Xiaohui's Academic Website

Example Talk

Sat, 01 Jun 2030 13:00:00 +0000

Click on the Slides button above to view the built-in slides feature.

Slides can be added in a few ways:

Create slides using Hugo Blox Builder’s Slides feature and link using slides parameter in the front matter of the talk file
Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file
Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes.

Further event details, including page elements such as image galleries, can be added to the body of this page.

On the stochastic fundamental diagram: A general micro-macroscopic traffic flow modeling framework

Mon, 01 Dec 2025 00:00:00 +0000

Stochastic fundamental diagram modeling of mixed traffic flow: A data-driven approach

Wed, 01 Oct 2025 00:00:00 +0000

Projects

Sun, 19 May 2024 00:00:00 +0000

On the string stability of neural network-based car-following models: A generic analysis framework

Fri, 01 Mar 2024 00:00:00 +0000

🎉 Easily create your own simple yet highly customizable blog

Fri, 27 Oct 2023 00:00:00 +0000

Welcome 👋

Table of Contents

Overview

The Hugo Blox website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site
The template can be modified and customised to suit your needs. It’s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS
You can work with all your favourite tools and apps with hundreds of plugins and integrations to speed up your workflows, interact with your readers, and much more

Get Started

👉 Create a new site
📚 Personalize your site
💬 Chat with the Hugo Blox community or Hugo community
🐦 Twitter: @GetResearchDev @GeorgeCushen #MadeWithHugoBlox
💡 Request a feature or report a bug for Hugo Blox
⬆️ Updating Hugo Blox? View the Update Guide and Release Notes

Crowd-funded open-source software

To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.

As a token of appreciation for sponsoring, you can unlock these awesome rewards and extra features 🦄✨

Ecosystem

Bibtex To Markdown: Automatically import publications from BibTeX

Inspiration

Learn what other creators are building with this template.

Features

Page builder - Create anything with no-code blocks and elements
Edit any type of content - Blog posts, publications, talks, slides, projects, and more!
Create content in Markdown, Jupyter, or RStudio
Plugin System - Fully customizable color and font themes
Display Code and Math - Code syntax highlighting and LaTeX math supported
Integrations - Google Analytics, Disqus commenting, Maps, Contact Forms, and more!
Beautiful Site - Simple and refreshing one-page design
Industry-Leading SEO - Help get your website found on search engines and social media
Media Galleries - Display your images and videos with captions in a customizable gallery
Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site
Multi-language - 35+ language packs including English, 中文, and Português
Multi-user - Each author gets their own profile page
Privacy Pack - Assists with GDPR
Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects
One-Click Deployment - No servers. No databases. Only files.

Themes

Hugo Blox and its templates come with automatic day (light) and night (dark) mode built-in. Visitors can choose their preferred mode by clicking the sun/moon icon in the header.

Choose a stunning theme and font for your site. Themes are fully customizable.

License

Released under the MIT license.

🧠 Sharpen your thinking with a second brain

Thu, 26 Oct 2023 00:00:00 +0000

Create a personal knowledge base and share your knowledge with your peers.

Hugo Blox web framework empowers you with one of the most flexible note-taking capabilities out there.

Create a powerful knowledge base that works on top of a local folder of plain text Markdown files.

Use it as your second brain, either publicly sharing your knowledge with your peers via your website, or via a private GitHub repository and password-protected site just for yourself.

Mindmaps

Hugo Blox supports a Markdown extension for mindmaps.

With this open format, can even edit your mindmaps in other popular tools such as Obsidian.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal
```

renders as

- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal

Anh here’s a more advanced mindmap with formatting, code blocks, and math:


```markmap
- Mindmaps
- Links
- [Hugo Blox Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$
```

renders as

- Mindmaps
- Links
- [Hugo Blox Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$

Highlighting

Highlight important text with mark:

<mark>Highlighted text</mark>

Callouts

Use callouts (aka asides, hints, or alerts) to draw attention to notes, tips, and warnings.

By wrapping a paragraph in {{% callout note %}} ... {{% /callout %}}, it will render as an aside.

{{% callout note %}}
A Markdown aside is useful for displaying notices, hints, or definitions to your readers.
{{% /callout %}}

renders as

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

Or use the warning callout type so your readers don’t miss critical details:

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

📈 Communicate your results effectively with the best data visualizations

Wed, 25 Oct 2023 00:00:00 +0000

Hugo Blox is designed to give technical content creators a seamless experience. You can focus on the content and Hugo Blox handles the rest.

Use popular tools such as Plotly, Mermaid, and data frames.

Charts

Hugo Blox supports the popular Plotly format for interactive data visualizations. With Plotly, you can design almost any kind of visualization you can imagine!

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the Plotly JSON Editor useful.

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example flowchart:

