2022 2nd International Conference on Intelligent Traffic Systems and Smart City(ITSSC 2022)

Keynote Speakers


Prof. Ming Zhong,Wuhan University of Technology, China 

Title:  An Integrated Modeling System for Decision-making Chains over Urban Transportation Planning and Management


Urban transportation planning and management involves decision-making chains from various stakeholders, such as those agencies responsible for socioeconomic development, land-use planning & management, transportation planning, transportation demand and infrastructure management, traffic operation and control and environmental protection agency, related businesses and general public. The level of service of urban transportation system has a profound and pervasive impact on the performance of above stakeholders. Therefore, a decision-making support system based on an integrated modeling technique is desired for examining relevant strategies, policies and measurements in the areas of urban transportation planning and management.

A demo urban integrated land-use transportation modeling system (U-ILUTMS) has been developed based on a technical support project funded by the World Bank, which is to support various decision-makings of above stakeholders. In particular, the system consists of a macroscopic economic model (MEM), a PECAS land-use model (LUM), a macroscopic transport demand model (MTDM), a mesoscopic and microscopic traffic simulation model and several data exchange protocols between these models. In particular, the MEM can be used to forecast the magnitude of each sector (e.g., agriculture vs. industry) over a planning window of 30 -50 years and therefore can provide decision-making support to urban agencies responsible for the socioeconomic development. The output from the MEM will be used as the input for the PECAS land use model within the system, which will allocate the forecasted economic totals by sector into different zones and simulate urban land-use patterns. The LUM will be used by urban planning agencies in supporting the decision-makings related to land use planning and control (e.g., zoning). The PECAS LUM will provide forecasted population/employment data to the macroscopic transport model through a “connected structure” and relevant data exchange protocol.  The MTDM will be used to provide decision-making support to various transportation planning and management agencies in the area of infrastructure planning and development. The output from the MTDM will be used to provide subarea OD matrices to a mesoscopic traffic simulation model (MTSM) for large-scale traffic simulations, which, in turn, interacts with microscopic simulation models (MSM) by passing relevant OD data.  The MTSM and MSM are found to be very useful in decision-making support of many urban transportation planning and management work, e.g., corridor planning and traffic signal timing.

It should be noted that, within the proposed integrated modeling system, the MSM and MTDM will provide updated network friction and/or land-use patterns tested to MTDM, and MTDM will interact with LUM through newly computed transport cost/time through relevant data exchange protocols. Such a design ensures that the entire modeling system can simulate the behavior of various sectors, their interactions and decision-chains in a holistic and realistic way. It is believed that the proposed integrated modeling system should provide urban planners/engineers/managers enhanced decision-making efficiency and capacity, due to its versatile policy analysis functionalities at multiple spatial scales.


Dr. Ming Zhong is a Professor at Intelligent Transportation Systems Research Center (ITSC), Wuhan University of Technology (WHUT) and an adjunct professor at Department of Civil and Environmental Engineering, University of Waterloo. Before he joined WHUT, he was an Associate/Assistant Professor of the Department of Civil Engineering, University of New Brunswick (UNB) from 2006 to 2013. He obtained his Bachelor and Masters degree of Transportation Management Engineering from Tongji University in 1995 and Beijing Jiaotong University in 1998 respectively, and his Ph.D. degree in transportation engineering at the University of Regina, Canada in 2004. Before he moved to UNB in 2006, he was a Research Associate and a Natural Science and Engineering Research Council (NSERC) Postdoctoral Fellow at the Department of Civil Engineering, University of Calgary during 2004 - 2005.


Prof.Xiaohua Zhao,Beijing University Of Technology, China 

Title:  Research on Design and Evaluation of Human-machine Interaction System of Intelligent Connected Vehicles

Abstract:  The evaluation of the public acceptance and product efficiency of the intelligent connected vehicles human-machine interaction system, and the quantification of the comprehensive impact of human-machine interaction system on driving performance are the prerequisites for exerting the potential safety benefits of human-machine interaction system and promoting the safety application of intelligent connected vehicles. To promote the application of human-machine interaction system in intelligent connected vehicle and improve its evaluation system of product efficiency, this study constructs a HUD acceptance model based on TAM theory, analyzes and quantifies the key factors that affect driver's acceptance. This study can provide strategic guidance for HUD design and development. Secondly, the comprehensive evaluation method of driver's visual performance and behavioral performance is proposed based on the driver's cognitive process, including driver's visual information processing mode, visual attention link map and driver's conflict avoidance ability. Finally, the influence of human-machine interaction system on driving performance is quantified by combining external environmental factors and driver's individual factors. The above research conclusions can provide theoretical reference and technical support for the design and promotion, utility evaluation and safety application of intelligent connected vehicles human-machine interaction system.


Xiaohua Zhao, doctor of Engineering, is the professor of College of Metropolitan Transportation in Beijing University of Technology. Prof. Zhao is also the member of the Vehicle User Characteristics Committee of Transportation Research Board (TRB), the member of traffic control facilities committee and the chairman of technical committee on traffic safety management of World Transport Convention(WTC), the member of editorial board of China Journal of Highway and Transport, the member of editorial board of Journal of Transport Information and Safety, the member of young scientists and engineers of China Communications and Transportation Association, the sixth director of China Road Transport Association, the member of Expert Committee of reflective materials branch of China Transportation Enterprise Management Association, the member of council of traffic engineering and information branch of China Highway Society, the member of supervisor and the member of technical committee of traffic information and control of Beijing Traffic Engineering Society.

