Friday, November 6, 2015

The Science of Cities by Michael Batty


Last Friday Michael Batty was presenting in Melbourne, thanks to a joint event by AURIN and CSIRO. I read Batty's book on "New Science of Cities" about two years ago. My interest in cities and networks formed when I first read Batty's book and then Newman's book on networks. Since then, I've been trying to apply theories from network science to urban/transportation data with a touch of visualization to better understand cities, mostly focusing on Melbourne.

Wednesday, October 28, 2015

Changing Melbourne 2.0: How socio-demographic characteristics of Melbourne are changing over time (2006-2011)


Cities are dynamic, connected, and complex systems that are constantly evolving in many and varied ways. Melbourne, identified as the most liveable city in the world by The Economist's liveability ranking 2011-2015, is home to more than 4 million residents. Melbourne’s population is expected to grow to between 7.6 and 9.8 million in 2061. Melbourne is also one of the largest contributors to wealth and energy consumption in Australia.

In this project, we use visual analytics to demonstrate how socio-demographic characteristics of Melbourne are changing over years. We use data from ABS Census 2006 and 2011 to provide a comparative analysis. We have developed a series of dot maps in which each dot is a person counted in the Census. Our goal here is to improve our understanding of Melbourne, as a “living” system, and to provide insights into creating a data-driven approach to urban design and planning. The project is supported by a joint seed funding from the Faculty of Engineering and Faculty of Information Technologies at Monash University in collaboration with Prof. Kim Marriott, Dr. Tim Dwyer, and Prof. Majid Sarvi.

Link to the project: https://t.co/rJU92cpac2

Thursday, September 24, 2015

A Complex Network Perspective for Characterizing Urban Travel Demand Patterns

Recent Working Paper:

Saberi, M., Hosseini, A., Mahmassani, H., Brockmann, D. A Complex Network Perspective for Characterizing Urban Travel Demand Patterns. [Working Paper]
Urban travel demand, consisting of millions of origin-destination trips, can be viewed as a large-scale network. The paper introduces a complex network-motivated approach to understand, characterize and model urban travel demand patterns. We compare selected network characteristics of travel demand patterns in two cities, presenting a comparative network-theoretic analysis of Chicago and Melbourne. The proposed approach develops an interdisciplinary and quantitative framework to understand and model dynamical aspects of mobility in urban areas. Statistical properties of the complex network of urban trips of the selected cities are explored. We show that travel demand networks exhibit similar properties despite their differences in topography and urban structure. Results suggest that the underlying dynamical processes in travel demand networks are similar and evolved by the distribution of activities and interaction between places in cities. Results provide a first step towards a new methodological base for calibration and validation of agent-based travel demand models.

Thursday, September 17, 2015

Exploring the Effects of Land Use, Travel Behavior, and Socio-Economic Characteristics on Safety at the Planning Level: Empirical Evidence from Melbourne

Recent Working Paper:

Amoh-Gyimah, R., Saberi, M., Sarvi, M. Exploring the Effects of Land Use, Travel Behavior, and Socio-Economic Characteristics on Safety at the Planning Level: Empirical Evidence from Melbourne. [Working Paper]
Understanding the relationship between road crashes, traffic, socio-economic, and land use characteristics is necessary in evaluating the safety impacts of urban policies at the planning level. The aim of this paper is to provide further empirical evidence on the significance and magnitude of various planning factors that influence transportation safety at the network level. We estimate multiple Negative Binomial (NB) regression models to explore the impact of different planning factors, individually and combined, at different geographical levels. Results suggest that vehicle kilometers traveled (VKT), percentage of population cycling to work, percentage of households with low income, and land use balance mix index significantly influence number of crashes. We also show that using different spatial units could produce different modeling outcomes, as expected. The study is a first step towards integration of safety modeling into the transportation planning.

Forecasting Vehicle Kilometers Traveled: Estimating an Autoregressive Integrated Moving Average Model (ARIMA) with Exogenous Variables

Recent working paper:

Nazemi, M., Shafiei, M.S., Saberi, M., Sarvi, M. (2015) Forecasting Vehicle Kilometers Traveled: Estimating an Autoregressive Integrated Moving Average Model (ARIMA) with Exogenous Variables. [Working Paper]
Vehicle kilometers traveled (VKT or VMT) is a key variable in long-term transportation planning and policy making, especially for road infrastructure investment. In this paper, we estimate an Autoregressive Integrated Moving Average (ARIMA) model with exogenous variables to forecast VKT in Australia. The estimated model produces forecasts based on lagged values in the time series and the errors made by previous predictions, which typically allows the model to rapidly adjust for sudden changes in trend, and at the same time includes exogenous variables resulting in more accurate forecasts. We found that the effects of unemployment rate, fuel price over Gross Domestic Product (GDP) per capita, and Consumer Price Index (CPI) on VKT are all statistically significant and negative. More importantly, number of vehicles per capita appears to be highly statistically significant with positive impact on VKT. We postulate that policies or behavioral changes towards less car ownership are more likely to have greater impact on VKT rather than fluctuations in fuel price, unemployment rate, or individual’s purchasing power.

