Public lecture

AI and Smart Mobility

  • 25 August 2022
  • 9:30 am - 4:30 pm
  • James France CC.0.21

As part of the IAS Annual Theme 'AI:Facts, Fictions, Futures', this in person event will bring together a range of academics to discuss AI and Smart Mobility

Smart mobility is rich in data. There is enormous potential within the transport sector for using AI to address pressing challenges in sustainability and resilience, as well as fulfilling broader societal objectives of inclusion and equity. AI is already commonplace in many everyday Transport Systems and will become even more prevalent as technology evolves, particularly with rapid developments in Autonomous Vehicles. Our Fellows will discuss state of the art and propose how Autonomous Vehicles and future Transport Systems based on AI implementation will better meet and shape the growing and ever-changing demand for mobility while reducing its carbon footprint to meet net-zero goals.

Convened by: Andrew Morris, Haitao He

IAS Visiting Fellows in residence:

Mohamed Abdel-Aty, University of Central Florida
Kay Axhausen, ETH Zurich
Sebastien Glaser, CARRS-Q, Queensland University of Technology
Nigel Wilson, MIT CEE
Andry Rakotonirainy, CARRS-Q, Queensland University of Technology
Juan de Dios Ortúzar, Department of Transport Engineering and Logistics, Pontificia Universidad Catolica de Chile
Xuesong Wang, Tongji University

For more details on the IAS Annual Theme of AI: Facts, Fictions, Futures, please click here.

In order to attend this in-person event, please click here.

Programme:

9:15

Refreshments and welcome. Introductory Remarks

Dr Andrew Morris, Dr Haitao He

9:30

Next Generation Roads: AI, Big Data & Computer Vision Applications

Mohamed Abdel-Aty, University of Central Florida

In-person presentation and Q&A

10:00

Talk title TBA

Sebastien Glaser, CARRS-Q, Queensland University of Technology

In-person presentation and Q&A

10:30

Talk title TBA

Xuesong Wang, Tongji University

Virtual presentation and Q&A

11:00

Coffee Break

11:50

Reconvene, and welcome on behalf of the IAS

Prof Marsha Meskimmon, Director of the Institute of Advanced Studies (IAS)

12:00

Is cycling a way out of transport policies' dead end?

Kay Axhausen, ETH Zurich

In-person presentation and Q&A

12:30

AI and Smart Urban Public Transport

Nigel Wilson, MIT CEE

In-person presentation and Q&A

13:00

Lunch with VC Nick Jennings

14:00

A human-centric eXplainable Automated Vehicle (XAV)

Andry Rakotonirainy, CARRS-Q, Queensland University of Technology

In-person presentation and Q&A

 14:30 AI and Smart Mobility - Contributions from the Deep South

Juan de Dios Ortúzar, Department of Transport Engineering and Logistics, Pontificia Universidad Catolica de Chile

In-person presentation and Q&A

15:00 Tea Break
15:30

Parallel Roundtable discussions

AI in connected and autonomous vehicles, Chaired by Dr Andrew Morris
(James France CC.0.14)

AI in transport systems, Chaired by Dr Haitao He
(James France CC.1.11)

16:30

Concluding remarks

 

Professor Mohamed Abdel-Aty, University of Central Florida

Next Generation Roads: AI, Big Data & Computer Vision Applications

This presentation will focus on the safety and operation benefits of AI, big data, technology, connected and Automated Vehicles (CAV) and active traffic management (ATM). The advent of AI and big data enables the real-time analysis of traffic safety and operation. By integrating multiple data sources, the data could help us understand the relationship between the presence of traffic conflicts and the real-time crash contributing factors (e.g. volume, speed, traffic control status, weather, etc), and quantify the impact of these factors on real-time crash risk. The same data could also be used to evaluate congestion and travel time reliability in real time. We also provide some insights from our studies that are related to ATM, using AI/big data analytics to improve safety and the potential of Connected and Automated Vehicles (CAV). For now, it is important to determine the expected effect that CAV technologies would have in reducing crashes. This is meaningful because we could provide important guidance for CAV-related policies, research, resource allocation, manufacturing and promotion of these systems.

This presentation would address the following topics:

• (Pro) Active Traffic Management (PTM)
• Machine Learning
• Big Data
• Real-Time Applications
• Computer Vision and Machine Learning
• Visualization

Professor Sebastien GlaserCARRS-Q, Queensland University of Technology

Talk Title TBA

Abstract forthcoming

Professor Xuesong WangTongji University

Talk Title TBA

Abstract forthcoming

Professor Kay AxhausenETH Zurich

Is cycling a way out of transport policies' dead end?

