Conference Sessions

All (123)
Keynote Ses­sion (8)
Reg­u­lar Ses­sion (63)
Light­ning Ses­sion (43)
Mon­day, June 21 (32)
Tues­day, June 22 (31)
Wednes­day, June 23 (29)
Thurs­day, June 24 (31)
Behav­ior (9)
Behav­ior & Demand (8)
Con­nect­ed & Auto­mat­ed Vehi­cles (4)
Data (16)
Data-Informed Deci­sion Mak­ing (4)
Elec­tri­fi­ca­tion (4)
Emerg­ing Mobil­i­ty (13)
Freight (4)
Impli­ca­tion of Auto­mat­ed Vehi­cles (8)
Mod­el­ing, Sim­u­la­tion & Opti­miza­tion (10)
Shared Mobil­i­ty (9)
Traf­fic Con­trol & Man­ag­ment (4)
Traf­fic Oper­a­tions (9)
Trans­porta­tion Net­work Mod­el­ing (4)
PRESENTERS (109)
Abdul­la, Bahrul­la (1)
Ahamed, Tan­vir (1)
Ali, Rafaqat (1)
Alshu­rafa, Ahmed (1)
Ansari, Reza (1)
Axhausen, Kay (1)
Azeve­­do-Sa, Hebert (1)
Bal­ac, Milos (1)
Bal­lare, Sud­heer (1)
Bayen, Alexan­dre (1)
Bhat, Chan­dra (1)
Bramich, Daniel (1)
Bur­sa, Bar­tosz (1)
Cai, Xiaolin (1)
Calderon, Fran­cis­co (1)
Caros, Nicholas (1)
Chang, Yohan (1)
Chen, Rong­sheng (1)
Chen, Xiang­dong (1)
Daus, Matthew (1)
Dean, Matthew (1)
Dong, Jiqian (1)
Du, Lili (1)
Dug­gal, Mausam (1)
Eftekhar, Zahra (1)
Fakhrmoosavi, Fate­meh (1)
Fer­nan­do, Cel­so (1)
Fil­ipovs­ka, Moni­ka (2)
Flan­na­gan, Car­ol (1)
Fourati, Walid (1)
Geroli­m­in­is, Niko­las (1)
Gong, Feng­min (1)
Gong, Yun­hai (1)
Gopalakr­ish­nan, Ragaven­dran (1)
Guan, Xiangyang (2)
Guo, Hao (1)
Guo, Xiao­tong (1)
Guo, Yi (1)
Hale, David (1)
Hu, Zijian (1)
Jayara­man, Suresh Kumaar (1)
Kaneko, Noriko (1)
Kavia­n­ipour, Moham­madreza (1)
Kawasa­ki, Yosuke (1)
Ke, Jin­tao (2)
Kleiber, Mar­cel (1)
Kon­tou, Eleft­he­ria (1)
Koushik, Gun­takan­ti Sai (1)
Ladi­no, Andres (1)
Lee, Tony (Yoon-Dong) (1)
Levin, Michael (1)
Li, Ang (1)
Li, Can (1)
Li, Qian­wen (1)
Li, Xiaopeng (1)
Liu, Hen­ry (1)
Liu, Xiao­hui (1)
Liu, Zhao­cai (1)
Lorente, Ester (1)
Lou, Yingyan (2)
Luo, Zhix­iong (1)
Ma, Jiaqi (1)
Ma, Mingy­ou (1)
Mah­mas­sani, Hani (1)
Mar­tinez, Irene (1)
Miah, Md Mintu (1)
Miller, Eric (1)
Mintsis, Evan­ge­los (1)
Mirali­naghi, Moham­mad (1)
Moham­ma­di­an, Abol­fa­zl (Kouros) (1)
Mol­nar, Tamas (1)
Nakan­ishi, Wataru (1)
Nam, Daisik (1)
Okuhara, Rui (1)
Rahi­mi, Ehsan (1)
Ros-Roca, Xavier (1)
Sal­lard, Aurore (1)
Sayed, Md Abu (1)
Seo, Toru (1)
Shen, Hui (2)
Song, Zhanguo (1)
Su, Qida (1)
Tafreshi­an, Amirmah­di (1)
Tang, Xin­di (1)
Tay, Tim­o­thy (1)
Tian, Qiong (1)
Tsub­o­ta, Takahi­ro (1)
Ume­da, Shogo (1)
Vacek, Lukas (1)
Wang, Jingx­ing (1)
Wang, Mengx­in (1)
Wang, Shen­hao (1)
Wang, Yineng (1)
Wang, Yiyang (1)
Wei, Bangyang (1)
Xie, Tingt­ing (1)
Xu, Min (1)
Xu, Zhengt­ian (1)
Yan, Huimin (1)
Yang, Chen (1)
Yang, Di (1)
Yang, Hai (1)
Yang, Jie (1)
Zhang, Guo­qing (1)
Zhang, Ke (1)
Zhang, Kenan (1)
Zhang, Wen­wen (1)
Zheng, Zhengfei (1)
Zock­aie, Ali (1)

