All (106)
Podi­um (36)
Poster (24)
Light­ning Talk (24)
Spe­cial Ses­sion (3)
Sun­day, July 14 (3)
Mon­day, July 15 (43)
Tues­day, July 16 (42)
Wednes­day, July 17 (17)
Advanced Traf­fic Con­trol (4)
AI and Machine Learn­ing Inno­va­tions in Traf­fic and Mobil­i­ty Analy­sis (4)
Curb­side Space Man­age­ment (3)
Dynam­ics and Safe­ty Impli­ca­tions in Auto­mat­ed Vehi­cle Sys­tems (3)
Emerg­ing Mobil­i­ty Sys­tems: I (3)
Emerg­ing Mobil­i­ty Sys­tems: II (3)
Inno­va­tions in Tran­sit Design and Opti­miza­tion (3)
Inno­va­tions in Trans­porta­tion Mod­el­ing (4)
Rout­ing and Equi­lib­ri­um in Traf­fic Net­works (4)
Traf­fic Flow The­o­ry (4)

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

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Lightning Talk 1: Two-Step Quadratic Programming for Physically Meaningful Smoothing of Longitudinal Vehicle Trajectories

Title: Two-Step Quadratic Programming for Physically Meaningful Smoothing of Longitudinal Vehicle Trajectories
Authors: Ximeng Fan, Wen-Long Jin, Penghang Yin
Abstract: Longitudinal vehicle trajectories suffer from errors and noise due to detection and extraction techniques, challenging their applications. Existing smoothing methods either lack physical meaning or cannot ensure solution existence and uniqueness. To address this, we propose a two-step quadratic programming method that aligns smoothed speeds and hig...
Keywords: Longitudinal vehicle trajectories; discrepancy and roughness; two-step quadratic programming; existence and uniqueness; NGSIM and highD data

Lightning Talk 1: Physics-Informed Machine Learning for Calibrating Macroscopic Traffic Flow Models

Title: Physics-Informed Machine Learning for Calibrating Macroscopic Traffic Flow Models
Authors: Yu Tang, Li Jin, Kaan Ozbay
Abstract: Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. Traditional calibration has been developed based on optimization methods. In this paper, we propose a novel physics-informed, learning-based calibration approach that achieves performances comparable to and even better than those...
Keywords: Physics-informed learning; Parameter identification; Traffic flow models

Poster Session 1: Two-Step Quadratic Programming for Physically Meaningful Smoothing of Longitudinal Vehicle Trajectories

Title: Two-Step Quadratic Programming for Physically Meaningful Smoothing of Longitudinal Vehicle Trajectories
Authors: Ximeng Fan, Wen-Long Jin, Penghang Yin
Abstract: Longitudinal vehicle trajectories suffer from errors and noise due to detection and extraction techniques, challenging their applications. Existing smoothing methods either lack physical meaning or cannot ensure solution existence and uniqueness. To address this, we propose a two-step quadratic programming method that aligns smoothed speeds and hig...
Keywords: Longitudinal vehicle trajectories; discrepancy and roughness; two-step quadratic programming; existence and uniqueness; NGSIM and highD data

Poster Session 1: Physics-Informed Machine Learning for Calibrating Macroscopic Traffic Flow Models

Title: Physics-Informed Machine Learning for Calibrating Macroscopic Traffic Flow Models
Authors: Yu Tang, Li Jin, Kaan Ozbay
Abstract: Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. Traditional calibration has been developed based on optimization methods. In this paper, we propose a novel physics-informed, learning-based calibration approach that achieves performances comparable to and even better than those...
Keywords: Physics-informed learning; Parameter identification; Traffic flow models

Lightning Talk 2: Distributionally Robust Origin Destination Demand Estimation

Title: Distributionally Robust Origin Destination Demand Estimation
Authors: Jingxing Wang, Jun Song, Chaoyue Zhao, Xuegang (Jeff) Ban
Abstract: Gaining a good understanding of the travel demands of a city or region is extremely important for many transportation applications. For stochastic origin-destination (OD) estimation problems, an accurate distribution assumption or observation of OD estimates or data is usually desired but not always available. In this paper, we establish a novel tw...
Keywords: OD demand estimation; distributionally robust optimization; quasi-sparsity

Lightning Talk 2: Estimation of Schedule Preference and Crowding Perception in Urban Rail Corridor Commuting: An Inverse Optimization Method

Title: Estimation of Schedule Preference and Crowding Perception in Urban Rail Corridor Commuting: An Inverse Optimization Method
Authors: Pu Xu, Tian-Liang Liu, Qiong Tian, Bingfeng Si, Wei Liu, Hai-Jun Huang
Abstract: This paper introduces an inverse optimization method to uncover commuters’ schedule preference and crowding perception based on aggregated observations from smart card data for an urban rail corridor system. The assessment of time-of-use preferences typically involves the use of econometric models of discrete choice based on detailed travel survey ...
Keywords: Travel behavior; Rail transit; Morning commute; Smart card data; Inverse optimization

Poster Session 2: Distributionally Robust Origin Destination Demand Estimation

Title: Distributionally Robust Origin Destination Demand Estimation
Authors: Jingxing Wang, Jun Song, Chaoyue Zhao, Xuegang (Jeff) Ban
Abstract: Gaining a good understanding of the travel demands of a city or region is extremely important for many transportation applications. For stochastic origin-destination (OD) estimation problems, an accurate distribution assumption or observation of OD estimates or data is usually desired but not always available. In this paper, we establish a novel tw...
Keywords: OD demand estimation; distributionally robust optimization; quasi-sparsity

Poster Session 2: Estimation of Schedule Preference and Crowding Perception in Urban Rail Corridor Commuting: An Inverse Optimization Method

Title: Estimation of Schedule Preference and Crowding Perception in Urban Rail Corridor Commuting: An Inverse Optimization Method
Authors: Pu Xu, Tian-Liang Liu, Qiong Tian, Bingfeng Si, Wei Liu, Hai-Jun Huang
Abstract: This paper introduces an inverse optimization method to uncover commuters’ schedule preference and crowding perception based on aggregated observations from smart card data for an urban rail corridor system. The assessment of time-of-use preferences typically involves the use of econometric models of discrete choice based on detailed travel survey ...
Keywords: Travel behavior; Rail transit; Morning commute; Smart card data; Inverse optimization

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

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