Inferring land use from mobile phone activity
|
|
- Peregrine Banks
- 6 years ago
- Views:
Transcription
1 Inferring land use from mobile phone activity Jameson L. Toole (MIT) Michael Ulm (AIT) Dietmar Bauer (AIT) Marta C. Gonzalez (MIT) UrbComp 2012 Beijing, China 1
2 The Big Questions Can land use be predicted from mobile phone activity? Can mobile phone data help measure temporal patterns of land use? Are these patterns robust across cities? 2
3 Sensing Urban Systems Data Scarcity Methods Outputs Abundance Traditional Data: Zoning Maps Novel Data: Mobile phone activity Statistical Physics Machine learning Geographic Boundaries (Ratti et al 2010) Intra-city Mobility (Cho et al 2011) Inter-city Mobility (Gonzalez et al 2008) (Wang et al 2009) (Simini 2012) Unsupervised Classification (Reades 2009) (Calabrese 2010) (Soto 2011)(Zheng 2012) 3
4 Land use and Travel Behavior travel demand Land-use supply Temporal Component opening hours of shops and services available time Individual Component income, gender, education level Travel supply Demand available time for activities vehicle ownership, etc. Demand where a person wants to go when a person can travel a persons ability to travel a persons desire to travel Static GIS data + = New micro level data from mobile phones Dynamic Land Use adapted from: K.T. Geurs, B. van Wee / Journal of Transport Geography 12 (2004)
5 Data Zoning Data GIS shapefiles at the parcel level Provided by MASSDOT Aggregated to 5 zoning classification: Residential, Commercial, Industrial, Parks, Other Mobile Phone ~600,000 Users in the Boston Metropolitan Area (Pop: ~3 million, Mkt Share: 30%-50%) 1 month of Call Detail Records (CDR) for voice calls and text messages. Each event geo-tagged with (Lat, Lon) triangulated from nearby towers Grid Partition the region into 200m X 200m cells Each cell is labeled with the most prevalent zoning type within Grid provides privacy All mobile phone events are aggregated hourly into an average week 5
6 6
7 ZONING DATA 40km Parks Other CBD 7
8 Zoned use & Dynamic Population Results: Absolute Activity Downtown Boston is mostly zoned as OTHER thus more activity is related to more people. Abs. Act Parks Other 8
9 Zoned use & Dynamic Population Results: Normalized Activity a norm ij (t) = aabs ij (t) µ a abs ij σ a abs ij 1 0 Parks Other Similarities are related to how individuals use mobile phones, not how they move. 9
10 Zoned use & Dynamic Population Results: Residual Activity a res ij (t) =a norm (t) ā ij norm (t) Inverse relationship between RESIDENTIAL and COMMERCIAL Hour Residential Industrial Parks Other Weekends are quantitatively different than weekdays. 10
11 Zoned use & Dynamic Population static population Abs. Act phone use 1 0 movement Hour Parks Other 11
12 Absolute Activity Results: Low Activity High Activity 12
13 Inferring Land Use Methods - Supervised Learning: Random Decision Tree Θ 1 1 <y 1 h(x, Θ k ) = c Θ 1 2 <y 2 Θ 1 3 <y 3 c 1 c 2 c 3 c 4 13
14 Inferring Land Use Methods - Supervised Learning: Random Forest Random Forest: (Breiman 2001) 1.1. A random forest i {h(x, k ), k = 1,...} ectors and each tree casts x is a T 1vector Θ k is a m 1vector,(m<T) Θ 1 1 <y Θ k 1 <z 1 Θ 1 2 <y 2 Θ 1 3 <y 3 Θ k 2 <z 2 Θ k 3 <z 3 c 1 c 2 c 3 c 4 c 1 c 2 c 3 c 4 14
15 Inferring Land Use Methods - Supervised Learning: Random Forest Random Forest: (Breiman 2001) 1.1. A random forest i {h(x, k ), k = 1,...} ectors and each tree casts x is a T 1vector Θ k is a m 1vector,(m<T) Θ 1 1 <y Θ k 1 <z 1 Θ 1 2 <y 2 Θ 1 3 <y 3 Θ k 2 <z 2 Θ k 3 <z 3 c 1 c 2 c 3 c 4 c 1 c 3 c 4 c 2 {h(x, k ) } = } {c2c2 c3 c2 c1 c2 c1 c4 ectors and c ^ = mode( {h(x, k ) }) perf = N ^ i I(c i = j) N ectors and 15
16 Results: Inferring Land Use Confusion Matrix Thresh Total Accuracy Weights Res Com Ind Prk Oth ( ) Res Com Ind Prk Oth Share: Res Com Ind Prk Oth 16
17 Inferring Land Use Results: Confusion Matrix Thresh Total Accuracy Weights Res Com Ind Prk Oth ( ) Res Com Ind Prk Oth Share: = 1 Res Com Ind Prk Oth
18 Results: Inferring Land Use Confusion Matrix Thresh Total Accuracy Weights Res Prk Ind Con Oth ( N/A ) N/A N/A N/A N/A N/A N/A N/A Res Com Ind Prk Oth N/A Share: N/A
19 Inferring Land Use Classification Group I Group II Group III Error Analysis Cells correctly predicted to be a Cells of a given use incorrectly Cells of a different use incorrectly predicted to be a given Residential I II III Commercial I II III 19
20 Robustness Across Cities Cross Tabulation Thresh Avg. Error Cutoffs Res Com Ind Par Oth ( ) Res Com Ind Prk Oth Share:
21 Robustness Across Cities 80 City wide Time Series Chicago LBS Data Abs. Act Norm. Act Res. Act Hour 21
22 Inferring Land Use Summary: Introduced a way to reconcile and normalize traditional and new data sources Used temporal activity patterns to identify land uses Performed error analysis to determine why classifications failed Similar results obtained for different cities Implications: Mobile phone activity can be radically different depending on zoned land use. Traditional zoning maps should be augmented to account for real-time use. Dynamic land use patterns seem relatively consistent despite differences in history and regulatory classification 22
23 Future Contributions Use more sophisticated feature spaces for zoning classification. Introduce new data sources. Measure urban system dynamics in multiple cities around the globe to identify similarities and differences in behavior. Apply normalization metrics to other data sources. Combine our understanding of temporal land use with measurements of human mobility to develop better models of intra-city travel behavior. 23
24 Thank You! Questions? 24
25 Errors 25
26 Correlation with Static Population 26
27 Group I Group II Group III Classification Error Analysis Cells correctly predicted to be a Cells of a given use incorrectly Cells of a different use incorrectly predicted to be a given Residential I II III Commercial Industrial I II III I II III Parks Other I II III I II III 27
Chapter 5 FUNCTIONAL CLASSIFICATION
Chapter 5 FUNCTIONAL CLASSIFICATION Functional classification is a system by which streets and roadways may be distinguished by types according to their function within the entire transportation network.
More informationImproving the Accuracy and Reliability of ACS Estimates for Non-Standard Geographies Used in Local Decision Making
Improving the Accuracy and Reliability of ACS Estimates for Non-Standard Geographies Used in Local Decision Making Warren Brown, Joe Francis, Xiaoling Li, and Jonnell Robinson Cornell University Outline
More informationAcknowledgements. Ms. Linda Banister Ms. Tracy With Mr. Hassan Shaheen Mr. Scott Johnston
Acknowledgements The 2005 Household Travel Survey was funded by the City of Edmonton and Alberta Infrastructure and Transportation (AIT). The survey was led by a steering committee comprised of: Dr. Alan
More informationRationale. Previous research. Social Network Theory. Main gap 4/13/2011. Main approaches (in offline studies)
Boots are Made for Walking: The Spatiality of Social Networks in a Pedestrian, Phone-Free Society Patterns of social interaction in regions without: Implications Rationale Annual Meeting of the Association
More informationDRAFT 5/31/2016. The Value of Self-Reported Frequently Visited Addresses in GPS Assisted Travel Surveys Timothy Michalowski Dara Seidl
DRAFT 5/31/2016 The Value of Self-Reported Frequently Visited Addresses in GPS Assisted Travel Surveys Timothy Michalowski Dara Seidl June 28, 2016 Agenda Travel Surveys Location Data Collection Methods
More informationUnderstanding the Pattern of Work Travel in India using the Census Data
Understanding the Pattern of Work Travel in India using the Census Data Presented at Urban Mobility India Hyderabad (India), November 5 th 2017 Nishant Singh Research Scholar Department of Civil Engineering
More informationSOUNDCAST CALIBRATION AND SENSITIVITY TEST RESULTS (DRAFT) TABLE OF CONTENTS. Puget Sound Regional Council. Suzanne Childress.
SOUNDCAST CALIBRATION AND SENSITIVITY TEST RESULTS (DRAFT) Puget Sound Regional Council Suzanne Childress June 2015 This document describes the activity-based model calibration to the 2010 using the following
More informationComparison of urban energy use and carbon emission in Tokyo, Beijing, Seoul and Shanghai
International Workshop on Urban Energy and Carbon Modeling, February 5-6, 28, AIT Centre, Asian Institute of Technology, Pathumthani, Thailand Comparison of urban energy use and carbon emission in Tokyo,
More informationPREDICTING the outcomes of sporting events
CS 229 FINAL PROJECT, AUTUMN 2014 1 Predicting National Basketball Association Winners Jasper Lin, Logan Short, and Vishnu Sundaresan Abstract We used National Basketball Associations box scores from 1991-1998
More informationVGI for mapping change in bike ridership
VGI for mapping change in bike ridership D. Boss 1, T.A. Nelson* 2 and M. Winters 3 1 Unviersity of Victoria, Victoria, Canada 2 Arizona State University, Arizona, USA 3 Simon Frasier University, Vancouver,
More informationSection I: Multiple Choice Select the best answer for each problem.