Keynote Session 5 — Henry Liu 

Title: Intel­li­gent Dri­ving Intel­li­gence Test for Autonomous Vehi­cles with Nat­u­ral­is­tic and Adver­sar­i­al Dri­ving Envi­ron­ment
Speak­er: Hen­ry Liu
Abstract: Dri­ving intel­li­gence tests are crit­i­cal to the devel­op­ment and deploy­ment of autonomous vehi­cles. The pre­vail­ing approach tests autonomous vehi­cles in life-like sim­u­la­tions of the nat­u­ral­is­tic dri­ving envi­ron­ment. How­ev­er, due to the high dimen­sion­al­i­ty of the envi­ron­ment and the rareness of safe­ty-crit­i­cal events, hun­dreds of mil­lions of miles would be required to demon­strate the safe­ty per­for­mance of autonomous vehi­cles, which is severe­ly inef­fi­cient. We dis­cov­er that sparse but adver­sar­i­al adjust­ments to the nat­u­ral­is­tic dri­ving envi­ron­ment, result­ing in the nat­u­ral­is­tic and adver­sar­i­al dri­ving envi­ron­ment, can sig­nif­i­cant­ly reduce the required test miles with­out loss of eval­u­a­tion unbi­ased­ness. By train­ing the back­ground vehi­cles to learn when to exe­cute what adver­sar­i­al maneu­ver, the pro­posed envi­ron­ment becomes an intel­li­gent envi­ron­ment for dri­ving intel­li­gence test­ing. We demon­strate the effec­tive­ness of the pro­posed envi­ron­ment in a high­way-dri­ving sim­u­la­tion. Com­par­ing with the nat­u­ral­is­tic dri­ving envi­ron­ment, the pro­posed envi­ron­ment can accel­er­ate the eval­u­a­tion process by mul­ti­ple orders of magnitude.

Break 

Break

W‑2: Regular Session/Traffic Operations — David Hale 

Sub­mis­sion: A Method­ol­o­gy for Tra­jec­to­ry-Based Cal­i­bra­tion of Microsim­u­la­tion Mod­els
Pre­sen­ter: David Hale
Authors: David K. Hale (Lei­dos, Inc.)*; Xiaopeng Li (Uni­ver­si­ty of South Flori­da); Amir Ghi­asi (Lei­dos, Inc.); Dong­fang Zhao (Uni­ver­si­ty of South Florida)

W‑3: Regular Session/Data-Informed Decision Making — Shogo Umeda 

Sub­mis­sion: Risk Eval­u­a­tion of Anom­aly Event Occur­rence Using Probe Vehi­cle Data
Pre­sen­ter: Shogo Ume­da
Authors: Shogo Ume­da (Tohoku Uni­ver­si­ty)*; Yosuke Kawasa­ki (Tohoku Uni­ver­si­ty); Masao Kuwa­hara (Tohoku University)

