AUTHORS (177)
Ahn, Soy­oung (Sue)
Aka­mat­su, Takashi
Alan­qary, Arwa
Alisoltani, Negin
Alon­so-Mora, Javier
Ameli, Mostafa
Ban, Jeff
Bandiera, Clau­dia
Bat­ley, Richard
Bayen, Alexan­dre M.
Bell, Michael G H
Beo­jone, Caio Vitor
Bhat­tachar­jya, Jyotir­moy­ee
Bliemer, Michiel
Cao, Yumin
Cas­sidy, Michael J.
Cen, Xuekai
Chen, Zhib­in
Chen, Dan­jue
Chen, Kehua
Chen, Xiqun (Michael)
Chen, Xu
Chen, Cyn­thia
Cheng, Xi
Coif­man, Ben­jamin
Cokyasar, Tan­er
Con­nors, Richard D.
Cook, Adi­an
Dagan­zo, Car­los F.
Dantsu­ji, Takao
Di, Xuan
Doig, Jean
Engel­hardt, Roman
Fan, Ximeng
Fan, Yueyue
Feng, Liyang
Feng, Yiheng
Fiel­baum, Andrés
Fu, Zhe
Gayah, Vikash V.
Geers, Glenn
Geroli­m­in­is, Niko­las
Gu, Ziyuan
Had­dad, Jack
Ham­dar, Samer
Haque, Mohaimin­ul
Hazel­ton, Mar­tin
He, Xiaozheng (Sean)
Her­ty, Michael
Hey­deck­er, Ben­jamin
Hong, Yuan
Hu, Simon
Hu, Xinyue
Huang, Hai-Jun
Huang, Shuai
Iaco­mi­ni, Elisa
Iryo, Taka­masa
Jia, Shaocheng
Jiang, Jiwan
Jin, Li
Jin, Wen­long
Ka, Eun­han
Khan, Zaid Saeed
Kobayashi, Shun-ichi
Krei­dieh, Abdul Rahman
Krish­naku­mari, Pan­chamy
Lau­riere, Math­ieu
Laval, Jorge
Le, Dat Tien
Lebacque, Jean-Patrick
Lecler­cq, Ludovic
Lee, Enoch
Lehe, Lewis J.
Li, Zihao
Li, Shen
Li, Jiayang
Li, Manzi
Li, Yifan
Lim, Jisoon
Lin, Jane
Liu, Yuhao
Liu, Jiachao
Liu, Wei
Liu, Hao
Liu, Ronghui
Liu, Yang
Liu, Tian-Liang
Liu, Zhiyuan
Lo, Hong K.
Loder, Allis­ter
Ma, Xiaoyu
Mah­mas­sani, Hani S.
Martínez, Irene
Masoud, Neda
Menén­dez, Móni­ca
Mo, Zhaobin
Mol­nar, Tamas G.
Nakaya­ma, Shoichi­ro
Ng, Max T.M.
Nie, Yu (Mar­co)
Orosz, Gábor
Oso­rio, Jesus
Ouyang, Yan­feng
Ozbay, Kaan
Pandey, Ayush
Pee­ta, Srini­vas
Qian, Sean
Qu, Xu
Ran, Bin
Ren, Kanghui
Rock­afel­larc, R. Tyrrell
Saberi, Meead
Safa­di, Yazan
Sakai, Takara
Sat­sukawa, Koki
Schmöck­er, Jan-Dirk
Segala, Chiara
Shen, Shiyu
Si, Bingfeng
Sir­matelb, Isik Ilber
Song, Jun
Sun, Xiao­tong
Sun, Lijun
Sun, Wen­zhe
Takaya­ma, Yuki
Talebpour, Alireza
Tang, Yu
Tian, Qiong
Uğurel, Ekin
Ukkusuri, Satish V.
van Lint, Hans
Ver­bas, Ömer
Viti, Francesco
Wada, Ken­taro
Wang, Xiaolei
Wang, Xin
Wang, Siy­ing
Wang, Qian­ni
Wang, David Z.W.
Wang, Jingx­ing
Wang, Fei­long
Wang, Qiqing
Wang, Zejiang
Wang, Wen­shuo
Watling, David
Wong, S.C.
Wong, Wai
Xie, Jun
Xu, Zhengt­ian
Xu, Pu
Xue, Jiawei
Yam­aguchi, Hiromichi
Yang, Hai
Yang, Chen
Yang, Shan
Yang, Kai­di
Yao, Rui
Ye, Anke
Yin, Peng­hang
Zang, Zhao­qi
Zhang, Yun­long
Zhang, Xiaon­ing
Zhang, Yu
Zhang, Zhuoye
Zhang, Fang­ni
Zhang, Kenan
Zhang, Chengyuan
Zhao, Chaoyue
Zheng, Yuan
Zheng, Zuduo
Zhou, Yang
Zhou, Yihe
Zhou, Bo
Zhou, Anye
Zhou, Hao
Zhu, Meix­in
Zhu, Peng­bo