```mermaid
graph TD
A[Hard] -->|Text| B(Round)
B --> C{Decision}
C -->|One| D[Result 1]
C -->|Two| E[Result 2]
```

renders as

graph TD A[Hard] -->|Text| B(Round) B --> C{Decision} C -->|One| D[Result 1] C -->|Two| E[Result 2]

An example sequence diagram:

```mermaid
sequenceDiagram
Alice->>John: Hello John, how are you?
loop Healthcheck
John->>John: Fight against hypochondria
end
Note right of John: Rational thoughts!
John-->>Alice: Great!
John->>Bob: How about you?
Bob-->>John: Jolly good!
```

renders as

sequenceDiagram Alice->>John: Hello John, how are you? loop Healthcheck John->>John: Fight against hypochondria end Note right of John: Rational thoughts! John-->>Alice: Great! John->>Bob: How about you? Bob-->>John: Jolly good!

An example class diagram:

```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```

renders as

classDiagram Class01 <|-- AveryLongClass : Cool Class03 *-- Class04 Class05 o-- Class06 Class07 .. Class08 Class09 --> C2 : Where am i? Class09 --* C3 Class09 --|> Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla Class08 <--> C2: Cool label

An example state diagram:

```mermaid
stateDiagram
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```

renders as

stateDiagram [*] --> Still Still --> [*] Still --> Moving Moving --> Still Moving --> Crash Crash --> [*]

Data Frames

Save your spreadsheet as a CSV file in your page’s folder and then render it by adding the Table shortcode to your page:

{{< table path="results.csv" header="true" caption="Table 1: My results" >}}

renders as

Table 1: My results
customer_id	score
1	0
2	0.5
3	1

👩🏼‍🏫 Teach academic courses

Tue, 24 Oct 2023 00:00:00 +0000

Hugo Blox Builder is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube D2vj0WcvH5c >}}

Bilibili:

{{< bilibili BV1WV4y1r7DF >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your page’s folder, and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
You found me!
{{< /spoiler >}}

renders as

👉 Click to view the solution

You found me 🎉

Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. Enable math by setting the math: true option in your page’s front matter, or enable math for your entire site by toggling math in your config/_default/params.yaml file:

features:
 math:
 enable: true

To render inline or block math, wrap your LaTeX math with $...$ or $$...$$, respectively.

Example math block:

$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$

renders as

$$\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}$$

Example inline math $\nabla F(\mathbf{x}_{n})$ renders as $\nabla F(\mathbf{x}_{n})$.

Example multi-line math using the math linebreak (\\):

$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Code

Hugo Blox Builder utilises Hugo’s Markdown extension for highlighting code syntax. The code theme can be selected in the config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Inline Images

{{< icon name="python" >}} Python

renders as

Python

Education

Tue, 24 Oct 2023 00:00:00 +0000

Learn JavaScript

Tue, 24 Oct 2023 00:00:00 +0000

Hugo Blox Builder is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube w7Ft2ymGmfc >}}

Bilibili:

{{< bilibili id="BV1WV4y1r7DF" >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your page’s folder, and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
You found me!
{{< /spoiler >}}

renders as

👉 Click to view the solution

You found me 🎉

Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.

To render inline or block math, wrap your LaTeX math with {{< math >}}$...${{< /math >}} or {{< math >}}$$...$${{< /math >}}, respectively.

We wrap the LaTeX math in the Hugo Blox math shortcode to prevent Hugo rendering our math as Markdown.

Example math block:

{{< math >}}
$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$
{{< /math >}}

renders as

Example inline math {{< math >}}$\nabla F(\mathbf{x}_{n})${{< /math >}} renders as $\nabla F(\mathbf{x}_{n})$ .

Example multi-line math using the math linebreak (\\):

{{< math >}}
$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$
{{< /math >}}

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Code

Hugo Blox Builder utilises Hugo’s Markdown extension for highlighting code syntax. The code theme can be selected in the config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Inline Images

{{< icon name="python" >}} Python

renders as

Python

Learn Python

Tue, 24 Oct 2023 00:00:00 +0000

Hugo Blox Builder is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube w7Ft2ymGmfc >}}

Bilibili:

{{< bilibili id="BV1WV4y1r7DF" >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your page’s folder, and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
You found me!
{{< /spoiler >}}

renders as

👉 Click to view the solution

You found me 🎉

Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.

To render inline or block math, wrap your LaTeX math with {{< math >}}$...${{< /math >}} or {{< math >}}$$...$${{< /math >}}, respectively.

We wrap the LaTeX math in the Hugo Blox math shortcode to prevent Hugo rendering our math as Markdown.

Example math block:

{{< math >}}
$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$
{{< /math >}}

renders as

Example inline math {{< math >}}$\nabla F(\mathbf{x}_{n})${{< /math >}} renders as $\nabla F(\mathbf{x}_{n})$ .

Example multi-line math using the math linebreak (\\):

{{< math >}}
$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$
{{< /math >}}

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Code

Hugo Blox Builder utilises Hugo’s Markdown extension for highlighting code syntax. The code theme can be selected in the config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Inline Images

{{< icon name="python" >}} Python

renders as

Python

✅ Manage your projects

Mon, 23 Oct 2023 00:00:00 +0000

Easily manage your projects - create ideation mind maps, Gantt charts, todo lists, and more!

Ideation

Hugo Blox supports a Markdown extension for mindmaps.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal
```

