Monday, December 16, 2013

Human Mobility Patterns: Power Law or Exponential?

Brockmann, D., Hufnagel, L. & Geisel, T.
The scaling laws of human travel.
Nature 439,462–465 (2006).
Several studies in the literature have suggested that human mobility patterns follow a power law (scaling law):
Brockmann, D., Hufnagel, L. & Geisel, T. The scaling laws of human travel. Nature 439,462–465 (2006).
González, M.C., Hidalgo, C.A., Barabási, A.L., 2008. Understanding individual human mobility patterns. Nature 453, 779-782
Woolley-Meza, O., Thiemann, C., Grady, D., Lee, J.J., Seebens, H., Blasius, B., Brockmann, D., 2011. Complexity in human transportation networks: A comparative analysis of worldwide air transportation and global cargo ship movements. European Physical Journal B 84. 589-600.
Song, C., Koren, T., Wang, P. & Barabási, A.-L. Modelling the scaling properties of human mobility. Nat. Phys. 6, 818–823 (2010).
Jiang, B., Yin, J. & Zhao, S. Characterizing the human mobility pattern in a large street network. Phys. Rev. E 80, 1–11 (2009).
Noulas, A., Scellato, S., Lambiotte, R., Pontil, M. & Mascolo, C.
A tale of many cities: universal patterns in human urban mobility.
PLoS ONE 7, e37027 (2012).
Other studies proposed that an exponential function provides a better fit:
Liang, X., Zheng, X., Lv, W., Zhu, T. & Xu, K. The scaling of human mobility by taxis is exponential. Physica A 391, 2135–2144 (2012).
Peng, C., Jin, X., Wong, K.-C., Shi, M. & Liò, P. Collective Human Mobility Pattern from Taxi Trips in Urban Area. PLoS ONE 7, e34487 (2012).
Bazzani, A., Giorgini, B., Rambaldi, S., Gallotti, R. & Giovannini, L. Statistical laws in urban mobility from microscopic GPS data in the area of Florence. J. Stat. Mech. 2010, P05001(2010).
Roth, C., Kang, S. M., Batty, M. & Barthélemy, M. Structure of urban movements: polycentric activity and entangled hierarchical flows. PLoS ONE 6, e15923 (2011).
Kang, C., Ma, X., Tong, D. & Liu, Y. Intra-urban human mobility patterns: An urban morphology perspective. Physica A 391, 1702–1717 (2012).
Noulas, A., Scellato, S., Lambiotte, R., Pontil, M. & Mascolo, C. A tale of many cities: universal patterns in human urban mobility. PLoS ONE 7, e37027 (2012).
The difference comes from the scale of the study and perhaps the data . The mobility patterns in the global scale (e.g. air transportation and cargo ship movements) or national scale (using bank notes and mobile data calls) tend to follow the power law. However, at the city scale, human mobility tends to follow an exponential law. Source of data could also affect the results. Use of mobile phone calls and taxi data do not completely represent individuals daily mobility patterns. Overall, the exponential law of human mobility in cities could be partly explained by the economies of agglomeration and thus, following a "natural decay". While at the global scale where the economies of agglomeration does not play a significant role, the power law seems to better describe the mobility patterns.

Success or Failure of Bike-share Systems

"But while Paris's bike-share scheme actually makes money for the city, London's 4,000 bikes cost local taxpayers an average of £1,400 per bike per year." - The Atlantic Cities
What are the driving factors that makes a bike-share system successful or financially self-sustained? I hope Chicago #Divvy does well in the coming years.
"Bank, whose logo covers thousands of 'Boris bikes' across London, will end association with flagship scheme in 2015." - The Guardian

Sunday, December 15, 2013

Household Car Ownership in London: Spatial Patterns

UK Data Explorer is an online visualization tool that uses UK Census 2011 data to visualize hundreds of different measures. Following is a series of maps showing the household car ownership in London including no car, 1 car, 3 cars, and 4 cars in the household. Dark blue roughly represents >2.5% of households and light blue to white represents <2.5% of households. Interestingly, as the number of cars in the households increases, the outer suburbs of London becomes darker and inner neighborhoods becomes pretty light or almost white. In other words, households with larger number of cars live in farther suburbs while households in the central areas have fewer cars. This is clearly linked to accessibility to destinations, cost of having a car, and perhaps number of persons in a household.


Saturday, December 14, 2013

Washington D.C.: The Changing City (Data Visualization)

Here are some nice visualizations of the demographic changes in Washington D.C over 10 years (2000-2010): http://datatools.urban.org/features/changingcities/#index

"Washington, DC, residents don't need census data to tell them what's obvious in their neighborhoods: the city is changing dramatically. But numbers can give us context. They can show us how shifts in population are reshaping the city and can help us prepare for changes to come.
In this series, we'll home in on changes from the past decade—2000 to 2010—when DC's population began growing again for the first time in 50 years. In this chapter, we look at demographic change, drilling down to wards and neighborhoods. Later, we'll explore changes in housing, crime, education, and more, using data from NeighborhoodInfo DCto tell the story of our changing city."

