Accessibility Analysis and Modeling in Public Transport Networks - A Raster based Approach

Accessibility Analysis and Modeling in Public Transport Networks - A

Raster based Approach

Morten Fuglsang1,2, Henning Sten Hansen2 & Bern Münier1

1Aarhus University – National Environmental research institute2Aalborg University Copenhagen

Structure of presentation

• Prensentation of topic

• Introduction to the case region

• The applied definition of accessibility

• The conducted modeling

• Results

• Evaluation

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Project topic

• For the Pashmina project, CA based land use change modelling is to be conducted using the LUCIA Cellular autometa model.

• The project outlines policy scenarios for paradigme shifts in transportation for the next 40 year period.

• Modeling is to be conducted in Denmark and in up to two other European cases.

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Project topic

• One of the ongoing tasks is to create a indicator of accessibility to jobs through public transportation.

• In order for it to be transferable, the data requirements was to be as simple as possible, to facilitate easy transfere to the other European regions.

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Introduction

• ‘Outskirt Denmark’ is a popular theme in Danish politics, describing regions with :

3.Declining population

4.Poor number og jobs

5.Low service level in terms of schools, hospital services and public transportation

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Study area

• The capitol region of Denmark

• 9200 km2 area coverage

• Large regional differences in terms of jobs and transportation services

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Defining accessibility

• Components of accessibility:

2.Land use component

3.Transportation component

4.Temporal component

5.Individual component(Geurs, K. T & van Wee, B. 2004)

• We use three of the four components, and a gravity based accessibility measurement.

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Data inputs

• Data used for the modeling:

2. Road dataset

3. Trainlines and stops

4. Buslines and stops

5. Metrolines and stops

6. Centers with population and number of jobs

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Cost surface creation

• Average tavel-speeds was appended to the vector data

• Out of network travel was set to walking pace

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Cost surface creation 2

• Data was rasterized to 100m resulution, and the differenet rasters where combined

• Finally the travelspeed was recalculated to CCT values

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Juliao, R. P (1999)

GIS modeling

• Using python, a model was created, that combines the data with the information from the centers, and the coast surface

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(Coffey, W & Shearmur R. G 2001)

GIS modeling

• The model creates a cost distance calculation for each center – calculating the centers contribution to the overall indicator.

• The center contributions are the summarized into one output layer

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Result post processing

• Result showed som inconsitency in two areas with high low coverage and few stations/stops

• Furthermore the effect of the constraints was removed for the result

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Results

• 285 centers was included in the calculation.

• High variability in terms of result scores

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Results

• Based upon upon the entire dataset, mean accessibility was calculated

• Result classified into +/- 1 and 2 std. deviation.

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Validation

• Municipality average was calculated based on the clssification scores

• Compared to commuting statistics.

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Validation 2

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Validation 3

• The cluster analysis highlights the regions where both accessibility and commuting is generaly low.

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Strengs and weaknesses

+ Fast calculation time based on raster math

+ Low data requirements

+ Describes commuting trends

+ In line with commuting statistics

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- Depiction of time in the model does not corrospond to traveltime

- Change of transportation mode and wait time is not modeled

- Region should and will be subdivided.

Conclusion

• Vector methods are much more precise, however this aproach was designed with low data requirements as main goal.

• The model illustrates the regional differences in service availability and align with commuting statistics

• Future work will focus on incorporating a more precise prediction of time to the raster based method.

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• Thank you for your attention…

[email protected]

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