Sunday, September 6, 2015

GovHack 2015: 1st Prize in Best Data Journalism Hack

I am happy to announce that my group (Monash City Science) is awarded the 1st prize in Best Data Journalism Hack in GovHack 2015 for the project "Mapping Pedestrian Activities and Safety in Melbourne."

In the project, we analyzed and visualized the relationship between pedestrian activities and safety over time in City of Melbourne using combined pedestrian count sensors data and pedestrian crash statistics.

Special thanks to Julian, Bryan, Emily, and Sajjad for being part of the Monash City Science team.

UPDATE 1: Here is a video from the award ceremony. Watch @38:57.



UPDATE 2: Photo with award sponsors ABC Australia and Australian Taxation Office



Wednesday, September 2, 2015

3D modeling with 123D Catch app

Here is a 3D model of mine sitting on a chair in the class. We built this on the spot in ENG1021 (Spatial Communication in Engineering) class today. The 3D model is built from more than twenty 2D pictures taken by a cell phone/mobile camera. Not too bad, ha? Make sure you click on "3D view". http://www.123dapp.com/Catch/ENG1021-Meead/4365088

The 3D model is built using an image-based technology, 123D Catch app: http://www.123dapp.com/catch 
It's a free app. So don't expect a powerful matching algorithm here.


Tuesday, September 1, 2015

Historic Papers on Network Traffic Flow Theory (NFD/MFD)

Here is a list of historic papers on network traffic flow theory which is currently known as Macroscopic Fundamental Diagram (MFD) or Network Fundamental Diagram (NFD). Thanks to the Northwestern University Transportation Library, I managed to get a scanned copy of the papers. Click on the title of each paper to view the scanned PDF.

1960's
1970's
1980's
  • Mahmassani, H.S., Williams, J.C., Herman, R., 1984. Investigation of network-level traffic flow relationships: some simulation results. Transportation Research Record: Journal of the Transportation Research Board, No. 971, 121-130.
  • Mahmassani, H.S., Williams, J.C., Herman, R., 1987. Performance of urban traffic networks. Proceedings of the 10th International Symposium on Transportation and Traffic Theory, Elsevier Science Publishing, 1-20.
  • Williams, J.C., Mahmassani, H.S., Herman, R., 1987. Urban traffic network flow modelsTransportation Research Record: Journal of the Transportation Research Board, No. 1112, 78-88.
1990's

Thursday, August 27, 2015

Bublcam 360 degree test image of St Kilda Beach

My bublcam has finally arrived. Here is the first outdoor test image in St Kilda Beach, Melbourne. Next step is to figure out how to view the image in VR gear. This will open up a lot of opportunities for pedestrian choice behavior experiments.

Wednesday, August 19, 2015

PhD Position in Big Data Analytics and Visualization in Public Transportation

We have 18 open PhD positions in public transport at Monash University starting January 2016. EOI submission deadline is September 30th. I'll be supervising one of the PhD projects on big data analytics and visualization: understanding and modeling public transport travel patterns using smart card data (myki data from Melbourne). Let me know if you're interested.

Saturday, August 15, 2015

City Science at Monash University: A Year After

Since June 2014 when Monash City Science group was first founded, we have proudly developed and delivered 18 #opendata #bigdata #urbanplanning #transportation #dataviz projects: from mapping pedestrian and bicycle safety in Melbourne to visualizing the spatial distribution of indigenous population in Australia. 

In the past 14 months, we have had more than 22,000 visitors (43% from Melbourne, 6% from Sydney, even 1.5% from London, and 1% from New York). Our most popular project has been the "Melbourne Bike Crash Map" http://goo.gl/nRZh9n with more than 16,000 hits. 

I have received many compliment emails from a wide range of people and organizations, from BreastScreen Victoria to the Federal Department of Infrastructure and Regional Development in Canberra. I am very happy that our work has been useful/helpful/educative/informative/attractive for many people. We're looking forward to many more challenges and opportunities ahead in the next couple of years. We welcome any financial and technical/scientific contribution from individuals and organizations. Please contact me at meead.saberi@monash.edu if you're interested.

Saturday, August 8, 2015

Places by Metro: Reinventing Public Transport User Experience


Places by Metro is an interactive map reinventing public transport users experience, connecting businesses to travelers across Melbourne. It helps travelers discover bars, restaurants, and cafes within walking distance from train stations (PTV). It uses Google’s Javascript API Library to source the highest rated venues and to provide specific details such as address, opening hours, and customer reviews. We also use Google Maps to reference walking directions to each venue from the selected train station.

