Encapsulating Urban Traffic Rhythms into Road Networks
|
|
- Crystal Phillips
- 5 years ago
- Views:
Transcription
1 Encapsulating Urban Traffic Rhythms into Road Networks Junjie Wang +, Dong Wei +, Kun He, Hang Gong, Pu Wang * School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, , P.R. China + These authors contributed equally to this work * Corresponding Author wangpu@csu.edu.cn Page 1 / 16
2 Supplementary Information Here, we briefly introduce the data and method used for generating transient ODs to maintain the completeness of the paper. This part is an abbreviation of Part II of the Supplementary Information with reference [1]. For more detailed information and analysis on transient ODs, please refer to [1]. PART 1. Mobile Phone Data and Road GIS Data The San Francisco Bay area mobile phone data were collected by a US mobile phone operator, having temporal and spatial records for nearly half a million customers. Each time a person uses a phone (call/text message/web browsing) the time and the mobile phone tower providing the service is recorded. In the three week observational period, we totally collect 374 million location records. A voronoi lattice is used to estimate the service area of a mobile phone tower [2, 3]. It provides the rough region where a mobile phone user can be located by the phone usage (Fig. S1a). In the Boston area mobile phone data, the coordinates of the recorded locations are estimated by a standard triangulation algorithm. In the three weeks observational period, more than 200,000 distinct locations are recorded, this data is aggregated at the census tract level to define the location of a phone user (Fig. S1b). We further find that a large majority of driver sources are located within dense mobile phone grids or small enough census tracts, thus providing accurate spatial resolution for the purpose of this study. Users privacy is protected by using anonymized user IDs. In addition, the spatial resolution of the voronoi lattice or the census tract provides sufficiently large areas to prevent personal location identification at an individual level. Furthermore, no individual trajectory is shown in our results. In both areas the selected mobile phone users have at least one location recorded between 9:00 p.m. to 7:00 a.m., allowing for the definition of home location in connection with a tower s service area or a census tract. Consequently, we select 356,670 Bay Area users and 683,001 Boston Area users, which represent 6.56% and 19.35% of the population in the two metropolitan areas respectively. We measure Page 2 / 16
3 the population in each census tract of the two areas, finding that the distributions of population can be approximated by two Gaussian distributions (Fig. S1c & d). The road networks, which include both highways and arterial roads, are provided by NAVTEQ, a commercial provider of geographical information systems data [4]. The data incorporate the attributes of roads needed for the computations presented in this work, in particular the road capacity. The road network in the Bay area contains 21,880 road segments and 11,096 intersections, while the road network in the Boston area contains 21,905 road segments and 9,643 intersections. For each road segment, the speed limit sl (miles/hr), the number of lanes l and the direction are extracted from the database. According to 2000 Highway Capacity Manual [5] and Reference [6], we estimate the capacity C of a road segment as follows: (1) when the speed limit of a road segment sl 45, it is defined as an arterial road: C=1,900 l q (vehicles/hour) (S1) for simplicity, the effective green time-to-cycle length ratio q is selected to be 0.5. (2) when the speed limit of a road segment 45<sl<60, it is defined as a highway: C=(1, sl) l (vehicles/hour) (S2) (3) when the speed limit of a road segment sl 60, it is defined as a freeway: C=(1, sl) l (vehicles/hour) (S3) Page 3 / 16
4 Figure S1 Human mobility and population data. (a) In the Bay area, 892 mobile phone towers (blue dots) are used by the carrier. The towers servicing areas are defined by a voronoi tessellation (blue polygons). The census tracts are represented by the light grey polygons. (b) Red polygons show the 750 census tracts in the Boston area. Mobile phone users coordinates were estimated by a standard triangulation algorithm, which resulted in more than 200,000 distinct locations with a 100m 100m spatial resolution (black dots). (c), (d) The distribution of population in the Bay area and Boston area census tracts. Two Gaussian distributions: and were plotted to guide the eyes. The maps in (a), (b) were generated using TransCAD 5.0 and ArcGIS. Page 4 / 16
5 Part 2. Estimation of the Transient ODs The major challenge when estimating travel demands with mobile phone data is embedded in the sparse and irregular records [7], in which user displacements (consecutive different recorded locations) are usually observed between a long period (i.e. the first location is observed at 8:00am and next location is observed at 6:00pm). To more accurately extract users travel demands between zones (mobile phone towers service areas for the Bay area and the census tracts for the Boston area), we only record displacements occurring within a short time window. However, the time window we select must be long enough in order to ensure that enough travel demand information is extracted. In our modelling framework, we set the time window to one hour and define a trip as a displacement occurring within one hour in each time period (i.e. Morning Period, Noon & Afternoon Period, etc). Fig. S2a illustrates a mobile user s time and location records, using the presented approach; in this example two trips are detected. Changes of locations C->D are not defined as a trip, because they do not occur within a one-hour time window. In this study, zones were defined by towers servicing areas in the Bay area and census tracts in the Boston area. The different zone definitions were resulted from the different features of location records in the two mobile phone datasets. The defined zones were only used in the process of generating the t-ods. All measurements regarding the dynamical driver sources (Fig. 1c-f) were based on the census tracts for both Bay area and Boston area. We next count the number of trips between zone i and zone j in a specific time period: (S4) where is the total number of selected users and is the total number of trips that user made between zone i and zone j in the observational period. One may note that the extracted distribution of travel demands did not take the population distribution into account. To avoid the bias caused by the Page 5 / 16