renders as

- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example Gantt diagram:

```mermaid
gantt
section Section
Completed :done, des1, 2014-01-06,2014-01-08
Active :active, des2, 2014-01-07, 3d
Parallel 1 : des3, after des1, 1d
Parallel 2 : des4, after des1, 1d
Parallel 3 : des5, after des3, 1d
Parallel 4 : des6, after des4, 1d
```

renders as

gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d

Todo lists

You can even write your todo lists in Markdown too:

- [x] Write math example
 - [x] Write diagram example
- [ ] Do something else

renders as

Write math example
- Write diagram example
Do something else

A Hierarchical Framework for Multi-Lane Autonomous Driving Based on Reinforcement Learning

Sun, 01 Jan 2023 00:00:00 +0000

Traffic Flow Theory and Simulation

Thu, 01 Sep 2022 00:00:00 +0000

My research in traffic flow theory and simulation focuses on bridging the fundamental gap between microscopic driving behaviors and macroscopic traffic flow properties through analytical modeling approaches and advance data-driven techniques to accurately reproduce human driving behaviors. The central challenge I address is understanding and quantifying the inherent stochasticity in traffic systems, where individual driver behaviors create uncertainty that propagates through the entire traffic network. Rather than treating traffic flow as deterministic, my work develops mathematical frameworks that explicitly account for the probabilistic nature of driving decisions and their collective impact on system-level performance.

Integrated Behavior Modeling Through Deep Learning

I have developed Long Short-Term Memory neural networks that simultaneously model car-following and lane-changing behaviors by observing only the positions of surrounding vehicles. This represents a fundamental shift from conventional approaches that model these behaviors separately. The LSTM architecture automatically extracts relevant features and captures the temporal dynamics of traffic interactions, while my Hybrid Retraining Constrained training method further optimizes performance.
Validation with real-world trajectory data demonstrates superior accuracy compared to classical models and excellent transferability across different traffic environments.

Analytical Micro-Macroscopic Modeling

Traditional traffic flow models have struggled to connect individual driving behaviors with aggregate traffic patterns in a mathematically rigorous way. I have developed the Leader-Follower Conditional Distribution-based Stochastic Traffic Modeling framework that provides this crucial analytical link. Through probabilistic modeling of leader-follower interactions using Brownian dynamics, I establish a general mathematical representation that encompasses the longitudinal interactions between vehicles while remaining compatible with classical car-following theories.
This approach describes the joint distribution of vehicle speeds within traffic platoons through Markov chain modeling, creating a mathematical pathway from individual behavioral uncertainty to system-wide flow characteristics. The framework provides analytical derivations of macroscopic traffic relations, including both mean flow-density relationships and their associated variances under equilibrium conditions. This represents a fundamental advancement in traffic flow theory by providing closed-form mathematical expressions that connect microscopic stochasticity to macroscopic uncertainty.

Stochastic Fundamental Diagram

My primary contribution lies in developing analytical models for the Stochastic Fundamental Diagram, which captures the inherent variability in traffic flow relationships that traditional deterministic models cannot explain. While previous research has attempted to reproduce stochastic traffic patterns through various empirical methods, my work provides the first comprehensive analytical framework for understanding why and how this stochasticity emerges from underlying driving behaviors.
Using maximum entropy principles within my micro-macroscopic framework, I derive theoretical expressions for both the mean and variance of fundamental traffic flow relationships. This maximum entropy approach provides specific conditional distributions for longitudinal vehicle interactions, enabling the solution of analytical functions that describe traffic flow uncertainty. The resulting model quantifies how individual driver variability compounds into system-level unpredictability, providing insights into the fundamental nature of traffic flow stochasticity.