Tuesday, August 4, 2015

ENG1021: Experiencing Virtual Reality (CAVE & Oculus Rift)

As part of ENG1021 (Spatial Communication in Engineering), I took my students to the CAVE. Students were given a 15 minutes tour of the Monash Immersive Visualization Platform including a demo of the structure model of the Oakland Bay Bridge in the US, ARUP point cloud of Melbourne city building plant room, NASA's Mars flight. I also brought my Samsung VR gear to the class and gave it to students to experience VR. Everyone were very impressed and loved it. Students were asked to describe how they relate their VR experience to spatial communication and our unit's learning objectives? I also asked them whether the experience generated any innovative engineering or business idea in their minds? Sorry for the not so high quality of the photos. It was dark in there.

 

Friday, July 31, 2015

Mapping Pedestrian Activities and Safety in Melbourne

As part of GovHack 2015, we have also developed a new analytics and visualization project on pedestrian activities and safety.


City of Melbourne has more than 40 pedestrian count sensors installed across the city, monitoring pedestrian activity in real-time. In this project, we integrate pedestrian count data and pedestrian crash data (obtained from VicRoads, downloaded from Victorian Government Data Directory) to explore the temporal and spatial distribution of pedestrian activities and crashes.

See the interactive project page here: http://monash.edu/research/city-science/pedsafety/

We would like to take this project to the next step, applying advanced analytical techniques and develop a predictive model to forecast both pedestrian activities and safety. We appreciate any financial support from relevant stakeholders, especially City of Melbourne, to continue the project.

Sunday, July 26, 2015

Art and Science: Indigenous Population Dot Map of Australia


A few weeks ago, we developed a new data visualization project: Indigenous Population Dot Map of Australia

This is special because of its relation to indigenous art. In this project, every single indigenous in Australia is mapped as a dot. Aboriginal dot paintings are a rich art work of indigenous population in this land. Now we're connecting art and science, showing the power of data science and visualization in a single map. Enjoy the interactive map:

Sunday, July 5, 2015

GovHack 2015 @Monash University: Hacking Government Open Data


GovHack 2015 is over with more than 400 teams competing over the past weekend. GovHack is an annual open data competition held all over Australia and New Zealand. GovHack is about bringing together the best and brightest, working with government data to innovate and create.

Cities around the world are increasingly capitalizing on public data through local and regional “Open Data” initiatives and platforms. A major goal of such programs is to improve transparency and efficiency of government services. However, using big open data to generate economic and social value relies on the methodological capacity to render the masses of data into meaningful and, most importantly, useful information. Being able to extract useful findings is crucial to making cities 'smarter', empowering new civic movements, and changing the way citizens experience urban life.

The potential benefits of open data often go beyond generating economic activity. The social impact of open data is significant too, although less recognized. It could improve political transparency, enhance education and research, support personal decision-makings, promote more inclusive developments, support advocacy efforts, and increase public data literacy.

Over the past weekend, we hosted a GovHack 2015 node at Monash University, Clayton with four enthusiastic teams getting together to blast the competition with six projects. Projects submitted from our node include:
  1. Mapping Pedestrian Activities & Safety in City of Melbourne by Monash City Science
  2. Indigenous Population Dot Map of Australia by Monash City Science
  3. Chinese Population Dot Map of Australia by Monash City Science
  4. Disaster Warning Service for Australian Transportation by The Four
  5. Affordably by XYW
  6. Don't Panic by the Two David's
We hope events like this improve industry-government-university relationships for a better and brighter future. Thanks to all the awesome volunteers and participants for such a great weekend. If you would like to discuss potential collaboration opportunities, please do not hesitate to contact us. We'd love to work with you to deliver amazing projects.

Tuesday, June 23, 2015

Point Cloud 3D Model of the Civil Engineering Department at Monash University

Thanks to Carlos Gonzalez from 3D Laser Mapping for demonstrating how their new product ZEB works. Following is a point cloud 3D model of the Civil Engineering department at Monash imported and visualized in AutoCAD.


Saturday, May 30, 2015

Melbourne Pedestrian Crash Map (Light Condition)

Animated Heatmap: Melbourne Pedestrian Crashes (2009-2013)

We have developed an animated heatmap of pedestrian crashes showing spatial and temporal distribution of pedestrian crashes in Melbourne over 5 years (2009-2013). This is an easy and quick tool to identify pedestrian crash hotspots. Thanks to my student Roshan Manage Don for his efforts with cleaning and analyzing the data (source: VicRoads). We will soon release a report summarizing some statistics on pedestrian crashes in Melbourne.