6 unevenly distributed mobile phone user market share, we define the down-scale ratio ( ) or the up-scale ratio ( ) as follows: (S5) where and are the population and the number of selected mobile phone users in zone i (the population of each Bay area zone was estimated based on the proportion of the zone area in each census tract). The measured distributions are shown in Fig. S2c. Note that in some regions the actual number of mobile phone users staying there may be larger than the number of residents registered by census. For both areas, they are relatively broad, thus it is necessary to adjust the number of trips by up-scaling or down-scaling the mobile phone users (Eq. S6). After this process, the total number of trips generated by residents in a zone is proportional with its actual population: (S6) where is the total number of users in the zone and is the total number of trips that user made between zone i and zone j during the three weeks of study. People use different transportation modes throughout their trips. Possible transportation modes include car (drive alone), carpool, public transportation, bicycle and walk. We define a user is a vehicle user if he/she uses car to commute. We calculate the vehicle using rate ( ) in a zone as follows: (S7) where and are the probabilities that residents in zone i drive alone or share a car. The average carpool size is 2.25 in California and 2.16 in Massachusetts (8). As shown in Fig. S2d, is low in downtown and high in the suburb areas. Using the calculated for each zone, we randomly assign the transportation mode (vehicle or non-vehicle) to the users living in each zone. We then filter the trips that are not made by vehicles and calculate the total number of trips generated by vehicles : Page 6 / 16
7 (S8) where user n is a vehicle user, is the number of users in zone. The average number of daily trips per person is about 4 in the US [9]. This generates about 22 million trips in the Bay area and 14 million trips in the Boston area. Based on the daily distribution of traffic volume obtained from [10], we estimate the average hourly trip production in the four time periods (Fig. S2e). Next, we upscale the obtained distribution of travel demands with the hourly trip production for the entire population, thus finally defining the estimated t-od. t- (S9) where is the number of zones. To assign trips to the road networks, we map each t-od pair from zone based t-od to intersection-based t-od. We find the road intersections within a zone and randomly select one intersection to be the origin or destination in the intersection-based t-od (Fig. S2b). In very few cases no intersection is found in a zone. In such cases we assign a trip s origin or destination to a randomly chosen intersection in the nearest neighbouring zone. We generate four 11,096 11,096 intersection based t-od from the four zone based t-od in the Bay Area (the Bay Area road network contains 11,096 intersections). For the Boston Area, we generate four 9,643 9,643 intersection based t-od from the four zone based t-od (the Boston road network contains 9,643 intersections). In conclusion, we selected census tracts as the Boston area zones because the mobile phone tower information was not available. The Bay Area t-ods were generated in the mobile phone tower resolution to avoid errors introduced by converting the tower-based trips to census tract-based trips. In the process of generating the zone-based t-ods, different zone definitions were used to adapt better to the data formats. After converting the zone based t-ods to intersection-based t-ods, only census tract definition was used to locate the dynamical driver sources in the Bay area and the Boston area. Page 7 / 16
8 Figure S2 Methodology to generate t-ods. (a) Illustration of trip definition from a mobile phone user s billing record. Black lines represent phone usage records; for each record, the time and the associated towers (A-D) routing the service were recorded. (b) Illustration of a mobile phone user s OD and t-od. Road segments in Boston are depicted by grey lines. A driver drives from zone A (origin) to zone D (destination); however, he/she may only be detected by phone records at zone B (transient origin) and zone C (transient destination). The thick red line indicates the predicted route from the observed t-od, whereas the thick yellow line represents the missing segment of the route. (c) The blue curve corresponds to the distribution of up-scaling/down-scaling ratios in the Bay area. The red curve corresponds to that of the Boston area. (d) Vehicle usage rates by geographical area in the Boston area. The vehicle usage in each Bay area mobile phone tower s servicing area was estimated based on the proportion of the servicing area in each census tract. (e) The average hourly total trip productions for the four time periods. For each time period, the hourly total trip productions were assigned as the average. The maps in (b), (d) were generated using TransCAD 5.0 and ArcGIS. Page 8 / 16
9 PART 3. Supplementary Figures Figure S3 Distributions of and. Comparison of the distributions of and, is much smaller than ; validating vehicle origins of a road segment during a specific time period has been confined to a much smaller scale than drivers home locations. Figure S4 Properties of road segments in different groups. Properties of road segments in the whole road network (group I), the giant road cluster (group II), the remaining 1,000 road segments (group III), and the remaining 500 road segments (group IV) were analyzed. (a) The distribution of extra travel time in the Bay area. (b) The distribution of traffic flow in the Bay area. (b) The distribution of in the Bay area. (d), (e), (f) Same as (a), (b), (c) but for the Boston area. Page 9 / 16
