Smarter, Resilient, and Fairer Cities

with Deep and Rich Models of Human

Activities and Mobility Behaviours

Flora Salim

Deputy Director, Centre for Information Discovery and Data Analytics

Associate Prof, CS&IT, School of Science, RMIT University, Melbourne, Australia

Humboldt Fellow, University of Kassel, Kassel, Germany

From Human Activity and Behavioural Patternsto Prediction and Optimisation of Urban Resources

Sadri, A., Salim, F.D., Ren, Y., Shao, W., Krumm, J., Mascolo, C. (2018) What Will You Do for the Rest of the Day?

An Approach to Continuous Trajectory Prediction, ACM IMWUT / Ubicomp), Vol. 2, No. 4, 186, Dec 2018

Observations: Morning and afternoon similarity

What Will You Do for the Rest of the Day? An Approach to Continuous Trajectory Prediction

Accurate energy use prediction with deep learning

Hourly average prediction error Weekly average prediction error Monthly average prediction error

H. Song, A. K. Qin & F. D. Salim, Evolutionary Model Construction for Electricity

Consumption Prediction, Neural Computing and Applications, pp. 1-19, 2019.

Pedestrian Traffic Forecasting SystemRMIT & City of Melbourne

Melbourne City Council Pedestrian Counting System

Models tested with datasets from New York JFK, La Guardia, and Newark airports

Outcomes published in:• IEEE Access• K-CAP• PACIS

From Human Mobility to Transport Demand Prediction

using taxi, flight, border control, and weather data

Mornington Peninsula Smart Parking and

Amenities for High Demand Areas

• Visitor demand prediction

• Parking recommendation

• Day trip planner

Parking Availability Prediction with Contextual Features

Predicting Parking

Occupancy in New Urban Areas with Clustering

of Contextual Features, IEEE Transactions on

Intelligent Transportation Systems, under review.

Clustering Big Spatiotemporal-Interval Data

case study: balancing parking demands across the whole city

W. Shao, F. Salim, A. Song, and A. Bouguettaya, “Clustering big spatiotemporal-interval data,” IEEE Transactions on Big Data, 2016.

From Human Mobility to Crime PredictionExample cities: Brisbane, New York, Chicago

Individual Risk Factor Analysis of Visitors

S. K. Rumi, K. Deng, and F. D. Salim, “Theft prediction with individual risk factor of

visitors,” in Proceedings of the 26th ACM SIGSPATIAL International Conference on

Advances in Geographic Information Systems. ACM, 2018, pp. 552–555.

Rumi, S.K., Deng, K., Salim, F.D. (2018), Crime event

prediction with dynamic features, EPJ Data Science

(2018) 7: 43.

Predicting when & where crime will strike next

Multiple Traveling Officer Problem

Shao, W., Salim, F. D., Gu, T., Dinh, T., Chan, J., “Travelling Officer Problem: Managing Car Parking Violations Efficiently Using

Sensor Data”, IEEE Internet of Things Journal, 2018.

Kyle K. Qin, Wei Shao, Yongli Ren, Y., Jeffrey Chan and Flora D. Salim, 2019. Solving Multiple Travelling Officers Problem with

Population-based Optimization Algorithms. Neural Computing and Applications, pp.1-27.

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Car Leaving

Probability

Model

Changing

State of

Map

Cooperation

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Thousands

of parking

bays

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Empowering Users with Personalised

Recommendation

© Westfield

From Individual and Group Activities to Personalised

Recommendations Indoors

, Y., Tomko, M., Salim, F.D., Ong, K., Sanderson, M. (2015). “Analyzing Web Behavior in Indoor Retail

Spaces. Journal of the Association for Information Science and Technology (JASIST). Vol. 68, 1, Jul 2015.

Cyber Physical Social (CPS) Contexts → Behaviors

, Y., Tomko, M., Salim, F.D., Ong, K., Sanderson, M. (2015). “Analyzing Web Behavior in Indoor Retail

Spaces. Journal of the Association for Information Science and Technology (JASIST). Vol. 68, 1, Jul 2015.

Analysing User Demographics

with CPS Behaviours

Physical: frequency,

weekdays, duration,

interests in shop

categories

Cyber: WiFi

frequency, search

frequency, what to

browse/search

Social: single, with kids, with another adult, in a group

–age: 18-24, 25-39, 40-54, 55+

–education level: Secondary/high school,

Honours degree?

–income: 0-$18,200, $18,201-$37,000,

$37,001-$80,000, $80,000+

–parental status: having kids?

–shopper category: Inner or Rest of Sydney

resident, CBD Worker, Domestic tourist,

International tourist

Ren, Y., Tomko, M., Salim, F.D., Chan, J., Sanderson, M.. “Understanding the Predictability of User

Demographics from Cyber-Physical-Social Behaviours in Indoor Retail Spaces”. EPJ Data Science 7(1), 2018.

Location-Query-Browse Graph for Contextual Recommendation

Y. Ren, M. Tomko, F. Salim, J. Chan, C. L.A. Clarke and M. Sanderson. “A Location-

Query-Browse Graph for Contextual Recommendation”. IEEE Transactions on

Knowledge and Data Engineering (TKDE). 30(2): 204-218 (2018)

From Cyber-

Physical-Social

Behaviours to Task

and Productivity

Assistance

RMIT-Microsoft Cortana

Intelligence InstituteOverview:

Cortana Intelligence Institute is driving the next-generation of

capabilities for Microsoft’s digital assistant, Cortana. Focused on

researching work-related tasks and using sensors in mobile

phones, the CII team builds a complex multidimensional data set,

used to model and predict user’s work-related tasks.

Impact:

● Task intelligence, to support complex tasks such as tracking

a person's progress on a task, reminders, or assisting with

completion of a task.

● Create a virtual assistant that can manage a calendar,

understand the user, be aware of context, and support

multi-turn dialogues.

Automated Decision-Making

and Society

https://www.arc.gov.au/2020-arc-centre-excellence-automated-decision-making-and-society

Context Recognition and Urban Intelligence (CRUISE), a part

of Centre for Information Discovery and Data Analytics (CIDDA)

Acknowledgment: Our Collaborators

flora.salim@rmit.edu.au