big data analysis for the high-resolution view of urban public transportation accessibility

Big Data Analysis for the High-Resolution View of Urban Public Transportation Accessibility

Itzhak Benenson1, Dmitry Geyzersky2, Karel Martens3, Yodan Rofe4

1Department of Geography and Human Environment, Tel Aviv University, Israel2Performit LTD, Israel (http://www.performit.co.il)

3Institute for Management Research, Radboud University Nijmegen, Holland4Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Israel

http://www.tau.ac.il/~bennya/[email protected]

Dresden, August 20131

http://www.performit.co.il/

http://www.tau.ac.il/~bennya/

mailto:[email protected]

What is accessibility?

The extent to which land-use transport system enables individuals to reach destinations by means of transport modes1

• Given a destination: The number of origins from which a destination can be reached, given the amount of effort

• Given an origin: The number of destinations that can be reached from the origin, given the amount of effort

1K.T. Geurs, J.T. Ritsema van Eck, 2001, “Accessibility measures: review and applications”, RIVM report 408505 006, Urban Research Center, Utrecht University


How many activities can be reached with the car from the given origin during the given time?

Transport-based component of accessibility is car-based and aggregate


Accessibility changes abruptly at the boundary of an area

Accessibility components

Transportation: Components of transportation system performance (modes, travel time, cost, effort to travel between origin and destination)

Land-use:Distribution of needs/activities (jobs, schools, shops) and population (workers, pupils, customers) in space and time

Individual utility:The demand for trips between certain origins and destination, benefits people derive from the access to facilities

Guangzhou, June 20134

The goal: To estimate accessibility from the user’s viewpoint

The idea: To compare accessibility with the private car and with the public transport (and, probably, other modes, as bike)


Accessibility depends on a transportation mode Public Transport Travel Time (PTT): PTT = Walk time from origin to a stop 1 of the PT + Waiting time of PT at stop 1 + Travel time of PT1 + [Transfer walk time to stop 2 of PT + Waiting time of PT 2 + Travel time of PT 2] + … + Walk time from the final stop to destination

Private Car Travel Time (CTT):CTT = Walk time from origin to the parking place + Car trip time + Parking search time + Walk time from the final parking place to destination.

Service area:Given origin O, transportation mode M and travel time t define Mode Service Area - MSAO(t) - as maximal area containing all destinations D that can be reached from O with M during MTT ≤ t.

Access area:Given destination D, transportation mode M and travel time t define Mode Access Area – MSAD(t) - as maximal area containing all origins O from which given destination D can be reached during MTT ≤ t.

We distinguish betweenPublic Transport Service Area PSAO(t), Public Transport Access Area PAAO(t),

Private Car Service Area CSAO(t), Private Car Access Area CAAO(t)


Service areas ratio: SAO(t) = PSAO(t)/CSAO(t)

Access area ratio: AAD(t) = PAAD(t)/CAAD(t)

We focus on measuring relative accessibility


IN A NEW ERA OF BIG DATA WE ARE ABLE TO ESTIMATE ACCESSIBILITY EXPLICITLY!


Tel Aviv Metro 600 km2

2.5*106 pop 300 bus lines

Utrecht Metro500 km2

0.6*106 pop 150 bus lines


BIG URBAN TRANSPORTATION

DATA

Street network 104 ÷ 105 links

Attributes: traffic directions,speed

Necessary for measuring accessibility by car


Bus lines –102 ÷ 103

Bus stops102 ÷ 103

Relation between bus lines and stops.

Necessary for measuring bus accessibility


Bus time-table 105 ÷ 106

Necessary for measuring bus accessibility


Buildings and jobs, 105 - 106

Necessary for measuring activity component of accessibility


Socio-economic level

Car ownership

Necessary for measuring activity component of bus accessibility

Socio-economic level by traffic zones


Land-uses, 105 ÷ 106

AccessCity



From transportation networks to graphs


Typical metropolitan road network graph has 104 - 105 nodes and links

Node JunctionLink Road segmentImpedance Travel time

Translation of Road network into Graph is easy…

Every travel should be represented explicitly

Origin

Initial Stop

Transfer Stop 1

Final Stop

Transfer Stop 2

Destination


Public Transport Graph, the idea

1 2 3 4 5 6 7 8

11 12 13 14 15 16 17 18

Route 1

Route 2

6:57 7:01 7:03 7:05 7:08 7:09 7:12 7:15

6:50 6:56 7:02 7:06 7:10 7:14 7:18 7:21

Start

Travel

Destination


1 2 3 4

Bus starts every 10 minutes

Bus starts every 30 minutes

[1, 6:57, 1, 6:57] [1,6:57, 2,7:01] [1, 6:57, 4,7:05][1,6:57, 3,7:03]