Inference for Linear Regression Review Section I: Multiple Choice Select the best answer for each problem. 1. Which of the following is NOT one of the conditions that must be satisfied in order to perform
More informationA Framework For Integrating Pedestrians into Travel Demand Models
A Framework For Integrating Pedestrians into Travel Demand Models Kelly J. Clifton Intersections Seminar University of Toronto September 22, 2017 Portland, Oregon, USA Region Population~ 2.4 M Urban Growth
More informationRIDERSHIP PREDICTION
RIDERSHIP PREDICTION Outline 1. Introduction: route ridership prediction needs and issues. 2. Alternative approaches to route ridership prediction. Professional judgement Survey-based methods Cross-sectional
More informationDesigning a Bicycle and Pedestrian Count Program in Blacksburg, VA
Designing a Bicycle and Pedestrian Count Program in Blacksburg, VA Steve Hankey (Virginia Tech) Andrew Mondschein (U or Virginia) Ralph Buehler (Virginia Tech) Issue/objective Issue No systematic traffic
More informationSoundCast Design Intro
SoundCast Design Intro Basic Design SoundCast and Daysim 3 Land use attributes Households & Individuals SoundCast DaySim Travel demand simulator Trips and Households, Excel Summary Sheets, EMME network
More informationChapter 12 Practice Test
Chapter 12 Practice Test 1. Which of the following is not one of the conditions that must be satisfied in order to perform inference about the slope of a least-squares regression line? (a) For each value
More informationSTRAVA - DATA ACCESS AND USES. March 9, 2016 Shaun Davis + Dewayne Carver Florida Dept. of Transportation
STRAVA - DATA ACCESS AND USES March 9, 2016 Shaun Davis + Dewayne Carver Florida Dept. of Transportation AGENDA Training Accessing the Data Data Characteristics Potential Uses Question and Answer WHAT
More informationReal-Time Electricity Pricing
Real-Time Electricity Pricing Xi Chen, Jonathan Hosking and Soumyadip Ghosh IBM Watson Research Center / Northwestern University Yorktown Heights, NY, USA X. Chen, J. Hosking & S. Ghosh (IBM) Real-Time
More informationWebinar: Development of a Pedestrian Demand Estimation Tool
Portland State University PDXScholar TREC Webinar Series Transportation Research and Education Center (TREC) 2-18-2016 Webinar: Development of a Pedestrian Demand Estimation Tool Kelly Clifton Portland
More informationSpatial Patterns / relationships. Model / Predict
Human Environment Spatial Patterns / relationships Model / Predict 2 3 4 5 6 Comparing Neighborhoods with high Quality of Life & health Overlap matrix NPUs with high NH & NQoL SEC High QoL High Health
More informationEnvironmental Science: An Indian Journal
Environmental Science: An Indian Journal Research Vol 14 Iss 1 Flow Pattern and Liquid Holdup Prediction in Multiphase Flow by Machine Learning Approach Chandrasekaran S *, Kumar S Petroleum Engineering
More informationMODELING THE ACTIVITY BASED TRAVEL PATTERN OF WORKERS OF AN INDIAN METROPOLITAN CITY: CASE STUDY OF KOLKATA
MODELING THE ACTIVITY BASED TRAVEL PATTERN OF WORKERS OF AN INDIAN METROPOLITAN CITY: CASE STUDY OF KOLKATA WORK ARITRA CHATTERJEE HOME SHOPPING PROF. DR. P.K. SARKAR SCHOOL OF PLANNING AND ARCHITECTURE,
More informationCSC242: Intro to AI. Lecture 21
CSC242: Intro to AI Lecture 21 Quiz Stop Time: 2:15 Learning (from Examples) Learning Learning gives computers the ability to learn without being explicitly programmed (Samuel, 1959)... agents that can
More informationKevin Proft NRS 509 Final Project: Written Overview & Annotated Bibliography GIS Applications in Active Transportation Planning
Kevin Proft NRS 509 Final Project: Written Overview & Annotated Bibliography 12.13.16 GIS Applications in Active Transportation Planning Geographic Information Systems (GIS) play an important role in developing
More informationJPEG-Compatibility Steganalysis Using Block-Histogram of Recompression Artifacts
JPEG-Compatibility Steganalysis Using Block-Histogram of Recompression Artifacts Jan Kodovský, Jessica Fridrich May 16, 2012 / IH Conference 1 / 19 What is JPEG-compatibility steganalysis? Detects embedding
More informationEvaluating and Classifying NBA Free Agents
