management and control;
- Innovative algorithms in urban computing/ITS;
- Networks and Communications in ITS;
- Public transportation system technologies;
- Public transport logistics;
- Urban emergency and incident management;
- Urban demand management;
- ITS industrial applications;
- Urban health check.
Prof. Dr. Jianbo Li
Dr. Junjie Pang
Prof. Dr. Antonio Comi
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- urban computing
- intelligent transportation systems
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Predicting Roadway Traffic Jam Propagation with Graph Neural Networks Utilizing User-Oriented Participatory Sensing (UPS) Data
Authors: Ahmad ALBarqawi; Mahmoud Nazzal; Joyoung Lee; Abdallah Khreishah; Dejan Besenski
Affiliation: New Jersey Institute of Technology
Abstract: Accurate and timely prediction of roadway traffic jam propagation is crucial for effective prevention and management of traffic congestion. Existing approaches mainly rely on sensor-collected data and overlook crowdsourced datasets. This restricts the applicability of these methods to areas equipped with sensory devices. Moreover, the increasing availability of crowdsourced transportation platforms such as WAZE suggests their potential for improved prediction. In this paper, we propose a data-driven graph neural network (GNN) approach that harnesses crowdsourced Waze traffic data to predict spatio-temporal traffic jam propagation patterns. This is achieved by training on a constructed dataset of subgraphs, each representing an extracted propagation pattern called a chain. A chain consists of multiple related singular jam data points. We design a set of algorithms targeted at extracting these chains and constructing the subgraphs. Then, a GNN is trained on this dataset of subgraphs to estimate the spatio-temporal effect of future propagations. Experiments on real Waze data show an F1 score of 81.03 on predicting road segments affected by jam propagation across the entirety of the Class-1 and Class-2 roadway network of the State of New Jersey. We also estimate the total time of traffic jams affecting these road segments due to propagation, with an average of 13.75% mean square error. The findings in this work demonstrate the promising potential of crowdsourced traffic data to enhance traffic jam propagation predictions across extensive roadway networks at no additional cost. These improvements can lead to more efficient traffic management, reduced congestion, and better urban planning decisions.
Title: Real-Time Incident Detection Through Predictive Modeling of Crowdsourced Waze Data
Authors: Md Tufajjal Hossain; Joyoiung Lee; Dejan Besenski; Branislav Dimitrijevic; Lazar Spasovic
Affiliation: New Jersey Institute of Technology
Abstract: Effective incident detection is essential for emergency response and transportation management. Traditional methods relying on stationary technologies are often costly and provide limited coverage, prompting the exploration of crowdsourced data such as Waze. While Waze offers extensive coverage, its data can be unverified and unreliable. This study aims to identify factors affecting the reliability of Waze
alerts and develop a predictive model to distinguish true incidents from false alerts using real-time Waze data, thereby improving emergency response times. Real crash data from the New Jersey Department of Transportation (NJDOT) and crowdsourced data from Waze were matched using the DBSCAN algorithm to differentiate true and false alerts. A binary logit model was constructed to reveal significant predictors such as time categories around peak hours, road type, report ratings, and crash type. Findings indicate that
the likelihood of accurate Waze alerts increases during peak hours, on streets, and with higher report ratings and major crashes. Moreover, two predictive models based on the XGBoost algorithm were developed: one using significant factors and the other incorporating all attributes. The model based on significant factors achieved an accuracy of 86.23%, while the model with all factors had an accuracy of
86.10%. Despite minimal differences in performance metrics, the significant factors model is computationally more efficient and suitable for real-time applications. The findings underscore the importance of user engagement and contextual factors in improving the reliability of crowdsourced traffic alerts, offering valuable insights for real-time traffic management and emergency response systems.