LiTrans @ 2018 IEEE Int. Smart Cities Conference

In the context of Smart Mobility research, LiTrans, Ryerson University is presenting following work at IEEE 4th International Smart Cities Conference (Sep 16-19, 2018):

Impact of Distributed Routing of Intelligent Vehicles on Urban Traffic
Lama Alfaseeh, Shadi Djavadian, and Bilal Farooq
Monday, September 17, 2018
Session 3: Transportation and Mobility – 1 (1:30 pm – 3:30 pm)

A blockchain framework for smart mobility
David Lopez and Bilal Farooq
Tuesday, September 18, 2018
Session 6: Transportation and Mobility – 2 (11:15 am – 12:10 pm)

Mobility Mode Detection Using WiFi Signals
Arash Kalatian and Bilal Farooq
Wednesday, September 19, 2018
Session 9: ICT – 2 (11:15 am – 12:10 pm)

We look forward to the great feedback from our colleagues.

AI@Civil.Eng 2018-2019 Seminar Series

AI@Civil.Eng

Department of Civil Engineering

Faculty of Engineering and Architecture Science

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The use of learning approaches in modeling traffic

Presenter: Professor Mario Cools, University of Liège

Location: EPH111

Date: Thursday August 16, 2018. 11:00AM—12:00PM

The presentation will tackle different examples of the use of learning approaches in the field of transportation, in particular in the case of transport modeling. First, the applications in the context of population synthesis and activity pattern generation are illustrated. Recent advances in agent-based micro-simulation modeling have further highlighted the importance of a thorough full synthetic population procedure for guaranteeing the correct characterization of real-world populations and underlying travel demands. In this regard, we propose an integrated approach including Markov Chain Monte Carlo (MCMC) simulation and profiling-based methods to capture the behavioral complexity and the great heterogeneity of agents of the true population through representative micro-samples. The population synthesis method is capable of building the joint distribution of a given population with its corresponding marginal distributions using either full or partial conditional probabilities or both of them simultaneously. In particular, the estimation of socio-demographic or transport-related variables and the characterization of daily activity-travel patterns are included within the framework. The fully probabilistic structure based on Markov Chains characterizing this framework makes it innovative compared to standard activity-based models. Moreover, data stemming from the 2010 Belgian Household Daily Travel Survey (BELDAM) are used to calibrate the modeling framework. We illustrate that this framework effectively captures the behavioral heterogeneity of travelers. Furthermore, we demonstrate that the proposed framework is adequately adapted to meeting the demand for large-scale micro-simulation scenarios of transportation and urban systems. Afterwards, a new Singular Value Decomposition-Latent Dirichlet Allocation (SVD-LDA) model will be presented to detect congestion patterns. The model is calibrated based on the taxi trip dataset, collected in the city of Chicago in 2016. The model specifies the trends in terms of traffic congestion patterns, also called topics in the literature. The main findings of this study reveal that, within day, SVD-LDA succeeds in distinguishing traffic patterns in terms of peak and off-peak hours with highly balanced topics. Furthermore, for day-to-day patterns, the model highlights stable traffic patterns across days. This lack of daily variability resulted in highly unbalanced share of topics.

Biography

Mario Cools (Lier, 1982) holds a master degree in applied economics, major quantitative business economics, minor operations research (University of Antwerp, 2004) and a master degree in applied statistics (Hasselt University, 2005). After obtaining his statistics degree, he started working as a PhD candidate at the Transportation Research Institute of Hasselt University, where he obtained his PhD in transportation sciences in 2009. After having continued his research as a post-doctoral researcher, Mario Cools was granted a post-doctoral fellowship from the Research Foundation Flanders (FWO) in 2010. Consequently, he worked at the KU Leuven Campus Brussels as an assistant professor in the Faculty of Economics and Management, and 10% as assistant professor the Faculty of Business Economics of Hasselt University. Currently, Mario Cools is appointed as full-time associate professor at the Faculty of Applied Sciences of the University of Liège. He is author and co-author of different scientific publications in research domains such as travel behaviour, transport policy and activity-based travel demand models.