10 Figure S5 The largest road cluster size and properties of remaining road segments in the noon/afternoon period and the evening period. Identical to Figure 2, but indicates the noon/afternoon period and the evening period. Page 10 / 16
11 Figure S6 The effects of congestion mitigation for different scales of targeted road clusters (Bay area). (a) The change of total extra travel time with the reduction of speed limit for different scales of targeted road clusters. (b) The change of total travel time with the reduction of speed limit for different scales of targeted road clusters. (c) The change in the number of congested road segments with the reduction of speed limit for different scales of targeted road clusters. (d), (e), (f) Same as (a), (b), (c) but indicated cases of increased capacity for different scales of targeted road clusters. Figure S7 The effects of congestion mitigation for different scales of targeted road clusters (Boston area). (a)-(f) Same with Figure S6, but for the Boston area Page 11 / 16
12 PART 4. Results for Weekdays and Weekends As Fig. S8 shows, the number of major dynamical driver sources followed similar exponential distributions during the weekdays and weekend days, and the distribution of total extra travel time of each census tract also follows similar power laws. These results show very tiny differences with the results depicted in Fig. 1 (where weekday and weekend records were not separated). Figure S8 Distributions of and. (a) The weekday follows an exponential distribution ( ) for the Bay area (Boston area). (b) The weekend follows an exponential distribution ( ) for the Bay area (Boston area). (c) The weekday extra travel time follows a power-law distribution ( ) for the Bay area (Boston area). (d) The weekend follows a power-law distribution ( ) for the Bay area (Boston area). ( for all fits) Page 12 / 16
13 We observed slightly different spatial distributions of the congested driver sources in weekdays and weekend days, suggesting that more detailed travel demand information can lead to more accurate estimation of congested driver sources (Fig. S9). Figure S9 Sources of traffic congestion in the Bay area and Boston area. The color of a census tract represents the total extra travel time experienced by drivers whose trips originated from that census tract during one of the peak morning hours. We defined the top 2% census tracts with the largest as congested driver sources and highlighted those using yellow polygons. (a) Weekday results in the Bay area. (b) Weekend morning in the Bay area. (c) Weekday results in the Boston area. (d) Weekend results in the Boston area. We also measured the size of the largest road cluster with the fraction of removed road segments for the morning, noon & afternoon and evening periods of the weekdays and weekend days. The properties of the remaining road segments show very similar patterns with the results shown in Fig. 2 and Fig. S5. Page 13 / 16
14 Figure S10 Locating the road segments used extensively by drivers from congested driver sources. Same with Fig. 2 and Fig. S5, but for the weekday and weekend cases. The top five road clusters targeted at different time periods of weekdays and weekend days were shown in Fig. S11 and Fig. S12 for the Bay area and the Boston area. We observed slightly different spatial distributions of targeted road clusters in different time periods and different types of days. The fundamental findings are well preserved when using weekday data and weekend data, indicating that our modeling framework and results show enough generality. Page 14 / 16
15 Figure S11 Spatial distribution of targeted road clusters in the Bay area when 500 road segments remain. (a), (b), (c) The weekday results. (d), (e), (f) The weekend results. Figure S12 Spatial distribution of targeted road clusters in the Boston area when 500 road segments remain. (a), (b), (c) The weekday results. (d), (e), (f) The weekend results. Page 15 / 16
16 REFERENCES: 1. Wang, P. et al. Understanding road usage patterns in urban areas. Scientific Reports 2, (2012). 2. Fu, T., Yin, X. & Zhang, Y. Voronoi algorithm model and the realization of its program. Computer Simulation 23, (2006). 3. Song, C., Qu, Z., Blumm, N. & Barabási, A.-L. Limits of predictability in human mobility. Science 327, (2010). 4. Navteq official website Transportation Research Board. Highway capacity manual: 2000 (Transportation Research Board, Washington, D.C. 2000). 6. Villalobos, J. R. et al. Logistics capacity study of the guaymas-tucson corridor (A report to the Arizona Department of Transportation, 2005). 7. Candia, J. et al. Uncovering individual and collective human dynamics from mobile phone records. J. Phys. A 41, (2008). 8. State averages for private vehicle occupancy, carpool size and vehicles per 100 workers National household travel survey daily travel quick facts Seto, Y. W., Holt, A., Rivard, T. & Bhatia, R. Spatial distribution of traffic induced noise exposures in a US city: an analytic tool for assessing the health impacts of urban planning decisions. International Journal of Health Geographics 6, 24 (2007). Page 16 / 16
Understanding Road Usage Patterns in Urban Areas
Understanding Road Usage Patterns in Urban Areas Pu Wang 1,2, Timothy Hunter 4, Alexandre M. Bayen 4,5, Katja Schechtner 6,7 and Marta C. González 2,3* 1 School of Traffic and Transportation Engineering,
More informationVISUAL EXPLORATION OF SPATIAL-TEMPORAL TRAFFIC CONGESTION PATTERNS USING FLOATING CAR DATA. Candra Kartika 2015
VISUAL EXPLORATION OF SPATIAL-TEMPORAL TRAFFIC CONGESTION PATTERNS USING FLOATING CAR DATA Candra Kartika 2015 OVERVIEW Motivation Background and State of The Art Test data Visualization methods Result
More information2014 Data Collection Project ITE Western District
2014 Data Collection Project ITE Western District Project Completed By: Oregon State University OSU ITE Student Chapter 101 Kearney Hall Corvallis, OR 97331 Student Coordinator: Sarah McCrea (OSU ITE Student
More informationMapping Accessibility Over Time
Journal of Maps, 2006, 76-87 Mapping Accessibility Over Time AHMED EL-GENEIDY and DAVID LEVINSON University of Minnesota, 500 Pillsbury Drive S.E., Minneapolis, MN 55455, USA; geneidy@umn.edu (Received
More informationAssessing spatial distribution and variability of destinations in inner-city Sydney from travel diary and smartphone location data