Click here to view the interactive tool (fullscreen): https://goo.gl/NSmJ5R




Last class of semester 1, 2015: CIV5305 Transport Modelling

Semester 1, 2015 ends with all good memories. I had the chance to teach CIV5305: Transport Modelling to a few bright and enthusiastic Master's students. Four came from the Monash-Southeast University joint master program in China and the rest were doing their Master's of Advanced Engineering. I also had about 20 online students doing a distance-education based Master's. The focus of the unit was mostly on travel demand modelling covering four-step modelling with a bit more into disaggregate choice modelling and traffic assignment, simulation-based modelling and lastly activity-based models. Thank you all for the fun class. Hope you're happy with your learning experience.

Tuesday, May 26, 2015

Transportation Data Scientist

Data Scientist is a newly introduced type of job in the data analytics community. It is also known as the sexiest job of the century. The job of a data scientist is to analyze complex or big data to understand and predict various patterns.

What do you need to become a data scientist?

  • You need to know analytics and programming in R, SAS, Python, Matlab, or other analytical/programming tools.
  • You also need to know how to work with databases (e.g. MySQL).
  • You probably improve your employability if you know some JavaScript and HTML to do cool data visualizations.
  • You also need to boost up your skills with some big data and machine learning skills.
Data plays a key role in transportation planning, operations, and management. Is it time for the transportation industry & government agencies to open up positions for transportation data scientists? Maybe hiring Chief Data Officers (CDO)?

I personally believe we can do much better with transportation data. There is certainly a need for highly skilled data scientists to pump some new blood into the transportation profession & research. Integrating advanced machine learning and big data techniques with traditional transportation analytics could revolutionize the transportation industry and research.


Monday, May 25, 2015

John Nash, Wardrop's principles, and the concept of equilibrium in traffic assignment

Here is an email that I sent to my "Transport Modelling" class today.

Hi class,

You might have heard the news that John Nash has died in a car crash today. I am not sure if anyone of you has taken a course or read a book on "Game Theory". However by now, you all know about Wardrop's principles and the User Equilibrium and System Optimal concepts in traffic assignment. This isn't very different from Nash equilibrium in game theory.

In a non-cooperative game of two or more players, if each player chooses a strategy and no player benefits by changing strategies while the other players stick to their strategies, then this is called Nash equilibrium. See this scene from the movie Beautiful Mind: https://www.youtube.com/watch?v=KT4fujOmPF8




Now compare this with Wardrop's UE in which no user will benefit by changing routes and SO in which the entire society benefits, just like the blond girl scene in the bar in the movie.

Anyways, just wanted to share my thoughts and maybe trigger more thinking in you.

Best,
Meead

Sunday, May 17, 2015

Melbourne Income Map (2011)


Income is a key component of an household economic wellbeing. According to the Australian Bureau of Statistics, transport is the highest household expenditure after housing costs and food and non-alcoholic beverages, accounting for 16% of total household income. Income is also an important socio-demographic variable that influences travel behavior and residential location choice patterns.

In this project, we are visualizing the spatial distribution of household income in greater Melbourne area. We use Census data from 2011. Household income is derived from personal income information collected for all persons aged 15 years and over. It includes all wages and salaries, government benefits, pensions, allowances and any other income before deductions for tax, superannuation, health insurance, salary sacrifice, or any other deduction.

We group the population into four groups. Households earning $0-$399 weekly account for about 13% of the number of households live in the greater Melbourne area. About 19% of households earn more than $2500 weekly. The rest of the households (representing lower and upper middle class families) are grouped into two groups, earning $400-$1249 and $1250-$2499 weekly.

Link to the visualization: http://monash.edu/research/city-science/MelbourneIncomeMap/#map

Victoria Connect: Crowd-sourced powered infrastructure asset management

I am very delighted to announce that we have recently published the BETA version of our new app on Google Play (for Android devices only for now). 

Victoria Connect helps Victorian residents make their neighborhood works better by reporting issues such as potholes, damaged signs, signal malfunction, unsafe locations, crashes, near-crashes, debris on the road, dead animal, etc.

Reports are collected and archived at a database hosted at Monash University for research purposes. This is part of the Transport Infrastructure Decision Support Platform being developed at Monash University.

If you have an Android device, I would appreciate if you install the app on your device and help us collect some data and make the app works better by providing feedback.

Our goal is to involve local governments, city councils, etc in this project. We would also like to take a step further, integrating the crowd-sourced and authoritative data for a better resource allocation in asset management, resulting in improved service to citizens.

Tuesday, May 5, 2015

How Monash University treats students from "sanctioned" countries


Monash University has a sanction compliance policy when it comes to admission and enrollment of students and provision of education and research training procedures. I believe this is somewhat against the academic/science freedom, preventing any human being to pursue education and research simply based on where they come from. Academic and science freedom works outside economically- and politically-oriented sanctions laws, for the benefit of the society and science.

