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 1 — Hai Yang 

Title: Smart Mobil­i­ty Man­age­ment in the Era of Smart Trans­porta­tion
Speak­er: Hai Yang
Abstract: The cur­rent rev­o­lu­tions of shar­ing, automa­tion and elec­tri­fi­ca­tion are reshap­ing the way we trav­el, with broad impli­ca­tions for future mobil­i­ty man­age­ment. While much uncer­tain­ty remains about how these dis­rup­tive tech­nolo­gies would exact­ly impact demand for future mobil­i­ty and enhance­ment of trans­porta­tion sup­ply, it is clear that Inno­v­a­tive demand man­age­ment is equal­ly impor­tant as smart sup­ply tech­nol­o­gy devel­op­ment in solv­ing wors­en­ing traf­fic prob­lems in big cities. In this talk, I will dis­cuss the oppor­tu­ni­ties and chal­lenges of smart mobil­i­ty man­age­ment in the era of smart trans­porta­tion. Inno­v­a­tive ways of trav­el demand man­age­ment are described, includ­ing trad­able trav­el cred­it scheme for road con­ges­tion mit­i­ga­tion, rev­enue-pre­serv­ing and Pare­to-improv­ing strate­gies for peak-hour tran­sit demand man­age­ment con­ges­tion, and a nov­el reward scheme inte­grat­ed with surge pric­ing in a ride-sourc­ing market.

Break 

Break

M‑1: Regular Session/Behavior and Demand — Milos Balac 

Sub­mis­sion: Dynam­ic Demand Esti­ma­tion for Sin­gle-Ride AMoD and Fleet Size Opti­miza­tion for Pooled AMoD across the Globe
Pre­sen­ter: Milos Bal­ac
Authors: Milos Bal­ac (IVT, ETHZ)*; Sebas­t­ian Hörl (IVT, ETHZ); Kay W. Axhausen (IVT, ETH)

M‑2: Regular Session/Emerging Mobility — Francisco Calderon 

Sub­mis­sion: On the Gen­er­al­i­ty of Emerg­ing Mobil­i­ty Ser­vices’ Oper­a­tional Process­es
Pre­sen­ter: Fran­cis­co Calderon
Authors: Fran­cis­co Calderón Per­al­vo (Uni­ver­si­ty of Toron­to)*; Eric Miller (Uni­ver­si­ty of Toronto)

M‑5: Lightning Session/Data — Yohan Chang 

Sub­mis­sion: Syn­TIS: Syn­thet­ic Trav­el­er Infor­ma­tion in Smart City
Pre­sen­ter: Yohan Chang
Authors: Yohan Chang (Korea Research Insti­tute for Human Settlements)*

M‑5: Lightning Session/Data — Yosuke Kawasaki 

Sub­mis­sion: Analy­sis of Fea­tures of the Routes Using Probe Tra­jec­to­ry Data
Pre­sen­ter: Yosuke Kawasa­ki
Authors: Yosuke Kawasa­ki (Tohoku Uni­ver­si­ty)*; Shogo Ume­da (Tohoku Uni­ver­si­ty); Masao Kuwa­hara (Tohoku University)

M‑5: Lightning Session/Data — Xavier Ros-Roca 

Sub­mis­sion: A Data Dri­ven Approach to Dynam­ic Ori­gin-Des­ti­na­tion Matrix Esti­ma­tion
Pre­sen­ter: Xavier Ros-Roca
Authors: Xavier Ros-Roca (Uni­ver­si­tat Politèc­ni­ca de Catalun­ya (UPC))*; Jaume Barceló (PTV Group); Lidia Mon­tero (Uni­ver­si­tat Politèc­ni­ca de Catalun­ya (UPC)); Gui­do Gen­tile (Sapien­za Uni­ver­sità di Roma); Klaus Nökel (PTV Group)

M‑5: Lightning Session/Data — Celso Fernando 

Sub­mis­sion: A Fac­tor Extrac­tion Method using Deep Learn­ing Tech­nique on Traf­fic Acci­dent Risk
Pre­sen­ter: Cel­so Fer­nan­do
Authors: Cel­so Luis Fer­nan­do (Ehime Uni­ver­si­ty)*; Toshio Yoshii (Ehime Uni­ver­si­ty); Takahi­ro Tsub­o­ta (Ehime uni­ver­si­ty); Hiro­to­shi Shi­rayana­gi (Ehime University)

