Using Transit ITS Data for Service Planning University of Arizona, Transit Research Unit Alireza Khani, Sanggu Lee, Hyunsoo Noh, Neema Nassir, Mark Hickman

Using Transit ITS Data for Service Planning

University of Arizona, Transit Research UnitAlireza Khani, Sanggu Lee, Hyunsoo Noh, Neema Nassir, Mark Hickman

ITS Arizona 18th Annual Conference, Sep 29, 2011

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Outline Introduction / Motivations Data Data Analysis Applications

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Introduction / Motivation How to transform the massive data into useful

information and support decision-making From fare card data,

• how to identify boarding stops• how to infer alighting stops (known boarding)• how to estimate travel/alighting time• how to analyze travel pattern/behavior

From Google’s transit data,• how to utilize a transit network• how to develop an intermodal network• how to find the shortest/optimal path

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Automated Data Collection System Data

Automatic Vehicle Location (AVL)

Automatic Passenger Counting (APC)

Automatic Fare Collection (AFC)

Bus location based on GPS Bus systems based on sensors in doors

Contactless smart card with unique ID

Information (location, ..) ofa given vehicle

Passenger boarding/alighting counts

for stops

Entry information (time, route, …)

at the individual level

4.2 M Records 3.4 M Records 2.2M Records

Metro Transit (www.metrotransit.org) data Serving Minneapolis/St. Paul area, MN November 2008 (30 days) Entry-only-control operation Implemented independently

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Google’s Transit Data General Transit Feed

Specification (GTFS) Open to public and

frequently updated by agencies

Stops, routes, trips, stop_times (schedules)

Metro Transit in 2008 Routes.txt: 220 Stops.txt: 14,601 Trips.txt: 25,417 Stop_times.txt: 1,471,150

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Relational Schema

STOP TIMESCALENDARTRIPSROUTESSTOPS

TRAVELERROUTE NO

DATE / TIME

USE TYPE

VEHICLE ID

LOCATION

AFC

ROUTE ID

ARRIVAL TIME

TRIP ID

DEPARTURE TIME

STOP ID

SEQUENCE

APC with LOCATION

ROUTE NO

VEHICLE ID

STOP ID

SEQUENCE

SCHEDULE

STOP LOCATION

STOP ID

STOP CITY

DESCRIPTION

ROUTE NO

TRIP ID SERVICE ID

GENERAL TRANSIT FEED SPECIFICATION (GOOGLE)

SCHEDULE

VEHICLE

Presented at 90th TRB Annual Meeting in Washington, DC on January 2011

Automated Data Collection System (ADCS)

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Data Analysis

DURATION 1 DURATION 2 DURATION 3

TRANSACTION 1DATE / TIME

ROUTE NUMBERUSE TYPEBUS ID

LOCATION

USER

SPECIAL SERIAL NOPurchases

FARE CARD

CARD TYPEMETRO PASSU-PASSC-PASS

Requests

STORED VALUE

TRANSACTION 2

DATE / TIME


LOCATION

TRANSACTION 3

DATE / TIME


LOCATION

TRANSACTION 4

DATE / TIME


LOCATION

Access TimeWaiting Time

Activity TimeTransfer TimeEgress Time

In-Vehicle Time


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Data Analysis – cont.

3:00

6:00

9:00

12:0

015

:00

18:0

021

:00

23:5

9

0%

5%

10%

15%

20%

25%

Metropass

Time

Pe

rce

nt

of

Tra

ns

ac

tion

s

3:00

6:00

9:00

12:0

015

:00

18:0

021

:00

23:5

9

0%

5%

10%

15%

20%

25%

U-Pass 17-Nov

18-Nov

19-Nov

20-Nov

21-Nov

Time

Pe

rce

nt

of

Tra

ns

ac

tion

s

3:00

6:00

9:00

12:0

015

:00

18:0

021

:00

23:5

9

0%

5%

10%

15%

20%

25%

Stored Value

Time

Pe

rce

nt

of

Tra

ns

ac

tion

s

3:00

6:00

9:00

12:0

015

:00

18:0

021

:00

23:5

9

0%

5%

10%

15%

20%

25%

C-Pass

Time

Pe

rce

nt

of

Tra

ns

ac

tion

s

1 2 3 4 5 6 7 8 9 10111213

0%

10%

20%

30%

40%

50%

Metropass

Duration (Hour)

