News Release

Bridging microscopic interactions and macroscopic traffic patterns: a novel approach to stochastic fundamental diagram modeling

Peer-Reviewed Publication

Tsinghua University Press

How we capture the stochasticity of traffic flow from microscopic interactions

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Overview of the proposed SFD modeling framework and its practice implications

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Credit: Communications in Transportation Research

Traffic congestion and its inherent stochasticity continue to challenge urban mobility worldwide. To address this, researchers have introduced a groundbreaking framework for modeling the Stochastic Fundamental Diagram (SFD) from microscopic interactions. It not only deepens our understanding of stochasticity in traffic flow, but also paves the way for advanced longitudinal control strategies in connected and automated vehicles (CAVs) to minimize the stochasticity and enhance the overall traffic.

 

                                                              

They published their study on 13 February 2025, in Communications in Transportation Research.

 

 

 

 

We propose the Leader-Follower Conditional Distribution-based Stochastic Traffic Modeling (LFCD-STM) framework to bridge the gap between microscopic driver interactions and macroscopic traffic flow patterns. By introducing a probabilistic representation of leader-follower behavior and leveraging the Markov chain modeling, the framework derives analytical functions describing the mean flow-density relation and its variance under equilibrium conditions. 

 

Using NGSIM I-80, US-101, and HighD datasets, the validation results demonstrate high consistency with real-world data. Applications of this framework span real-time traffic flow estimation, enhanced simulation of macroscopic traffic dynamics, and development of robust traffic control strategies that account for uncertainty. 

 

The implications are profound: from providing traffic engineers with tools for better policy-making to promoting smoother driving behaviors for congestion mitigation. Furthermore, as connected and automated vehicles (CAVs) continue to evolve, this analytical model offers a foundation for optimizing mixed traffic systems and developing efficient CAV control strategies. 

 

This research paves the way for more reliable and adaptive traffic management systems, addressing long-standing challenges in transportation engineering.

 

The above research is published in Communications in Transportation Research (COMMTR), which is a fully open access journal co-published by Tsinghua University Press and Elsevier. COMMTR publishes peer-reviewed high-quality research representing important advances of significance to emerging transport systems. COMMTR is also among the first transportation journals to make the Replication Package mandatory to facilitate researchers, practitioners, and the general public in understanding and advancing existing knowledge. At its discretion, Tsinghua University Press will pay the open access fee for all published papers from 2021 to 2025.


About Communications in Transportation Research

Communications in Transportation Research was launched in 2021, with academic support provided by Tsinghua University and China Intelligent Transportation Systems Association. The Editors-in-Chief are Professor Xiaobo Qu, a member of the Academia Europaea from Tsinghua University and Professor Shuai’an Wang from Hong Kong Polytechnic University. The journal mainly publishes high-quality, original research and review articles that are of significant importance to emerging transportation systems,aiming to serve as an international platform for showcasing and exchanging innovative achievements in transportation and related fields, fostering academic exchange and development between China and the global community.

It has been indexed in SCIE, SSCI, Ei Compendex, Scopus, DOAJ, TRID and other databases. In 2022, it was selected as a High-Starting-Point new journal project of the “China Science and Technology Journal Excellence Action Plan”. In 2024, it was selected as the Support the Development Project of “High-Level International Scientific and Technological Journals”. The same year, it was also chosen as an English Journal Tier Project of the “China Science and Technology Journal Excellence Action Plan PhaseⅡ”. In 2024, it received the first impact factor of 12.5. The 2023 IF is 12.5, ranking in the Top1 (1/58, Q1) among all journals in "TRANSPORTATION" category. Tsinghua University Press will cover the open access fee for all published papers in 2025.

 

About Tsinghua University Press

Established in 1980, as a department of Tsinghua University, Tsinghua University Press (TUP) is a leading comprehensive higher education and professional publisher in China. TUP publishes 58 journals and 38 of them are in English. There are 18 journals indexed by SClE/ESCl. Three of them have the highest impact factor in their respective fields. In 2022, TUP launched SciOpen. As a publishing platform of TUP, SciOpen provides free access to an online collection of journals across diverse academic disciplines and serves to meet there search needs of scientific communities. SciOpen provides end-to-end services across manuscript submission, peer review, content hosting, analytics, identity management, and expert advice to ensure each journal's development.


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