Assessing spatial distribution and variability of destinations in inner-city Sydney from travel diary and smartphone location data Richard B. Ellison 1, Adrian B. Ellison 1 and Stephen P. Greaves 1 1 Institute
More informationPATREC PERSPECTIVES Sensing Technology Innovations for Tracking Congestion
PATREC PERSPECTIVES Sensing Technology Innovations for Tracking Congestion Drivers have increasingly been using inexpensive mapping applications imbedded into mobile devices (like Google Maps, MapFactor,
More informationImproving the travel time prediction by using the real-time floating car data
Improving the travel time prediction by using the real-time floating car data Krzysztof Dembczyński Przemys law Gawe l Andrzej Jaszkiewicz Wojciech Kot lowski Adam Szarecki Institute of Computing Science,
More informationA route map to calibrate spatial interaction models from GPS movement data
A route map to calibrate spatial interaction models from GPS movement data K. Sila-Nowicka 1, A.S. Fotheringham 2 1 Urban Big Data Centre School of Political and Social Sciences University of Glasgow Lilybank
More informationDetecting Origin-Destination Mobility Flows From Geotagged Tweets in Greater Los Angeles Area
Detecting Origin-Destination Mobility Flows From Geotagged Tweets in Greater Los Angeles Area Song Gao 1, Jiue-An Yang 1,2, Bo Yan 1, Yingjie Hu 1, Krzysztof Janowicz 1, Grant McKenzie 1 1 STKO Lab, Department
More informationEstimating Large Scale Population Movement ML Dublin Meetup
Deutsche Bank COO Chief Data Office Estimating Large Scale Population Movement ML Dublin Meetup John Doyle PhD Assistant Vice President CDO Research & Development Science & Innovation john.doyle@db.com
More informationExploring Human Mobility with Multi-Source Data at Extremely Large Metropolitan Scales. ACM MobiCom 2014, Maui, HI
Exploring Human Mobility with Multi-Source Data at Extremely Large Metropolitan Scales Desheng Zhang & Tian He University of Minnesota, USA Jun Huang, Ye Li, Fan Zhang, Chengzhong Xu Shenzhen Institute
More informationRegional Performance Measures
G Performance Measures Regional Performance Measures Introduction This appendix highlights the performance of the MTP/SCS for 2035. The performance of the Revenue Constrained network also is compared to
More informationAnalyzing the Market Share of Commuter Rail Stations using LEHD Data
Analyzing the Market Share of Commuter Rail Stations using LEHD Data Using Census Data for Transportation Applications Conference, Irvine, CA October 26, 2011 1. What is the size of Metrolink s commute
More informationCity of Hermosa Beach Beach Access and Parking Study. Submitted by. 600 Wilshire Blvd., Suite 1050 Los Angeles, CA
City of Hermosa Beach Beach Access and Parking Study Submitted by 600 Wilshire Blvd., Suite 1050 Los Angeles, CA 90017 213.261.3050 January 2015 TABLE OF CONTENTS Introduction to the Beach Access and Parking
More informationTraffic Demand Forecast
Chapter 5 Traffic Demand Forecast One of the important objectives of traffic demand forecast in a transportation master plan study is to examine the concepts and policies in proposed plans by numerically
More informationWOODRUFF ROAD CORRIDOR ORIGIN-DESTINATION ANALYSIS
2018 WOODRUFF ROAD CORRIDOR ORIGIN-DESTINATION ANALYSIS Introduction Woodruff Road is the main road to and through the commercial area in Greenville, South Carolina. Businesses along the corridor have
More informationUnderstanding Individual Daily Activity Space Based on Large Scale Mobile Phone Location Data
Understanding Individual Daily Activity Space Based on Large Scale Mobile Phone Location Data Yang Xu 1, Shih-Lung Shaw 1 2 *, Ling Yin 3, Ziliang Zhao 1 1 Department of Geography, University of Tennessee,
More informationBROOKINGS May
Appendix 1. Technical Methodology This study combines detailed data on transit systems, demographics, and employment to determine the accessibility of jobs via transit within and across the country s 100
More informationAPPENDIX IV MODELLING
APPENDIX IV MODELLING Kingston Transportation Master Plan Final Report, July 2004 Appendix IV: Modelling i TABLE OF CONTENTS Page 1.0 INTRODUCTION... 1 2.0 OBJECTIVE... 1 3.0 URBAN TRANSPORTATION MODELLING
More informationAdvancing Transportation Performance Management and Metrics with Census Data
Advancing Transportation Performance Management and Metrics with Census Data Authors: Ivana Tasic, University of Utah, Department of Civil and Environmental Engineering, 110 Central Campus Drive, Salt
More informationPrepared for: San Diego Association Of Governments 401 B Street, Suite 800 San Diego, California 92101
Activity-Based Travel Model Validation for 2012 Using Series 13 Data: Coordinated Travel Regional Activity Based Modeling Platform (CT-RAMP) for San Diego County Prepared for: San Diego Association Of
More informationTrip Generation Study: A 7-Eleven Gas Station with a Convenience Store Land Use Code: 945
Trip Generation Study: A 7-Eleven Gas Station with a Convenience Store Land Use Code: 945 Introduction The Brigham Young University Institute of Transportation Engineers student chapter (BYU ITE) completed
More informationNeighborhood Locations and Amenities
University of Maryland School of Architecture, Planning and Preservation Fall, 2014 Neighborhood Locations and Amenities Authors: Cole Greene Jacob Johnson Maha Tariq Under the Supervision of: Dr. Chao
More informationRegional Performance Measures
G Performance Measures Regional Performance Measures Introduction This appendix highlights the performance of the MTP/SCS for 2035. The performance of the Revenue Constrained network also is compared to
More informationSpatial and Socioeconomic Analysis of Commuting Patterns in Southern California Using LODES, CTPP, and ACS PUMS
Spatial and Socioeconomic Analysis of Commuting Patterns in Southern California Using LODES, CTPP, and ACS PUMS Census for Transportation Planning Subcommittee meeting TRB 95th Annual Meeting January 11,
More informationFigure 8.2a Variation of suburban character, transit access and pedestrian accessibility by TAZ label in the study area
Figure 8.2a Variation of suburban character, transit access and pedestrian accessibility by TAZ label in the study area Figure 8.2b Variation of suburban character, commercial residential balance and mix