M‑5: Lightning Session/Data — Jintao Ke 

Sub­mis­sion: Ori­gin-Des­ti­na­tion Demand Pre­dic­tion Via Spa­tial-Tem­po­ral Mul­ti-Graph CNN
Pre­sen­ter: Jin­tao Ke
Authors: Siyuan Feng (Hong Kong Uni­ver­si­ty of Sci­ence and Tech­nol­o­gy); Jin­tao Ke (Hong Kong Uni­ver­si­ty of Sci­ence and Tech­nol­o­gy)*; Hai Yang (Hong Kong Uni­ver­si­ty of Sci­ence and Technology)

M‑6: Lightning Session/Modeling, Simulation and Optimization — Marcel Kleiber 

Sub­mis­sion: Sim­u­lat­ing Traf­fic Dynam­ics Sub­ject To Per­cep­tion­al Errors
Pre­sen­ter: Mar­cel Kleiber
Authors: Volk­er Berkhahn (Leib­niz Uni­ver­sität Han­nover); Mar­cel Kleiber (Leib­niz Uni­ver­sität Han­nover)*; Chris Tim­mer­mann (Leib­niz Uni­ver­sität Han­nover); Johannes Langn­er (Leib­niz Uni­ver­sität Han­nover); Ste­fan Weber (Leib­niz Uni­ver­sität Hannover)

M‑3: Regular Session/Implication of Automated Vehicles — Fatemeh Fakhrmoosavi 

Sub­mis­sion: Incor­po­rat­ing a Mixed Fleet of Autonomous, Con­nect­ed, and Human-Dri­ven Vehi­cles into a Meso­scop­ic Sim­u­la­tion Tool Con­sid­er­ing Net­work Capac­i­ty Vari­a­tions with Het­ero­ge­neous Dri­vers
Pre­sen­ter: Fate­meh Fakhrmoosavi
Authors: Fate­meh Fakhrmoosavi (Michi­gan State Uni­ver­si­ty); Ramin Sae­di (Michi­gan State Uni­ver­si­ty); Ali Zock­aie (Michi­gan State Uni­ver­si­ty)*; Alireza Talebpour (Uni­ver­si­ty of Illi­nois at Urbana-Champaign)

M‑2: Regular Session/Emerging Mobility — Xiaohui Liu 

Sub­mis­sion: A Data-Dri­ven Approach to Man­age the Curb­side Ride-Hail­ing Pick-ups and Drop-offs
Pre­sen­ter: Xiao­hui Liu
Authors: Xiao­hui Liu (The Hong Kong Poly­tech­nic Uni­ver­si­ty); Lei Xu (Shen­zhen Research Insti­tute of Big Data); Sean Qian (Carnegie Mel­lon Uni­ver­si­ty); Wei Ma (The Hong Kong Poly­tech­nic University)*

M‑4: Regular Session/Traffic Control and Management — Toru Seo 

Sub­mis­sion: Eval­u­a­tion of Large-Scale Com­plete Vehi­cle Tra­jec­to­ries Dataset on Two Kilo­me­ters High­way Seg­ment for One Hour Dura­tion: Zen Traf­fic Data
Pre­sen­ter: Toru Seo
Authors: Toru Seo (The Uni­ver­si­ty of Tokyo)*; Yusuke Tago (Region­al Futures Research Cen­ter); Nori­hi­to Shinkai (Region­al Futures Research Cen­ter); Masakazu Nakan­ishi (Region­al Futures Research Cen­ter); Jun Tan­abe (Region­al Futures Research Cen­ter); Daisuke Ushi­rogochi (Omron Social Solu­tions Co.,Ltd); Shota Kanamori (Omron Social Solu­tions Co.,Ltd); Atsushi Abe (Omron Social Solu­tions Co.,Ltd); Takashi Kodama (Han­shin Express­way Com­pa­ny Lim­it­ed); Satoshi Yoshimu­ra (Han­shin Express­way Com­pa­ny Lim­it­ed); Masaa­ki Ishi­hara (Han­shin Express­way Com­pa­ny Lim­it­ed); Wataru Nakan­ishi (Tokyo Insti­tute of Technology)