Evaluating and Classifying NBA Free Agents Shanwei Yan In this project, I applied machine learning techniques to perform multiclass classification on free agents by using game statistics, which is useful
More informationActive Travel and Exposure to Air Pollution: Implications for Transportation and Land Use Planning
Active Travel and Exposure to Air Pollution: Implications for Transportation and Land Use Planning Steve Hankey School of Public and International Affairs, Virginia Tech, 140 Otey Street, Blacksburg, VA
More informationUnderstanding Transit Demand. E. Beimborn, University of Wisconsin-Milwaukee
Understanding Transit Demand E. Beimborn, University of Wisconsin-Milwaukee 1 Purpose To provide a basic understanding of transit ridership and some common misunderstandings. To explain concepts of choice
More informationNACTO Designing Cities Conference Project Evaluation: Tools for Measuring Success and Building Support. October 29, 2015
NACTO Designing Cities Conference Project Evaluation: Tools for Measuring Success and Building Support October 29, 2015 The case for evaluation: Have a social contract with City Council, staff and community
More informationRoad Congestion Measures Using Instantaneous Information From the Canadian Vehicle Use Study (CVUS)
Proceedings of Statistics Canada Symposium 2016 Growth in Statistical Information: Challenges and Benefits Road Congestion Measures Using Instantaneous Information From the Canadian Vehicle Use Study (CVUS)
More informationPlanning Daily Work Trip under Congested Abuja Keffi Road Corridor
ISBN 978-93-84468-19-4 Proceedings of International Conference on Transportation and Civil Engineering (ICTCE'15) London, March 21-22, 2015, pp. 43-47 Planning Daily Work Trip under Congested Abuja Keffi
More informationForecasting High Speed Rail Ridership Using Aggregate Data:
Transportation Research Board 2015, Washington D.C. Forecasting High Speed Rail Ridership Using Aggregate Data: A Case Revisit of High Speed Rail in Taiwan Yu-Ting Hsu, Wei-Ren Lin, Yung-Cheng (Rex) Lai,
More informationBike Counter Correlation
Bike Counter Correlation A Story of Synergy: Bike Counts and Strava Metro For decades, transportation planners have used manual and automatic bicycle counters to collect hard data on where and when people
More informationBASKETBALL PREDICTION ANALYSIS OF MARCH MADNESS GAMES CHRIS TSENG YIBO WANG
BASKETBALL PREDICTION ANALYSIS OF MARCH MADNESS GAMES CHRIS TSENG YIBO WANG GOAL OF PROJECT The goal is to predict the winners between college men s basketball teams competing in the 2018 (NCAA) s March
More informationPreliminary Exploration of Pedestrian Destinations using Traces from WiFi Infrastructures
Preliminary Exploration of Pedestrian Destinations using Traces from WiFi Infrastructures Antonin Danalet, Michel Bierlaire, Bilal Farooq STRC 2012 1 Presentation Outline 1. Motivation 2. Data collections
More informationGIS Based Data Collection / Network Planning On a City Scale. Healthy Communities Active Transportation Workshop, Cleveland, Ohio May 10, 2011
The Purpose of GIS Based Network Planning GIS Based Data Collection / Network Planning Healthy Communities Active Transportation Conference Tuesday, May 10, 2011 10:00 AM Norman Cox, LLA, ASLA. Ann Arbor,
More informationBriefing Paper #1. An Overview of Regional Demand and Mode Share
2011 Metro Vancouver Regional Trip Diary Survey Briefing Paper #1 An Overview of Regional Demand and Mode Share Introduction The 2011 Metro Vancouver Regional Trip Diary Survey is the latest survey conducted
More informationSiła-Nowicka, K. (2018) Analysis of Actual Versus Permitted Driving Speed: a Case Study from Glasgow, Scotland. In: 26th Annual GIScience Research UK Conference (GISRUK 2018), Leicester, UK, 17-20 Apr
More informationTransport Poverty in Scotland. August 2016
Transport Poverty in Scotland August 2016 About Sustrans Sustrans makes smarter travel choices possible, desirable and inevitable. We re a leading UK charity enabling people to travel by foot, bike or
More informationDeconstructing Data Science
Deconstructing Data Science David Bamman, UC Berkele Info 29 Lecture 4: Regression overview Jan 26, 217 Regression A mapping from input data (drawn from instance space ) to a point in R (R = the set of
More informationExploring the relationship between Strava cyclists and all cyclists*.