W‑4: Regular Session/Shared Mobility — Jintao Ke 

Sub­mis­sion: Online Opti­miza­tion and Offline Learn­ing for On-Demand Match­ing in Ride-Sourc­ing Ser­vices
Pre­sen­ter: Jin­tao Ke
Authors: Xiao­ran Qin (Hong Kong Uni­ver­si­ty of Sci­ence and Tech­nol­o­gy); Jin­tao Ke (Hong Kong Uni­ver­si­ty of Sci­ence and Tech­nol­o­gy)*; Wei Liu (Uni­ver­si­ty of New South Wales); Hai Yang (Hong Kong Uni­ver­si­ty of Sci­ence and Technology)

W‑6: Lightning Session/Modeling, Simulation and Optimization — Zhixiong Luo 

Sub­mis­sion: Joint Deploy­ment of Low Emis­sion Zones and Elec­tric Vehi­cle Charg­ing Sta­tions
Pre­sen­ter: Zhix­iong Luo
Authors: Zhix­iong Luo (Tsinghua Uni­ver­si­ty)*; Fang He (Tsinghua Uni­ver­si­ty); Xi Lin (Tsinghua Uni­ver­si­ty); Meng Li (Tsinghua University)

W‑6: Lightning Session/Modeling, Simulation and Optimization — Mohammad Miralinaghi 

Sub­mis­sion: On the Opti­miza­tion of Elec­tric Charg­ing Infra­struc­ture to Address Vehic­u­lar Emis­sions
Pre­sen­ter: Moham­mad Mirali­naghi
Authors: Moham­mad Mirali­naghi (Pur­due Uni­ver­si­ty)*; Gonca­lo Cor­reia (TU Delft); Sania Esmaeilzadeh Seil­abi (Pur­due Uni­ver­si­ty); Mah­mood T. Tabesh (Pur­due Uni­ver­si­ty); Samuel Labi (Pur­due University)

W‑6: Lightning Session/Modeling, Simulation and Optimization — Yi Guo 

Sub­mis­sion: Sig­nal­ized Cor­ri­dor Man­age­ment with Tra­jec­to­ry Pre­dic­tion and Opti­miza­tion under Mixed-Auton­o­my Traf­fic Envi­ron­ment
Pre­sen­ter: Yi Guo
Authors: Yi Guo (Uni­ver­si­ty of Cincin­nati); Jiaqi Ma (Uni­ver­si­ty of Cal­i­for­nia, Los Angeles)*

W‑5: Lightning Session/Behavior — Hui Shen 

Sub­mis­sion: Trav­el Mode Choice of Young Peo­ple with Dif­fer­en­ti­at­ed E‑Hailing Ride Ser­vices: A Case Study in Nan­jing Chi­na
Pre­sen­ter: Hui Shen
Authors: Hui Shen (Uni­ver­si­ty of Illi­nois at Chica­go); Bo Zou (Uni­ver­si­ty of Illi­nois at Chica­go); Jane Lin (Uni­ver­si­ty of Illi­nois at Chicago)*

W‑5: Lightning Session/Behavior — Jingxing Wang 

Sub­mis­sion: Neigh­bor­hood Lev­el Impacts in Human Trav­el Pat­terns: Find­ings from the Clo­sure of Alaskan Way Viaduct
Pre­sen­ter: Jingx­ing Wang
Authors: Jingx­ing Wang (Uni­ver­si­ty of Wash­ing­ton)*; Xue­gang Ban (Uni­ver­si­ty of Wash­ing­ton); He Zhu (Uni­ver­si­ty of Washington)