M‑1: Regular Session/Behavior and Demand — Abolfazl (Kouros) Mohammadian 

Sub­mis­sion: Com­plex­i­ty of Trav­el-Based Mul­ti­task­ing and Its Asso­ci­a­tion to Latent Lifestyles
Pre­sen­ter: Abol­fa­zl (Kouros) Moham­ma­di­an
Authors: Ali Shamshiripour (UIC)*; Ehsan Rahi­mi (UIC); Abol­fa­zl (Kouros) Moham­ma­di­an (UIC); Joshua Auld (Argonne Nation­al Laboratory )

M‑2: Regular Session/Emerging Mobility — Bangyang Wei 

Sub­mis­sion: Tem­po­ral Capac­i­ty Allo­ca­tion and Tolling Schemes for Morn­ing Com­mute with Car­pool­ing
Pre­sen­ter: Bangyang Wei
Authors: Bangyang Wei (Uni­ver­si­ty of New South Wales); Wei Liu (Uni­ver­si­ty of New South Wales)*; meead saberi (Uni­ver­si­ty of New South Wales); Fang­ni Zhang (UNSW Syd­ney); Travis Waller (Uni­ver­si­ty of New South Wales)

M‑1: Regular Session/Behavior and Demand — Wataru Nakanishi 

Sub­mis­sion: Appli­ca­tion of Eigen­vec­tor Spa­tial Fil­ter­ing to Trav­el Des­ti­na­tion Choice Mod­el: A Case Study of Munic­i­pal­i­ty-Size Choice in Hokkai­do Island, Japan
Pre­sen­ter: Wataru Nakan­ishi
Authors: Wataru Nakan­ishi (Tokyo Insti­tute of Tech­nol­o­gy)*; Hiromichi Yam­aguchi (Kanaza­wa University)

M‑3: Regular Session/Implication of Automated Vehicles — Carol Flannagan 

Sub­mis­sion: Urban Taxi vs Non-taxi Crash­es: Impli­ca­tions for Auto­mat­ed Vehi­cles in the Rideshare Envi­ron­ment
Pre­sen­ter: Car­ol Flan­na­gan
Authors: Adi­ti Mis­ra (Uni­ver­si­ty of Michi­gan)*; Andrew Leslie (Uni­ver­si­ty of Michi­gan); Car­ol Flan­na­gan (Uni­ver­si­ty of Michi­gan, Trans­port Research Institute)

M‑2: Regular Session/Emerging Mobility — Zhengfei Zheng 

Sub­mis­sion: The Crit­i­cal Pas­sen­ger Mass for Achiev­ing a Soci­etal­ly Ben­e­fi­cial Ride-Split­ting Pro­gram
Pre­sen­ter: Zhengfei Zheng
Authors: Jin­tao Ke (Hong Kong Uni­ver­si­ty of Sci­ence and Tech­nol­o­gy)*; Hai Yang (Hong Kong Uni­ver­si­ty of Sci­ence and Tech­nol­o­gy); Zhengfei Zheng (Hong Kong Uni­ver­si­ty of Sci­ence and Technology)

M‑4: Regular Session/Traffic Control and Management — Monika Filipovska 

Sub­mis­sion: A Pri­ori and Adap­tive Reli­able Rout­ing in Sto­chas­tic Dynam­ic Net­works with Cor­re­la­tions
Pre­sen­ter: Moni­ka Fil­ipovs­ka
Authors: Moni­ka Fil­ipovs­ka (North­west­ern Uni­ver­si­ty); Hani S. Mah­mas­sani (North­west­ern University)*

Break 

Break

Keynote Session 2 — Hani Mahmassani 

Title: Oper­a­tional Strate­gies for Urban Air Mobil­i­ty and 4D Sys­tem Fun­da­men­tal Dia­grams
Speak­er: Hani Mah­mas­sani
Abstract: We take urban mobil­i­ty to the next lev­el by con­sid­er­ing shared mobil­i­ty ser­vices offered through auto­mat­ed elec­tric ver­ti­cal take-off and land­ing (eVTOL) vehi­cles (“fly­ing taxis”), enabled by new gen­er­a­tion of eVTOL air­craft. We present var­i­ous con­cepts for ser­vice oper­a­tions at urban/regional lev­els, along with algo­rithms adapt­ed for the real-time oper­a­tion of shared air mobil­i­ty fleets. We also exam­ine the con­gesta­bil­i­ty of urban air space through a micro­scop­ic sim­u­la­tion and illus­trate the emer­gence of sys­tem fun­da­men­tal dia­gram (for prop­er­ly defined aver­ages tak­en over four-dimen­sion­al space) com­pa­ra­ble in shape to urban road traf­fic networks.

