Analysis of the urban travel structure using smartcard and GPS data from Santiago, Chile

Transcription

1 Analysis of the urban travel structure using smartcard and GPS data from Santiago, Chile Marcela Munizaga, Felipe Hernández, Universidad de Chile Ricardo Hurtubia, Pontificia Universidad Católica de Chile

2 Introduction Cities are complex and they will become even more complex in the future If we want to improve life quality in cities, we need to understand the activities/travel structure OD surveys: detailed information, based on respondants declarations, usually small samples Passive data: provided (and limited) by the technological devices available, limited human intervation, usually huge samples

3 Objective To analyze the urban spatial structure of Santiago de Chile using passive data from the public transport system (AFV, AVL) URBAN FORM SPATIAL STRUCTURE

4 Background Methods developed for: Alighting estimation - Zhao et al 2007; Trepanier et al 2007; Munizaga & Palma 2012 Trip linking, transfer behavior - Seaborn et al 2009; Charikov & Erath 2011; Devillaine et al 2013 Trip purpose estimation - Charikov & Erath 2011; Devillaine et al 2013; Lee & Hickman 2014 Zone of residence estimation - Amaya et al Analysis of the dynamics of urban structure in Singapore using passive data - Zhong et al. 2014

5 Case of study: Santiago Santiago. Capital City of Chile: Population: 6.6 million 2 million households Area: 1,400 km2 34 Municipalities 18M trips per day 28% public transport 35% walk 26% car

6 Description of Santiago in 6 large zones Source: 2012 ODS

7 Transantiago: Multimodal integrated public transport system (bus, metro) introduced in 2007 Equipped with Smartcard and GPS Transactions sequence travel structure 6,500 buses all equipped with GPS 70 km of segregated busways 10,000 bus stops 125 bus stations (off-bus fare collection) Metro: 5 lines, 100 km, 54 trains Only smartcard payment in buses (global penetration rate 97%) 4.6M trips per day

8 The Data Buses GPS: 1 record every 30s, M records per week bip! transactions: M records per week Other information: Routes paths Route assignments Position of bus stops Position of Metro stations Position of bus stations

9 Processing Estimation of alighting stop Min Tg Second transac on of the day First transac on of the day Min Tg Min dist Last transac on of the day Boarding point min Tg = t + d i->x y post post i i s walk s.t. d post d (qwalk /qtravel) GPS Point Bus stop Metro sta on

10 Processing: Stages and Trips Trip Estimated alighting Transfer or activity? Observed boarding Trip t Criteria to distinguish destination from transfer Time elapsed Transaction sequence Frequency of PT services Ratio: distance on the route / Euclidean distance Trip purpose estimation Work : Card type is adult, activity is longer than 2h, trip before is not the last of the day Study : Card type is student, activity is longer than 2h, trip before is not the last of the day Home : Trip before is the last of the day Other : Activity is between 1 min and 2h long. Trip before is not the last of the day

11 Zone of residence estimation for frequent users Day 1. 07:18 am Day 4. 09:02 am Enrichment Day 2. 07:38 am Day 3. 10:53 am

12 Zone of residence estimation for frequent users Enrichment Day 1. 07:18 am Day 4. 09:02 am Day 2. 07:38 am Day 3. 10:53 am R

13 Data Period: 15/04/ /04/2013 Trips observed: 25,244,791 1 stage 63,93% 2 stages 29,91% 3 stages 5,89% 4 or more stages 0,27% Smart cards: 3,419,038 Bus stops: 11,183 Metro stations: 100 Trips used: 18,535,153 (73.42%) 13

14 Zone of residence estimation for frequent users 1,294,049 (39%) satisfied the minimum number of trips observed in the time window 888,970 (68%) have radii less than 1,000 m circumscribing all first-inthe-morning transactions

15 We want to recover information Network Bus stops Administrative limits URBAN SPATIAL STRUCTURE 15

16 Methodology Network Weighted directed graph G = (N,L,W) N: Bus stops L: Link between two bus stops (trip) W: Trip volume Center Hubs Page rank Betweenness centrality (network, trips) Communities Infomap algorithm

17 Results -> Page Rank

18 Results-> Network Betweenness Centrality

19 Results-> Trip stages Betweenness Centrality

20 Results-> Communities (10 most significant) North East West Sout h Southeast

21 Results-> Communities (10 most significant) West North East Sout h Southeast

22 Results-> Communities (10 most significant) West North East Sout h Southeast

23 Results-> Communities (10 most significant) North East West Sout h Southeast

24 Results-> Communities (10 most significant) North East West South Southeast

25 Results-> Communities (10 most significant) North East West South Southeast

26 Results-> Communities (10 most significant) North East West South Southeast

27 Results-> Communities (10 most significant) North East West South Southeast

28 Results-> Communities (10 most significant) North East West South Southeast

29 Results-> Communities (10 most significant) North East West South Southeast

30 Results-> Communities (10 most significant) North East West South Southeast

31 Synthesis and Conclusions Successful application of network analysis methodology (Zhong et al) to Santiago Santiago different than Singapore Identification of CBD center-east (Page Rank) Transfer points (hubs) do not vary during the day Can be identified with original and modified methodology Communities -> identifiable, but all have a conection to the CBD

32 Future Work Incorporate other data sources (mobile phone traces, GPS data from other transport modes) Analyze different periods of time (per hour, per day, weekday, weekend, etc) Analyze network structure Look at the evolution

33 Thanks!

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