Exploring the relationship between Strava cyclists and all cyclists*. Dr. David McArthur, Dr. Jinhyun Hong, Dr. Mark Livingston, Kirstie English *This presentation contains preliminary results which are
More informationAnalyzing Gainesville s Bicycle Infrastructure
Mateo Van Thienen 1 DCP 2002: Intro to GIS II Final Project Paper Abstract: Analyzing Gainesville s Bicycle Infrastructure The main purpose of this project is to determine which areas within Gainesville
More informationThe relationship between sea level and bottom pressure in an eddy permitting ocean model
The relationship between sea level and bottom pressure in an eddy permitting ocean model Rory Bingham and Chris Hughes Proudman Oceanographic Laboratory Introduction Motivation: Clearer understanding on
More information2017 Northwest Arkansas Trail Usage Monitoring Report
2017 Northwest Arkansas Trail Usage Monitoring Report Summary Findings: The study showed that average daily weekday bicycle volumes per study site increased by about 32% between 2015 and 2017, from 142
More informationCongestion and Safety: A Spatial Analysis of London
Congestion and Safety: A Spatial Analysis of London Robert B. Noland Mohammed A. Quddus Centre for Transport Studies Dept. of Civil and Environmental Engineering Imperial College London London SW7 2AZ
More informationA computer program that improves its performance at some task through experience.
1 A computer program that improves its performance at some task through experience. 2 Example: Learn to Diagnose Patients T: Diagnose tumors from images P: Percent of patients correctly diagnosed E: Pre
More informationPedestrian Demand Modeling: Evaluating Pedestrian Risk Exposures
Pedestrian Demand Modeling: Evaluating Pedestrian Risk Exposures Kelly J. Clifton National Center for Smart Growth University of Maryland May 19, 2008 Study Team University of Maryland National Center
More information2011 Origin-Destination Survey Bicycle Profile
TRANS Committee 2011 Origin-Destination Survey National Capital Region December 2012 TRANS Committee Members: City of Ottawa, including OC Transpo Ville de Gatineau Société de transport de l Outaouais
More informationSummary of NWA Trail Usage Report November 2, 2015
Summary of NWA Trail Usage Report November 2, 2015 Summary Findings: The study showed that Northwest Arkansas (NWA) had relatively high cyclist user counts per capita aggregated across the top three usage
More informationINTRODUCTION TO PATTERN RECOGNITION
INTRODUCTION TO PATTERN RECOGNITION 3 Introduction Our ability to recognize a face, to understand spoken words, to read handwritten characters all these abilities belong to the complex processes of pattern
More informationA MULTI-MODAL PUBLIC TRANSPORT SOLUTION FOR MALE, MALDIVES
A MULTI-MODAL PUBLIC TRANSPORT SOLUTION FOR MALE, MALDIVES 11 TH CONFERENCE ON COMPETITION AND OWNERSHIP IN LAND PASSENGER TRANSPORT Delft University of Technology (The Netherlands) 20-25 September 2009
More informationGIS Based Non-Motorized Transportation Planning APA Ohio Statewide Planning Conference. GIS Assisted Non-Motorized Transportation Planning
The Purpose of GIS Assisted Network GIS Assisted Non-Motorized Transportation 2011 APA Ohio Statewide Conference Friday, 10:45 AM to Noon Focus on near-term projects wwwgreenwaycollabcom The purpose of
More informationNational Bicycle and Pedestrian Documentation Project INSTRUCTIONS
National Bicycle and Pedestrian Documentation Project INSTRUCTIONS The National Documentation Project (NBPD) is an annual bicycle and pedestrian count and survey effort sponsored by the Institute of Transportation
More informationPredicting NBA Shots
Predicting NBA Shots Brett Meehan Stanford University https://github.com/brettmeehan/cs229 Final Project bmeehan2@stanford.edu Abstract This paper examines the application of various machine learning algorithms
More information1999 On-Board Sacramento Regional Transit District Survey
SACOG-00-009 1999 On-Board Sacramento Regional Transit District Survey June 2000 Sacramento Area Council of Governments 1999 On-Board Sacramento Regional Transit District Survey June 2000 Table of Contents
More informationDiscriminative Feature Selection for Uncertain Graph Classification
Discriminative Feature Selection for Uncertain Graph Classification Xiangnan Kong University of Illinois at Chicago joint work with Philip S. Yu (Univ. Illinois at Chicago) Xue Wang & Ann B. Ragin (Northwestern