Keynote Session 3 — Nikolas Geroliminis 

Title: On the Inef­fi­cien­cy and Man­age­ment of Ride-Sourc­ing Ser­vices towards Urban Con­ges­tion
Speak­er: Niko­las Geroli­m­in­is
Abstract: Human mobil­i­ty in con­gest­ed city cen­ters is a com­plex dynam­i­cal sys­tem with high den­si­ty of pop­u­la­tion, many trans­port modes to com­pete for lim­it­ed avail­able space and many oper­a­tors that try to effi­cient­ly man­age dif­fer­ent parts of this sys­tem. New emerg­ing modes of trans­porta­tion, such as ride-hail­ing and on-demand ser­vices cre­ate addi­tion­al oppor­tu­ni­ties, but also more com­plex­i­ty. Lit­tle is known about to what degree its oper­a­tions can inter­fere in traf­fic con­di­tions, while replac­ing oth­er trans­porta­tion modes, or when a large num­ber of idle vehi­cles is cruis­ing for pas­sen­gers. We exper­i­men­tal­ly ana­lyze the effi­cien­cy of TNCs using taxi trip data from a Chi­nese megac­i­ty and an agent-based sim­u­la­tion with a trip-based MFD mod­el for deter­min­ing the speed. We inves­ti­gate the effect of expand­ing fleet sizes for TNCs, pas­sen­gers’ incli­na­tion towards shar­ing rides, and strate­gies to alle­vi­ate urban con­ges­tion. We observe that, although a larg­er fleet size reduces wait­ing time, it also inten­si­fies con­ges­tion, which, in turn, pro­longs the total trav­el time. Such con­ges­tion effect is so sig­nif­i­cant that it is near­ly insen­si­tive to pas­sen­gers’ will­ing­ness to share and flex­i­ble sup­ply. Final­ly, park­ing man­age­ment strate­gies can pre­vent idle vehi­cles from cruis­ing with­out assigned pas­sen­gers, mit­i­gat­ing the neg­a­tive impacts of ride-sourc­ing over con­ges­tion, and improv­ing the ser­vice qual­i­ty. We are also devel­op­ing dif­fer­ent type of con­trol strate­gies, such as relo­ca­tion of emp­ty vehi­cles, park­ing man­age­ment and pric­ing incen­tives to alle­vi­ate the neg­a­tive effects.

Break 

Break

T‑4: Regular Session/Behavior — Mingyou Ma 

Sub­mis­sion: Quan­ti­fy­ing Day-to-Day Evo­lu­tion of Choice Pat­terns in Pub­lic Tran­sit Sys­tem with Smart Tran­sit Card Data
Pre­sen­ter: Mingy­ou Ma
Authors: Mingy­ou Ma (UNSW Syd­ney)*; Wei Liu (Uni­ver­si­ty of New South Wales); Xin­wei Li (Bei­hang Uni­ver­si­ty); Fang­ni Zhang (UNSW Syd­ney); Sisi Jian (); Vinayak Dix­it (UNSW)

T‑1: Regular Session/Emerging Mobility — Yingyan Lou 

Sub­mis­sion: Con­ges­tion Mit­i­ga­tion for Planned Spe­cial Event: Smart Park­ing, Ride-Shar­ing Drop-off Loca­tions and Net­work Con­fig­u­ra­tion
Pre­sen­ter: Yingyan Lou
Authors: Jun Xiao (Ari­zona State Uni­ver­si­ty); Yingyan Lou (Ari­zona State University)*

T‑2: Regular Session/Freight — Tanvir Ahamed 

Sub­mis­sion: Deep Rein­force­ment Learn­ing for Crowd­sourced Urban Deliv­ery: Sys­tem States Char­ac­ter­i­za­tion, Heuris­tics-guid­ed Action Choice, and Rule-Inter­pos­ing Inte­gra­tion
Pre­sen­ter: Tan­vir Ahamed
Authors: Tan­vir Ahamed (Uni­ver­si­ty of Illi­nois at Chica­go); Bo Zou (Uni­ver­si­ty of Illi­nois at Chica­go)*; Nahid Farazi (Uni­ver­si­ty of Illi­nois at Chica­go); The­ja Tula­band­hu­la (UIC)