Thursday, December 12, 2013

Gender Disparities in Science

Recently published in Science: Bibliometrics: Global gender disparities in science
"Despite many good intentions and initiatives, gender inequality is still rife in science. Although there are more female than male undergraduate and graduate students in many countries, there are relatively few female full professors, and gender inequalities in hiring, earnings, funding, satisfaction and patenting persist."
Since I am a member of the Traffic Flow Theory and Characteristics Committee (AHB45) of the Transportation Research Board of the National Academies, I was thinking that maybe I could start looking at our own committee. As of December 2013, the TFTC committee has 36 members (including 4 young and 2 emeritus). Only 6 out of 36 members are female resulting in a female/male ratio of 0.167

Also, the Network Modeling Committee (ADB30) has 42 members (including 4 young and 4 emeritus). Only 8 out 42 members are female resulting in a female/male ratio of 0.190


Predictive vs. Prescriptive Analytics


Here is an interview with Jack Levis from UPS discussing predictive vs. prescriptive analytics. He actually discusses more about implementation of ORION in figuring out the best route to deliver. Despite the low scholarly value of the article, it's nice to read what UPS does to improve their system.
"Our digital journey started with an early adoption of data and analytics tools for improving our operations. As our operations became more complex and distributed in nature, the focus has been to improve business processes, increase efficiency and cut costs. We had been following a descriptive and predictive analytics-based system for a long time but what has recently changed is our shift to prescriptive analytics. I can safely say that UPS is one of the few companies to effectively use prescriptive analytics to gain insight for successful optimization."
See the entire January issue of "Digital Transformation Review" here: http://ebooks.capgemini-consulting.com/Digital-Transformation-Review-5/files/assets/common/downloads/Digital-Transformation-Review-5.pdf

Tuesday, December 10, 2013

Featured Article: The Structure of Spatial Networks and Communities in Bicycle Sharing Systems

Abstract
Bicycle sharing systems exist in hundreds of cities around the world, with the aim of providing a form of public transport with the associated health and environmental benefits of cycling without the burden of private ownership and maintenance. Five cities have provided research data on the journeys (start and end time and location) taking place in their bicycle sharing system. In this paper, we employ visualization, descriptive statistics and spatial and network analysis tools to explore system usage in these cities, using techniques to investigate features specific to the unique geographies of each, and uncovering similarities between different systems. Journey displacement analysis demonstrates similar journey distances across the cities sampled, and the (out)strength rank curve for the top 50 stands in each city displays a similar scaling law for each. Community detection in the derived network can identify local pockets of use, and spatial network corrections provide the opportunity for insight above and beyond proximity/popularity correlations predicted by simple spatial interaction models.
Read the full article here: Zaltz Austwick M, O’Brien O, Strano E, Viana M (2013) The Structure of Spatial Networks and Communities in Bicycle Sharing Systems. PLoS ONE 8(9): e74685. doi:10.1371/journal.pone.0074685

Monday, December 9, 2013

Verification of the Zipf's Law for Cities in Iran

Zipf's law establishes a simple relationship between the size/population of N samples and their frequency ranking. The original study by Zipf in 1935* proposed that the frequency of any word in a natural language is inversely proportional to its rank in the frequency table. Before Zipf, others including Auerbach (1913)** proposed a similar law that the size distribution of cities in a country can be approximated by a Pareto distribution meaning that the size of cities is inversely related to their ranking. In other words, if you list cities of a country and rank them by their population, the population of each city would be inversely related to its ranking. If you're interested to learn more, see the following articles:
Jiang B. and Jia T (2011). Zipf’s Law for All the Natural Cities in the United States: A Geospatial Perspective. International Journal of Geographical Information Science, Volume 25, Issue 8, pp. 1269-1281.
Cristelli M, Batty M, Pietronero L (2012). There is more than a power law in Zipf. Nature, Scientific reports 2, pp. 1-7.
I decided to verify whether the Zipf's law holds for cities in Iran (my birth country). Following is a bar chart showing the first 20 cities in Iran sorted from the largest to the smallest, based on population data from 2006. Obviously, Tehran has the largest population with near 8 million followed by Mashhad (my hometown), Isfahan, Tabriz, Karaj, and Shiraz.

Now let's plot the log (population) against the log (ranking). In fact, results imply that the Zipf's law holds (approximately) for these cities (R-squared = 0.9752). Therefore one could predict the population of a city based on its ranking in a country or vice versa. Note that doing a simple regression here to get the coefficients of the Zipf's law is not exactly correct. More correct methods exist in the literature for estimating the Zipf's coefficients which I do not discuss in this post. The performed regression gives a reasonable approximation in my opinion. The underlying mechanism of the Zipf's law is not yet fully understood specially in the context of cities. It would be interesting to see how the following graph has evolved over time when cities shift in ranking and with increase/decline of population. Does the Zipf's law holds true for other self-formed communities (e.g. at the neighborhood level)? And most importantly, why do we see what we see here? Honestly, I am a little skeptical about the Zipf's law and its application in predicting cities population. I think there is something there that we're missing. The recent paper published in Nature by Cristelli et al. (listed above) sheds some light into it.
* George K. Zipf (1935) The Psychobiology of Language. Houghton-Mifflin.
** Auerbach F. (1913) Das gesetz der bevolkerungskoncentration (The Law of Population Concentration). Petermanns Geographische Mitteilungen, 59, pp. 74–76.

