KTH-Texxi Project 2010
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Transcript of KTH-Texxi Project 2010
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!