W‑5: Lightning Session/Behavior — Ehsan Rahimi 

Sub­mis­sion: Ana­lyz­ing the Usage Fre­quen­cy of Shared E‑Scooters Dur­ing the COVID-19 Pan­dem­ic
Pre­sen­ter: Ehsan Rahi­mi
Authors: Ali Shamshiripour (UIC)*; Ehsan Rahi­mi (UIC); Ramin Sha­ban­pour (UIC); Abol­fa­zl (Kouros) Moham­ma­di­an (UIC)

W‑5: Lightning Session/Behavior — Bartosz Bursa 

Sub­mis­sion: Mod­el­ling Tourist On-Site Mode Choice Deci­sions dur­ing Vaca­tion Stays
Pre­sen­ter: Bar­tosz Bur­sa
Authors: Bar­tosz Bur­sa (Uni­ver­si­ty of Inns­bruck)*; Markus Mail­er (Uni­ver­si­ty of Innsbruck)

W‑5: Lightning Session/Behavior — Xiangyang Guan 

Sub­mis­sion: A Nov­el State-Tran­si­tion Mod­el for Real-Time Fore­cast­ing of Evac­u­a­tion Demand
Pre­sen­ter: Xiangyang Guan
Authors: Xiangyang Guan (Uni­ver­si­ty of Wash­ing­ton)*; Cyn­thia Chen (Uni­ver­si­ty of Washington)

W‑1: Regular Session/Connected and Automated Vehicles — Tamas Molnar 

Sub­mis­sion: On-Board Traffic Pre­dic­tion Via V2X Con­nec­tiv­i­ty
Pre­sen­ter: Tamas Mol­nar
Authors: Tamas G. Mol­nar (Uni­ver­si­ty of Michi­gan)*; Devesh Upad­hyay (Ford Motor Co.); Michael Hop­ka (Ford Motor Co.); Michiel Van Nieuw­stadt (Ford Motor Co.); Gabor Orosz (Uni­ver­si­ty of Michigan)

W‑2: Regular Session/Traffic Operations — Ali Zockaie 

Sub­mis­sion: Inves­ti­gat­ing Weath­er Impacts on Net­work-Wide Traf­fic Flow Rela­tion­ships
Pre­sen­ter: Ali Zock­aie
Authors: Ramin Sae­di (Michi­gan State Uni­ver­si­ty); Ali Zock­aie (Michi­gan State University)*

W‑3: Regular Session/Data-Informed Decision Making — Jiqian Dong 

Sub­mis­sion: Lane-Change Deci­sions of Con­nect­ed Autonomous Vehi­cles Using Spa­tial­ly-Weight­ed Infor­ma­tion and Deep Rein­force­ment Learn­ing
Pre­sen­ter: Jiqian Dong
Authors: Jiqian Dong (Pur­due Uni­ver­si­ty); Sikai Chen (Pur­due Uni­ver­si­ty)*; Paul (Young Joun) Ha (Pur­due Uni­ver­si­ty); Run­jia Du (Pur­due Uni­ver­si­ty); Yujie Li (South­east Uni­ver­si­ty); Samuel Labi (Pur­due University)

W‑4: Regular Session/Shared Mobility — Ester Lorente 

Sub­mis­sion: An Agent-based Sim­u­la­tion Mod­el for Inter­modal Assign­ment of Pub­lic Trans­port and Ride Pool­ing Ser­vices
Pre­sen­ter: Ester Lorente
Authors: Ester Lorente (PTV Group)*; Jaime Barce­lo (Tech. Univ. of Catalun­ya); Esteve Cod­i­na (Uni­ver­si­tat Politèc­ni­ca de Catalun­ya); Klaus Nökel (PTV Group)

W‑1: Regular Session/Connected and Automated Vehicles — Xiaopeng Li 

Sub­mis­sion: Vehi­cle Tra­jec­to­ry Opti­miza­tion at a Sig­nal­ized Inter­sec­tion in Mixed Traf­fic: Mod­el and Field Exper­i­ments
Pre­sen­ter: Xiaopeng Li
Authors: Zhen Wang (Chang'an Uni­veristy)*; Xiaopeng Li (Uni­ver­si­ty of South Flori­da); Xiang­mo Zhao (Changan Uni­ver­si­ty); Zhi­gang Xu (Chang'an University)

