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General Transit Feed Specification (GTFS)-based GIS Tool for Creating Practical Applications East-West Gateway Council of Governments Sang Gu Lee GIS in.

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Presentation on theme: "General Transit Feed Specification (GTFS)-based GIS Tool for Creating Practical Applications East-West Gateway Council of Governments Sang Gu Lee GIS in."— Presentation transcript:

1 General Transit Feed Specification (GTFS)-based GIS Tool for Creating Practical Applications East-West Gateway Council of Governments Sang Gu Lee GIS in Transit Conference October 16, 2013 │ Washington, DC

2 Propose use of Google’s General Transit Feed Specification (GTFS) for a transit stop aggregation model (SAM) The idea of using GTFS has been drawing attention in the public transit planning area these days One area in which GTFS can be very useful is in developing and updating transit networks used in service planning We explore how to use this innovative data source in various areas by proposing a SAM Introduction

3 Open data format for transit schedules First released with TriMet (Portland, OR) in 2005 Incorporating transit information in the Google Maps application A de facto standard for data describing transit stops, schedules, and route geometry, … Currently, many transit agencies in the US have made their GTFS data publicly available, which helps developers and transit agencies efficiently share and retrieve GTFS data (e.g., General Transit Feed Specification (GTFS)

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5 Stop-level boarding and alighting counts aggregated to the segment level for generating a transit route origin- destination matrix (Furth and Navick, 1992) The need for relevant stop aggregation was discussed to match the scheduled time between bus stops from the transaction data collected (Barry et al. 2002) Each pair of stops on the opposite sides of a road at the same general location might be combined for predicting transit-related activities (Chu, 2004) Previous Research

6 Conceptual Approach Transit users’ activity may not be originated from or destined to an individual stop per se The activity is associated with a specific location in the vicinity of the stop Three parameters: Distance, Text, and Catchment area Three parameters: Distance, Text, and Catchment area This location may be “covered” by several adjacent transit stops This location may be “covered” by several adjacent transit stops

7 Developing Stop Aggregation Model (SAM) Stop Aggregation Model (SAM) ParameterDistance-basedText-basedCatchment-based SimilaritySpatialTextualLand use or activity StageLower-level Upper-level Scope in algorithmRegional Route-level Data in GTFSStops.txt Stop_times.txt ImplementationArcGISMicrosoft SQL server Advantage Geographical proximity Textual comparison Service characteristics in the catchment area Drawbacks - Distance threshold dependent (e.g., individual or overlapping) - Unique and various types of text - Geographical location issue (e.g., curves in transit line) - Not easy to extend to a regional scope Stop Aggregation Model: Development and Application (Lee et al. 2012)

8 Distance-based SAM 8 Stop Aggregation Model: Development and Application (Lee et al. 2012)

9 Distance-based SAM: Sensitivity of Distance SB TripNB Trip SB TripNB Trip SB Trip CBD One-way Downtown Minneapolis University of Minnesota Study routeOpposite directionSame direction Stop Aggregation Model: Development and Application (Lee et al. 2012)

10 Minneapolis /St. Paul (MN) and Sacramento (CA) Case Study Number of Groups Minneapolis/St. Paul Area (Total: 14,601)Sacramento Area (Total: 4,366) Number of individual stops in the group DBSAM (50 m) TBSAM Integrated DBSAM and TBSAM DBSAM (100 m) Integrated DBSAM and TBSAM 12,3834,4222,1351, ,2254,9785,3011,2481, Total8,1019,4627,9512,5652,238 Stop Aggregation Model: Development and Application (Lee et al. 2012)

11 Use of a Stop Aggregation Model Land-Use patternTransit demand Aggregate-level O-D estimation Measuring accessibility Observing land use and activity location Identification of boarding and alighting locations AFCGTFSParcels Network Development Intermodal Network (e.g., Park-n-Ride) Intersection-level Transit Network Mutually Exclusive Service Areas Stop Aggregation Model (SAM) Are Transit Trips Symmetrical in Time and Space? Evidence from the Twin Cities (Lee and Hickman, in press)

12 Integrating Transit Demand and Land Use General Transit Feed Specification (GTFS) Stop Aggregation Model Automated Fare Collection (AFC) Data Determination of Transit Service Area Measurement of Land Use Types Measurement of Land Use Types Time-varying Transit Demand Time-varying Transit Demand Street Network Parcel-level Land Use Linkage Development of a Temporal and Spatial Linkage between Transit Demand Land Use Patterns (Lee et al. 2013)