More informationThresholds and Impacts of Walkable Distance for Active School Transportation in Different Contexts
Thresholds and Impacts of Walkable Distance for Active School Transportation in Different Contexts Xuemei Zhu, Chanam Lee, Zhipeng Lu, Chia-Yuan Yu College of Architecture, Texas A&M University CONTENT
More informationPocatello Regional Transit Master Transit Plan Draft Recommendations
Pocatello Regional Transit Master Transit Plan Draft Recommendations Presentation Outline 1. 2. 3. 4. What is the Master Transit Plan? An overview of the study Where Are We Today? Key take-aways from existing
More informationFixed Guideway Transit Outcomes on Rents, Jobs, and People and Housing
Fixed Guideway Transit Outcomes on Rents, Jobs, and People and Housing Arthur C. Nelson, Ph.D., ASCE, FAICP Professor of Planning and Real Estate Development University of Arizona 1 Changing Transportation
More informationBrazil Baseline and Mitigation Scenarios
Brazil Baseline and Mitigation Scenarios The 12 th AIM International Workshop William Wills ww@ufrj.br Tsukuba, Japan 19-21, February 2007 CCAP (Center for Clean Air Policy): Dialogue on Future International
More informationJournal of Emerging Trends in Computing and Information Sciences
A Study on Methods to Calculate the Coefficient of Variance in Daily Traffic According to the Change in Hourly Traffic Volume Jung-Ah Ha Research Specialist, Korea Institute of Construction Technology,
More informationAppendix C. Corridor Spacing Research
Appendix C. Corridor Spacing Research Task 3 of the Twin Cities Bicycle Study called for the development of bicycle corridor spacing guidelines. This section summarizes research of the spacing of planned
More informationDynamic Cluster-Based Over-Demand Prediction in Bike Sharing Systems
Dynamic Cluster-Based Over-Demand Prediction in Bike Sharing Systems Longbiao Chen, Daqing Zhang, Leye Wang, Dingqi Yang, Xiaojuan Ma, Shijian Li, Gang Pan, Thi-Mai-Trang Nguyen, Jérémie Jakubowicz Zhejiang
More informationModeling Driving Behavior at Roundabouts: Impact of Roundabout Layout and Surrounding Traffic on Driving Behavior
Modeling Driving Behavior at Roundabouts: Impact of Roundabout Layout and Surrounding Traffic on Driving Behavior M. Zhao D. Käthner D. Söffker M. Jipp K. Lemmer Institute of Transportation Systems, German
More informationA N E X P L O R AT I O N W I T H N E W Y O R K C I T Y TA X I D ATA S E T
A N E X P L O R AT I O N W I T H N E W Y O R K C I T Y TA X I D ATA S E T GAO, Zheng 26 May 2016 Abstract The data analysis is two-part: an exploratory data analysis, and an attempt to answer an inferential
More informationWebinar: The Association Between Light Rail Transit, Streetcars and Bus Rapid Transit on Jobs, People and Rents
Portland State University PDXScholar TREC Webinar Series Transportation Research and Education Center (TREC) 11-15-2016 Webinar: The Association Between Light Rail Transit, Streetcars and Bus Rapid Transit
More informationValidation of 12.5 km Resolution Coastal Winds. Barry Vanhoff, COAS/OSU Funding by NASA/NOAA
Validation of 12.5 km Resolution Coastal Winds Barry Vanhoff, COAS/OSU Funding by NASA/NOAA Outline Part 1: Determining empirical land mask Characterizing σ 0 near coast Part 2: Wind retrieval using new
More informationPREDICTING THE NCAA BASKETBALL TOURNAMENT WITH MACHINE LEARNING. The Ringer/Getty Images
PREDICTING THE NCAA BASKETBALL TOURNAMENT WITH MACHINE LEARNING A N D R E W L E V A N D O S K I A N D J O N A T H A N L O B O The Ringer/Getty Images THE TOURNAMENT MARCH MADNESS 68 teams (4 play-in games)
More informationMobility Detection Using Everyday GSM Traces
Mobility Detection Using Everyday GSM Traces Timothy Sohn et al Philip Cootey pcootey@wpi.edu (03/22/2011) Mobility Detection High level activity discerned from course Grained GSM Data provides immediate
More informationResource Allocation for Malaria Prevention
Resource Allocation for Malaria Prevention Final Presentation April 17, 2008 Michele Cataldi Christina Cho Cesar Gutierrez Jeff Hull Phillip Kim Andrew Park Sponsor Contact: Jason Pickering, PhD. Faculty
More informationLecture 5. Optimisation. Regularisation
Lecture 5. Optimisation. Regularisation COMP90051 Statistical Machine Learning Semester 2, 2017 Lecturer: Andrey Kan Copyright: University of Melbourne Iterative optimisation Loss functions Coordinate
More informationDeconstructing Data Science