Sanctioned countries recognized by Monash include:
  1. Central African Republic
  2. Côte d'Ivoire
  3. Crimea and Sevastopol*
  4. Democratic Republic of 
  5. Congo
  6. DPRK (North Korea)*
  7. Eritrea 
  8. Former Federal Republic of 
  9. Yugoslavia
  10. Guinea – Bissan
  11. Iran*
  12. Iraq
  13. Lebanon
  14. Liberia
  15. Libya
  16. Myanmar
  17. Russia*
  18. Somalia
  19. Sudan
  20. Syria*
  21. Ukraine
  22. Yemen
  23. Zimbabwe
* represents "harsher sanction law countries"!! Whatever it means.
Note: There are currently 196 countries in the world.
"Under the Sanctions Laws, the University is prohibited from dealing with specific individuals and entities, or providing those individuals, entities and specified countries with access to specific types of training, services and resources. The training, services or resources targeted by the sanctions are those relevant to military purposes or the development of weapons of mass destruction, and for a small number of sanctioned countries also specified dual use goods (being resources that have a military purpose and also have a legitimate civilian purpose). The Sanctions Laws aim to ensure the University does not equip targeted individuals, entities or nations with these resources or the skills to utilise these resources." Source: Monash sanctions compliance policy
When a student from any of the sanctioned countries apply to study at Monash, either being undergraduate or postgraduate, his application will go through a sanction compliance process. The applicant's supervisor  has to fill different forms and do an assessment whether the research or education could involve sanctioned activities.

There is also a "Sanctioned Good Risk Management Plan" which specifically deals with sanctioned materials. Following is a ridiculous part of the management plan:
"Monash University will permit the researcher to continue to undertake the research activity if the following conditions are strictly adhered to:
  1. The researcher must not enter into the laboratory where the sanctioned good is located, except under supervision.
  2. The researcher will not personally conduct any research using the sanctioned good.
  3. The researcher will issue instructions to the research assistant for all activity involving the sanctioned good.
  4. The research assistant will conduct all activity pursuant to those instructions without further reference to the researcher.  Requests for advice and assistance relating to use of the sanctioned good will be directed to some other person, and not the researcher.
  5. The researcher will not be present when the research assistant uses the sanctioned good.  
  6. The researcher will not observe, supervise, monitor or direct the use of the sanctioned good.
  7. When the research assistant has completed the activity using the sanctioned good, the research assistant will provide the outputs to the researcher.  The research assistant will not discuss the process or activity of the use of the sanctioned good with the researcher.
  8. The role of the researcher will be limited to issuing the initial instructions to the research assistant, and receiving the outputs from the use of the sanctioned good by the research assistant.
  9. No Monash University staff member or student will provide the researcher with access to, instruction about, assistance with, or training in the use of the sanctioned good."
APPENDIX

Following is a specific assessment currently (as of today) being done for students from Iran.

Australia Group Common Controls List at http://www.australiagroup.net/en/controllists.html which deals with:
- Chemical Weapons Precursors
- Dual-use chemical manufacturing facilities and equipment and related technology and software
- Dual-use biological equipment and related technology and software
- Biological agents
- Plant pathogens
- Animal pathogens.

INFCIRC/254/Rev.9/Part 1 at http://www.un.org/ar/sc/committees/1737/pdf/INFCIRC1.pdf which deals with export of nuclear materials, equipment and technology.

 INFCIRC/254/Rev.7/Part 2 at http://www.un.org/sc/committees/1737/pdf/INFCIRC_254_Rev.7_Part2.pdf which deals with transfer of nuclear-related dual-use equipment, materials, software and related technology.

S/2010/263 at http://www.un.org/ga/search/view_doc.asp?symbol=S/2010/263 which deals with items, materials, equipment, goods and technology related to ballistic missile-related programmes.

 The annex to A/RES/46/36 L dated 6 December 1991 at http://www.un.org/Depts/ddar/Register/4636.html which deals with arms and related materiel.

 S/RES/1737 at paragraph 3(d) at http://www.securitycouncilreport.org/atf/cf/%7B65BFCF9B-6D27-4E9C-8CD3-CF6E4FF96FF9%7D/Iran%20SRES%201737.pdf which deals with materials, equipment, goods and technology that could contribute to enrichment-related, or reprocessing, or heavy water-related activities or the development of nuclear weapon delivery systems.

 S/RES/1929 at paragraph 8 http://www.iaea.org/newscenter/focus/iaeairan/unsc_res1929-2010.pdf which deals with arms and related materiel.