Podium Session 8: Design an Intermediary Mobility-As-A-Service (MaaS) Platform Using Many-To-Many Stable Matching Framework

Title: Design an Intermediary Mobility-As-A-Service (MaaS) Platform Using Many-To-Many Stable Matching Framework
Authors: Rui Yao, Kenan Zhang
Abstract: Mobility-as-a-service (MaaS) provides seamless door-to-door trips by integrating different transport modes. Although many MaaS platforms have emerged in recent years, most of them remain at a limited integration level. This study investigates the assignment and pricing problem for a MaaS platform as an intermediary in a multi-modal transportation n...
Keywords: mobility-as-a-service (MaaS); many-to-many stable matching; network design; multi-modal traffic assignment

Podium Session 8: A Hierarchical Control Framework for Vehicle Repositioning in Ride-Hailing Systems

Title: A Hierarchical Control Framework for Vehicle Repositioning in Ride-Hailing Systems
Authors: Caio Vitor Beojone, Pengbo Zhu, Isik Ilber Sirmatelb, Nikolas Geroliminis
Abstract: This paper introduces a multi-layer control strategy for efficiently repositioning empty ride-hailing vehicles, aiming to bridge the gap between proactive repositioning strategies and micro-management. The proposed framework consists of three layers: an upper-layer employing an aggregated model based on the Macroscopic Fundamental Diagram (MFD) and...
Keywords: Vehicle Repositioning; Hierarchical Control; Model Predictive Control (MPC); Coverage Control; Macroscopic Fundamental Diagram (MFD)

Podium Session 8: On-Demand Meal Delivery: A Markov Model for Circulating Couriers

Title: On-Demand Meal Delivery: A Markov Model for Circulating Couriers
Authors: Michael G H Bell, Dat Tien Le, Jyotirmoyee Bhattacharjya, Glenn Geers
Abstract: On-demand meal delivery has become a feature of most cities around the world as a result of platforms and apps that facilitate it, as well as the pandemic, which for a period closed restaurants. Meals are delivered by couriers, typically on bikes, e-bikes or scooters, who circulate collecting meals from kitchens and delivering them to customers, wh...
Keywords: Ghost kitchens; On-demand delivery; Markov chains

Podium Session 9: Microscopic Discontinuities Disrupting Hydrodynamic and Continuum Traffic Flow Models

Title: Microscopic Discontinuities Disrupting Hydrodynamic and Continuum Traffic Flow Models
Authors: Benjamin Coifman
Abstract: Hydrodynamic and continuum traffic flow models usually require that traffic states are stationary for the model assumptions to hold. The reproducibility of a concave fundamental diagram, FD, is typically assumed to also demonstrate that the underlying states are sufficiently near stationary. This paper uses loop detector data from five locations to...
Keywords: Fundamental diagram; Vehicle detection; Traffic flow theory; Hydrodynamic traffic flow; Continuum traffic flow model

Podium Session 9: A Game-Theoretic Framework for Generic Second Order Traffic Flow Using Mean Field Games and Adversarial Inverse Reinforcement Learning