 

AI@Civil.Eng March Seminar

 

AI@Civil.Eng

Department of Civil Engineering

Faculty of Engineering and Architecture Science

Ryerson-rgb

Using Wi-Fi data to infer transportation mode

Presenter: Arash Kalatian, Ryerson University

Location: MON316

Date: Thursday March 22, 1:00PM—2:00PM

The intrinsic problems of conventional data collection methods have led researchers to explore the automated alternative approaches for acquiring data. One of the most prevailing methods of such approaches is the smartphone based data collection. In this study, we have utilized Wi-Fi communications obtained from smartphones of participants to predict their mode of transportation, i.e. walking, biking and driving. We deployed Wi-Fi sensors on four strategic locations in a closed loop of urban streets. These sensors are capable of recording and saving signal data of Wi-Fi enabled smartphones within their range, with no need for participant to log on. Participants were asked to walk, bike or drive in the area with their Wi-Fi enabled smartphones in their pockets. A dataset of 400 observations is created with 15 features, which are based on travel time, signal strength and connection time. The collected features were then used to classify participants based on their mode of transport. Three decision tree-based classifiers were trained: Decision Tree, Bagged Decision Tree and Random Forest, and more advanced methods are to be trained in the future. Validation results revealed that using the Random Forest method, transportation mode was predicted correctly in more than 80% of cases, with 87.72% accuracy for walking, 77.08% for biking and 83.64% for driving mode.

Biography

Arash Kalatian is a PhD student in transportation engineering at Ryerson university. Working as part of LITrans team under supervision of Dr. Bilal Farooq, his research mainly involves replacing conventional tools used in transportation engineering with modern alternatives. Currently, he is working on analyzing the interactions between pedestrians and autonomous vehicles, using a virtual reality environment. Besides that, he is also developing a framework to utilize Wi-Fi data for inferring traffic parameters, specifically mode of transportation.

New Positions Available at LITrans

We have following 3 positions available for CarbonCount project (https://carboncount.io/). Please contact me with your C.V.

1. Android Developer – CarbonCount Project
Responsibilities
Develop a mobility app for Android platforms to log users’ activity pattern and communicate personalized messages to them. Implementation of the communication with the GHG emissions platform. Assist in the implementation of a web interface, dashboard system for viewing data collected from the app and implementation of customized behavioural change strategies.

Requirements
Experience with Android Studio. Experience in Java, Objective C, JSON, C++ or similar languages. Exposure to collaborative tools such as Git. Strong communication & collaboration skills. Experience in Agile software development.

Preferred
Experience writing multi-threaded and networked apps. Experience with reducing battery consumption. Experience with using sensory devices on Android phones. Experience with logging MAC address of routers in the vicinity. Advanced UI skills (Fragments, Custom View components). Understanding of API design, RESTful concepts. Experience with web development, backend development or iOS development.

2. iOS Developer – CarbonCount Project
Responsibilities
Develop a mobility app for iOS platforms to log users’ activity pattern and communicate personalized messages to them. Implementation of the communication with the GHG emissions platform. Assist in the implementation of a web interface, dashboard system for viewing data collected from the app and implementation of customized behavioural change strategies

Requirements
Experience in developing mobile applications and libraries for various iOS platforms including the latest iOS versions. Experience in Swift, Objective C, Java, JSON, C++ or similar languages. Exposure to collaborative tools such as Git. Strong communication & collaboration skills. Experience in Agile software development.

Preferred
Experience writing multi-threaded and networked apps. Experience with reducing battery consumption. Experience with using sensory devices on iPhone phones. Experience with logging MAC address of routers in the vicinity. Experience with web development, backend development or Android development. Understanding of API design, RESTful concepts.

3. Backend Developer – CarbonCount Project
Responsibilities
Integration of user-facing elements with server side logic. Implementation of user profiles, security, and data protection layers. Optimization of the application for maximum speed and scalability. Design and implementation of data storage solutions. Assist in the implementation of a web interface, dashboard system for viewing data collected from the app and implementation of customized behavioural change strategies

Requirements
Basic understanding of front-end technologies and platforms, such as HTML5 and CSS3. Knowledge of one or more of Python, Ruby, PHP, Java, .NET, JavaScript, or similar. Exposure to collaborative tools such as Git. Experience in Agile software development. Strong communication & collaboration skills.

Preferred
Experience developing with Angular JS and Django/Python or similar. Experience running server-side automation scripts. Management of hosting environment, including database administration. Experience with web development, Android or iOS development.

LITrans has moved to Toronto

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Starting May 1, 2017, Laboratory of Innovations in Transportation has moved to Ryerson University. Dr. Farooq has been nominated as Canada Research Chair in Disruptive Transportation Technologies and Services by Ryerson. This is a very exciting position where he will be tackling various planning, design, and operational issues related to sustainable development of cyber-physical transportation systems of tomorrow. LITrans will be part of the new multidisciplinary Ryerson Centre for Urban Innovation starting April 2018.