Deconstructing Data Science David Bamman, UC Berkele Info 29 Lecture 4: Regression overview Feb 1, 216 Regression A mapping from input data (drawn from instance space ) to a point in R (R = the set of
More informationRolling Out Measures of Non-Motorized Accessibility: What Can We Now Say? Kevin J. Krizek University of Colorado
Rolling Out Measures of Non-Motorized Accessibility: What Can We Now Say? Kevin J. Krizek University of Colorado www.kevinjkrizek.org Acknowledgements Mike Iacono Ahmed El-Geneidy Chen-Fu Liao Outline
More informationAnalyzing Spatial Patterns, Statistics Based on FAST Act Safety Performance Measures
Analyzing Spatial Patterns, Statistics Based on FAST Act Safety Performance Measures Abhishek Bhargava, PhD April 13, 2017 Agenda Objective Highway Safety Improvement Program (HSIP) & FAST Act Performance
More informationUsing Spatio-Temporal Data To Create A Shot Probability Model
Using Spatio-Temporal Data To Create A Shot Probability Model Eli Shayer, Ankit Goyal, Younes Bensouda Mourri June 2, 2016 1 Introduction Basketball is an invasion sport, which means that players move
More informationBicycle Demand Forecasting for Bloomingdale Trail in Chicago
Bicycle Demand Forecasting for Bloomingdale Trail in Chicago Corresponding Author: Zuxuan Deng Arup Water Street, New York, NY 000 zuxuan.deng@arup.com Phone:.. Fax:..0 Matthew Sheren Arup Water Street,
More informationTravel Behavior of Baby Boomers in Suburban Age Restricted Communities
Travel Behavior of Baby Boomers in Suburban Age Restricted Communities TRB Conference Impact of Changing Demographics on the Transportation System 27 October 2008 P. Christopher Zegras Frank Hebbert Eran
More informationFeasibility Analysis of China s Traffic Congestion Charge Legislation
International Conference on Social Science and Technology Education (ICSSTE 2015) Feasibility Analysis of China s Traffic Congestion Charge Legislation Wang Jiyun Beijing Jiaotong University Law School
More informationTemporal and Spatial Variation in Non-motorized Traffic in Minneapolis: Some Preliminary Analyses
Temporal and Spatial Variation in Non-motorized Traffic in Minneapolis: Some Preliminary Analyses Spencer Agnew, Jason Borah, Steve Hankey, Kristopher Hoff, Brad Utecht, Zhiyi Xu, Greg Lindsey Thanks to:
More informationName May 3, 2007 Math Probability and Statistics
Name May 3, 2007 Math 341 - Probability and Statistics Long Exam IV Instructions: Please include all relevant work to get full credit. Encircle your final answers. 1. An article in Professional Geographer
More informationFrequently asked questions about how the Transport Walkability Index was calculated are answered below.
Transport Walkability Index The Transport Walkability Index is a relative indicator of how well the built environment in different areas supports walking for transport. The index is frequently used in
More informationProjecting Three-Point Percentages for the NBA Draft
Projecting Three-Point Percentages for the NBA Draft Hilary Sun hsun3@stanford.edu Jerold Yu jeroldyu@stanford.edu December 16, 2017 Roland Centeno rcenteno@stanford.edu 1 Introduction As NBA teams have
More informationUrban planners have invested a lot of energy in the idea of transit-oriented
DOES TRANSIT-ORIENTED DEVELOPMENT NEED THE TRANSIT? D A N I E L G. C H AT M A N Urban planners have invested a lot of energy in the idea of transit-oriented developments (TODs). Developing dense housing
More informationR&D Initiatives for Sustainable Mobility
Centre for Infrastructure Systems (CIS) Director: A/Prof Wong Yiik Diew Email: cydwong@ntu.edu.sg 27 June 2014 R&D Initiatives for Sustainable Mobility 1 Population Density (thousand persons per square
More informationAnalysis On Night-time Public Transportation Access In Seoul: How Do People Travel At Night In Seoul Using Taxies?
ANNUAL TECHNOLOGY SUMMIT ORLANDO, FL MARCH 31-APRIL 2, 2019 Analysis On Night-time Public Transportation Access In Seoul: How Do People Travel At Night In Seoul Using Taxies? O. NAMKUNG Graduate School
More informationABSTRACT PEDESTRIAN - VEHICULAR CRASHES: THE INFLUENCE OF PERSONAL AND ENVIRONMENTAL FACTORS. Carolina V. Burnier, M.Sc., 2005
ABSTRACT Title: PEDESTRIAN - VEHICULAR CRASHES: THE INFLUENCE OF PERSONAL AND ENVIRONMENTAL FACTORS Carolina V. Burnier, M.Sc., 2005 Directed By: Dr. Kelly J. Clifton, Department of Civil and Environmental
More informationExploring Factors Affecting Metrorail Ridership in Washington D.C.