Title: A Game-Theoretic Framework for Generic Second Order Traffic Flow Using Mean Field Games and Adversarial Inverse Reinforcement Learning
Authors: Zhaobin Mo, Xu Chen, Xuan Di, Elisa Iacomini, Chiara Segala, Michael Herty, Mathieu Lauriere
Abstract: A traffic system can be interpreted as a multi-agent system, wherein vehicles choose the most efficient driving approaches guided by inter-connected goals or strategies. This paper aims to develop a family of mean field games (MFG) for generic second-order traffic flow models (GSOM), in which cars control individual velocity to optimize their objec...
Keywords: Mean field game (MFG); Generic second traffic flow model; Adversarial Inverse Reinforcement Learning (AIRL)

Podium Session 9: Priority Queue Formulation of Agent-Based Bathtub Model for Network Trip Flows in the Relative Space

Title: Priority Queue Formulation of Agent-Based Bathtub Model for Network Trip Flows in the Relative Space
Authors: Irene Martínez, Wenlong Jin
Abstract: Agent-based models have been extensively used to simulate the behavior of travelers in transportation systems because they allow for realistic and versatile modeling of interactions. However, traditional agent-based models suffer from high computational costs and rely on tracking physical locations, raising privacy concerns. This paper proposes an ...
Keywords: Agent-based bathtub model; efficient simulation model; priority queue relative space; trip travel time distribution

Podium Session 9: Data Poisoning Attacks on Traffic State Estimation and Prediction

Title: Data Poisoning Attacks on Traffic State Estimation and Prediction
Authors: Feilong Wang, Xin Wang, Yuan Hong, R. Tyrrell Rockafellarc, Jeff Ban
Abstract: Data has become ubiquitous nowadays in transportation, including vehicular data and infrastructure-generated data. The growing reliance on data poses potential cybersecurity issues to transportation systems, among which the so-called “data poisoning” attacks by adversaries are becoming increasingly critical. Such attacks aim to compromise a system’...
Keywords: Data poisoning attacks; Attack model; Traffic state estimation and prediction; Lipschitz continuity; Semi-derivatives

Lunch

Podium Session 10: Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach

Title: Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach
Authors: Qiqing Wang, Kaidi Yang
Abstract: This paper proposes a privacy-preserving data fusion method for traffic state estimation (TSE). Unlike existing works that assume all data sources to be accessible by a single trusted party, we explicitly address data privacy concerns that arise in the collaboration and data sharing between multiple data owners, such as municipal authorities (MAs) ...
Keywords: Data Fusion; Federated Learning; Data Privacy; Traffic State Estimation; Traffic Flow Theory

Podium Session 10: Implications of Stop-and-Go Traffic on Training Learning-Based Car-Following Control

Title: Implications of Stop-and-Go Traffic on Training Learning-Based Car-Following Control
Authors: Anye Zhou, Srinivas Peeta, Hao Zhou, Jorge Laval, Zejiang Wang, Adian Cook
Abstract: Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving t...
Keywords: Car-following control; System identification; Behavior cloning; Deep reinforcement learning; Generalizability

Podium Session 10: Uncovering Physics-Regularized Data Generation Processes for Individual Human Mobility: A Multi-Task Gaussian Process Approach Based on Multiple Kernel Learning

Title: Uncovering Physics-Regularized Data Generation Processes for Individual Human Mobility: A Multi-Task Gaussian Process Approach Based on Multiple Kernel Learning
Authors: Ekin Uğurel, Shuai Huang, Cynthia Chen
Abstract: Passively-generated mobile data has grown increasingly popular in the travel behavior (or human mobility) literature. A relatively untapped potential for passively-generated mobile data is synthetic population generation, which is the basis for any large-scale simulations for purposes ranging from state monitoring, policy evaluation, and digital tw...
Keywords: Synthetic mobile data; Gaussian process; Multiple kernel Learning; Physics-regularization; Travel behavior

Podium Session 10: Calibrating Car-Following Models via Bayesian Dynamic Regression

Title: Calibrating Car-Following Models via Bayesian Dynamic Regression
Authors: Chengyuan Zhang, Wenshuo Wang, Lijun Sun
Abstract: Car-following behavior modeling is critical for understanding traffic flow dynamics and developing high-fidelity microscopic simulation models. Most existing impulse-response car-following models prioritize computational efficiency and interpretability by using a parsimonious nonlinear function based on immediate preceding state observations. Howev...
Keywords: car-following models; dynamic regression; Bayesian inference; microscopic traffic simulation

Edited by Guoyang Qin

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