Xiaotong Sun

Xiaotong Sun

Xiaotong Sun is an assistant professor in the Intelligent Transportation Thrust (Guangzhou Campus)

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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

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about_pi

experience

group

Chenglin Zhuang (2024 Fall)

Role: Ph.D. Student
Research Interests: Generalized nash equilibrium problem in intelligent transportation
Bio: Chenglin received his ​Bachelor of Information Management and Information Systems from Northeastern University, followed by a ​Master of Management Science and Engineering from Shenzhen University. His research centers on ​Generalized Nash Equilibrium Problems (GNEPs) in intelligent transportation systems. Chenglin Zhuang

Junying MA (2025 Fall)

Role: Ph.D. Student
Research Interests: Data Mining, AI for Transportation Bio: Junying received his Bachelor’s degree in Economics from Jinan University and M.Phil. degree in Financial Technology from the Hong Kong University of Science and Technology, Guangzhou Campus. His research interests includes Data Mining and AI for Transportation. Junying

Ziye Guo

Role: Research Assistant. Research Interests: Federated Learning.
Bio: Ziye graduated from Bejing University of Post and Telecommunications. His research interst includes federated learning and LLM agents.

portfolio

publications

Methods for the design of safety service patrol beats: the Florida Road Ranger case study

Published in Transportation Research Record, 2018

This paper discusses methods for designing safety service patrol beats in Florida.

Recommended citation: Sun, X., Shahabi, M., Carrick, G., Yin, Y*., Srinivasan, S., & Shirmohammadi, N. (2018). "Methods for the design of safety service patrol beats: the Florida Road Ranger case study." Transportation Research Record, 2672(14), 50-60.
Download Paper | Download Slides

Integrated Planning of Static and Dynamic Charging Infrastructure to Support Electric Vehicles for Inter-city Trips

Published in Transportation Research Part D: Transport and Environment, 2020

This paper discusses integrated planning of static and dynamic charging infrastructure for electric vehicles.

Recommended citation: Sun, X., Chen, Z., and Yin, Y*., (2020). "Integrated Planning of Static and Dynamic Charging Infrastructure to Support Electric Vehicles for Inter-city Trips." Transportation Research Part D: Transport and Environment, 83, 102331.
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Decentralized game-theoretical approaches for behaviorally-stable and efficient vehicle platooning

Published in Transportation Research Part B: Methodological, 2021

This paper proposes a decentralized game-theoretical approach for vehicle platooning.

Recommended citation: Sun, X., & Yin, Y*. (2021). "Decentralized game-theoretical approaches for behaviorally-stable and efficient vehicle platooning." Transportation Research Part B: Methodological, 153, 45-69. [TR-Part B][PDF(pre-print)]
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Investigating the Potential of Truck Platooning for Energy Savings: Empirical Study of the US National Highway Freight Network

Published in Transportation Research Record, 2021

This paper investigates the potential of truck platooning for energy savings in the US national highway freight network.

Recommended citation: Sun, X., Wu, H., Abdolmaleki, M., Yin, Y*., & Zou, B. (2021). "Investigating the Potential of Truck Platooning for Energy Savings: Empirical Study of the US National Highway Freight Network." Transportation Research Record, 03611981211031231. [TRR][PDF(pre-print)]
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Hub-Based Platoon Formation: Optimal Release Policies and Approximate Solutions

Published in IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2024

This study optimizes truck platoon formation at highway hubs under decentralized, distributed, and centralized policies by developing dynamic programming-driven release thresholds and receding horizon solutions, revealing that decentralized strategies—despite yielding ~3.5% and 8% lower rewards than distributed and centralized approaches—achieve near-optimal performance without inter-hub coordination, validated via simulations on Swedish highway hubs.

Recommended citation: Johansson, A., Nekouei, E., Sun, X., Johansson, K. H., & Mårtensson, J. (2023). Hub-based platoon formation: Optimal release policies and approximate solutions. IEEE Transactions on Intelligent Transportation Systems, 25(6), 5755-5766.

Economic Analysis of On-Street Parking with Urban Delivery

Published in TRANSPORTATION SCIENCE, 2024

This study examines the impact of delivery-driven curb parking demands on urban curb dynamics through continuum modeling and comparative statics, revealing that optimized metered parking pricing for general users outperforms dedicated delivery bays and duration caps, with demand source-dependent strategies and adaptive bay sizing proving critical to curb management efficiency, thereby challenging the necessity of delivery-specific infrastructure under optimal pricing.

Recommended citation: Xu, Z., & Sun, X. (2024). Economic Analysis of On-Street Parking with Urban Delivery. Transportation Science, 58(6), 1300-1318.

A data-driven approach to uncovering the charging demand of electrified ride-hailing services

Published in TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2025

This study analyzes the distinct charging demand patterns of electric ride-hailing vehicles using trip data from 96,716 Shanghai-based vehicles, employs spatial regression models to reveal significant correlations between charging needs and built environment variables, and integrates supply-side data to evaluate public charging infrastructure sufficiency, uncovering spatiotemporal demand variations and spatial lag effects.

