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General Transit Feed Specification (GTFS)-based

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Presentation on theme: "General Transit Feed Specification (GTFS)-based"— 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 Introduction 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

3 General Transit Feed Specification (GTFS)
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.,

4 General Transit Feed Specification (GTFS)

5 Previous Research 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)

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 This location may be “covered” by several adjacent transit stops Three parameters: Distance, Text, and Catchment area

7 Stop Aggregation Model (SAM)
Developing Stop Aggregation Model (SAM) Stop Aggregation Model (SAM) Parameter Distance-based Text-based Catchment-based Similarity Spatial Textual Land use or activity Stage Lower-level Upper-level Scope in algorithm Regional Route-level Data in GTFS Stops.txt Stop_times.txt Implementation ArcGIS Microsoft 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 Stop Aggregation Model: Development and Application (Lee et al. 2012)
Distance-based SAM Stop Aggregation Model: Development and Application (Lee et al. 2012)

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

10 Case Study Minneapolis /St. Paul (MN) and Sacramento (CA)
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 (100 m) 1 2,383 4,422 2,135 1,107 665 2 5,225 4,978 5,301 1,248 1,272 3 265 43 262 116 142 4 192 11 213 73 106 5 23 27 13 6 8 17 7 9 10 Total 8,101 9,462 7,951 2,565 2,238 Stop Aggregation Model: Development and Application (Lee et al. 2012)

11 Use of a Stop Aggregation Model
Land-Use pattern Transit demand Aggregate-level O-D estimation Measuring accessibility Observing land use and activity location Identification of boarding and alighting locations AFC GTFS Parcels 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 Stop Aggregation Model
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 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 Sunrise Park-and-Ride at Sacramento, CA
Developing Intermodal Network SAM Access point to P&R Street Junction Auto Bus Stop LRT Station P&R Centroid Transit Walk Vehicle Sunrise Park-and-Ride at Sacramento, CA An Intermodal Shortest and Optimal Path Algorithm using a Transit Trip-based Shortest Path (Khani et al. 2012)

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

15 Stop Aggregation Model: Development and Application (Lee et al. 2012)
Linkage with On-Board Survey Data Spatial references are typically asked of each respondent about where they are coming from and going to Boarding and alighting information from the on-board survey data Stop names in SAM Stop IDs only along Route 25 Record 10513 SurveyID 4334 Silver Lake Rd & 36 Av NE 14157 Route 25 Silver Lake Rd & 37 Av NE 14155 ServiceType Local Silver Lake Rd & 39 Av NE 14154 TimePeriod Peak BoardIntersect Silver lake & 39th NE Hennepin Av E & 4 St SE 42008 BoardCity Minneapolis Hennepin Av E & 5 Av SE 14943 AlightIntersect Hennepin & 6th St Hennepin Av E & 6 St SE 14955 AlightCity Hennepin Av E & 8 St SE 14946 Stop Aggregation Model: Development and Application (Lee et al. 2012)

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

17 Express Service Available
Accessibility: The Nth Nearest Stop Group 1st 2nd 3rd 4th Parcel ID Nth Nearest Stop ID Stop Group Length (in meter) Express Service Available 7 - 8 am pm 5 - 6 pm XXXXXX 1 44952 662 - 2 45006 668 3 7049 981 O 4 7059 987 5 45007 1026 6 44951 1042 7 52924 1082 8 52923 1105 What if 4th stop is better choice with express service at a specific time?

18 Measuring Transit Accessibility
Meters 1,700 ~ 1,800 700 ~ 800 The 1st nearest stop group The 2nd nearest stop group 0 ~ 100 Arbitrary points assigned as facilities in Network Analyst in GIS due to the observance of isolated street network The 3rd nearest stop group The 4th nearest stop group

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 Conclusions 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

23 Acknowledgements 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


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