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 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 — 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 — 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 — 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/