Using smart card and GPS data for policy and planning: the case of

Transantiago

Marcela A. Munizaga Universidad de Chile

Visiting CTS

Research Team: Universidad de Chile – Transantiago

Research grants: CONICYT PBCT, Milenio Scientific Initiative, FONDEF

Introduction. Transantiago: public transport system Santiago, Chile

¤ Santiago. Capital City of Chile: ¤ Population: 6 million ¤ Area: 1,400 km2 ¤ 34 Municipalities ¤ Modal Split: 50%

Introduction. Transantiago: public transport system Santiago, Chile

¤  Transantiago q  Introduced in 2007 q  6.500 buses (65% low entry) with GPS q  70 km of segregated busways q  10.000 bus stops q  125 bus stations (off-bus fare collection) q  12 private bus operators q  600 trunk and feeder services q  Metro: 5 lines, 100 km, 54 trains q  Only smartcard payment in buses

(global 97% penetration rate)

Map

Transantiago structure

¤ Operators: bus (private) + metro (public) ¤ Provide transport services, receive payment

(per passenger, per km, regularity,…)

¤ AFT (financial administrator) ¤ Collects and distributes money ¤ Collects and store data

¤ Transantiago authority DMTP ¤ Regulates (spatial coverage, fare, frequency) ¤ Controls

Quoting the Transantiago authority:

¤  “Before this project we were: “

§  Advancing slowly

§  With very little information

§  Lack of support tools

Carolina Simonetti, Director of Planning and Research, DMTP. XVI Chilean Transport Conference, Santiago, October 2013

The Data

OD trip matrices, buses speeds, travel patterns, level of service

indicators…

Other informa-

tion

AFC bip!

(metro & buses)

AVL Buses GPS

•  Buses GPS: 1 record every 30s, 80–100 M records per week

•  bip! transactions: 35-40 M records per week

•  Other information: •  Routes paths •  Route assignments •  Position of bus stops •  Position of Metro

stations •  Position of bus

stations

What can we do with the data?

Observe buses

What can we do with the data?

Analyze transactions

What can we do with the data?

Analyze transactions

What can we do with the data?

Link vehicles and passengers through vehicle id

Processing

¤  Estimation of alighting stop

Second'transac,on'of'the'day'

Last'transac,on'of'the'day'

First'transac,on'of'the'day'

Min'Tg'

Min'Tg'

Min'dist'

Boarding'point'

GPS'Point'

Metro'sta,on'

Bus'stop'imin Tg = ti +

di−>xpost ypostswalk

⋅ (θwalk /θtravel)

s.t. dpost ≤ d

tTrip Trip

Observed boarding

Estimated alighting

Transfer or activity?

Post-Processing: Stages and Trips

Determination of:

–  Trips/stages

–  Time. distance and speed of transfers. stages and trips

–  Walking and waiting time

Criteria to distinguish destination from transfer –  Time elapsed –  Transaction sequence –  Land use –  Frequency of PT services –  Ratio: distance on the route /

Euclidean distance

Post-Processing: Visualization ¤  Passengers boarding and alighting in origin and destination

Validation

¤  We are able to estimate alighting location-time in 80% of trip stages, generating over 20M trip observations in a week

¤  Validation with small OD survey:

¤  84% correct estimation of alighting position-time

¤  Validation with a sample of volunteers:

¤  90% correct estimation of trip/trip stage separation

q  Disclaimers: q  Validation with large ODS to be conducted

q  Fare evasion not included

q  Exact Origin/Destination unknown

q  Sociodemographic characteristics unknown

Commercial speed of buses

q Estimation of commercial speed of buses q  Associate position to linear route distance q  Define time-space disaggregation q  Monitor in time-space diagram

q Estimation of commercial speed for bus corridors q  Modelling

Time-space diagram for buses of a route

Bus following

No se puede mostrar la imagen. Puede que su equipo no tenga suficiente memoria para abrir la imagen o que ésta esté dañada. Reinicie el equipo y, a continuación, abra el archivo de nuevo. Si sigue apareciendo la x roja, puede que tenga que borrar la imagen e insertarla de nuevo.

