Workshop on Big Data Analytics for Enhancing Public Transport (BigTransport17)
November 8, 2017, Singapore
In Conjunction with ACM International Conference on Information and Knowledge Management (CIKM17), Singapore, November 7-10, 2017
Dr. Xing Xie
Microsoft Research Asia
Public transport is a critical component of smart city. In a dense urban city, public transport system is the preferred means to move people around. As the most sustainable and scalable solution, public transport now needs innovation to respond to new challenges brought by improving commuting experience.
These challenges include:
- Increased expectation of service quality, comfort and efficiency from commuters;
- Influx of new commuters working or visiting cities;
- Imbalanced supply and demand; and
- Last mile commuting gaps.
Meanwhile, public commuters today generate massive amount of data traces. These include: (i) sensor, image, and video data collected by the existing public transport infrastructure; (ii) train and bus trips recorded by electronic farecard systems; (iii) taxi bookings and taxi trips recorded by mobile apps; (iv) bicycle rental and biking trips recorded by bike sharing apps; and (v) social media posts on public transport events.
These rich data traces offer new opportunities for research in information retrieval, database, data mining and machine learning to enhance commuting experience enhancement, namely:
- Identifying areas for public transport service improvement;
- Discovering regular travel patterns of commuters;
- Modelling and monitoring of commuting experience;
- Personalizing public transport services to improve individual commuting experience; and
- Engaging commuters in crowdsourcing resources to address unmet demand
The BigTransport workshop aims to bring researchers and practitioners across different Big Data and Public Transport research communities together in a unique forum to share the state-of-the-art technologies.
It welcomes researchers and practitioners to share the latest breakthroughs in analysing transport related data for improving commuting experience in public transport systems. These could include data science studies on commuter behavior, public transport data analytics applications and systems, large-scale behavioural experiments on public transport users, and simulation and visualization using massive public transport data. BigTransport will focus on application inspired novel findings, methods, systems and solutions which demonstrate the impact of big data analytics on public transport experience.
The workshop invites original contributions in the form of poster and demo papers covering but not limited to the following topics:
- Public transport event detection
- Ride/Taxi sharing system design
- Commuting experience modelling and measurement
- Emergency response systems
- Walkability in urban cities
- Real-time public transport system management
- Social media analytics for commuter feedback
- Last-mile commuting
- Resilient public transport systems
- Bike sharing
- Recommender systems for commuters
- Inclusive public transport
- Intelligent commuter assistant
- Commuter behavior sensing
Authors of all accepted submissions will be required to present their posters and demos at the workshop.
Prospective authors are required to submit their papers electronically through EasyChair. Every poster/demo paper is limited to 4 pages in ACM proceedings format. All accepted papers will be shared with participants in an informal proceedings as well as on the workshop’s website.
Submission site: https://easychair.org/conferences/?conf=bigtransport17
The following are the important deadlines:
15 August: Paper submission
31 August: Paper acceptance notice
15 September: Camera-ready submission
Registration Fee Waiver for Poster/Demo Presenters
One presenter per poster/demo will be invited to join the workshop with registration fee waiver. If the presenter wishes to attend other parts of CIKM17 (workshops, tutorials, main conference tracks, etc.), the normal CIKM17 registration fees apply.
Ling-Jyh Chen Academia Sinica
Shih-Fen Cheng Singapore Management University
Meng-Fen Chiang Living Analytic Research Centre, Singapore Management University
Chi-Yin Chow City University of Hong Kong
Yunjun Gao Zhejiang University
Hui-Huang Hsu Tamkang University
Haibo Hu Hong Kong Polytechnic University
Siyuan Liu Penn State University
Meng-Shiuan Pan Tamkang University
Chih-Ya Shen National Tsing Hua University
Hong-Han Shuai National Chiao Tung University
Yazhe Wang Living Analytic Research Centre, Singapore Management University
Hai Wang Singapore Management University
Fang-Jing Wu NEC Europe Labs
Jianliang Xu Hong Kong Baptist University
Dongxiang Zhang University of Electronic Science and Technology of China