More informationAppendixx C Travel Demand Model Development and Forecasting Lubbock Outer Route Study June 2014
Appendix C Travel Demand Model Development and Forecasting Lubbock Outer Route Study June 2014 CONTENTS List of Figures-... 3 List of Tables... 4 Introduction... 1 Application of the Lubbock Travel Demand
More informationTransit Time Shed Analyzing Accessibility to Employment and Services
Transit Time Shed Analyzing Accessibility to Employment and Services presented by Ammar Naji, Liz Thompson and Abdulnaser Arafat Shimberg Center for Housing Studies at the University of Florida www.shimberg.ufl.edu
More informationEconomic consequences of floods: impacts in urban areas
Economic consequences of floods: impacts in urban areas SWITCH Paris Conference Paris, 24 th 26 th January 2011 Economic consequences of floods: impacts in urban areas Institutions: Authors Vanessa Cançado
More informationCensus Transportation Planning Products (CTPP)
Census Transportation Planning Products (CTPP) Penelope Weinberger CTPP Program Manager - AASHTO September 15, 2010 1 What is the CTPP Program Today? The CTPP is an umbrella program of data products, custom
More informationGeospatial Analysis of Job-Housing Mismatch Using ArcGIS and Python
Geospatial Analysis of Job-Housing Mismatch Using ArcGIS and Python 2016 ESRI User Conference June 29, 2016 San Diego, CA Jung Seo, Frank Wen, Simon Choi and Tom Vo, Research & Analysis Southern California
More informationStanCOG Transportation Model Program. General Summary
StanCOG Transportation Model Program Adopted By the StanCOG Policy Board March 17, 2010 What are Transportation Models? General Summary Transportation Models are technical planning and decision support
More informationROUNDTABLE ON SOCIAL IMPACTS OF TIME AND SPACE-BASED ROAD PRICING Luis Martinez (with Olga Petrik, Francisco Furtado and Jari Kaupilla)
ROUNDTABLE ON SOCIAL IMPACTS OF TIME AND SPACE-BASED ROAD PRICING Luis Martinez (with Olga Petrik, Francisco Furtado and Jari Kaupilla) AUCKLAND, NOVEMBER, 2017 Objective and approach (I) Create a detailed
More informationCOMBINATION OF MACROSCOPIC AND MICROSCOPIC TRANSPORT SIMULATION MODELS: USE CASE IN CYPRUS
International Journal for Traffic and Transport Engineering, 2014, 4(2): 220-233 DOI: http://dx.doi.org/10.7708/ijtte.2014.4(2).08 UDC: 656:519.87(564.3) COMBINATION OF MACROSCOPIC AND MICROSCOPIC TRANSPORT
More informationTypical information required from the data collection can be grouped into four categories, enumerated as below.
Chapter 6 Data Collection 6.1 Overview The four-stage modeling, an important tool for forecasting future demand and performance of a transportation system, was developed for evaluating large-scale infrastructure
More informationCIV3703 Transport Engineering. Module 2 Transport Modelling
CIV3703 Transport Engineering Module Transport Modelling Objectives Upon successful completion of this module you should be able to: carry out trip generation calculations using linear regression and category
More informationGIS-BASED VISUALIZATION FOR ESTIMATING LEVEL OF SERVICE Gozde BAKIOGLU 1 and Asli DOGRU 2
Presented at the FIG Congress 2018, May 6-11, 2018 in Istanbul, Turkey GIS-BASED VISUALIZATION FOR ESTIMATING LEVEL OF SERVICE Gozde BAKIOGLU 1 and Asli DOGRU 2 1 Department of Transportation Engineering,
More informationINSTITUTE OF POLICY AND PLANNING SCIENCES. Discussion Paper Series
INSTITUTE OF POLICY AND PLANNING SCIENCES Discussion Paper Series No. 1102 Modeling with GIS: OD Commuting Times by Car and Public Transit in Tokyo by Mizuki Kawabata, Akiko Takahashi December, 2004 UNIVERSITY
More informationCalifornia Urban Infill Trip Generation Study. Jim Daisa, P.E.
California Urban Infill Trip Generation Study Jim Daisa, P.E. What We Did in the Study Develop trip generation rates for land uses in urban areas of California Establish a California urban land use trip
More informationBus Landscapes: Analyzing Commuting Pattern using Bus Smart Card Data in Beijing
Bus Landscapes: Analyzing Commuting Pattern using Bus Smart Card Data in Beijing Ying Long, Beijing Institute of City Planning 龙瀛 Jean-Claude Thill, The University of North Carolina at Charlotte 1 INTRODUCTION
More informationVisualization of Commuter Flow Using CTPP Data and GIS
Visualization of Commuter Flow Using CTPP Data and GIS Research & Analysis Department Southern California Association of Governments 2015 ESRI User Conference l July 23, 2015 l San Diego, CA Jung Seo,
More informationSpatiotemporal Analysis of Commuting Patterns: Using ArcGIS and Big Data
Spatiotemporal Analysis of Commuting Patterns: Using ArcGIS and Big Data 2017 ESRI User Conference July 13, 2017 San Diego, VA Jung Seo, Tom Vo, Frank Wen and Simon Choi Research & Analysis Southern California
More informationTrip and Parking Generation Study of Orem Fitness Center-Abstract
Trip and Parking Generation Study of Orem Fitness Center-Abstract The Brigham Young University Institute of Transportation Engineers student chapter (BYU ITE) completed a trip and parking generation study
More informationTrip Generation Model Development for Albany
Trip Generation Model Development for Albany Hui (Clare) Yu Department for Planning and Infrastructure Email: hui.yu@dpi.wa.gov.au and Peter Lawrence Department for Planning and Infrastructure Email: lawrence.peter@dpi.wa.gov.au
More informationSpatiotemporal Analysis of Commuting Patterns in Southern California Using ACS PUMS, CTPP and LODES
Spatiotemporal Analysis of Commuting Patterns in Southern California Using ACS PUMS, CTPP and LODES 2017 ACS Data Users Conference May 11-12, 2017 Alexandria, VA Jung Seo, Tom Vo, Frank Wen and Simon Choi
More information3.0 ANALYSIS OF FUTURE TRANSPORTATION NEEDS
3.0 ANALYSIS OF FUTURE TRANSPORTATION NEEDS In order to better determine future roadway expansion and connectivity needs, future population growth and land development patterns were analyzed as part of
More informationData Collection. Lecture Notes in Transportation Systems Engineering. Prof. Tom V. Mathew. 1 Overview 1
Data Collection Lecture Notes in Transportation Systems Engineering Prof. Tom V. Mathew Contents 1 Overview 1 2 Survey design 2 2.1 Information needed................................. 2 2.2 Study area.....................................