No se puede mostrar la imagen. Puede que su equipo no tenga suficiente memoria para abrir la imagen o que ésta esté dañada. Reinicie el equipo y, a continuación, abra el archivo de nuevo. Si sigue apareciendo la x roja, puede que tenga que borrar la imagen e insertarla de nuevo.

Speed range definition sR=20[km/hr]

Condition Sijk [Km/h] Color

Very bad ≤ 15 Red

Bad >15 a ≤19 Orange

Regular >19 a ≤20 Yellow

Acceptable >20 to ≤25 Light green

Good >25 to ≤30 Dark green

Excelent >30 Blue

n.a.: Grey

Global results (all services)

September 2008

March 2009

April 2009

Other visualizations

¤  Spatial visualization by service

¤  Worst cases in a map

¤  Speed of a corridor ¤  For all services

¤  All times of day

¤  Divided into segments

Exclussive way 7:30-10 & 17-21 Mixed traffic 3 lanes 10-17

Seggregated corridor 2 continue lanes per direction

Mixed traffic 2 lanes

Segment 7 6 5 4 3 2 1

Length (km) 0.99 1.39 1.97 1.63 1.18 1.41 0.97

Traffic light controlled int/ km 3.03 4.32 3.55 3.68 2.54 3.55 3.09

Bus stops service/km 2.02 2.88 2.54 2.45 2.54 2.13 2.06

N Santa Rosa corridor S

Average commercial speed Santa Rosa corridor(km/h) Segment

Period 1 2 3 4 5 6 7 Total 7:30 18.5 15.1 29.3 24.8 24.4 17.2 10.4 18.1 8:00 14.6 16.3 30.9 26.5 25.2 17.3 9.7 19.3

8:30 13.7 18.8 32.7 27.2 27.4 18.0 9.2 18.2 9:00 15.2 20.2 34.9 28.0 29.3 19.2 14.7 21.5 9:30 16.3 19.8 33.7 27.5 29.7 18.5 14.9 21.5

10:00 16.2 21.6 34.5 30.6 31.6 22.2 17.1 22.8 10:30 19.9 21.0 33.7 30.2 31.6 21.9 17.4 23.8 11:00 19.7 21.6 30.4 31.2 31.6 21.2 17.1 23.2 11:30 19.6 21.2 33.0 32.0 31.4 21.4 16.8 23.9

12:00 18.5 20.3 36.3 31.1 32.0 20.9 15.1 24.4 12:30 18.5 21.0 35.2 30.0 32.9 21.1 14.8 23.5 13:00 18.9 21.2 35.1 31.6 32.1 21.1 15.5 24.2 13:30 20.4 21.0 35.6 32.2 32.7 22.4 16.8 24.8 14:00 19.8 21.4 36.3 31.0 33.1 22.7 17.8 25.1 14:30 21.1 21.4 33.2 28.8 31.1 21.6 20.1 24.7 15:00 22.1 19.9 32.4 29.5 30.6 22.4 17.9 24.3 15:30 20.5 21.4 30.0 28.7 31.1 21.3 17.1 23.4 16:00 17.0 21.4 30.5 28.3 32.2 21.8 16.9 22.7 16:30 18.1 20.3 32.6 28.2 31.4 22.0 16.9 23.1 17:00 15.7 20.5 31.2 29.1 27.2 22.7 15.0 21.7 17:30 17.7 20.3 29.7 29.2 27.4 22.9 15.2 22.7 18:00 14.5 20.8 30.5 28.8 27.5 23.1 15.8 21.7 18:30 22.6 21.5 30.2 29.8 29.0 25.8 15.6 23.8 19:00 23.1 23.5 30.0 29.6 29.5 26.5 19.0 25.3 19:30 23.6 23.6 30.5 31.5 29.5 28.7 20.2 26.3 20:00 26.7 24.9 31.3 31.6 30.5 29.8 22.3 27.8 20:30 27.7 26.7 32.5 33.5 31.3 33.2 25.7 29.8