Sunday, December 8, 2013

20-minute neighborhood and bikeability

Following the previous post on the idea of "twenty-minute city", here are presentation slides by Nathan MacNeil from Portland State University on "Exploring How Infrastructure and Destinations Influence Bicycle Accessibility":
http://www.cts.pdx.edu/pdf/TransSeminar-McNeil-100810.pdf

"This paper explores a methodology for assessing a neighborhood’s bikeability based on its mix of infrastructure and destinations –essentially the 20-minute neighborhood for bicycles.
Background: Dense, well-connected neighborhoods where residents can access services, shopping, transit, restaurants and employment centers without the use of a car are often lauded as an important next step in urban and suburban development. These goals have come up in the aftermath of decades of federally-subsidized automobile and highway-centric planning that encouraged development of cheap land on the periphery of metropolitan areas, tore up existing urban streetcar systems, and disconnected urban neighborhoods with highway projects. Given that much of the current urban landscape was created for the automobile, it is no surprise that most people view the car as a necessity.
However, many places are now embracing the idea that auto-dependent cities are not sustainable from an environmental, economic and national-security standpoint. Efforts to recreate neighborhoods where residents can manage (and want to manage) without cars usually focus on providing transportation options, attracting a diversity of uses (including all essential uses) and attaining a certain threshold of population density within a limited space.
The area of outer east Portland provides an interesting case study of a community largely shaped by the automobile, but struggling to become increasingly urban and decreasingly auto-dependent. Among the goals expressed in the 2009 plan are to improve the area's land use mix by encourage mixed-use development and multi-use commercial areas, to increase the safety and accessibility of bicycling, and to improve connectivity.
This paper explores a methodology for assessing a neighborhood's bikeability based on its mix of infrastructure and destinations – essentially the 20-minute neighborhood for bicycles. The area of outer east Portland, an area east of 82nd Avenue with substantially lower bicycling rates than other Portland neighborhoods, is used as a case study and compared to an assessment of neighborhoods that are considered to be bike-friendly (downtown, inner-east and north Portland). The paper examines prior approaches to assessing bikeability, details a new method to measure bikeability, presents the findings, and explores what impact expected or potential transportation and land use changes might have on bikeability."
The full paper can be downloaded here: http://www.pdx.edu/ibpi/sites/www.pdx.edu.ibpi/files/McNeil_Bikeability_June2010.pdf

Saturday, December 7, 2013

20-minute City



The idea of a "20-minute city" is appealing but could it really be achieved when a huge employment hub called CBD and almost-purely residential suburbs around it exist? The complex relationship of mobility and accessibility need to be well understood first.
"One of the better objects of Plan Melbourne is to create '20-minute neighbourhoods' in which jobs, schools, shops and community services within a 20-minute walk, bike or public transport ride from home. Matching employment with residential location is difficult at the best of times. Proximity can be encouraged via a good distribution of affordable housing and job types, with multiple transport links so people have choices about where they live and work."

Thursday, December 5, 2013

Integrated Corridor Management Analysis, Modeling, and Simulation Guide

See the report from the RITA, U.S. DOT on "Integrated Corridor Management 
Analysis, Modeling, and Simulation Guide" as part of the "Traffic Analysis Toolbox":
"As part of the Federal Highway Administration (FHWA) Traffic Analysis Toolbox (Volume XIII), this guide was designed to help corridor stakeholders implement the ICM AMS methodology successfully and effectively. It provides a step-by-step approach to implementation of the ICM AMS methodology and reflects lessons learned in its application to the three ICM Pioneer Sites and a test corridor. It is specifically targeted at technical and/or program managers in transportation agencies at the State or local level who may oversee implementation of ICM and/or an ICM AMS initiative. This Guide will also be a helpful reference to all stakeholders involved in AMS, including technical modelers, by providing a framework for developing an effective analysis plan to support selection and application of available tools and models specifically conducive to ICM." - Traffic Analysis Toolbox

Monday, December 2, 2013

UrtheCast (pronounced as Earth Cast)

Urthecast (pronounced as Earth+Cast) will provide near-real time satellite images. You can view time-lapses of your selected places and see how they evolve over time: http://www.urthecast.com


Tuesday, November 26, 2013

Connecting Poor and Rich: Transit, Equity, and Crime


Historically, rich neighborhoods in cities (like Lake Oswego in Portland, OR) have opposed building transit lines that connect them with poor neighborhoods in fear of their own safety. With the recent availability of large amount of urban data and "hotness" of big data analytics, an interesting idea of research is performing a comparative analysis of different transit routes with respect to equity and safety/crime. Thanks to the Transit Quality and Equity visualization project by the vudlab at UC Berkeley, one could qualitatively/visually realize that there exist some transit routes in San Francisco that connects poor neighborhoods to rich neighborhoods (or poor-to-poor and rich-to-rich). One could compute an equity score for each route (based on income for example) and compare different transit routes in the entire transit network with respect to that equity score. In addition to an average equity score, one could also look at the variations throughout each route and identify those routes that goes through highly different neighborhoods (from poor to rich for example). Using detailed crime data (if available publicly), a safety analysis (for example, # of crimes inside the transit vehicle or in the vicinity of the transit station) could also be performed for each route and comparatively. Can we verify whether the routes that connect rich and poor neighborhoods are associated with high crime rates? Does transit reduce safety in rich neighborhoods?