Theoretical Validation and Practical Implications

Through comprehensive validation using multiple real-world datasets including NGSIM I-80, US-101, and HighD trajectories, I demonstrate remarkable consistency between theoretical predictions and empirical observations. This validation confirms both the mathematical soundness of the Leader-Follower Conditional Distribution framework and the practical accuracy of the maximum entropy-based Stochastic Fundamental Diagram model.
The theoretical insights from this work have direct implications for traffic management and control strategies. By understanding how microscopic behavioral variations propagate to macroscopic flow uncertainty, the model provides guidance for developing interventions that promote smoother driving behaviors to suppress stochasticity and improve overall traffic flow efficiency. This represents a shift from reactive traffic management toward predictive strategies based on fundamental understanding of traffic flow uncertainty.

Research Impact and Contribution

My work establishes a new paradigm in traffic flow theory by providing the mathematical tools to analytically connect individual driving behaviors with system-level traffic performance under uncertainty. This bridges a critical gap that has limited the development of robust traffic control strategies and provides a theoretical foundation for understanding complex traffic phenomena. The analytical nature of the framework enables deeper insights into traffic dynamics than purely empirical approaches, while the validation with real-world data ensures practical relevance.
The Leader-Follower Conditional Distribution framework represents a general analytical tool that can be applied to various traffic scenarios and extended to incorporate different behavioral models. This flexibility, combined with the rigorous mathematical foundation, positions the work as a significant contribution to both theoretical traffic flow research and practical transportation engineering applications.

References

Zhang, X., Sun, Jie, Sun, Jian, 2025. On the stochastic fundamental diagram: A general micro-macroscopic traffic flow modeling framework. Communications in Transportation Research 5, 100163.
Zhang, X., Sun, Jie, Qi, X., Sun, Jian, 2019. Simultaneous modeling of car-following and lane-changing behaviors using deep learning. Transportation Research Part C: Emerging Technologies 104, 287–304.

Autonomous vehicles’ intended cooperative motion planning for unprotected turning at intersections

Mon, 01 Aug 2022 00:00:00 +0000

Autonomous Driving's Impacts on Traffic

Wed, 01 Sep 2021 00:00:00 +0000

Understanding the impacts of autonomous vehicles (AVs) on traffic requires bridging the gap between vehicle-level behaviors and system-level outcomes. My research approaches this challenge through two complementary studies: the stability of AV car-following dynamics and the fundamental diagram of mixed traffic flow.

String stability analysis of NN-based car-following model

The first line of work addresses string stability. Stability is critical because even minor fluctuations in car-following behavior can propagate and amplify, creating stop-and-go waves that degrade traffic efficiency. While traditional analytical models have been widely studied, no theoretical framework existed for analyzing the string stability of neural network–based car-following models, which are increasingly used to capture realistic AV behavior.
To fill this gap, I developed the EADC framework (Estimation–Approximation–Derivation–Calculation), which derives stability criteria for both feedforward and recurrent neural network models.
Validated on representative NN-based models, the framework demonstrates consistency between theoretical and simulation results, establishing the first rigorous method for assessing the stability of AI-driven vehicle behavior.

Stochastic Fundamental Diagram modeling for mixed traffic

The second line of work focuses on the Stochastic Fundamental Diagram (SFD) for mixed traffic. Current AV impact studies often oversimplify traffic dynamics or ignore stochasticity, limiting their realism. To overcome this, I proposed a data-driven probabilistic modeling framework that learns car-following interactions for all leader–follower types (AV–AV, AV–HV, HV–AV, HV–HV) using mixture density networks. By modeling platoons as Markov chains, the framework derives the SFD from trajectory data, validated against NGSIM and Waymo datasets.
Results show that higher AV penetration decreases mean capacity and critical density while reducing capacity uncertainty, highlighting the conservative yet stabilizing effects of current AVs.

Research Impact

Together, these studies reveal how AVs influence traffic from the perspective of stability and capacity: string stability analysis ensures the reliability of AI-based AV behavior, while SFD modeling quantifies the systemic effects of AV penetration. My contributions establish new theoretical and data-driven foundations for assessing AV impacts, with implications for both traffic management strategies and policy-making in the era of mixed traffic.