Pe

rce

nt

of

Tra

ns

ac

tion

s

1 2 3 4 5 6 7 8 9 10111213

0%

10%

20%

30%

40%

50%

U-Pass 17-Nov

18-Nov

19-Nov

20-Nov

21-Nov

Duration (Hour)

Pe

rce

nt

of

Tra

ns

ac

tion

s

1 2 3 4 5 6 7 8 9 10111213

0%

10%

20%

30%

40%

50%

Stored Value

Duration (Hour)

Pe

rce

nt

of

Tra

ns

ac

tion

s

1 2 3 4 5 6 7 8 9 10111213

0%

10%

20%

30%

40%

50%

C-Pass

Duration (Hour)

Pe

rce

nt

of

Tra

ns

ac

tion

s

Between-day Hourly Variation in Transactions by Card Type

Duration Distribution by Card Type


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Application Conference Name

Travel Pattern Analysis 90th TRB Annual Meeting

Transit O-D Estimation 90th TRB Annual Meeting (TRR in press)

Transit Path Choice Model 13th TRB National Planning Applications

Transit Path Algorithms

Trip-Based Shortest Path 91st TRB Annual Meeting (under review)

Intermodal Optimal Path 91st TRB Annual Meeting (under review)

Intermodal Tour Path 91st TRB Annual Meeting (under review)

Link-Based Transit Hyperpath 91st TRB Annual Meeting (under review)

Transit Assignment and Simulation 13th TRB National Planning Applications

Integration of Land Use and Transportation

Temporal and Spatial Linkage between Land Use and Transit Demand

1st World Symposium for Transport and Land Use Research

Stop Aggregation Model 91st TRB Annual Meeting (under review)

Applications

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Transit O-D Estimation Using Fare Card Objective: To find the origin, destination, and transfer

stops of each passenger in Minneapolis/St. Paul transit system

Bus

Walk Transaction

Bus Stop

Home

ShoppingWork

Transfer


Application 1

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Transit O-D Estimation – cont. This model

Finds the boarding and alighting stop for each AFC transaction, as well as the possible trip taken by the passenger

Infers the transfers made by passenger by looking at the boarding and alighting times, service headway, etc

Verifies the results by comparing with APC sample data

Results for a typical weekday 90,154 transactions in AFC data set 84,413 transaction after refining the data set 33,514 transactions with estimated OD (28,260 persons) 98% percent verified by APC sample data


Application 1

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Transit O-D Estimation – cont.AM Mid-day PM

Ori

gin

Dest

inati

on


Application 1

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Transit Path Choice Model Using Fare Card Objective: To calibrate a logit model for transit path

choice using the results of the O-D estimation study (Minneapolis/St. Paul)

Origin

Destination

Passenger 1

Origin

Destination

Passenger 2

Origin

Destination

Choice set

Presented at 13th TRB National Planning Applications Conference in Reno, NV on May 2011

Application 2

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Transit Path Choice Model – cont.