Reproducibility Initiative on Transportation and Traffic Phenomena

Inspired by the Reproducibility Initiative in psychology launched by Science Exchange, PLOS ONE, figshare, and Mendeley, I think there is a need to start a similar work to verify the reproducibility of some of the transportation and traffic phenomena. For example, transportation journals could announce a call for papers for a special issue on "reproducibility". The ideal initiative would be a world-wide collaborative project that first identifies some of the main phenomena/effects that need to be verified and then different research groups work together to collect/obtain new data and examine the reproducibility of the selected phenomena.
"It’s time to start rewarding the people who take the extra time to do the most careful and reproducible work. Current academic incentives place an emphasis on novelty, which comes at the expense of rigor...we believe that the scientific consensus on the most robust, as opposed to simply the most cited, work is a valuable signal to help identify high quality reproducible findings that can be reliably built upon to advance scientific understanding." - Reproducibility Initiative

Transferability and Reproducibility of Travel Behavior Phenomena

A recent article in Nature reports on a large collaborative study done by 36 labs across the globe to examine the reproduciblity of the results of several classic and contemporary studies in psychology. It's reported that they could successfully reproduce the results of 10 out of 13 past experiments. Further details are published in an article on "Investigating variation in replicability: The “Many Labs” Replication Project".

The question remains open for reproduciblity of some of the observed travel behavior phenomena. It would be interesting to test, for example, the shape of the response function to waiting time, or existence of thresholds in price sensitivity, etc. (Idea: courtesy of Hani Mahmassani).

With regard to temporal or spatial transferability of travel behavior models, there are numerous studies in the literature. Perhaps the following dissertation by Sujan Sikder (University of South Florida) provides a comprehensive background on the transferability issue: Spatial Transferability of Activity-Based Travel
Forecasting Models.

Growing Smart Cities Industry/Market

Here are a few readings on the growing "smart cities industry/market" which will be worth more than $400 billion globally by 2020:

15 Strategic Research Priorities of Australia

15 Strategic Research Priorities (released on 21 June 2013 by the Australian government) include:

Living in a changing environment
  1. Identify vulnerabilities and boundaries to the adaptability of changing natural and human systems
  2. Manage risk and capture opportunities for sustainable natural and human systems
  3. Enable societal transformation to enhance sustainability and wellbeing
Promoting population health and wellbeing
  1. Optimise effective delivery of health care and related systems and services
  2. Maximise social and economic participation in society
  3. Improve the health and wellbeing of Aboriginal and Torres Strait Islander people
Managing our food and water assets
  1. Optimise food and fibre production using our land and marine resources
  2. Develop knowledge of the changing distribution, connectivity, transformation and sustainable use of water in the Australian landscape
  3. Maximize the effectiveness of the production value chain from primary to processed food
Securing Australia's place in a changing world
  1. Improve cybersecurity for all Australians
  2. Manage the flow of goods, information, money and people across our national and international boundaries
  3. Understand political, cultural, economic and technological change, particularly in our region
Lifting productivity and economic growth
  1. Identify the means by which Australia can lift productivity and economic growth
  2. Maximise Australia’s competitive advantage in critical sectors
  3. Deliver skills for the new economy

Transportation & Health: Effects of Light Rail Transit Extension on Physical Activity and Travel Behavior in Houston

See the following articles:

UTHealth researchers to measure impact of METRO on physical activity of Houston residents

TTI and UTHealth Join Forces on 5-Year Transportation and Health Study Granted by NIH
"For the first time, the Texas A&M Transportation Institute (TTI) will team up with the University of Texas Health Science Center in Houston (UTHealth), for a new project funded by the National Institutes of Health (NIH). The five-year project will look at how the Metropolitan Transit Authority of Harris County’s (METRO’s) light rail transit (LRT) line extensions affect the physical activity and travel behavior of adults in Houston. Researchers are particularly interested in understanding this effect in the low-income, ethnically diverse adult population residing along the new LRT lines."

Example in Chile: Increasing Metro transit station capacity? (in Spanish)

Friday, November 22, 2013

Call for Papers: Big Data in Transportation and Traffic Engineering

Call for papers for a special issue in Transportation Research Part C:
"Big Data has been expanding into the transportation arena. However, the methods, models and algorithms that are used today in our domain to mine and explore data - think of estimation, prediction, validation of traffic and transportation theories and models - may not scale and/or perform well under these new conditions. This special issue solicits papers that advance the fundamental understanding, concepts and technologies related to Big Data applications to transportation and traffic engineering. Original contributions that provide novel theories, frameworks, and solutions to challenging problems of Big Data analytics are welcome."
Inquiries should be directed to Drs. Park (bpark@virginia.edu), van Lint (j.w.c.vanlint@tudelft.nl), Vlahogianni (elenivl@central.ntua.gr), and to the Editor-in-Chief, Dr. Karlaftis (mgk@mail.ntua.gr).

Australian Urban Research Infrastructure Network (AURIN)

"The Australian Urban Research Infrastructure Network (AURIN) is a $20 million initiative funded by the Australian Government’s Super Science scheme. AURIN will provide built environment and urban researchers, designers and planners with infrastructure to facilitate access to a distributed network of aggregated datasets and information services."https://aurin.org.au/
The final product will be a huge database including hundreds of valuable datasets from different urban-related organizations in Australia. It provides a basis for further development and understanding of cities.