15 16 17 18

[2, 6:50, 1,7:10] [1, 6:50, 3,7:14] [1, 6:50,1,7:18] [1, 6:50,1,7:21]

[Bus route = 1, Start Time = 6:57, Stop = 4,Arrival time = 7:05]

0:04 0:02 0:02

0:05

0:04 0:04 0:03

Node BuildingNode [PTLine_ID, Stop_ID, ArrivalTime] (triple)Link (a) Possible path between building and PT stop accessible by foot; Link (b) Possible path between two sequential stops connected by the PT line; Link (c) Possible path stops connected by the transfer walkNode impedance (a) Population, Number of jobsLink Impedance (a) Walk time Link Impedance (b) PT travel time Link Impedance (c) Walk time + waiting time (Transfer time)


Public Transport Graph, the process

Public Transport Graph, the outcome

AccessCity parameters

Day of the weekTrip start/finish time

Max time of waiting at initial stop

Walk speed when changing lines

Max travel time Max number of line changes

Calculate access areaCalculate service area


AccessCity works with any partition of the urban space: Cells



AccessCity works with any partition of the urban space: buildings

AccessCity is built on the neo4j graph database http://www.neo4j.org/


http://www.neo4j.org/


Service and access area in AccessCity are currently implemented as a part of the Dijkstra shortest path algorithm

We calculate service area based on Dijkstra algorithm, starting from every building


AccessCity is a scalable applicationCALCULATION FOR ALL BUILDINGS CAN BE DONE IN PARALLEL

Performance: Service area for one building, 1-hour trip ~ 0.1 sec

Processor

Threads

Processor

Two-level parallelization

SOME RESULTS


Car service area is essentially larger than bus service areas

Entire metropolitan area Urban Land-uses

Car service areas versus bus service area


07:0007:0507:1007:1507:2007:2507:30

The center of Tel-Aviv metropolitan: Accessibility maps between 07:00 – 07:30

Job Accessibility


Average accessibility: 0.336 Average accessibility: 0.356Relatively higher in the center Relatively higher at the periphery

We must work at high-resolution!


Hig

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Passengers waste more time with the short trips!Trip start: 7:00, No of transfers: 1

50 minutes tripHigh-resolution: 0.257Low-resolution: 0.308





We could not see that at the low resolution

50 minutes tripAv improvement: 2.5%

Light rail, if combined with the existing bus network does not improve much…

Trip start: 7:00, No of transfers: 1





Towards transportation justice7:00, trip duration 60 min, 1 transfer

r2 = 0.054 (r = 0.23)

TAZ Socio-economic index (1 - 20)

Acc

essi

bili

ty

Socio-economic level


TA public transportation system is not just!

Applications of the tool in transportation planning

• Assessment of public transport service improvements, e.g. impacts of increase in frequencies for different population groups, areas, land uses

• Identification of ‘pockets of inaccessibility’ in metropolitan area

• Accessibility planning for services

• Assessment of (public) transport investments, e.g., light rail


The future: Trial-And-Error public transport planning with AccessCity


I. Benenson, K. Martens, Y. Rofé and A. Kwartler, 2010, Measuring the Gap Between Car and Transit Accessibility Estimating Access Using a High-Resolution Transit Network Geographic Information System, Transportation Research Record: Journal of the Transportation Research Board, N2144, 28–35

I. Benenson, K. Martens, Y. Rofé and A. Kwartler, 2011, Public transport versus private car: GIS-based estimation of accessibility applied to the Tel Aviv metropolitan area, Annals of Regional Science, 47:499–515

Questions?

big data analysis for the high-resolution view of urban public transportation accessibility

Technology

given time

travel time t

given origin

time individual utility

maximal area

private car access area

mode access area msadt

destination d