References

Zhang, X., Sun, Jie, Zheng, Z., Sun, Jian, 2024. On the string stability of neural network-based car-following models: A generic analysis framework. Transportation Research Part C: Emerging Technologies 160, 104525.
Zhang, X., Yang, K., Sun, Jie, Sun, Jian, 2025. Stochastic fundamental diagram modeling of mixed traffic flow: A data-driven approach. Transportation Research Part C: Emerging Technologies 179, 105279.

Automated vehicle’s behavior decision making using deep reinforcement learning and high-fidelity simulation environment

Tue, 01 Oct 2019 00:00:00 +0000

Artificial Intelligence in Transportation Research

Sat, 01 Jun 2019 00:00:00 +0000

My research applies advanced artificial intelligence to revolutionize autonomous vehicle behavior and decision-making in transportation systems. The central challenge I address is that traditional approaches treat driving behaviors as isolated processes, failing to capture the interconnected nature of real-world traffic dynamics. Through deep learning and reinforcement learning techniques, I develop AI systems that understand and replicate the integrated, adaptive decision-making that characterizes skilled human driving.

Reinforcement Learning for Decision-Making

While autonomous vehicle perception has advanced rapidly, decision-making capabilities remain underdeveloped. I address this through a comprehensive framework combining deep reinforcement learning with high-fidelity simulation environments. This enables autonomous vehicles to learn optimal decision strategies that achieve nearly eight percent efficiency improvements over established adaptive cruise control systems while maintaining safety and comfort.
Extending to complex multi-lane scenarios, my integrated models achieve additional performance gains by coordinating longitudinal and lateral movements as unified decision processes.

Hierarchical AI Integration

Recognizing the need for both learning capability and interpretability in safety-critical applications, I have developed hierarchical frameworks that strategically combine reinforcement learning with rule-based methods. Through the concept of Instantaneous Desired Speed as an intermediate action, high-level AI algorithms focus on strategic decisions while low-level systems handle tactical execution. This approach maintains the adaptability of machine learning while preserving the transparency of traditional control systems.
My Traffic Flow Adaptive strategy extends this hierarchical approach to system-level optimization, enabling individually controlled autonomous vehicles to coordinate for improved overall traffic flow. This shift from isolated decision-making to collaborative traffic participation represents a crucial advancement toward practical autonomous vehicle deployment.

Research Impact

Through rigorous validation using real-world data and comprehensive benchmarking, my research demonstrates that artificial intelligence can create autonomous driving systems that are both more human-like and more efficient than existing approaches. By developing models that integrate multiple driving behaviors, learn from experience, and maintain interpretability, I am establishing the foundation for autonomous vehicles that can operate effectively in mixed traffic environments alongside human drivers. This work contributes directly to the vision of intelligent transportation systems where AI-enabled vehicles enhance both individual mobility and overall traffic system performance.

References

Ye, Y.*, Zhang, X.*, Sun, J., 2019. Automated vehicle’s behavior decision making using deep reinforcement learning and high-fidelity simulation environment. Transportation Research Part C: Emerging Technologies 107, 155–170.
Zhang, X., Sun, Jie, Wang, Y., Sun, Jian, 2023. A Hierarchical Framework for Multi-Lane Autonomous Driving Based on Reinforcement Learning. IEEE Open Journal of Intelligent Transportation Systems 4, 626–638.

Simultaneous modeling of car-following and lane-changing behaviors using deep learning

Tue, 01 Jan 2019 00:00:00 +0000

Xiaohui's Academic Website

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On the stochastic fundamental diagram: A general micro-macroscopic traffic flow modeling framework

Stochastic fundamental diagram modeling of mixed traffic flow: A data-driven approach

Projects

On the string stability of neural network-based car-following models: A generic analysis framework

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A Hierarchical Framework for Multi-Lane Autonomous Driving Based on Reinforcement Learning

Traffic Flow Theory and Simulation

Integrated Behavior Modeling Through Deep Learning

Analytical Micro-Macroscopic Modeling

Stochastic Fundamental Diagram

Theoretical Validation and Practical Implications

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References

Autonomous vehicles’ intended cooperative motion planning for unprotected turning at intersections

Autonomous Driving's Impacts on Traffic

String stability analysis of NN-based car-following model

Stochastic Fundamental Diagram modeling for mixed traffic

Research Impact

References

Automated vehicle’s behavior decision making using deep reinforcement learning and high-fidelity simulation environment

Artificial Intelligence in Transportation Research

Reinforcement Learning for Decision-Making

Hierarchical AI Integration

Research Impact

References

Simultaneous modeling of car-following and lane-changing behaviors using deep learning