Ports of Australia

The top five busiest ports in Australia by total cargo volume are:
  1. Port Hedland: 246,672 thousand of tons
  2. Dampier: 171,844 thousand of tons
  3. Newcastle: 129,283 thousand of tons
  4. Gladstone: 83,790 thousand of tons
  5. Hay Point: 82,854 thousand of tons
Reference: Ports Australia Trade Statistics


Thursday, November 21, 2013

United Parcel Service (UPS) and the Traveling Salesman Problem

Recently published in INFORMS Analytics Magazine
"UPS recently launched its route optimization software known as ORION (On-Road Integrated Optimization and Navigation), just in time for the busy holiday season. The rollout of ORION will optimize 10,000 delivery routes by the end of the year, thus reducing miles driven and reinforcing UPS’ sustainability efforts by saving more than 1.5 million gallons of fuel and reducing carbon dioxide emissions by more than 14,000 metric tones in that time frame. A reduction of just one mile each day per driver over the course of a year saves the company up to $50 million."

Sunday, November 17, 2013

Kerner's new article: "Criticism of generally accepted fundamentals and methodologies of traffic and transportation theory"

Recently published by Boris Kerner in Physica A: "Criticism of generally accepted fundamentals and methodologies of traffic and transportation theory: A brief review"
Abstract. "It is explained why the set of the fundamental empirical features of traffic breakdown (a transition from free flow to congested traffic) should be the empirical basis for any traffic and transportation theory that can be reliably used for control and optimization in traffic networks. It is shown that the generally accepted fundamentals and methodologies of the traffic and transportation theory are not consistent with the set of the fundamental empirical features of traffic breakdown at a highway bottleneck. To these fundamentals and methodologies of the traffic and transportation theory belong (i) Lighthill–Whitham–Richards (LWR) theory, (ii) the General Motors (GM) model class (for example, Herman, Gazis et al. GM model, Gipps’s model, Payne’s model, Newell’s optimal velocity (OV) model, Wiedemann’s model, Bando et al. OV model, Treiber’s IDM, Krauß’s model), (iii) the understanding of highway capacity as a particular (fixed or stochastic) value, and (iv) principles for traffic and transportation network optimization and control (for example, Wardrop’s user equilibrium (UE) and system optimum (SO) principles). Alternatively to these generally accepted fundamentals and methodologies of the traffic and transportation theory, we discuss the three-phase traffic theory as the basis for traffic flow modeling as well as briefly consider the network breakdown minimization (BM) principle for the optimization of traffic and transportation networks with road bottlenecks."

Wednesday, November 13, 2013

Visualizing Urban Data IdeaLab @ UC Berkeley

If you are interested in urban data visualization, you may regret missing visiting the "Visualizing Urban Data IdeaLab" at UC Berkeley.
"The Visualizing Urban Data Idealab (VUDlab) is a student-led organization formed at University of California-Berkeley in May 2013. It is a project of the Blum Center for Developing Economies IdeaLabs program.

The past decade has witnessed explosive growth in the world’s urban population alongside equally quick growth in digital record-keeping. The Visualizing Urban Data IdeaLab builds on both trends, to create a space where participants can learn and invent visual ways to clarify urban data. The IdeaLab hosts speakers, teaches software and computer languages, and encourages collaboration across departments on projects that exploit they Berkeley community's strengths in the social and information sciences. We are open to off-campus participation. Follow @vudlab

Tuesday, November 12, 2013

MIT Big Data Challenge: Predict Taxi Demand

The goal is to predict taxi demand in downtown Boston based on historical taxi data (2.3 million rides) and other related data sets (weather, transit, events, and social media data).
"We have collected several months worth of twitter, MBTA, taxi pickup and drop off, and other datasets (see datasets page). In this challenge we will give you all of the data except for a number of 2 hour time spans at several locations in downtown Boston that we will remove from the taxi pickup dataset. Your job is to predict the total number of taxi trips within 250 meters of the locations and the 2 hour time spans. We will give you a list of location (latitude, longitude points) and timespan pairs. Your score will be computed as the root-mean-squared error of your predictions against the ground truth."

Monday, November 11, 2013

Hyper data vs. Big data

Big Data’s Little Brother: Start-Ups Are Mining Hyperlocal Information for Global Insights
Published in the NY Times.
"A picture of a pile of tomatoes in Asia may not lead anyone to a great conclusion other than how tasty those tomatoes may or may not look. But connect pictures of food piles around the world to weather forecasts and rainfall totals and you have meaningful information that people like stockbrokers or buyers for grocery chains could use."

Urban Network Gridlock

Recently published.