Charter of United Nations (Sanctions – Iran)(Export Sanctioned Goods) List Determination 2008 at http://www.comlaw.gov.au/details/f2011c00901/download open PDF and see schedule 1, which deals with:
- nuclear materials, facilities and equipment
- nuclear materials, chemicals, microorganisms and toxins
- material processing
- electronics, sensors and lasers
- navigation and avionics
- technology
- dual use goods of utility in a nuclear program.
Autonomous Sanctions (Export Sanctioned Goods – Iran) Specification 2012 at http://www.comlaw.gov.au/Details/C2014G00117 which deals with:
- exploration and production of crude oil and natural gas
- refining crude oil and liquefaction of natural gas
- petrochemical industry.
Autonomous Sanctions (Export Sanctioned Goods – Iran) Amendment Specification 2013 at http://www.comlaw.gov.au/Details/C2014G00121 which deals with:
- graphite, iron and steel, copper and articles thereof, nickel and articles thereof, aluminium, lead, zinc, tin and other base metals, cermets and articles thereof
- key naval equipment and technology
- software for integrating industrial processes

Monday, April 27, 2015

Spatial Fluctuations of Pedestrian Velocities in Bidirectional Streams: Exploring the Effects of Self-Organization

Saberi, M., Aghabayk, K., Sobhani, A. (2015) Spatial Fluctuations of Pedestrian Velocities in Bidirectional Streams: Exploring the Effects of Self-Organization, Physica A: Statistical Mechanics and Its Applications (in press) doi:10.1016/j.physa.2015.04.008
Abstract 
Individual pedestrian velocities vary over time and space depending on the crowd size, location of individuals’ within the crowd, and formation of self-organized lanes. We use empirical data to explore the spatial fluctuations of pedestrian velocities in bidirectional streams. We find that, unlike ordinary fluids, the velocity profile in bidirectional pedestrian streams does not necessarily follow a hyperbolic form. Rather, the shape of the velocity profile is highly dependent on the formation of self-organized lanes. We also show that the spatial fluctuations of pedestrian velocities along and transverse to the flow direction are widely distributed and can be modeled by a sum of Gaussian distributions. Results suggest that the effect of self-organization phenomenon is strong enough that for the same crowd size, the velocity distribution does not significantly change when pedestrians are highly mixed compared to when separate lanes are formed.
Link to the published paper: http://www.sciencedirect.com/science/article/pii/S0378437115003672

Tuesday, April 14, 2015

Melbourne Truck Volume Ratio Map (2012)

Extending our previously released Melbourne Truck Volume Map (2012) <http://goo.gl/JfRGyh>, we have developed a new visualization, this time showing the truck volume ratios (Average Daily Truck Traffic, ADTT over Annual Average Daily Traffic, AADT). The new map provides a different picture of exposure to truck traffic in Melbourne. Please see the new map here: http://goo.gl/pPxziX


Thursday, April 2, 2015

Prof. Brian Wolshon and Dr. Vinayak Dixit visit ITS Monash

Prof. Brian Wolshon from Louisiana State University (LSU) and Dr. Vinayak Dixit from University of New South Wales (UNSW) visit ITS Monash and presented some of their recent research projects.


Friday, March 20, 2015

ITEANZ Automated Vehicles seminar/panel


Automated Vehicles seminar/panel with Carl Liersch, General Manager at Bosch Australia. 
Thanks to ITEANZ and ARRB Group.

Visiting Transurban

This week I had a meeting with the traffic modeling group at Transurban discussing a possible project to improve the predictive accuracy of their existing toll traffic and revenue model. Fingers crossed to get the project.




Monday, March 2, 2015

Professor Armin Seyfried and Dr. Maik Boltes visit ITS Monash University

Professor Armin Seyfried and Dr. Maik Boltes from Bergische Universität Wuppertal, Germany visit Monash University.


Friday, February 27, 2015

Pedestrian Crowd Experiment at Monash University


Congratulations to Prof. Majid Sarvi and his team on completing an exciting experiment on pedestrian crowd dynamics today. The complex experiment was held at Clayton campus and was covered by Channel 10 News. Results of the study will be come out in a few months.

Link to the video: http://goo.gl/Z6MvkI




Thursday, February 26, 2015

Monash City Science web analytics by Google Analytics

Web analytics (thanks to Google Analytics) are quite interesting and addictive to follow. During the past week, my research webpage has had 1,695 views from 1,093 viewers mainly because of releasing two new visualizations. 70% of the viewers were from Melbourne, 5% from Sydney, 1.5% from Brisbane, 1% from Canberra, and the rest from different cities in and out of Australia. On average every viewers spent 1 minute and 6 seconds surfing around the page, perhaps interacting with the visualizations.

This is all great to know because I now have a better understanding of where the majority of my audience are and whether really people interact with my maps. I think one minute interaction time is a pretty good number.