Attribute Definition

In Vehicle Time VT Sum of the times spent on rides of all legs of the path

Number of Transfers TR Number of bus transfers for the path

Waiting Time WT Sum of waiting times for all the transfers in the path

Walking Distance WD Sum of walking distances for all the transfers in the path

Express Route EX Indicates whether path contains any express routes or not

Downtown Route DT Indicates whether path contains a leg in downtown or not

Covers Express CEX Indicates whether the user’s pass covers the express fare or not

Covers Downtown CDT Indicates whether the user’s pass covers the downtown fare or not


Application 2

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Transit Path Choice Model – cont. Calibration Tools

Easy Logit Modeler (ELM) (http://www.elm-works.com)

Biogeme (http://biogeme.epfl.ch)

Results

Time Period # of Records Model Rho2 t-statistics

Rush-Hours 1225 -1.076 TR 0.029 -6.41

Non-Rush Hours 695 -1.010 TR 0.038 -4.76

All-Day 1922 -1.055 TR 0.032 -7.96


Application 2

http://www.elm-works.com/

http://biogeme.epfl.ch/

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Transit Path Algorithms Using GTFS Data Objective: To develop efficient algorithm for transit and

intermodal shortest/optimal path Trip-Based Shortest Path (TBSP)

Shortest path algorithm utilizing hierarchical transit network

Intermodal Optimal Path (IOP) Optimal path with combined modes (auto and transit)

Intermodal Optimal Tour (IOT) Optimal path for a tour with multiple destinations with

combined modes

Link-Based Transit Hyperpath Optimal strategy for schedule-based transit systems

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Trip-Based Shortest Path (TBSP) The idea

Using trips as the elements of the network (instead of links) Distinguish between transfer stops and non-transfer stops in

the labeling algorithm

Performance in computation

Sacramento, CA San Francisco (MUNI)

No. of Stops (transfer)

2880(1028)

4424(2865)

Improvement in Label Setting

83% 54%

Improvement in Label Correcting

32% 26%

Application 3

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Intermodal Optimal Path (IOP) This model

Using TBSP for the transit side Using a multi-source time-dependent shortest path for the

auto side Modeling Park-and-Ride locations to make the connections

Contributions Solve for the optimal intermodal path and transfer point

between an origin and a destination for a preferred arrival time

The complexity is the same as single-mode path (e.g. TBSP)

Application 4

DestinationOrigin

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Intermodal Optimal Tour (IOT) Objective

Capture the effect of back trip on park-and-ride choice Find optimal tour and optimal park-and-ride facilities for a

sequence of activities

Application 5

Origin

D1

D2

P1

P2

D3

Origin

D10

D20

P10

P20

D11

D12

D22

D21

P11

P22

D32

D31

D30

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Link-Based Transit Hyperpath Objective: To search an optimal strategy path on a

transit schedule network using logit model Link-Based Time-Expanded Network

A run (or trip) segment between two consecutive stops is considered a unique link

Search Model

Application 6

A B C Des

e1

e2 e7

e8

e10

e12

et

[25, 7]

Route 1Route 2

Route 3Route 4

[7, 1] [6, 28]

[4, 11]

[4, 15]

[10, 13]

[cost, departure time]

e3 [8, 5]

e4 [7, 10] e9[5, 16]

e6[6, 19]e5 [6, 8]

e14 [10, 43]

e11 [4, 21]

[10, 23]e13

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Transit Assignment and Simulation Integration among transportation models

Activity-Based Model (ABM) for demand Dynamic Traffic Assignment (DTA) model

FAST-TrIPsPassenger assignment and simulation on GTFS

Transit Assignment

and Simulation

DynusT MALTAVehicle assignment and

simulation

DTA

ABM

DaySim OpenAMOS


Application 7

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Transit Assignment and Simulation – cont. Contributions

Capability to model travel behaviors according to Google’s GTFS which allows to connect to DTA model, such as boarding and alighting

As well as integrating between ABM and DTA, the schedule-based transit assignment and simulation supports the intermodal capability

Funded by FHWA EAR program and SHRP2 C-10B

Application 7

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Thank you!! University of Arizona, Transit Research Unit

Documents

Using Transit ITS Data for Service Planning University of Arizona, Transit Research Unit Alireza Khani, Sanggu Lee, Hyunsoo Noh, Neema Nassir, Mark Hickman