T‑3: Regular Session/Data — Zijian Hu 

Sub­mis­sion: Self-Cal­i­bra­tion of Traf­fic Sur­veil­lance Cam­era Sys­tems for Traf­fic Den­si­ty Esti­ma­tion on Urban Roads
Pre­sen­ter: Zijian Hu
Authors: Zijian Hu (The Hong Kong Poly­tech­nic Uni­ver­si­ty); Wei Ma (The Hong Kong Poly­tech­nic Uni­ver­si­ty)*; William Lam (The Hong Kong Poly­tech­nic Uni­ver­si­ty); S. C. Wong (The Uni­ver­si­ty of Hong Kong); Andy Chow (City Uni­ver­si­ty of Hong Kong)

T‑6: Lightning Session/Traffic Operations — Zhanguo Song 

Sub­mis­sion: Short-Term Traf­fic Flow Uncer­tain­ty Pre­dic­tion Using an Improved Grey Pre­dic­tion Mod­el under Dif­fer­ent Time Inter­vals
Pre­sen­ter: Zhanguo Song
Authors: ZHanguo Song (South­east Uni­ver­si­ty)*; Xiao Qin (Uni­ver­si­ty of Wisconsin-Milwaukee)

T‑6: Lightning Session/Traffic Operations — Rui Okuhara 

Sub­mis­sion: Effect of Traf­fic Acci­dent on Arte­r­i­al Road Net­work
Pre­sen­ter: Rui Okuhara
Authors: Rui Okuhara (Ehime Uni­verci­ty)*; Toshio Yoshii (Ehime Uni­ver­si­ty); Takahi­ro Tsub­o­ta (Ehime uni­ver­si­ty); Hiro­to­shi Shi­rayana­gi (Ehime University)

T‑6: Lightning Session/Traffic Operations — Md Abu Sayed 

Sub­mis­sion: Pre­dict Short-Term Traf­fic Flow with Pre­dic­tion Error from Traf­fic Sen­sor Data Using Deep Learn­ing
Pre­sen­ter: Md Abu Sayed
Authors: Md Abu Sayed (Uni­ver­si­ty of Wis­con­sin-Mil­wau­kee)*; Xiao Qin (Uni­ver­si­ty of Wisconsin-Milwaukee)

T‑6: Lightning Session/Traffic Operations — Rongsheng Chen 

Sub­mis­sion: Traf­fic Assign­ment Analy­sis of Traf­fic Net­works with Max-Pres­sure Con­trol
Pre­sen­ter: Rong­sheng Chen
Authors: Rong­sheng Chen (Uni­ver­si­ty of Min­neso­ta)*; Michael W. Levin (Uni­ver­si­ty of Minnesota)

T‑6: Lightning Session/Traffic Operations — Monika Filipovska 

Sub­mis­sion: Com­pu­ta­tion and Esti­ma­tion of Path Trav­el Time Vari­abil­i­ty with Sparse Vehi­cle Tra­jec­to­ry Data
Pre­sen­ter: Moni­ka Fil­ipovs­ka
Authors: Moni­ka Fil­ipovs­ka (North­west­ern Uni­ver­si­ty); Hani S. Mah­mas­sani (North­west­ern University)*

T‑5: Lightning Session/Emerging Mobility — Qianwen Li 

Sub­mis­sion: Autonomous Vehi­cle Iden­ti­fi­ca­tion Based on Car-Fol­low­ing Data
Pre­sen­ter: Qian­wen Li
Authors: Qian­wen Li (Uni­ver­si­ty of South Flori­da)*; Xiaopeng Li (Uni­ver­si­ty of South Flori­da); Han­dong Yao (Uni­ver­si­ty of South Florid)

T‑5: Lightning Session/Emerging Mobility — Rafaqat Ali 

Sub­mis­sion: A Mul­ti­modal Trav­el­ing Itin­er­ary Prob­lem in a Time Depen­dent Mul­ti­modal Trans­porta­tion Net­work for a Fixed Sequence of Nodes with Time Win­dows
Pre­sen­ter: Rafaqat Ali
Authors: Rafaqat Ali (Tsinghua University)*