Since the beginning, when I first launched the website in June 2014, I have had 27,865 views from 16,304 viewers: 46% from Melbourne, 6% from Sydney, 3% from Brisbane, 2% from London, and the rest from other cities including Adelaide, Canberra, and Perth.

I wonder if we develop a visualization for Sydney or Brisbane, are we going to get more viewers from those cities? I would say yes, probably.

Sunday, February 22, 2015

Connected Future by Telstra

Nice to see "connected" business initiatives by mobile companies such as Telstra. I think Optus has also started something similar in Australia. Picture downloaded from Telstra Enterprise twitter account.


Melbourne Car Ownership Map

I am happy to announce the release of our latest visualization developed at the Monash City Science Research Group: Melbourne Car Ownership Map [link].
An important question in urban planning and travel demand forecasting is where households with fewer or more cars live? and why? In this project, we visualize the spatial distribution of car ownership by dwelling in Melbourne metropolitan area using Census (2011) data. Every dot in the map represents a dwelling.
As expected, households who live within CBD and inner city areas have fewer cars, mostly 0 or 1 car per dwelling. As we go further out to the suburbs, car ownership rate increases. 
Click on the map below to re-direct to the interactive visualization.


Wednesday, February 18, 2015

Visualization: Melbourne Truck AADT Map (2012)


Yesterday I had a discussion with one of my colleagues, Colin Caprani who is a lecturer in the Civil Engineering Department at Monash University on a potential collaboration. Colin is in the structure group. His research specialization includes bridge traffic loading and ITS-infrastructure interaction.

The following visualization is a result of our conversation plus a couple of my hours today. I spent about an hour to find and process the data and a good few more hours to visualize it in CartoDB. What I see in the map is a clear larger truck volumes on freeways, around the Port of Melbourne, and Essendon airport. I could also identify a few other truck corridors in Melbourne such as the Princess Highway, Nepean Highway, Bell Street, etc. Perhaps a more comprehensive analysis could reveal more insightful patterns and findings.

Link to the map: http://goo.gl/ZQ3jx5

At first, I was trying to develop a heatmap showing the spatial distribution of truck volumes as an indication of where road damages are more likely to occur. I used "Torque Heat" which is a combination of heatmaps and Torque in CartoDB but I struggled. 

The default torque-aggregation-function in torque heat is count(cartodb_id). Since we wanted to visualize where truck volumes are higher, I changed the function to (round(avg(trucks)). But it didn't work as I expected. See the code below. It took me a few hours to figure out why. The problem was not with the aggregation function; rather, it was the image-filters: colorize-alpha () which caused a confusion. Apparently the colorize-alpha() filter only works with count data. According to its developer website, "Colorize-alpha is an image-filter and works at the layer level. Technically, we accumulate alpha channel values from densely positioned markers and convert this value to the color."

Well, I ended up using a bubble chart which is as informative but simpler to implement. If I figure out how to fix the image-filtering problem, I'll update the map.

CartCSS code for the Heatmap (which didn't give me what I wanted)
/** torque_heat visualization */
Map {
-torque-frame-count:1;
-torque-animation-duration:10;
-torque-time-attribute:"cartodb_id";
-torque-aggregation-function:"round(avg(trucks))";
-torque-resolution:4;
-torque-data-aggregation:cumulative;
}
#truck_data_2012_updated{
  image-filters: colorize-alpha(blue, cyan, lightgreen, yellow , orange, red);
  marker-file: url(http://s3.amazonaws.com/com.cartodb.assets.static/alphamarker.png);
  marker-fill-opacity: 0.4;
  marker-width: 35;
}
#truck_data_2012_updated[frame-offset=1] {
 marker-width:37;
 marker-fill-opacity:0.2;
}
#truck_data_2012_updated[frame-offset=2] {
 marker-width:39;
 marker-fill-opacity:0.1;
}

Tuesday, February 17, 2015

Three Dimensional (3D) Trajectories

Interested to learn more about 3D trajectories? See the following two papers recently published in Transportation Research Record.

Estimating Network Fundamental Diagram Using Three-Dimensional Vehicle Trajectories: Extending Edie’s Definitions of Traffic Flow Variables to Networks
ABSTRACT. This paper evaluates measurement methods for traffic flow variables taken at the network level. Generalized Edie’s definitions of fundamental traffic flow variables along highways are extended for considering vehicles traveling in networks. These definitions are used to characterize traffic flow in networks and form the basis for estimating relationships among network density, flow, and speed in the form of a network fundamental diagram. The method relies on three-dimensional vehicle trajectories to provide estimates of network flow, density, and speed. Such trajectories may be routinely obtained from particle-based microscopic and mesoscopic simulation models and are increasingly available from tracking devices on vehicles. Numerical results from the simulation of two networks, in Chicago, Illinois, and Salt Lake City, Utah, are presented to illustrate and validate the estimation methodology. As part of the verification process, the study confirms that the traffic flow fundamental identity (Q = K • V) holds at the network level only when networkwide traffic flow variables are defined consistently with Edie’s definitions.
How to cite this paper?
Saberi, M., Mahmassani, H., Hou, T., Zockaie, A. (2014) Estimating Network Fundamental Diagram using Three-Dimensional Vehicle Trajectories: Extending Edie's Definitions of Traffic Flow Variables to Networks. Transportation Research Record: Journal of the Transportation Research Board. No. 2422, 12-20.

