Demand-Responsive Transit (DRT) Service in the Stockholm Area
Group 1
Adeel Anwar_Alexander Jacob_Mahnaz Narooie_Ehsan Saq...
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 ...
Methodology
1. Literature review:
• Methodology overview
• Indicators
2. Data cleaning & selection (relevant data):
• Trip...
4. Database:
• Set up Postgres database
• Creation & import of tables (.shp)
• Population tool (JAVA) – (.csv) files into ...
O/D Population segmentation ->
Potential customer
Potential customer flows
+
Extended O/D
Methodology
5. Demand generation...
6. Visualization - Open Layers
• Dynamic map by changing parameters
Methodology
Data
Import
Database
Analysis preparation
Analysis
Results
O/D Matrices Mosaic data
Public
Transports
Points of
interest
R...
GRAVITY MODEL
• Attraction
• Friction factor (Travel time)
• Find Trips generated by potential customers
Gravity model giv...
Areas with HIGH probability of car sharing members (similar group):
POPULATION BASED:
• Age distribution: 20-39 years
AND
...
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...
Attraction and friction factor
Sum(trips pointing to one zone)
All O/D demand included
Aggregated inflow per zone
1/ trave...
Gravity model
i j ij
ij
j ij
1
PA F
T
A F
n
j


Tij = Trips between i and j
Pi = Trips produced in zone i
Aj = Trips at...
Clustering
• Heuristic based!
• For every zone a subset with the biggest
amount of trips to ,is selected and all inner
tri...
1
2
3
3
5
12
7
8 10 4
6
58
4
9
7
12 7
  4519188   48191712
Rank 2 Rank 1
Clustering
20-59
150-400+
20-39
150-399
20-59
150-399
20-39
400+
20-59
400+
Input
Cluster size
5 – 20 zones
Demographic based selecti...
Selection of zones using extended flows
Top 3 clusters
1. Sollentuna (235 trips/day)
2. Hammarbyhöjden/Björkhagen (228 tri...
Selection of zones using exteflows
Top 3 clusters
1. Sollentuna
Cluster includes Greater Sollentuna, Kista, Akalla, Husby
Selection of zones using extended flows
Top 3 clusters
2. Hammarbyhöjden/Björkhagen
Cluster includes Älta, Kärrtorp , Baga...
Selection of zones using extended flows
Top 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 So...
Discussion
• Travelling itself is usually no purpose
• Further analysis of characteristics of resulting zones can give clu...
Discussion
• Data usage
• Not all data is used in the current analysis due to different problems:
1. Mosaic population pro...
Conclusion
• We created a web application that can be used for finding
suitable areas for a pilot project!
• It currently ...
Thank you for your attention!
Feel free to open the discussion!
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KTH-Texxi Project 2010

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KTH-Texxi Project Final Presentation Group 1

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KTH-Texxi Project 2010

  1. 1. 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 Gidofalvi T.A. Jan Haas
  2. 2. Outline • Introduction • Methodology Overview • Methodology Detail • Results • Discussion • Conclusion
  3. 3. 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.
  4. 4. 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
  5. 5. 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 data Public Transports Points of interest Road network Taxi data External Java program ArcGIS, Microsoft Access Digitalization P.T. system Given data Given Data Postgres import function of shape files Methodology
  6. 6. O/D Population segmentation -> Potential customer Potential customer flows + Extended O/D Methodology 5. Demand generation and distribution (conceptual model):
  7. 7. 6. Visualization - Open Layers • Dynamic map by changing parameters Methodology
  8. 8. Data Import Database Analysis preparation Analysis Results O/D Matrices Mosaic data Public Transports Points of interest Road network Taxi data External Java program Attraction based on O/D, aggregated on flows Gravity model Trips with DRT service ArcGIS, Microsoft Access Digitalization P.T. system Trips produced by Potential customers AHP weighting Literature review Clustering 10 TOP ZONES Control with Points of interests P.T. Friction factor Car travel time peek (and off peek) Given Data Postgres import function of shape files Methodology
  9. 9. 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 Attraction based on O/D, aggregated on flows Gravity model Trips with DRT service Friction factor Car travel time peek (and off peek) Trips produced by Potential customers AHP weighting Demand generation and distribution
  10. 10. Areas with HIGH probability of car sharing members (similar group): POPULATION BASED: • Age distribution: 20-39 years AND • Level of education: University degree AND • Number of cars/household: 0-1 GEOGRAPHIC BASED: • High density areas – Housing SENSITIVITY ANALYSIS: • Age distribution AND • Income Trips produced by Potential customer
  11. 11. 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
  12. 12. 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
  13. 13. Attraction and friction factor Sum(trips pointing to one zone) All O/D demand included Aggregated inflow per zone 1/ travel time 2 3 10 4 5 8 9 7 1 3 10 9 8 7 3 37    attraction
  14. 14. Gravity model i j ij ij j ij 1 PA F T A F n j   Tij = Trips between i and j Pi = Trips produced in zone i Aj = Trips attracted to zone j Fij = 1 / travel time
  15. 15. Clustering • Heuristic based! • For every zone a subset with the biggest amount of trips to ,is selected and all inner trips out of this “cluster” selection are counted. • Those are ordered by the inner-trip-count and the top results are high-lighted on the map
  16. 16. 1 2 3 3 5 12 7 8 10 4 6 58 4 9 7 12 7   4519188   48191712 Rank 2 Rank 1 Clustering
  17. 17. 20-59 150-400+ 20-39 150-399 20-59 150-399 20-39 400+ 20-59 400+ Input Cluster size 5 – 20 zones Demographic based selection Demand filter min Distance filter min, max Cluster center zone & inner trip count Choices Cluster Top 10 – 30 zones Ranked by inner trip count Clustering
  18. 18. Selection of zones using extended flows Top 3 clusters 1. Sollentuna (235 trips/day) 2. Hammarbyhöjden/Björkhagen (228 trips/day) 3. Södertälje (213 trips/day) Parameters: Type 4 3-8 km 0.5 minimum demand Cluster size 10 zones 1 2 3
  19. 19. Selection of zones using exteflows Top 3 clusters 1. Sollentuna Cluster includes Greater Sollentuna, Kista, Akalla, Husby
  20. 20. Selection of zones using extended flows Top 3 clusters 2. Hammarbyhöjden/Björkhagen Cluster includes Älta, Kärrtorp , Bagarmossen
  21. 21. Selection of zones using extended flows Top 3 clusters 3. Södertälje Northern part of Södertälje
  22. 22. 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.
  23. 23. 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
  24. 24. 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 but amount 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
  25. 25. 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 • …
  26. 26. Thank you for your attention! Feel free to open the discussion!

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