T‑5: Lightning Session/Emerging Mobility — Evangelos Mintsis 

Sub­mis­sion: Man­age­ment of Con­nect­ed and Auto­mat­ed Vehi­cle Dis­en­gage­ments in the prox­im­i­ty of Work Zones
Pre­sen­ter: Evan­ge­los Mintsis
Authors: Evan­ge­los Mintsis (Hel­lenic Insti­tute of Trans­port (HIT))*

T‑6: Lightning Session/Traffic Operations — Lukas Vacek 

Sub­mis­sion: Dis­con­tin­u­ous Galerkin Method for Macro­scop­ic Traf­fic Flow Mod­els on Net­works using Numer­i­cal Flux­es at Junc­tions
Pre­sen­ter: Lukas Vacek
Authors: Lukáš Vacek (Charles Uni­ver­si­ty)*; Václav Kučera (Charles University)

T‑4: Regular Session/Behavior — Hebert Azevedo-Sa 

Sub­mis­sion: Using Trust in Automa­tion to Enhance Driver-(Semi)AutonomousVehicle Inter­ac­tion and Improve Team Per­for­mance
Pre­sen­ter: Hebert Azeve­do-Sa
Authors: Hebert Azeve­do Sa (Uni­ver­si­ty of Michigan)*

T‑1: Regular Session/Emerging Mobility — Amirmahdi Tafreshian 

Sub­mis­sion: Proac­tive Vehi­cle Dis­patch­ing in Large-Scale Ride-Sourc­ing Sys­tems
Pre­sen­ter: Amirmah­di Tafreshi­an
Authors: Amirmah­di Tafreshi­an (Uni­ver­si­ty of Michi­gan)*; Mojta­ba Abdol­male­ki (Uni­ver­si­ty of Michi­gan); Neda Masoud (Uni­ver­si­ty of Michi­gan); Huizhu Wang (Ford Motor Company)

T‑2: Regular Session/Freight — Sudheer Ballare 

Sub­mis­sion: A Many-to-Many Vehi­cle Rout­ing Prob­lem with Split Loads
Pre­sen­ter: Sud­heer Bal­lare
Authors: Jane Lin (Uni­ver­si­ty of Illi­nois at Chica­go)*; Sud­heer Bal­lare (Uni­ver­si­ty of Illi­nois at Chicago)

T‑3: Regular Session/Data — Ang Li 

Sub­mis­sion: With­in-Day Pre­dic­tion of Path Trav­el Times with Use of Mul­ti-Source of Traf­fic Data
Pre­sen­ter: Ang Li
Authors: Ang Li (The Hong Kong Poly­tech­nic Uni­ver­si­ty)*; William Lam (The Hong Kong Poly­tech­nic Uni­ver­si­ty); Renx­in Zhong (Sun Yat-sen University)

T‑4: Regular Session/Behavior — Ragavendran Gopalakrishnan 

Sub­mis­sion: Behav­ioral Mod­els of Users in Ride-Shar­ing
Pre­sen­ter: Ragaven­dran Gopalakr­ish­nan
Authors: The­ja Tula­ban­du­la (Uni­ver­si­ty of Illi­nois at Chica­go)*; Ragaven­dran Gopalakr­ish­nan (Queens University)

T‑1: Regular Session/Emerging Mobility — Kenan Zhang 

Sub­mis­sion: A Gen­er­al Spa­tiotem­po­ral Equi­lib­ri­um Mod­el of Ride-Hail Mar­ket
Pre­sen­ter: Kenan Zhang
Authors: Yu (Mar­co) Nie (North­west­ern Uni­ver­si­ty)*; Kenan Zhang (North­west­ern University)

T‑2: Regular Session/Freight — Mausam Duggal 

Sub­mis­sion: Unknown to Known: Pre­dict­ing Truck GPS Com­mod­i­ty Using Machine Learn­ing
Pre­sen­ter: Mausam Dug­gal
Authors: Mausam Dug­gal (WSP); Bryce W Shar­man (WSP)*; Rick Don­nel­ly (WSP); Matthew Roor­da (Uni­ver­si­ty of Toron­to); Sun­dar Damodaran (Min­istry of Trans­porta­tion of Ontario); Shan Sure­shan (Min­istry of Trans­porta­tion of Ontario)