Exploring Areawide Dynamics of Pedestrian Crowds: Three-Dimensional Approach
ABSTRACT. The main objectives of this paper are to evaluate existing measurement methods of pedestrian traffic flow and to propose a three-dimensional approach that extends Edie’s definitions of fundamental traffic variables to multidirectional walking areas by using three-dimensional pedestrian trajectories. Pedestrian crowds have an areawide fundamental diagram that is similar to a network fundamental diagram of vehicular traffic. Pedestrian traffic in a multidirectional area exhibits hysteretic behavior similar to that of some other many-particle physical systems. Some of the underlying dynamics of bidirectional pedestrian streams are explored with empirical data. Pedestrian streams behave somewhat differently from ordinary fluids with regard to the viscosity concept in the models based on fluid dynamics. The velocity profile for both unidirectional and bidirectional pedestrian streams is hyperbolic (with higher values on the boundaries and lower values in the middle), opposite that of fluids. The formation and dissipation of self-organized pedestrian lanes also are explored. A modification to Helbing’s social force model is proposed with regard to the attractive force between pedestrians.

How to cite this paper?
Saberi, M., and Mahmassani, H. (2014) Exploring Areawide Dynamics of Pedestrian Crowds: Three-Dimensional Approach. Transportation Research Record: Journal of the Transportation Research Board. No. 2421, 31-40.

Dynamics of Urban Network Traffic Flow During a Large-Scale Evacuation

Recently published in Transportation Research Record
http://dx.doi.org/10.3141/2422-03

ABSTRACT
This paper explores some of the dynamics of urban network traffic flow during a large-scale evacuation in the context of the network fundamental diagram (NFD). The structure of the evacuation demand can significantly affect network performance. A radial-shaped structure results in a more stable network recovery compared with a directional evacuation structure. This study confirms the existence of unloading-reloading hysteresis when a network is subject to successive cycles of loading and unloading. If a network undergoes a complete or near-complete recovery, the reloading path in the NFD follows almost the same path as in the initial loading. Results suggest that the linear relationship between average network flow and trip completion rate does not always hold, as previously thought. The relationship becomes highly scattered and nonlinear when the network is highly congested and under disruption and the number of adaptive drivers is sufficiently large. Frequent route switching by adaptive drivers can artificially increase the average network flow but does not necessarily increase the network output (trip completion rate). Adaptive driving increases fluctuations in the NFD; however, it reduces hysteresis and gridlock while increasing network capacity.
How to cite this paper?
Zockaie, A., Mahmassani, H., Saberi, M., Verbas, O. (2014) Dynamics of Urban Network Traffic Flow during a Large-Scale Evacuation. Transportation Research Record: Journal of the Transportation Research Board. No. 2422, 21-33.

Tuesday, January 20, 2015

Complex Network of Urban Trips in Melbourne



We're releasing a new visualization showing a sample of urban trips in Melbourne from a complex network perspective. This is an unofficial release. We'll do the official release when the working paper is published. Click on the picture below to see the interactive visualization.

Link to the interactive visualization (Beta version) here: 

"In 2013, an estimated 14.2 million trips were made on an average weekday in greater Melbourne area. As population increases, the number of daily trips is estimated to increase to 24.9 million in 2051. Such large scale demand for travel between hundreds/thousands of origins and destinations can actually be viewed as a complex network.

In this project, we visualize a sample of daily trips in Melbourne obtained from the Victorian Integrated Survey of Travel and Activity (VISTA) . The visualized complex network includes 46,335 trips (edges) and 183 Statistical Local Areas (nodes).

Each colour represents a cluster of nodes that are strongly connected and form a spatial coherent community. Links are coloured according to their origins' colour. This uncovers the structure of communities in Melbourne inherent to its human mobility patterns."

Monday, January 5, 2015

"Connected Vehicle" Technology: Will Australia Embrace the Change?

A recent video by the USDOT on the benefits of "Connected Vehicle" technology. Perhaps not entirely new for some of you but good for communicating the concept with public and policy makers. The application has already began shaping in the US and a few other countries. Will Australia embrace the change? Are we going to be followers or we may/shall lead the movement?