Recommended citation: Jin, Z., Sun, X., Xu, Z., & Tu, H. (2025). A data-driven approach to uncovering the charging demand of electrified ride-hailing services. Transportation Research Part D: Transport and Environment, 139, 104599.

Hierarchical prediction uncertainty-aware motion planning for autonomous driving in lane-changing scenarios

Published in TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2025

This study evaluates the energy-saving benefits of truck platooning across the US national highway freight network by integrating aerodynamic modeling with large-scale traffic simulations, revealing significant fuel efficiency gains and reduced emissions through optimized inter-vehicle coordination, validated via real-world freight corridor data.

Recommended citation: Yao, R., & Sun, X. (2025). Hierarchical prediction uncertainty-aware motion planning for autonomous driving in lane-changing scenarios. Transportation Research Part C: Emerging Technologies, 171, 104962.

Planning of truck platooning for road-network capacitated vehicle routing problem

Published in TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2025

This study pioneers the integration of truck platooning into capacitated vehicle routing problems with time windows (CVRPTW) by developing a road network-based optimization framework and a three-stage algorithm to minimize total costs—including dispatch and energy expenses—while serving multi-customer demands, with numerical experiments validating the model’s efficacy and quantifying platooning’s cost-saving potential in real-world logistics operations.

Recommended citation: Hao, Y., Chen, Z., Sun, X., & Tong, L. (2025). Planning of truck platooning for road-network capacitated vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 194, 103898.

On the robotic uncertainty of fully autonomous traffic: From stochastic car-following to mobility–safety trade-offs

Published in TRANSPORTATION RESEARCH PART C:Emerging Technologies, 2025

This paper presents an analytical model framework that delineates the endogenous reciprocity between traffic safety and mobility that arises from AVs’ robotic uncertainties. Using both realistic car-following data and a stochastic intelligent driving model (IDM), the stochastic car-following distance is derived as a key parameter, enabling analysis of single-lane capacity and collision probability. A semiMarkov process is then employed to model the dynamics of the lane capacity, and the resulting collision-inclusive capacity, representing expected lane capacity under stationary conditions, serves as the primary performance metric for fully autonomous traffic. The analytical results are further utilized to investigate the impacts of critical parameters in AV and roadway designs on traffic performance, as well as the properties of optimal speed and headway under mobilitytargeted or safety-dominated management objectives.

Recommended citation: Li, H., Sun, X., Zhuang, C., & Li, X. (2025). On the robotic uncertainty of fully autonomous traffic: From stochastic car-following to mobility–safety trade-offs. Transportation Research Part C: Emerging Technologies, 178, 105254.
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resources

Autonomous Vehicles Car-following Behavior Data

Car-following behavior data in autonomous driving scenarios(https://doi.org/10.5281/zenodo.14995837). This dataset contains processed longitudinal car-following behavior data derived from the Waymo Open Dataset, specifically focusing on autonomous vehicle (AV) operations in stable traffic scenarios. The data captures interactions between leading vehicles (LVs) and following AVs (FAVs) to analyze stochastic car-following distances and speed regulation patterns.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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updates

Xiaotong attended TSL2023 in Chicago, U.S., and made presentations.

Published:

Xiaotong attended TSL2023 in Chicago, U.S., and made presentations. Xiaotong’s proposals, entitled “Autonomous Traffic Efficiency and Safety under Perception Uncertainty: The Integrated Analysis and Countermeasures”, and “Mechanism Design and Optimization for the Mobility-as-a-Service” are awarded by the Guangzhou Municipal Science and Technology Bureau, granting RMB 300,000 in total.

Xiaotong‘s proposals, entitled “Autonomous Traffic Efficiency and Safety under Perception Uncertainty: The Integrated Analysis and Countermeasures”, and “Mechanism Design and Optimization for the Mobility-as-a-Service” are awarded by the Guangzhou Municipal Science and Technology Bureau, granting RMB 300,000 in total.

Published:

Xiaotong‘s proposals, entitled “Autonomous Traffic Efficiency and Safety under Perception Uncertainty: The Integrated Analysis and Countermeasures”, and “Mechanism Design and Optimization for the Mobility-as-a-Service” are awarded by the Guangzhou Municipal Science and Technology Bureau, granting RMB 300,000 in total.

Manlian, Miaowei, and Wenjie attended the 16th International Workshop on Computational Transportation Science, held in Wuhan, China, from July 25 to 27, 2025, where they delivered presentations.

Published:

Manlian, Miaowei, and Wenjie attended the 16th International Workshop on Computational Transportation Science, held in Wuhan, China, from July 25 to 27, 2025. At the workshop, Manlian delivered a presentation titled “What Determines Travelers’ Acceptance of MaaS? Insights from a Meta-Analysis”, Miaowei presented “Data-Driven Agent Simulation for Urban Delivery: Integrating Personality via Large Language Models”, and Wenjie presented “Unlocking Low-Altitude Airspace Supply.” Image