T‑3: Regular Session/Data — Di Yang 

Sub­mis­sion: Explor­ing the Pos­si­bil­i­ty of Out­lier Detec­tion Using Func­tion­al Data Analy­sis for Proac­tive Safe­ty Man­age­ment
Pre­sen­ter: Di Yang
Authors: Di Yang (New York Uni­ver­si­ty)*; Kaan Ozbay (New York Uni­ver­si­ty); Kun Xie (Old Domin­ion Uni­ver­si­ty); Hong Yang (Old Domin­ion Uni­ver­si­ty); Fan Zuo (New York Uni­ver­si­ty); Di Sha (New York University)

T‑4: Regular Session/Behavior — Zhengtian Xu 

Sub­mis­sion: Under­stand­ing Ride-Sourc­ing Dri­vers’ Cus­tomer-Search Behav­ior
Pre­sen­ter: Zhengt­ian Xu
Authors: Jun­ji Ura­ta (Uni­ver­si­ty of Michi­gan)*; Jin­tao Ke (Hong Kong Uni­ver­si­ty of Sci­ence and Tech­nol­o­gy); Zhengt­ian Xu (Uni­ver­si­ty of Michi­gan); Guo­jun Wu (Worces­ter Poly­tech­nic Insti­tute); Yafeng Yin (Uni­ver­si­ty of Michi­gan); Hai Yang (Hong Kong Uni­ver­si­ty of Sci­ence and Tech­nol­o­gy); Jieping Ye (Didi Chuxing)

T‑1: Regular Session/Emerging Mobility — Min Xu 

Sub­mis­sion: Address­ing the Fleet Siz­ing Prob­lem for Shared-and-Autonomous-Mobil­i­ty Ser­vices
Pre­sen­ter: Min Xu
Authors: Min Xu (The Hong Kong Poly­tech­nic University)*

T‑2: Regular Session/Freight — Guoqing Zhang 

Sub­mis­sion: An Inte­grat­ed Loca­tion-Inven­to­ry Mod­el for the Health­care Sup­ply Net­work under Sto­chas­tic Demands
Pre­sen­ter: Guo­qing Zhang
Authors: Guo­qing Zhang (Uni­ver­si­ty of Wind­sor)*; Mohammed Almanaseer (Uni­ver­si­ty of Wind­sor); Xiaot­ing Shang (Uni­ver­si­ty of Windsor)

T‑3: Regular Session/Data — Xiangyang Guan 

Sub­mis­sion: Cor­rect­ing Bias­es in Using Emerg­ing Big Data for Mobil­i­ty Research: A Like­li­hood-Based Approach
Pre­sen­ter: Xiangyang Guan
Authors: Xiangyang Guan (Uni­ver­si­ty of Wash­ing­ton)*; Cyn­thia Chen (Uni­ver­si­ty of Wash­ing­ton); Shuai Huang (Uni­ver­si­ty of Washington)

Break 

Break

Keynote Session 4 — Alexandre Bayen 

Title: Lagrangian Con­trol at Large and Local Scales in Mixed Auton­o­my Traf­fic Flow
Speak­er: Alexan­dre Bayen
Abstract: This talk inves­ti­gates Lagrangian (mobile) con­trol of traf­fic flow at local scale (vehic­u­lar lev­el). The ques­tion of how self-dri­ving vehi­cles will change traf­fic flow pat­terns is inves­ti­gat­ed. We describe approach­es based on deep rein­force­ment learn­ing pre­sent­ed in the con­text of enabling mixed-auton­o­my mobil­i­ty. The talk explores the grad­ual and com­plex inte­gra­tion of auto­mat­ed vehi­cles into the exist­ing traf­fic sys­tem. We present the poten­tial impact of a small frac­tion of auto­mat­ed vehi­cles on low-lev­el traf­fic flow dynam­ics, using nov­el tech­niques in mod­el-free deep rein­force­ment learn­ing, in which the auto­mat­ed vehi­cles act as mobile (Lagrangian) con­trollers to traf­fic flow. Illus­tra­tive exam­ples will be pre­sent­ed in the con­text of a new open-source com­pu­ta­tion­al plat­form called FLOW, which inte­grates state of the art microsim­u­la­tion tools with deep-RL libraries on AWS EC2. Inter­est­ing behav­ior of mixed auton­o­my traf­fic will be revealed in the con­text of emer­gent behav­ior of traf­fic: https://flow-project.github.io/

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)