General Transit Feed Specification (GTFS)-based GIS Tool for Creating Practical Applications

East-West GatewayCouncil of Governments

Sang Gu Lee

GIS in Transit Conference

October 16, 2013 │ Washington, DC

• 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

• 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., http://www.gtfs-data-exchange.com)

General Transit Feed Specification (GTFS)

General Transit Feed Specification (GTFS)

• 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

Conceptual ApproachTransit 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

This location may be “covered” by several adjacent transit stops

Developing Stop Aggregation Model (SAM)

Stop Aggregation Model (SAM)Parameter Distance-based Text-based Catchment-basedSimilarity Spatial Textual Land use or activityStage Lower-level Lower-level Upper-levelScope in algorithm Regional Regional Route-levelData in GTFS Stops.txt Stops.txt Stop_times.txtImplementation ArcGIS Microsoft SQL server 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)

Distance-based SAM

8Stop Aggregation Model: Development and Application (Lee et al. 2012)

Distance-based SAM: Sensitivity of Distance200 150 100 70 50 50 70 100 150 200 200 150 100 70 50 50 70 100 150 200

SB Trip NB TripSB Trip NB Trip

NB Trip

SB Trip

CBD

One-way

Downtown Minneapolis

University of Minnesota

Study route Opposite directionSame direction

Stop Aggregation Model: Development and Application (Lee et al. 2012)

• 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 groupDBSAM (50 m) TBSAM

Integrated DBSAM and

TBSAMDBSAM (100 m)

Integrated DBSAM and

TBSAM

1 2,383 4,422 2,135 1,107 6652 5,225 4,978 5,301 1,248 1,2723 265 43 262 116 1424 192 11 213 73 1065 23 5 27 13 276 8 8 4 177 2 1 1 1 58 1 1 2 1 29 1 1

10 1 1 2 1 1Total 8,101 9,462 7,951 2,565 2,238

Stop Aggregation Model: Development and Application (Lee et al. 2012)

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

AFC GTFS Parcels

Network Development

Intermodal Network(e.g., Park-n-Ride)

Intersection-levelTransit Network

Mutually Exclusive Service Areas

StopAggregation

Model(SAM)

Are Transit Trips Symmetrical in Time and Space? Evidence from the Twin Cities (Lee and Hickman, in press)

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)

Developing Intermodal Network

Access point to P&R

Street Junction

Auto

Bus Stop LRT Station

P&R Centroid

Transit Walk

Vehicle

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

• Using AFC data

Intersection-level Origin-Destination Estimation

Stops serving by Orange Route

B1 T1 A1

B2

A2

T2

B3

A3

T3

A4 B4T4

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)

• 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 informationfrom 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 Minneapolis Hennepin Av E & 8 St SE 14946

… … … …

Stop Aggregation Model: Development and Application (Lee et al. 2012)

• 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 6 CTPB Route-level Mutually Exclusive Service Areas

Accessibility: The Nth Nearest Stop Group

1st 2nd 3rd 4th

What if 4th stop is better choice with express service at a specific time?

Parcel ID Nth Nearest Stop ID Stop Group Length(in meter)

Express Service Available

7 - 8 am 12 - 1 pm 5 - 6 pm

053-0311722XXXXXX

1 44952 1 662 - - -2 45006 1 668 - - -3 7049 2 981 O - O4 7059 2 987 O - O5 45007 3 1026 - - -6 44951 3 1042 - - -7 52924 4 1082 O - -8 52923 4 1105 - - O

Measuring Transit Accessibility

The 1st nearest stop group The 2nd nearest stop group

The 4th nearest stop groupThe 3rd nearest stop group

Arbitrary points assigned as facilities in Network Analyst in GIS due to the observance of isolated street network

0 ~ 100

700 ~ 800

1,700 ~ 1,800Meters

East-West Gateway Travel Demand Model

Passenger Behavior Data

Behavioral Richness

Quantity of Data

Passenger Counts

Farecard Data

On-board Surveys

Household Surveys

SAM

Enhancing the Modeling CapabilitiesTravel 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)

• 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

• 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