More information2014 Certification Review Regional Data & Modeling
2014 Certification Review Regional Data & Modeling July 22, 2014 Regional Data Census Program Coordination PAG works with and for member agencies to ensure full participation in all Census Bureau programs
More informationRefinement of the OECD regional typology: Economic Performance of Remote Rural Regions
[Preliminary draft April 2010] Refinement of the OECD regional typology: Economic Performance of Remote Rural Regions by Lewis Dijkstra* and Vicente Ruiz** Abstract To account for differences among rural
More informationUpdating the Urban Boundary and Functional Classification of New Jersey Roadways using 2010 Census data
Updating the Urban Boundary and Functional Classification of New Jersey Roadways using 2010 Census data By: Glenn Locke, GISP, PMP 1 GIS-T May, 2013 Presentation Overview Purpose of Project Methodology
More informationSupplementary Figures
Supplementary Figures 10 1 Roma Milano Napoli Torino Palermo Genova 0.3 0.25 (a) P(t) 10 1 (b) t (h) 0.2 0 0.2 0.4 0.6 0.8 1 t (h) 0.15 10 4 10 5 10 6 10 7 Population Supplementary Figure 1: Variation
More informationLand Use Modeling at ABAG. Mike Reilly October 3, 2011
Land Use Modeling at ABAG Mike Reilly michaelr@abag.ca.gov October 3, 2011 Overview What and Why Details Integration Use Visualization Questions What is a Land Use Model? Statistical relationships between
More informationKeywords: Travel Behavior; Transportation Finance; Global Positioning Systems (GPS)
0 0 0 How Local is Travel? Michael Scharenbroich Humphrey Institute of Public Affairs University of Minnesota 0 th Ave. S Minneapolis, MN scha0@umn.edu Michael Iacono* Department of Civil Engineering University
More informationHow ACS Data Can Make Smart Cities Even Smarter: A method for combining bike sensor data with ACS demographic data Peter Viechnicki, Deloitte Center
How ACS Data Can Make Smart Cities Even Smarter: A method for combining bike sensor data with ACS demographic data Peter Viechnicki, Deloitte Center for Government Insights, 12 May 2017 pviechnicki@deloitte.com,
More informationMeasuring connectivity in London
Measuring connectivity in London OECD, Paris 30 th October 2017 Simon Cooper TfL City Planning 1 Overview TfL Connectivity measures in TfL PTALs Travel time mapping Catchment analysis WebCAT Current and
More informationNATHAN HALE HIGH SCHOOL PARKING AND TRAFFIC ANALYSIS. Table of Contents
Parking and Traffic Analysis Seattle, WA Prepared for: URS Corporation 1501 4th Avenue, Suite 1400 Seattle, WA 98101-1616 Prepared by: Mirai Transportation Planning & Engineering 11410 NE 122nd Way, Suite
More informationSBCAG Travel Model Upgrade Project 3rd Model TAC Meeting. Jim Lam, Stewart Berry, Srini Sundaram, Caliper Corporation December.
SBCAG Travel Model Upgrade Project 3rd Model TAC Meeting Jim Lam, Stewart Berry, Srini Sundaram, Caliper Corporation December. 7, 2011 1 Outline Model TAZs Highway and Transit Networks Land Use Database
More informationVisualization of Origin- Destination Commuter Flow Using CTPP Data and ArcGIS
Visualization of Origin- Destination Commuter Flow Using CTPP Data and ArcGIS Research & Analysis Department Southern California Association of Governments 2015 ESRI User Conference l July 23, 2015 l San
More informationINTRODUCTION PURPOSE DATA COLLECTION
DETERMINATION OF VEHICLE OCCUPANCY ON THE KATY AND NORTHWEST FREEWAY MAIN LANES AND FRONTAGE ROADS Mark Ojah and Mark Burris Houston Value Pricing Project, March 2004 INTRODUCTION In the late 1990s, an
More informationExploring the Impact of Ambient Population Measures on Crime Hotspots
Exploring the Impact of Ambient Population Measures on Crime Hotspots Nick Malleson School of Geography, University of Leeds http://nickmalleson.co.uk/ N.S.Malleson@leeds.ac.uk Martin Andresen Institute
More informationMetro SafeTrack Impact on Individual Travel Behavior & Regional Traffic Conditions. 1. Introduction. 2. Focus of this Volume & Issue
Metro SafeTrack Impact on Individual Travel Behavior & Regional Traffic Conditions Volume 1 Issue 1 June 10, 16 1. Introduction The National Transportation Center (NTC@Maryland) at the University of Maryland
More informationVHD Daily Totals. Population 14.5% change. VMT Daily Totals Suffolk 24-hour VMT. 49.3% change. 14.4% change VMT
6.9 Suffolk 6-54 VMT Population and Travel Characteristics Population 14.5% change 2014 1,529,202 VHD Daily Totals 2014 251,060 49.3% change 2040 1,788,175 2040 374,850 VMT Daily Totals 2014 39,731,990
More informationExtracting mobility behavior from cell phone data DATA SIM Summer School 2013
Extracting mobility behavior from cell phone data DATA SIM Summer School 2013 PETER WIDHALM Mobility Department Dynamic Transportation Systems T +43(0) 50550-6655 F +43(0) 50550-6439 peter.widhalm@ait.ac.at
More informationSpatiotemporal Analysis of Urban Traffic Accidents: A Case Study of Tehran City, Iran
Spatiotemporal Analysis of Urban Traffic Accidents: A Case Study of Tehran City, Iran January 2018 Niloofar HAJI MIRZA AGHASI Spatiotemporal Analysis of Urban Traffic Accidents: A Case Study of Tehran
More informationLeaving the Ivory Tower of a System Theory: From Geosimulation of Parking Search to Urban Parking Policy-Making