W‑2: Regular Session/Traffic Operations — Daniel Bramich 

Sub­mis­sion: Fit­Fun: Improved noise mod­els for Fun­da­men­tal Dia­grams
Pre­sen­ter: Daniel Bramich
Authors: Dan Bramich (New York Uni­ver­si­ty Abu Dhabi)*; Mon­i­ca Menen­dez (New York Uni­ver­si­ty Abu Dhabi); Lukas Ambuhl (ETH Zurich)

W‑4: Regular Session/Shared Mobility — Xiaolin Cai 

Sub­mis­sion: Mod­el­ing and Sim­u­la­tion of Poten­tial Use-Cas­es for Shared Mobil­i­ty Ser­vices in the City of Ann Arbor
Pre­sen­ter: Xiaolin Cai
Authors: Richard Twu­masi-Boakye (Ford Motor Com­pa­ny)*; Xiaolin Cai (Ford Motor Com­pa­ny); James Fishel­son (Ford Motor Company)

W‑1: Regular Session/Connected and Automated Vehicles — Jiaqi Ma 

Sub­mis­sion: DTEM: Dynam­ic Traf­fic Envi­ron­ment Map­ping for Con­nect­ed and Auto­mat­ed Traf­fic Con­trol
Pre­sen­ter: Jiaqi Ma
Authors: Tao Li (Uni­ver­si­ty of Cincin­nati); Jiaqi Ma (Uni­ver­si­ty of Cal­i­for­nia, Los Angeles)*

W‑3: Regular Session/Data-Informed Decision Making — Yiyang Wang 

Sub­mis­sion: Real-Time Sen­sor Anom­aly Detec­tion and Recov­ery in Con­nect­ed Auto­mat­ed Vehi­cle Sen­sors
Pre­sen­ter: Yiyang Wang
Authors: Yiyang Wang (Uni­ver­si­ty of Michi­gan)*; Neda Masoud (Uni­ver­si­ty of Michi­gan); Anahi­ta Kho­jan­di (Uni­ver­si­ty of Tennessee)

W‑4: Regular Session/Shared Mobility — Nicholas Caros 

Sub­mis­sion: Lever­ag­ing Des­ti­na­tion Flex­i­bil­i­ty to Increase Rideshar­ing Par­tic­i­pa­tion: An Inte­grat­ed Mod­el and Case Study
Pre­sen­ter: Nicholas Caros
Authors: Nicholas Caros (MIT); Jin­hua Zhao (MIT)*

Break 

Break

Keynote Session 6 — Fengmin Gong 

Title: Bet­ter Jour­neys For All Through Impact, Inno­va­tion & Respon­si­bil­i­ty
Speak­er: Feng­min Gong
Abstract: Data sci­ence and AI are at the core of the “fourth indus­tri­al res­o­lu­tion”. While the sci­ence and tech­nol­o­gy com­mu­ni­ty are dili­gent­ly push­ing the fron­tier for the ben­e­fits of human­i­ty, some fear the neg­a­tive impact of the same. The crust of the mat­ter is that, Data sci­ence and AI are pow­er­ful tools with huge poten­tial, HOW we har­ness this pow­er is the most crit­i­cal fac­tor to suc­cess or dis­as­ter. In this talk, I will share three main guid­ing prin­ci­ples — impact, inno­va­tion, and respon­si­bil­i­ty, which should help us to do the right things the right way in apply­ing AI. DiDi has been at the fore­front in trans­form­ing trans­porta­tion through AI. To illus­trate these prin­ci­ples, I will use some exam­ples in rein­force­ment learn­ing for opti­miza­tion, NLP for safe rides, and use-case dri­ven sim­u­la­tion for AV.