Abstract
This study explores the limiting properties of network-wide traffic flow relations under heavily congested conditions in a large-scale complex urban street network; these limiting conditions are emulated in the context of dynamic traffic assignment (DTA) experiments on an actual large network. The primary objectives are to characterize gridlock and understand its dynamics. This study addresses a gap in the literature with regard to the existence of exit flow and recovery period. The one-dimensional theoretical Network Fundamental Diagram (NFD) only represents steady-state behavior and holds only when the inputs change slowly in time and traffic is distributed homogenously in space. Also, it does not describe the hysteretic behavior of the network traffic when a gridlock forms or when network recovers. Thus, a model is proposed to reproduce hysteresis and gridlock when homogeneity and steady-state conditions do not hold. It is conjectured that the network average flow can be approximated as a non-linear function of network average density and variation in link densities. The proposed model is calibrated for the Chicago Central Business District (CBD) network. We also show that complex urban networks with multiple route choices, similar to the idealized network tested previously in the literature, tend to jam at a range of densities that are smaller than the theoretical average network jam density. Also it is demonstrated that networks tend to gridlock in many different ways with different configurations. This study examines how mobility of urban street networks could be improved by managing vehicle accumulation and redistributing network traffic via strategies such as demand management and disseminating real-time traveler information (adaptive driving). This study thus defines and explores some key characteristics and dynamics of urban street network gridlocks including gridlock formation, propagation, recovery, size, etc.

Sunday, November 10, 2013

Wednesday, November 6, 2013

Journal of "Scientific Data" by Nature

Nature is launching "Scientific Data", an open-access, online-only, peer-reviewed journal for detailed descriptions of data sets: http://www.nature.com/scientificdata/


Initiatives on Smart Cities in the UK and EC

New initiative to support $40 billion smart cities in the UK
"It comes as a new report published today values the smart cities industry at more than $400 billion globally by 2020, with the UK expected to gain a 10% share ($40 billion)."
European Initiative on Smart Cities
"This Initiative will support cities and regions in taking ambitious and pioneering measures to progress by 2020 towards a 40% reduction of greenhouse gas emissions through sustainable use and production of energy. This will require systemic approaches and organisational innovation, encompassing energy efficiency, low carbon technologies and the smart management of supply and demand. In particular, measures on buildings, local energy networks and transport would be the main components of the Initiative."

Monday, November 4, 2013

Bike-Share as Mass Transit

The Atlantic Cities - In a recent issue of the Journal of Public Transportation, UCLA planning scholars Rui Wang and Chen Liu make a strong case that U.S. cities should do a better job integrating bikes into metro transit systems.
"This paper analyzes the recent trend in bicycle-transit integration in the U.S. It reviews data from the National Household Travel Surveys (NHTS) to show the characteristics of bicycle-transit integrated trips, where the integrators were from, and to which population groups the integrators belonged. Bicycle-transit integration was increasingly observed in commuters and younger travelers, and became more imbalanced by gender. Results indicate the rise in socio-economic diversity of bicycle-transit integrators, despite a racial gap. There was a clear concentration of bicycle-transit integrators in large and high-density urban areas, where most transit users lived. Evidence does not support that rail attracts more bike access/egress trips than bus. More transit users used bicycles to access/egress in the Pacific, East North Central, and Mountain regions. Given the non-trivial role of bicycles compared to transit in the U.S., the focus on bicycle use and the marriage between bicycle and transit should be further emphasized."
View the full article.

Friday, October 25, 2013

Value of Time

What is the value of time? We are still not sure how to estimate that. An article by Kenneth Small on "Valuation of Travel Time" discusses what we know about value of time and what we need to know.
"After decades of study, the value of travel time remains incompletely understood and ripe for further theoretical and empirical investigation. Research has revealed many regularities and connections between willingness to pay for time savings and other economic factors including time of day choice, aversion to unreliability, labor supply, taxation, activity scheduling, intra-household time allocation, and out-of-office productivity. Some of these connections have been addressed through sophisticated modeling, revealing a plethora of reasons for heterogeneity in value of time rooted in behavior at a micro scale. This paper reviews what we know and what we need to know. A recurrent theme is that the value of time for a particular travel movement depends strongly on very specific factors, and that understanding how these factors work will provide new insights into travel behavior and into more general economic choices."

Wednesday, October 23, 2013

Ride-Thru Cafe


"Zurich’s city council has installed ‘Velokafi’; a drive-in for bicycles on the outdoor terrace of the Rathaus Cafe, which is a popular spot next to the Limmat river. Two wooden docking stations with tabletops enable cyclists to pull up and have some food and a coffee without getting off their bike. A slot in the front for their front wheel to fit through keeps the bicycle steady and there are raised sides for resting their feet on."
See the article here: http://www.psfk.com/2013/04/bicycle-drive-in-cafe.html

Open Data City

A recent artilce on The Atlantics on "The Neighborhood Data Portal Every City Needs" highlights the need for visualizing urban data. Every city should have such open data portal for a better understanding and communicating the linkage between public health, land use, economics, education, crime, transportation and housing.
"Los Angeles has recently done just this, rolling out a web tool as part of its Plan for a Healthy Los Angeles that maps a tremendous number of metrics about life in the region, at both the city and neighborhood scales. "

Tuesday, October 15, 2013

Time Maps

Check out this article on "Mapping the 'Time Boundaries' of a City"
"Maps don't typically convey time very well. They're static snapshots of a moment in history. They tell you what exists, not when people go there, or how the value of a place might be tied to time – whether it's a nightlife district or a public park most popular with early-morning joggers."