Leaving the Ivory Tower of a System Theory: From Geosimulation of Parking Search to Urban Parking Policy-Making Itzhak Benenson 1, Nadav Levy 1, Karel Martens 2 1 Department of Geography and Human Environment,
More informationAn Implementation of Mobile Sensing for Large-Scale Urban Monitoring
An Implementation of Mobile Sensing for Large-Scale Urban Monitoring Teerayut Horanont 1, Ryosuke Shibasaki 1,2 1 Department of Civil Engineering, University of Tokyo, Meguro, Tokyo 153-8505, JAPAN Email:
More informationDM-Group Meeting. Subhodip Biswas 10/16/2014
DM-Group Meeting Subhodip Biswas 10/16/2014 Papers to be discussed 1. Crowdsourcing Land Use Maps via Twitter Vanessa Frias-Martinez and Enrique Frias-Martinez in KDD 2014 2. Tracking Climate Change Opinions
More informationParking Occupancy Prediction and Pattern Analysis
Parking Occupancy Prediction and Pattern Analysis Xiao Chen markcx@stanford.edu Abstract Finding a parking space in San Francisco City Area is really a headache issue. We try to find a reliable way to
More informationESTIMATION OF THE NUMBER OF RAILWAY PASSENGERS BASED ON INDIVIDUAL MOVEMENT TRAJECTORIES
ESTIMATION OF THE NUMBER OF RAILWAY PASSENGERS BASED ON INDIVIDUAL MOVEMENT TRAJECTORIES Shun Ikezawa 1, Hiroshi Kanasugi 2, Go Matsubara 3, Yuki Akiyama 4, Ryutaro Adachi 5, Ryosuke Shibasaki 6 1 Master,
More informationEmployment Decentralization and Commuting in U.S. Metropolitan Areas. Symposium on the Work of Leon Moses
Employment Decentralization and Commuting in U.S. Metropolitan Areas Alex Anas Professor of Economics University at Buffalo Symposium on the Work of Leon Moses February 7, 2014 9:30-11:15am, and 2:30-4:30pm
More informationCONGESTION REPORT 1 st Quarter 2016
CONGESTION REPORT 1 st Quarter 2016 A quarterly update of the National Capital Region s traffic congestion, travel time reliability, top-10 bottlenecks and featured spotlight April 20, 2016 ABOUT TPB Transportation
More informationURBAN TRANSPORTATION SYSTEM (ASSIGNMENT)
BRANCH : CIVIL ENGINEERING SEMESTER : 6th Assignment-1 CHAPTER-1 URBANIZATION 1. What is Urbanization? Explain by drawing Urbanization cycle. 2. What is urban agglomeration? 3. Explain Urban Class Groups.
More informationEstimating Origin-Destination flows using opportunistically collected mobile phone location data from one million users in Boston Metropolitan Area
Estimating Origin-Destination flows using opportunistically collected mobile phone location data from one million users in Boston Metropolitan Area The MIT Faculty has made this article openly available.
More informationKeywords: Air Quality, Environmental Justice, Vehicle Emissions, Public Health, Monitoring Network
NOTICE: this is the author s version of a work that was accepted for publication in Transportation Research Part D: Transport and Environment. Changes resulting from the publishing process, such as peer
More informationSpatial Variation in Local Road Pedestrian and Bicycle Crashes
2015 Esri International User Conference July 20 24, 2015 San Diego, California Spatial Variation in Local Road Pedestrian and Bicycle Crashes Musinguzi, Abram, Graduate Research Assistant Chimba,Deo, PhD.,
More informationModelling exploration and preferential attachment properties in individual human trajectories
1.204 Final Project 11 December 2012 J. Cressica Brazier Modelling exploration and preferential attachment properties in individual human trajectories using the methods presented in Song, Chaoming, Tal
More informationIdentifying transit deserts in major Texas cities where the supplies missed the demands
THE JOURNAL OF TRANSPORT AND LAND USE http://jtlu.org VOL. 10 NO. 1 [2017] pp. 529 540 Identifying transit deserts in major Texas cities where the supplies missed the demands Junfeng Jiao The University
More informationMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
AP Test 13 Review Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) Compared to the United States, poor families in European cities are more
More informationThe Journal of Database Marketing, Vol. 6, No. 3, 1999, pp Retail Trade Area Analysis: Concepts and New Approaches
Retail Trade Area Analysis: Concepts and New Approaches By Donald B. Segal Spatial Insights, Inc. 4938 Hampden Lane, PMB 338 Bethesda, MD 20814 Abstract: The process of estimating or measuring store trade
More informationTRAFFIC IMPACT STUDY. Platte Canyon Villas Arapahoe County, Colorado (Arapahoe County Case Number: Z16-001) For
TRAFFIC IMPACT STUDY For Platte Canyon Villas Arapahoe County, Colorado (Arapahoe County Case Number: Z16-001) February 2015 Revised: August 2015 April 2016 July 2016 September 2016 Prepared for: KB Home
More informationProject Appraisal Guidelines
Project Appraisal Guidelines Unit 16.2 Expansion Factors for Short Period Traffic Counts August 2012 Project Appraisal Guidelines Unit 16.2 Expansion Factors for Short Period Traffic Counts Version Date
More informationStatus Report: Ongoing review of O-D cellular data for the TPB modeled area
Item #4 Status Report: Ongoing review of O-D cellular data for the TPB modeled area Presentation to the Travel Forecasting Subcommittee September 19, 2014 Ronald Milone, COG/TPB staff National Capital
More informationUncovering the Digital Divide and the Physical Divide in Senegal Using Mobile Phone Data
