11 Dec 2024 (Wed) 11:00 AM-12:30 PM | Seminar by Dr. Prateek Bansal

An Analytical Approach to Generate Synthetic Population by Harnessing Household Travel Survey Data with Mobile Phone Data

Speaker:

Dr. Prateek Bansal, National University of Singapore

Date:    11 December 2024 (Wednesday)

Time:   11:00 am - 12:30 pm

Venue:  Innovation Wing Two, G/F Run Run Shaw Building, HKU

Abstract

Conventional methods to synthesize population use household travel survey (HTS) data. HTS encounters a low spatial heterogeneity issue due to a low sampling rate of the HTS data. Passively collected mobility (PCM) data (e.g., cellular traces) provides extensive spatial coverage but poses integration challenges with HTS data due to differences in spatial resolution and attributes. This study introduces a novel cluster-based data fusion method to address these limitations and simultaneously generate synthetic populations with accurate sociodemographics and homework locations at high spatial heterogeneity. Analytical properties are derived to retain essential distributional characteristics from both datasets in the fused distribution. The data fusion properties are validated using HTS and LTE/5G cellular signaling data from Seoul, South Korea. Validation against census data confirms the method's efficacy in maintaining distributional consistency while increasing spatial heterogeneity, with 97% of the generated population being unobserved in the HTS data. This research advances methods to synthesize a population by leveraging the complementary strengths of HTS and PCM data, providing a robust framework for generating spatially diverse synthetic populations essential for urban planning.

Speaker Bio:

Dr Prateek Bansal is a Presidential Young (Assistant) Professor at the National University of Singapore (NUS). Before joining NUS in 2022, he was a Leverhulme Trust Early Career Fellow at Imperial College London and did a Ph.D. from Cornell, an MSc from UT Austin, a BTech from IIT Delhi. Prateek leads the Behavioural Cognitive Science Lab at NUS and is a co-principal investigator of the Adaptive Mobility module at Future Cities Laboratory, Singapore. His research group is interested in creating new methods to address challenging questions related to mobility behavior and the adoption of emerging technologies at an individual level and an urban scale. His research has led to over 55 journal articles. Apart from top Transportation journals, he regularly publishes in interdisciplinary journals like Energy Economics and Statistics and Computing. He also serves as the editorial board member of Transportation Research Part A: Policy and Practice, Transportation Research Part B: Methodological, and Journal of Choice Modelling, among others. He is a member of the TRB’s standing committees on Travel Survey Methods (AEP25) and Travel Forecasting (AEP50), and a regular board member of the International Association of Travel Behavior Research (IATBR).

Student Seminar by Shaocheng Jia on Sep 27, 2024 (10AM)

Title: Uncertainty Estimation of the Connected Vehicle Penetration Rate: Modeling Complex Residual-vehicle Effects

Speaker: Mr. Shaocheng Jia (Department of Civil Engineering)

Date: September 27, 2024 (Friday)

Time: 10:00 am - 11:00 am

Venue: Room 612B, 6/F, Haking Wong Building, The University of Hong Kong

About the talk: In the transition to full deployment of connected vehicles (CVs), the CV penetration rate plays a key role in bridging the gap between partial and complete traffic information. Several innovative methods have been proposed to estimate the CV penetration rate using only CV data. However, these methods, as point estimators, may lead to biased estimations or suboptimal solutions when applied directly in modeling or system optimization. To avoid these problems, the uncertainty and variability in the CV penetration rate must be considered. Recently, a probabilistic penetration rate (PPR) model was developed for estimating such uncertainties. The key model input is a constrained queue length distribution composed exclusively of queues formed by red signals in undersaturation conditions with no residual vehicles. However, in real-world scenarios, due to random arrivals, residual vehicles are commonly carried over from one cycle to another in temporary overflow cycles in undersaturation conditions, which seriously restricts the applicability of the PPR model. To address this limitation, this talk will introduce a Markov-constrained queue length (MCQL) model that can model the complex effects of residual vehicles on the CV penetration rate uncertainty. Comprehensive VISSIM simulations and applications to real-world datasets demonstrate that the proposed MCQL model can accurately model the residual-vehicle effect and estimate the uncertainty. Thus, the applicability of the PPR model is truly extended to real-world settings, regardless of the presence of residual vehicles. A simple stochastic CV-based adaptive signal control example illustrates the potential of the proposed model in real-world applications.

About the speaker: Shaocheng Jia is currently a Ph.D. candidate at The University of Hong Kong and a (student) member of IEEE, IEEE ITSS, INFORMS, INFORMS TSL, CHTS, IACIP, and ITS of HKU. He received his B.E. degree from the Department of Electronic Information Engineering, China University of Petroleum (Beijing), and M.E. degree from the Department of Automation, Tsinghua University, in 2014 and 2018, respectively. His research interests include intelligent perception and control, connected and automated transportation, stochastic modeling and optimization, and machine learning. Shaocheng has published over 10 papers in prestigious journals and conferences, including Transportation Science, IEEE TITS, TR-Part C, the International Symposium on Transportation and Traffic Theory (ISTTT), etc., as well as 6 national invention patents. Shaocheng is also a recipient of the Outstanding Master’s Dissertation Awards from CHTS and Tsinghua University. He has chaired a regular session in the 26th IEEE ITSC and a Young Scholar Tech Talk at HKU, co-organized two workshops in IEEE ITSC, and been a reviewer for IEEE TITS, IEEE TNNLS, IEEE TMC, TRR, IEEE ITSC, TRB, etc.

1 Nov 2024 (Fri) 15:00-16:00 | Seminar by Prof. Feng Xiao

Cognitive Hierarchy in Day-to-day Network Flow Dynamics

Speaker:

Prof. Feng Xiao

Business School, Sichuan University, China

Date:    Nov. 01, 2024 (Friday)

Time:   3:00pm – 4:00pm

Venue:  Room 612B, 6/F Haking Wong Building, The University of Hong Kong

Abstract

When making route decisions, travelers may engage in a certain degree of reasoning about what the others will do in the upcoming day, rendering yesterday’s shortest routes less attractive. This phenomenon was manifested in a recent virtual experiment that mimicked travelers’ repeated daily trip-making process. Unfortunately, prevailing day-to-day traffic dynamical models failed to faithfully reproduce the collected flow evolution data therein. To this end, we propose a day-to-day traffic behavior modeling framework based on the Cognitive Hierarchy theory, in which travelers with different levels of strategic-reasoning capabilities form their own beliefs about lower-step travelers’ capabilities when choosing their routes. Two widely studied day-to-day models, the Network Tatonnement Process dynamic and the Logit dynamic, are extended into the framework and studied as examples. Calibration of the virtual experiment is performed using the extended Network Tatonnement Process dynamic, which fits the experimental data reasonably well. We show that the two extended dynamics have multiple equilibria, one of which is the classical user equilibrium. While analyzing global stability is intractable due to the presence of multiple equilibria, local stabilities near equilibria are developed analytically and verified by numerical experiments. General insights on how key parameters affect the stability of user equilibria are unveiled.

About the Speaker

Feng Xiao is currently a professor at Business School of Sichuan University, recipient of the National Science Fund for Distinguished Young Scholars, China. Dr. Xiao’s research interests include artificial intelligence algorithms and data mining, modeling and optimization of complex traffic systems, financial risk management and quantitative trading, healthcare data management, etc. He has published more than 70 papers in internationally renowned journals and conferences in the fields of management science and engineering, transportation technology and data mining, such as Transportation Science, Transportation Research Part A, B, C, D, E, IEEE TKDE, ISTTT, etc.