Defining Downtown

What's the definition of downtown?
See the following article on the Atlantic Cities:
http://www.theatlanticcities.com/jobs-and-economy/2013/10/problem-defining-downtown/7144/

"Last year, the U.S. Census Bureau released a report on population trends in American downtowns, a helpful step toward quantifying the claims made by many cities that residents (and jobs) are moving there in droves (you can view the original report here... whenever the federal government reopens and the Census Bureau's shuttered website comes back online). The Census' blunt definition of "downtown," though, inevitably produced some grousing about over-and under-counts of local populations. It measured “downtown,” for lack of a better universal definition, as everything within a 2-mile radius of the local city hall."

Wednesday, September 25, 2013

Vehicle-Miles-Traveled Fee in Oregon, USA

Check out the recent article on "Ten Questions (and Answers) About Oregon’s New VMT Charge" on DC.STREETBLOG.ORG.
"Will a VMT charge better match the needs of the system than the gas tax? Mostly, yes. Fuel consumption used to be a reasonably good proxy for road use, but it isn’t anymore. Charging people directly for their use of the roads makes more sense. If revenues go down, it will be because people are driving less, and therefore creating less wear and tear on the roads, as well as less demand for system expansion. It’s not perfect, though: Lightly used roads will still get beaten up by severe weather, and plants will still show up in the cracks."

Tuesday, September 17, 2013

Streetcar and Economic Development

A recent article on streetcars and economic development in The Atlantic Cities, have highlighted the need for more empirical study to better understand how streetcars affect urban development. Melbourne Tram system is a perfect case for such study.
"A 2010 survey of 13 U.S. streetcar systems, sponsored by the Federal Transit Administration, concluded that the economic impact of streetcars remains largely unknown. System representatives "believed" that streetcars enhance development but didn't actually "seek information" about this economic impact — perhaps because there's not much to seek"

Monday, September 2, 2013

"Decline in driving is about more than an economic aftershock."

"After sixty years of almost constant increases in the annual number of miles Americans drive, since 2004 Americans have decreased their driving per-capita for eight years in a row. Driving miles per person are down especially sharply among Millennials, America’s largest generation that will increasingly dominate national transportation trends.
But some skeptics have suggested that the apparent end of the Driving Boom might be just a temporary hiccup in the trend toward more driving for Americans. By the time Americans took notice of the decline in driving, the economy was in deep recession. Would economic growth bring back rapid increases in driving?
Doubts about whether the Driving Boom has ended make it easier to postpone choices about transforming our transportation system or enacting reforms that disrupt well-established interest groups."  
- Source: http://uspirg.org/reports/usp/moving-road

Monday, August 19, 2013

Hot or Not? by Janson & Levinson

HOT or Not?
Driver Elasticity to Price on the MnPASS HOT Lanes

Michael Janson and David Levinson

"The Minnesota Department of Transportation (MnDOT) has added MnPASS High Occupancy Toll (HOT) lanes on two freeway corridors. While not the fi rst HOT lanes in the country, the MnPASS lanes are the fi rst implementation of road pricing in Minnesota and possess a dynamic pricing schedule. Tolls charged to single occupancy vehicles (SOVs) are adjusted every three minutes according to HOT lane vehicle density. Given the infancy of systems like MnPASS, questions remain about drivers' responses to toll prices. Three fi eld experiments were conducted on the corridors during which prices were changed. Data from the fi eld experiments as well as two years of toll and traffi c data were analyzed to measure driver responses to pricing changes. Driver elasticity to price was positive with magnitudes less than 1.0. This positive relationship between price and demand is in contrast with the previously held belief that raising the price would discourage demand. In addition, drivers consistently paid between approximately $60-120 per hour of travel time savings, much higher than the average value of time. Reasoning for these results is discussed as well as the implications these results have on the pricing of HOT lanes."

Also see the following article on The Atlantic Cities:
HOT Lanes Are Even More Popular When They're Expensive

Thursday, August 15, 2013

Travel Behavior, Self-formed Urban Communities, and Sociodemographic Characteristics

Following is the racial dot map of Chicago. 1 dot = 1 person


Following is a map of the identified community clusters based on urban trips reported in the 2007 Household Travel Survey data of Chicago using a modularity maximization algorithm (courtesy of Dirk Brockmann). Modularity is often used in optimization methods for detecting community structure in networks.


Qualitatively observing, there is somewhat a correlation between the two maps. It would be interesting to quantitatively explore that how sociodemographic factors affect the travel-based community structure in the Chicago region: understanding the relationship between travel behavior, community formation, and sociodemographic characteristics.

Sunday, August 11, 2013

Smart Growth and Urban Goods Movement

See the NCFRP report here: http://onlinepubs.trb.org/onlinepubs/ncfrp/ncfrp_rpt_024.pdf
"TRB’s National Cooperative Freight Research Program (NCFRP) Report 24: Smart Growth and Urban Goods Movement identifies the interrelationships between goods movement and smart growth applications, in particular, the relationship between the transportation of goods in the urban environment and land-use patterns."