Bicycles and e-bikes, in particular, are competitors for the car, but also public transport in urban areas. The limited amount of road space now made available to them and the generally sorry maintenance of the generally limited amount of dedicated facilities make them unattractive for a large share of the population. The high historical usage of the cycle tells us, that there is no law of nature behind the currently low shares.
Transport policy has to guide the transport system to a net zero future by 2050, i.e. removing the about 30% share of CO2 due to the transport sector. It should also continue to increase (maintain) the accessibility levels fairly. All major policy levers have known downsides, which make them hard to impossible to implement: Urban road building is both expensive and invites unwanted modal shifts; all pricing alternatives (road use, congestion charging, parking, transit peak pricing) are too unpopular to implement unless special circumstances prevail; electric cars are helpful, but still produce too much CO2 in either production or use; automated cars are likely to produce so much additional travel, that the net balance might be negative, as more persons can drive in these cheap to use and comfortable vehicles. Small public transport vehicles (4-8 seaters) for on-demand and pooled services are generally uneconomical away from rare concentrated demands, such as a train arrival, low private car ownership, or a group of friends travelling together.
In this dead end it is necessary to envision positive futures. The IVT is currently testing a scenario where cycles and other slow modes are allocated 50% of the existing road space to make it a safe and comfortable option. We will check all of the technical constraints, especially due to transit, and assess the required behavioural changes. The e-bike-city is intended to be the focus of discussion to move the discussion towards a new starting point for the necessary changes.

Professor Nigel WilsonMIT CEE

AI and Smart Urban Public Transport

On behalf of Jinhua Zhao, Haris Koutsopoulos, Shenhao Wang, Joseph Rodriguez, Qingyi Wang, and Dingyi Zhuang

In this talk we illustrate the potential of AI application in public transit using two examples: 1) Uncertainty Quantification of Spatiotemporal Transit Demand with Probabilistic Graph Neural Networks; 2) Cooperative Bus Holding and Stop-skipping: A Deep Reinforcement Learning Framework. The first study examines transit demand prediction. Recently deterministic neural networks have significantly improved the prediction accuracy of average travel demand but they have largely ignored the uncertainty that inevitably exists in transit demand prediction. Our study proposes a framework of probabilistic graph neural networks (GNN) to quantify the spatiotemporal uncertainty of travel demand. Applying it to the transit demand prediction in Chicago for pre- and post- COVID periods, we found that the probabilistic GNNs can successfully describe the spatial and temporal uncertainty patterns at a high spatial and temporal resolution. The second study focuses on the bus control problem that combines holding and stop-skipping strategies. We formulated it as a multi-agent reinforcement learning (MARL) problem with a design of the state and reward function that increases the observability of the impact of agents’ actions during training. An event-based mesoscopic simulation model is built to train the agents. We evaluate the proposed approach in a case study from the Chicago transit network. The results show that the proposed method not only improves the level of service but it is also more robust towards uncertainties in operations such as travel times and operator compliance with the recommended action. We conclude the talk with an initial research agenda on the AI applications in public transit.

Professor Andry RakotonirainyCARRS-Q, Queensland University of Technology

A human-centric eXplainable Automated Vehicle (XAV)

The presentation will address the inability of Automated Vehicles, powered by Artificial intelligence, to self-explain their behaviours. I will present a project which applies novel multidisciplinary methodologies in a real-world self-driving setting to formalise the essence of driving explanations. It explores the when, why and how a driver is seeking an explanation and what type of automated explanation is truly human-interpretable.

Professor Juan de Dios OrtúzarDepartment of Transport Engineering and Logistics, Pontificia Universidad Catolica de Chile

AI and Smart Mobility - Contributions from the Deep South

On behalf of Hans Löbel

Five years ago, the School of Engineering at PUC decided to finance a joint appointment, at the Assistant Professor level, for the departments of Computing Science and Transport Engineering and Logistics. This move has started to give some fruits in the form of several research projects related to the use of AI in modelling the perceptions of urban space, public transport satisfaction and the behaviour of public transport drivers. In this short presentation, we will give a glimpse of the methods and results obtained in these applications, and will also mention some plans for future work.

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Contact and booking details

Email address
ias@lboro.ac.uk
Cost
Free
Booking required?
Yes