Uncovering the Digital Divide and the Physical Divide in Senegal Using Mobile Phone Data Song Gao, Bo Yan, Li Gong, Blake Regalia, Yiting Ju, Yingjie Hu STKO Lab, Department of Geography, University of
More informationSocio-Economic Levels and Human Mobility
1 Socio-Economic Levels and Human Mobility V. Frias-Martinez, J. Virseda, E. Frias-Martinez Abstract Socio-economic levels provide an understanding of the population s access to housing, education, health
More informationInternational Journal of Scientific & Engineering Research Volume 9, Issue 6, June ISSN
International Journal of Scientific & Engineering Research Volume 9, Issue 6, June-2018 109 Quantifying Traffic Congestion by Studying Traffic Flow Characteristics in Wolaita Sodo Town, Ethiopia Mengistu
More informationPredicting freeway traffic in the Bay Area
Predicting freeway traffic in the Bay Area Jacob Baldwin Email: jtb5np@stanford.edu Chen-Hsuan Sun Email: chsun@stanford.edu Ya-Ting Wang Email: yatingw@stanford.edu Abstract The hourly occupancy rate
More informationForecasts from the Strategy Planning Model
Forecasts from the Strategy Planning Model Appendix A A12.1 As reported in Chapter 4, we used the Greater Manchester Strategy Planning Model (SPM) to test our long-term transport strategy. A12.2 The origins
More informationTravel behavior of low-income residents: Studying two contrasting locations in the city of Chennai, India
Travel behavior of low-income residents: Studying two contrasting locations in the city of Chennai, India Sumeeta Srinivasan Peter Rogers TRB Annual Meet, Washington D.C. January 2003 Environmental Systems,
More informationLecture 19: Common property resources
Lecture 19: Common property resources Economics 336 Economics 336 (Toronto) Lecture 19: Common property resources 1 / 19 Introduction Common property resource: A resource for which no agent has full property
More informationDeveloping Built Environment Indicators for Urban Oregon. Dan Rubado, MPH EPHT Epidemiologist Oregon Public Health Division
Developing Built Environment Indicators for Urban Oregon Dan Rubado, MPH EPHT Epidemiologist Oregon Public Health Division What is the built environment? The built environment encompasses spaces and places
More informationYu (Marco) Nie. Appointment Northwestern University Assistant Professor, Department of Civil and Environmental Engineering, Fall present.
Yu (Marco) Nie A328 Technological Institute Civil and Environmental Engineering 2145 Sheridan Road, Evanston, IL 60202-3129 Phone: (847) 467-0502 Fax: (847) 491-4011 Email: y-nie@northwestern.edu Appointment
More informationAppendix BAL Baltimore, Maryland 2003 Annual Report on Freeway Mobility and Reliability
(http://mobility.tamu.edu/mmp) Office of Operations, Federal Highway Administration Appendix BAL Baltimore, Maryland 2003 Annual Report on Freeway Mobility and Reliability This report is a supplement to:
More informationPath and travel time inference from GPS probe vehicle data
Path and travel time inference from GPS probe vehicle data Timothy Hunter Department of Electrical Engineering and Computer Science University of California, Berkeley tjhunter@eecs.berkeley.edu Pieter
More informationGIS Analysis of Crenshaw/LAX Line
PDD 631 Geographic Information Systems for Public Policy, Planning & Development GIS Analysis of Crenshaw/LAX Line Biying Zhao 6679361256 Professor Barry Waite and Bonnie Shrewsbury May 12 th, 2015 Introduction
More informationDensity and Walkable Communities
Density and Walkable Communities Reid Ewing Professor & Chair City and Metropolitan Planning University of Utah ewing@arch.utah.edu Department of City & Metropolitan Planning, University of Utah MRC Research
More informationTransforming Geospatial Data for Visualization with D3
Transforming Geospatial Data for Visualization with D3 FOSS4G Boston August 17, 2017 Beatrice Jin and Benjamin Krepp Boston Region Metropolitan Planning Organization Agenda Who we are Project context Implementation
More informationRegional Snapshot Series: Transportation and Transit. Commuting and Places of Work in the Fraser Valley Regional District
Regional Snapshot Series: Transportation and Transit Commuting and Places of Work in the Fraser Valley Regional District TABLE OF CONTENTS Complete Communities Daily Trips Live/Work Ratio Commuting Local
More informationThe 3V Approach. Transforming the Urban Space through Transit Oriented Development. Gerald Ollivier Transport Cluster Leader World Bank Hub Singapore
Transforming the Urban Space through Transit Oriented Development The 3V Approach Gerald Ollivier Transport Cluster Leader World Bank Hub Singapore MDTF on Sustainable Urbanization The China-World Bank
More informationUrban Planning Word Search Level 1
Urban Planning Word Search Level 1 B C P U E C O S Y S T E M P A R E U O E U R B A N P L A N N E R T N S T D H E C O U N T Y G E R E R D W R E N I C I T Y C O U N C I L A A A S U G G C I L A G P R I R
More informationExamining travel time variability using AVI data
CAITR 2004 Examining travel time variability using AVI data by Ruimin li ruimin.li@eng.monash.edu.au 8-10 December 2004 INSTITUTE OF TRANSPORT STUDIES The Australian Key Centre in Transport Management
More information