Demand-Responsive Transit (DRT) Service in the Stockholm Area

Group 1

Adeel Anwar_Alexander Jacob_Mahnaz Narooie_Ehsan Saqib _Annmari Skrifvare‎_Elisabetta Troglio‎

AG2421 – A GIS Project

Geoinformatics, KTH, Period 2, 2010

Gyozo GidofalviT.A. Jan Haas

Outline

• Introduction

• Methodology Overview

• Methodology Detail

• Results

• Discussion

• Conclusion

Introduction

Objective:• Create a decision-making-support tool for finding the optimal area to implement a pilot project for a taxi service in a DRT manner.

• DRT stands for demand responsive transit!• Created service based on a demand model• Model contains also distribution of demand in terms of trips between zones

• Core of our analysis is a database combining a variety of different data sources of both spatial and non-spatial character.

Methodology

1. Literature review:

• Methodology overview

• Indicators

2. Data cleaning & selection (relevant data):

• Trip zones /OD matrices, road network cleaning

• Mosaic – finding the useful indicators according to literature

3. Data fusion – ArcGIS level

• Fusing mosaic data to trip zones

• Fusing population data to trip zones

4. Database:

• Set up Postgres database

• Creation & import of tables (.shp)

• Population tool (JAVA) – (.csv) files into database

Data in the database:

• One common reference system• Basemma.shp – trip zones as reference zone • OD matrices – main information • Own calculations

O/D Matrices Mosaic dataPublic

TransportsPoints of interest

Road network Taxi data

External Java

program

ArcGIS,Microsoft

Access

DigitalizationP.T. system

Given data

Given DataPostgres import function

of shape files

Methodology

O/D Population segmentation -> Potential customer

Potential customer flows

+

Extended O/D

Methodology5. Demand generation and distribution (conceptual model):

6. Visualization - Open Layers

• Dynamic map by changing parameters

Methodology

Data

Import

Database

Analysis preparation

Analysis

Results

O/D Matrices Mosaic dataPublic

TransportsPoints of interest

Road network Taxi data

External Java

program

Attractionbased on O/D, aggregated on

flows

Gravity model Trips with DRT service

ArcGIS,Microsoft

Access

DigitalizationP.T. system

Trips produced byPotential customers

AHP weighting

Literature review

Clustering

10 TOP ZONES

Control withPoints of interests

P.T.

Friction factorCar travel time

peek (and off peek)

Given DataPostgres import function

of shape files

Methodology

GRAVITY MODEL

• Attraction• Friction factor (Travel time) • Find Trips generated by potential customers

Gravity model gives the opportunity to analyze potential flows by clustering analysis - find most interesting zones

Attractionbased on O/D, aggregated on

flows

Gravity model Trips with DRT service

Friction factorCar travel time peek

(and off peek)

Trips produced byPotential customers

AHP weighting

Demand generation and distribution

Areas with HIGH probability of car sharing members (similar group):

POPULATION BASED:

• Age distribution: 20-39 yearsAND

• Level of education: University degreeAND

• Number of cars/household: 0-1

GEOGRAPHIC BASED:

• High density areas – Housing

SENSITIVITY ANALYSIS:

• Age distributionAND

• Income

Trips produced by Potential customer

20 - 39 40 - 59 150 - 399 400+

1 X X X X

2 X X

3 X X X

4 X X X

5 X X X

Defining potential customers

Age Income

AHP- weights

Age Income Education Housing

Age 1 1/0.144 0.208 0.488

Income 0.144 1 0.228 1/0.184

Education 1/0.208 1/0.228 1 0.357

Housing 1/0.488 0.184 1/0.357 1

0.199, 0.224, 0.309, 0.267

Attraction and friction factor

Sum(trips pointing to one zone)All O/D demand includedAggregated inflow per zone

1/ travel time

2

3

10 4

58

9

7

1

3

10 9 8 7 3 37 attraction

Gravity model

i j ij

ij

j ij

1

PA FT  

A Fn

j

Tij = Trips between i and j Pi = Trips produced in zone iAj = Trips attracted to zone jFij = 1 / travel time

Clustering

• Heuristic based!

• For every zone a subset with the biggestamount of trips to ,is selected and all innertrips out of this “cluster” selection arecounted.

• Those are ordered by the inner-trip-countand the top results are high-lighted on themap

1

2

3

3

5

12

7

8 10 4

6

58

4

9

7

12 7

4519188 48191712

Rank 2 Rank 1

Clustering

20-59150-400+

20-39150-399

20-59150-399

20-39400+

20-59400+

Input

Cluster size5 – 20 zones

Demographic based selection

Demand filtermin

Distance filtermin, max

Cluster center zone & inner trip

count

Choices

Cluster

Top 10 – 30 zonesRanked by inner trip

count

Clustering

Selection of zones using extended flowsTop 3 clusters

1. Sollentuna (235 trips/day)2. Hammarbyhöjden/Björkhagen (228 trips/day) 3. Södertälje (213 trips/day)

Parameters:Type 43-8 km0.5 minimum demandCluster size 10 zones 1

2

3

Selection of zones using exteflowsTop 3 clusters

1. Sollentuna

Cluster includes Greater Sollentuna, Kista, Akalla, Husby

Selection of zones using extended flowsTop 3 clusters

2. Hammarbyhöjden/Björkhagen

Cluster includes Älta, Kärrtorp , Bagarmossen

Selection of zones using extended flowsTop 3 clusters

3. Södertälje

Northern part of Södertälje

Resulting recomendation

Based on our analysis we suggest that the pilot project of the DRT service should be located in Sollentuna and its neighboring areas.

It should however be noted that this is only one possible result, based on one specific set of parameters. Different parameter sets might produce different outcomes. We chose a set that we found reasonable based on some assumption what range and cluster size is suitable for a taxi service pilot project as well as the demographic group most promising from the literature review.

Discussion

• Travelling itself is usually no purpose• Further analysis of characteristics of resulting zones can give clues of

more specific customer purposes (shopping, corporate, evening/night etc.)

• POI (points of interest) can be used

• Price• probably has a strong influence on acceptance of service

• should be oriented on competitors such as existing public transport

• maybe slighter higher due to better convenience

• Time• Now using peak hour for worst case scenario, with possibility to extend the

analysis to off-peak hours

Discussion

• Data usage• Not all data is used in the current analysis due to different problems:

1. Mosaic population profiles only in percentage for day and night butamount of day and night population not given!

2. Taxi data neither includes all zones nor covers a 24h period, thus a model first needs to be created to use them parallel to O/D matrices.

3. Public transport availability is high but not included in the analysis

• Clustering• many possible solutions (e.g. Ripleys K, K – means, etc.)

• for most exact result every trip needs to be compared with every other.

• computational efficient – on the fly

Conclusion

• We created a web application that can be used for finding suitable areas for a pilot project!

• It currently enables the customer to select from a set of pre-calculated demand sets and perform simple clustering based on mainly three parameters!

• This could be improved further by: • for example creating demand sets freely based on all available

demographic indicators at run time.

• including a price based model

• more advanced clustering methods

• …

Thank you for your attention!

Feel free to open the discussion!