Total 18.8 20.8 32.2 29.5 29.9 22.1 16.0 23.1

Post processing

¤ Load profiles Built using ¤ Bus trajectory ¤ Observed boarding with expansion

factors ¤ Estimated alighting with expansion

factors à Aggregated at bus or route level

Route load profile

0  0.1  0.2  0.3  0.4  0.5  0.6  0.7  0.8  0.9  

1   2   3   4   5   6   7  

cards  

(million)  

days  

 Regular  card  –  days  used  in  a  week  

sep.08  

ago.09  

jun.10  

abr.11  

abr.12  

0  20  40  60  80  100  120  140  160  180  200  

1   2   3   4   5   6   7  

cards  (thou

sand

s)  

days  

Student  card  –  days  used  in  a  week  

sep.08  

ago.09  

jun.10  

abr.11  

abr.12  

The  most  frequent  user  is  the  unfrequent  traveller,  but…  there  is  an  important  number  of  regular  users  

The  most  frequent  behavior  for  students  is  frequent  traveller  

Travel patterns

Zone of residence estimation for frequent users

Day  1.  07:18  am  

Day  2.  07:38  am  

Day  3.  10:53  am  

Day  4.  09:02  am  

R  =  500  m  

Day  1.  07:18  am   Day  4.  09:02  am  

Day  2.  07:38  am  

Day  3.  10:53  am  

Zone of residence estimation for frequent users

Other developments

¤ Trip purpose ¤ Level of service indicators ¤ Time use patterns ¤ Fare evasion

Applications

¤ OD matrix at different levels of aggregation (XY, bus stop, zone, municipality) ¤ Route/service design ¤ Infrastructure decisions ¤ Design of information

campaigns

Recoleta 203-208

Lira-Carmen 204

Fusion

Applications

¤ Speed profiles ¤ Operational interventions ¤ Bus priority decisions ¤ Infrastructure investment

decisions

B

F

F

C

C

A

D

D

E

E

G

G

Applications

¤ Load profiles ¤ Frequency optimization ¤ Design of express or short

variations of services

0

100

200

300

400

500

600

700

800

Perfil  de  carga  Servicio  104  Puente  Alto  -­‐ Providencia  (7:30)

subidas bajadas Carga

Quality of the information?

b. Santiago 2001 ODS data

One example:

Applications

¤ Third party applications (open access policy)

Conclusions

q Quantum leap on information availability and cost

q Many tools can be developed to improve planning, operation and control

q We can advance on understanding behavior and test hypothesis

q Solid grounds to formulate new policies

Further research

¤ Additional information: ¤ Vehicle detectors ¤ Private GPS equipment ¤ Mobile phone traces ¤ Online applications (waze) ¤ Surveys!

¤ New age for transport engineering

Thanks!

Cortés, C., Gibson, J., Gschwender, A., Munizaga, M.A., Zúñiga, M. (2011) Commercial bus speed diagnosis based on GPS-monitored data. Transportation Research C 19(4), 695-707. Devillaine, F., Munizaga, M.A., Trepanier, M. (2012) Detection activities of public transport users by analyzing smart card data. Transportation Research Record 2276, 48-55. Gschwender, A., Ibarra, R., Munizaga, M., Palma, C. (2012) Monitoring Transantiago through enriched load profiles obtained from GPS and smartcard data. CASPT Santiago, Chile 23-29 Julio. Munizaga, M.A., Palma, C. (2012) Estimation of a disaggregate multimodal public transport origin-destination matrix from passive Smart card data from Santiago, Chile. Transportation Research 24C(12), 9-18. Munizaga, M.A., Devillaine, F., Navarrete, C., Silva, D. (2014) Validating travel behavior estimated from smartcard data. Transportation Research 44C, 70-79.