13 Developing Intermodal Network Access point to P&R Street Junction Auto Bus StopLRT Station P&R Centroid Transit Walk Vehicle An Intermodal Shortest and Optimal Path Algorithm using a Transit Trip-based Shortest Path (Khani et al. 2012) Sunrise Park-and-Ride at Sacramento, CA SAM

14 Using AFC data Intersection-level Origin-Destination Estimation Stops serving by Orange Route B1 T1 A1 B2 A2 T2 B3 A3 T3 A4 B4 T4 Stops serving by Red Route Location of Transaction B A Boarding stop Alighting stop Stop Group of SAM Stop Aggregation Model: Development and Application (Lee et al. 2012)

15 Spatial references are typically asked of each respondent about where they are coming from and going to Linkage with On-Board Survey Data Boarding and alighting information from the on-board survey data Stop names in SAM Stop IDs only along Route 25 Record10513 …… SurveyID4334Silver Lake Rd & 36 Av NE14157 Route25Silver Lake Rd & 37 Av NE14155 ServiceTypeLocalSilver Lake Rd & 39 Av NE14154 TimePeriodPeak… … BoardIntersectSilver lake & 39th NEHennepin Av E & 4 St SE42008 BoardCityMinneapolisHennepin Av E & 5 Av SE14943 AlightIntersectHennepin & 6th StHennepin Av E & 6 St SE14955 AlightCityMinneapolisHennepin Av E & 8 St SE14946 ………… Stop Aggregation Model: Development and Application (Lee et al. 2012)

16 Combination of Thiessen Polygon and Buffer (CTPB) CTPB approach improves the capability of spatial data integration in direct demand models Generating Mutually Exclusive Service Areas Comparative Study of alternative methods for generating route-level mutually exclusive service areas (Lee et al., in press) Stop Group by SAMCase Study: Route 6CTPBRoute-level Mutually Exclusive Service Areas

17 Accessibility: The N th Nearest Stop Group 1st2nd3rd4th What if 4 th stop is better choice with express service at a specific time? Parcel IDN th NearestStop IDStop Group Length (in meter) Express Service Available am pm5 - 6 pm XXXXXX O-O O-O O O

18 Measuring Transit Accessibility The 1 st nearest stop groupThe 2 nd nearest stop group The 4 th nearest stop groupThe 3 rd nearest stop group Arbitrary points assigned as facilities in Network Analyst in GIS due to the observance of isolated street network 0 ~ ~ 800 1,700 ~ 1,800 Meters

19 East-West Gateway Travel Demand Model

20 Passenger Behavior Data Behavioral Richness Quantity of Data Passenger Counts Farecard Data On-board Surveys Household Surveys SAM

21 Enhancing the Modeling Capabilities Travel Behavior Analysis and Accessibility Measure Travel Pattern Analysis (Lee and Hickman 2011) Modified Empty Space Distance for Measuring Transit Accessibility (Lee et al. 2012) Trip purpose inference using AFC data (Lee and Hickman, in press) Generating Mutually Exclusive Service Areas (Lee et al., in press) Symmetry of Boardings and Alightings (Lee and Hickman, in press) Relational Database Modeling Integration with GTFS (Nassir et al. 2011) Integration of Land Use and Transportation Temporal and Spatial Linkage between Transit Demand and Land Use Patterns (Lee et al. 2013) Stop Aggregation Model: Development and Applications (Lee et al. 2012) An Intermodal Shortest and Optimal Path Algorithm (Khani et al. 2012) Demand Modeling Time-varying Transit Patronage Models (Lee et al. 2013) Transit O-D Estimation AFC data: Stop-level (Nassir et al. 2011) and Aggregate-level (Lee et al. 2011) APC data: Time-varying Alighting Probability Matrices (Lee and Hickman, under review)

22 Provides the development and application of a stop aggregation model for a transit network based on Google’s General Transit Feed Specification (GTFS) Aggregate representation of transit stops –Stop groups that serve common or similar land use patterns and activities can be represented by a single node, which is able to reduce the complexity of the transit network –Easily applicable to model passenger transfers, and access time and distance within these stop groups Utilization of Google’s GTFS –Frequently updated by transit agencies, as it provides detailed information on transit supply-side characteristics Conclusions

23 Dr. Mark Hickman (University of Queensland, Australia) Dr. Daoqin Tong (University of Arizona) University of Arizona Transit Research Unit (UATRU) East-West Gateway Council of Governments Acknowledgements


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