Tuesday, August 6, 2013

Transportation Journals Ranking

Following ranking includes SJR 4-yr and 2-yr (2011) impact factor and Google Scholar H5 index (2008-2012)

Integration of Land Use and Transportation Modeling in Ohio

The Ohio Department of Transportation released a report on integration of land use and travel behavior modeling in the state of Ohio:
"Facing major challenges related to energy consumption, global warming, environmental quality, and economic viability, metropolitan regions around the world are examining the consequences of alternative growth patterns on resource consumption. As we plan for new land use policies and investments in the transportation system over the next decade, we will face a new set of challenges tied to the changing demographic and economic conditions in Ohio, in addition to the rising costs of energy and related policies aimed at reducing the carbon footprint of our economy. The first step in understanding the possible implications of these changes is a deeper understanding of the current relationships between land use and travel behavior, and how these might be impacted by future land use, transportation and energy policies."

Friday, August 2, 2013

Bipartite Networks: Application in Transit Networks

A bipartite network is a kind of network in which the nodes are grouped into two independent classes of U and V and every link connects a node in U to a node in V.

A transit network can be viewed as a bipartite network such that group U consists of different geographical locations in the city (origins and destinations) and V consists of different transit lines or routes. A node in U (e.g. an origin) can only connect to another node in U (e.g. a destination) if and only if a transit line/route exists that connects them. Therefore, an origin will be connected to a destination (both nodes in U group) via a transit line/route (a node in V group). Such network representation illustrates a transit network with no transfer on-route. Note that transfers are possible in this representation but only through reaching an intermediate destination in U.


The bipartite network can be modified to represent transit networks with one or more transfers. The modified bipartite network includes two independent groups of nodes (U and V) in which every link connects a node in U to a node in V. Also, there are links that connects nodes in V, representing transfers between different transit routes/lines. Thus, an origin in U can also be connected to a destination, also in U, via two or more transit lines/routes.

The discussed application of bipartite networks in transit networks and the proposed modification to illustrate transit networks with transfers is an ongoing work and not yet published. No reproduction is allowed without written permission.


Following is an example of the bipartite and modified partite of Chicago transit network.

 


Wednesday, July 31, 2013

Real-time Visualization of the Bikeshare Systems Data around the World


Integration of Land Use & Tranport Planning in Victoria, Australia

Check out the following presentation slides on the integration of land use and transportation planning in Victoria, Australia:
http://www.mtf.org.au/site/files/ul/data_text12/1076625.pdf

"The Victorian Transport Plan is based on an integrated approach to transport and land use planning. The VTP and Melbourne @ 5 million bring together future transport and land use decisions to:
  • Increase development and job opportunities through strategic transport investment
  • Develop future housing in the established areas of Melbourne along the tram and rail network
  • Invest in new transport links to promote more jobs closer to new housing in Melbourne’s fast-growing west and north
  • Take pressure off the city and inner Melbourne by facilitating substantial employment growth at six designated Central Activities Districts (CADs), and along employment corridors in middle and outer areas
  • Support Melbourne’s growth areas with high capacity public transport links, nominating CADs, creating employment corridors and investigating the proposed extension of the Urban Growth Boundary
  • Support regional population growth through significant investment in more transport services that link regional centers to Melbourne and smaller towns to regional cities."
- Victorian Transport Plan 

Tuesday, July 30, 2013

MOCoPo: Measuring and Modeling Traffic Congestion and Pollution

The MOCoPo project is funded by the French Ministry in charge of Transport (MEDDTL), through the PREDIT (Research and Innovation in Land Transport Program).

"The MOCoPo offers following data and information:
  • A detailed description of the project with the presentation of the various data collection and modeling tasks; 
  • A map to present the detectors and zone where data where collected on the two lanes highway in south of the city of Grenoble (a urban area of about 700,000 inhabitants located in the French Alps); 
  • A document page where you will find photos, videos, reports on data collection description and eventually papers published (by the MOCoPo team or by you) with the help of the MOCoPo data; 
  • A data page presenting an interface that will allow you to download the data you need."

SF-CHAMP Model

SF-CHAMP activity-based model of the San Francisco area:
http://www.sfcta.org/modeling-and-travel-forecasting

"The San Francisco Chained Activity Modeling Process (known as SF-CHAMP) is a regional travel demand model that is used to assess the impacts of land use, socioeconomic, and transportation system changes on the performance of the local transportation system. SF-CHAMP was developed to reflect San Francisco’s unique transportation system and socioeconomic and land use characteristics. It uses San Francisco residents’ observed travel patterns, detailed representations of San Francisco’s transportation system, population and employment characteristics, transit line boardings, roadway volumes, and the number of vehicles available to San Francisco households to produce measures relevant to transportation and land use planning. Using future year transportation, land use, and socioeconomic inputs, the model forecasts future travel demand."

Wednesday, July 24, 2013

Cities as Complex Systems

"Cities have been treated as systems for fifty years but only in the last two decades has the focus changed from aggregate equilibrium systems to more evolving systems whose structure emerges from the bottom up.
Cities were first conceived as complex systems in the 1960's when architects and urban planners began to change their perceptions that cities be treated as ‘works of art’ to something much more akin to a functioning economic system that required social engineering."


Wednesday, July 17, 2013

Active Traffic Management for Arterials

TRB released an NCHRP report on Active Traffic Management for Arterials:
"TRB’s National Cooperative Highway Research Program (NCHRP) Synthesis 447: Active Traffic Management for Arterials explores practices associated with the design, implementation, and operation of active traffic management (ATM) on arterial roadways.

ATM includes a suite of strategies that are used to manage traffic flow in order enhance capacity and safety." - Transportation Research Board