Prof. Zhao has been committed to research on driving behaviors, traffic safety, as well as traffic information and control technology. At present, Prof. Zhao is the author or co-author of 210 academic papers in tenure and the first author of 162 academic papers. Among all published papers, 71 papers were indexed by SCI/SSCI and 65 papers were indexed by EI. Prof. Zhao was editor-in-chief of 3 monographs,1 textbook and 1 translation of MUTCD, and participated in editing 4 monographs. Prof. Zhao won the titles of famous teachers in Colleges and universities in Beijing, scientific and technological talents of China highway construction industry association, outstanding talents of Beijing Organization Department and backbone of Beijing middle-young teachers, etc.


Prof. Celimuge Wu,The University of Electro-Communications,Japan


Title:  Toward Efficient Federated Learning for Internet of Vehicles

Abstract: In order to support advanced Internet-of-Vehicles (IoV) applications, such as collaborative autonomous driving and intelligent transport systems, information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular environments. Federated learning (FL), a well-known distributed learning technology, has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user privacy. This talk will focus on client selection and communication technologies for supporting FL in vehicular environments in order to facilitate emerging IoV applications.


Celimuge Wu received his PhD degree from the University of Electro-Communications, Tokyo, Japan, in 2010. He is a professor at the University of Electro-Communications. His research interests include Vehicular Networks, Internet-of-Things, Edge Computing, and Application of Machine Learning in Wireless Networking and Computing. He serves as an associate editor of IEEE Transactions on Network Science and Engineering, IEEE Transactions on Green Communications and Networking, and IEEE Open Journal of the Computer Society. He also has been a guest editor of IEEE Transaction on Intelligent Transportation Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Computational Intelligence Magazine etc. He is a recipient of the 2021 IEEE Communications Society Outstanding Paper Award, 2021 IEEE Internet of Things Journal Best Paper Award, IEEE Computer Society 2020 Best Paper Award, and IEEE Computer Society 2019 Best Paper Award Runner-Up. He is/has been a symposium Co-Chair IEEE ICC 2023, a TPC Co-Chair of IEEE International Smart Cities Conference 2021, a TPC Co-chair of the 2021 IEEE Autonomous Driving AI Test Challenge, a General Chair (Lead) of ICT-DM 2021, a TPC Co-chair of Wireless Days 2021, a Track Chair (Lead) of IEEE VTC-Spring 2020, a Track Chair (Lead) of ICCCN 2019, and a Track Chair of IEEE PIMRC 2016. He is the chair of IEEE TCBD Special Interest Group on Big Data with Computational Intelligence, and IEEE TCGCC Special Interest Group on Green Internet of Vehicles. He is a senior member of IEEE.


Prof. Hui Liu,Central South University,China


Title:  Smart City Big Data Prediction Technology and Its Transportation, Medical and Power Grid Applications

Abstract: Artificial intelligence and big data have brought new opportunities and challenges to the development of the smart cities. Based on some interesting and important studying results from the presenter and his research team, this report will provide the latest research progress, key technologies and important breakthroughs of smart cities from three specific perspectives: smart transportation, smart medical care, and smart grid.


Liu Hui, born in 1983, male, Professor, doctoral supervisor, and vice dean of Faculty of Traffic & Transportation Engineering, Central South University. He is a World 2% Top Scientist and Elsevier China Highly Cited Scholar. He is awarded the top national young talents of China. He obtained double PhD degrees from Central South University in China and University of Rostock in Germany and Germany professorship certification. He leaded and completed the excellent youth fund of Ministry of Education and Research of Germany. He received one second prize of Natural Science Award of the Ministry of education and one national science and Technology Progress Award (innovation team). He authorized 3 international patents and authorized 89 national invention patents. He has been leading 1 national key R&D project and 3 national natural science Foundation of China. He published 7 English Monographs from Springer and Elsevier Press, 1 Chinese monograph of Science Press and 18 ESI Hot/Highly Cited papers as the first author.

Invited  Speaker:


Dr. Shangjing Lin, Beijing University of Posts and Telecommunications, China

Title:   Federated learning enabled Internet of Vehicle: coalition formation、resource allocation and incentive mechanism.

Abstract: Modern vehicles are equipped with numerous sensors to collect data. These data can be useful to optimize the performance of the individual vehicles and the transportation network as a whole. However, uploading large amounts of raw data from the vehicles to the IoV edge requires enormous communication bandwidth and introduces significant privacy concerns. Therefore, federated learning technologies are proposed to address the data privacy concerns, communication bandwidth limitations, and data heterogeneity in IoV. However, the mobility of vehicles will lead to the problem of unstable communication connections. To solve the above problems, we propose a framework of joint dynamic coalition formation and resource allocation. Moreover, to incentivize vehicle users (VUs) to cooperatively joint to FL, we further design an iterative double auction mechanism to stimulate VUs with good channel states and sufficient resources to participate in FL. 


Lin Shangjing, Ph.D., postdoctor, lecturer of Beijing University of Posts and Telecommunications, master supervisor, IEEE member, expert of ISO/IEC JTC1 SC6, senior member of the China Communications Society, and member of the Internet of Things Expert Committee of the China Communications Industry Association. She presided over the National Natural Science Foundation of China Youth Fund "Research on the Evolution and Dynamic of Amorphous Formation Ultra-dense Heterogeneous Networks ", the National Natural Science Foundation of China and the Royal Society of Edinburgh, the " Edge-cloud based self-organization of aerial robotics assisted cellular communications ", participated in the key research and development programs "Development of Small Multifunctional High-Mobility reliable Rescue Robot Equipment for Major Natural Disasters", "Research on mobile ubiquitous business environment architecture and Model of  Beijing Winter Olympic Theme" and other national and provincial and ministerial projects. She has published more than 50 academic papers, obtained 18 authorized invention patents, and 3 software copyrights.