Exploring Factors Affecting Metrorail Ridership in Washington D.C. Chao Liu, Ph.D., Hiro Iseki, Ph.D. National Center for Smart Growth University of Maryland, College Park September 2015, GIS in Transit
More informationTransport Poverty in Scotland. August 2016
Transport Poverty in Scotland August 2016 About Sustrans Sustrans is the charity making it easier for people to walk and cycle. We connect people and places, create liveable neighbourhoods, transform the
More informationTravel Characteristics on Weekends:
Travel Characteristics on Weekends: Implications for Planning and Policy Making Ram M. Pendyala Department of Civil and Environmental Engineering University of South Florida, Tampa Ashish Agarwal Cambridge
More informationStation heterogeneity and the dynamics of retail gasoline prices
Station heterogeneity and the dynamics of retail gasoline prices Julia González Carlos Hurtado April 2018 INCOMPLETE AND PRELIMINARY DRAFT. Abstract One of the main stylized facts in the dynamics of retail
More informationMarch 6, 2013 Tony Giarrusso, Rama Sivakumar Center for GIS, Georgia Institute of Technology
March 6, 2013 Tony Giarrusso, Rama Sivakumar Center for GIS, Georgia Institute of Technology 33 46 35.74 N, 83 23 48.33 W Location: Georgia Institute of Technology, College of Architecture Established:1996
More informationDEVELOPMENT OF A SET OF TRIP GENERATION MODELS FOR TRAVEL DEMAND ESTIMATION IN THE COLOMBO METROPOLITAN REGION
DEVELOPMENT OF A SET OF TRIP GENERATION MODELS FOR TRAVEL DEMAND ESTIMATION IN THE COLOMBO METROPOLITAN REGION Ravindra Wijesundera and Amal S. Kumarage Dept. of Civil Engineering, University of Moratuwa
More information2014 QUICK FACTS ILLINOIS CRASH INFORMATION. Illinois Emergency Medical Services for Children February 2016 Edition
2014 QUICK FACTS ILLINOIS CRASH INFORMATION February 2016 Edition Illinois Emergency Medical Services for Children www.luhs.org/emsc Illinois Emergency Medical Services for Children TABLE OF CONTENTS
More information2012 QUICK FACTS ILLINOIS CRASH INFORMATION. Illinois Emergency Medical Services for Children September 2014 Edition
2012 QUICK FACTS ILLINOIS CRASH INFORMATION September 2014 Edition Illinois Emergency Medical Services for Children www.luhs.org/emsc Illinois Emergency Medical Services for Children TABLE OF CONTENTS
More informationMarket Factors and Demand Analysis. World Bank
Market Factors and Demand Analysis Bank Workshop and Training on Urban Transport Planning and Reform. Baku, April 14-16, 2009 Market Factors The market for Public Transport is affected by a variety of
More informationCITI BIKE S NETWORK, ACCESSIBILITY AND PROSPECTS FOR EXPANSION. Is New York City s Premier Bike Sharing Program Accesssible to All?
CITI BIKE S NETWORK, ACCESSIBILITY AND PROSPECTS FOR EXPANSION Is New York City s Premier Bike Sharing Program Accesssible to All? How accessible is Citi Bike s network in New York City? Who benefits from
More informationIVR Caller Experience Metrics. World IA Day 2013 March 2
IVR Caller Experience Metrics World IA Day 2013 March 2 What is Caller Experience? Processes and Design Principles that put the caller at the center of attention, to ensure that an IVR is: Useful Usable
More informationFebruary Funded by NIEHS Grant #P50ES RAND Center for Population Health and Health Disparities
Urban Use and Physical Activity Deborah Cohen, Thom McKenzie, Amber Sehgal, Stephanie Williamson, Daniela Golinelli, Multi-Cultural Area Health Education Center (MAHEC) Funded by NIEHS Grant #P50ES012383
More informationAn Analysis of Central Business District Pedestrian Circulation Patterns
An Analysis of Central Business District Pedestrian Circulation Patterns M. P. NESS, Greater London Council, J. F. MORRALL, Department of Highways, Ontario, and B. G. HUTCHINSON, University of Waterloo,
More informationPresentation Summary Why Use GIS for Ped Planning? What Tools are Most Useful? How Can They be Applied? Pedestrian GIS Tools What are they good for?
1 2 Pedestrian GIS Tools What are they good for? Pro Walk / Pro Bike 2006 Presentation Summary Why Use GIS for Ped Planning? What Tools are Most Useful? How Can They be Applied? Matt Haynes Fehr & Peers
More information