Politecnico di Milano School of Architecture, Urban Planning and Construction Engineering M.Sc. Urban Planning and Policy design
Mutations of self-organized mobility
Mapping and Evaluating Alexandria’s minibus movement patterns
Prof. Paola Pucci
Prof. Frank Witlox
Mohamed El Gohary Student:
10702842
Academic year:
Co-supervisor: Supervisor: 2020-21
Research question
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
How could urban mobility data measure the performance of informal mobility in the Global South
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Research question ??????
How could urban mobility data measure the performance of informal mobility in the Global South?
What are the available data sources in the study region?
How would urban modelling of paratransit help forecast the minibuses’ correspondence to different mobility policies/emergencies?
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Research outline
0111010011010111101 000101010010101101 1010111010111010010 1010101111010101010 1010101111010101101 10111110101010111110 0101010101111010101
Figure 1: Thesis outline. Source: Author.
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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Investigating minibus challenges in Global South Mobility data And mobility models Digital Mapping of Matatu In Nairobi Mapping and Ground-thrusting The Minibuses MATSim Model for the minibuses Urban Mobility Policy recommendations Ch.1 Intoduction Ch.2 Literature review Ch.3 Good Practice Ch.4 Data ane Methdology Ch.5 Modelling and analysis Ch.6 Conclusions
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Nairobi, Kenya
Chapter 1: Mobility challenges in Global South
What paratransit challenges in Global South?
Chapter 1 Introduction:
-Unplanned Sprawl -Informal transport: paratransit -Mobility challenges
Chapter 4 data and methodology:
- Alexandria unplanned sprawl
- Alexandria mobility market
- Minibus as transport supply
- Available data for mapping and analysing minibus supply
Could data map the informal mobility in Alexandria ?
How data could be contained?
Chapter 2 literature review: -Urban Mobility data -Urban Mobility models
Chapter 3 Good practice: -Matatu, Nairobi, Kenya. Chapas, Maputo, Mozambique
What could be learn from good practices?
Chapter 5 mapping and analyzing: - MATSim data framework
- Executing the model - Scoring - Replanning
What is recommended to shift a traditional mobility Paradigm into a smart one? Kenya.
How could MATSim model address minibus inadequacy?
Chapter 6 conclusions: - Conclusions - Recommendations
Figure 2: Minibus terminal, central Kampala. Source: Flickr Greg James Wade.
Urban Sprawl and mobility
Rural areas
Unpredicted population growth immigration
Informal/Unplanned Sprawl
Urban areas Foretasted population growth Planned Sprawl
Demand for Mobility
Informal transport (paratransit)
Formal /Public transport
Figure 3: The relation between Unplanned Sprawl and informal mobility in developing countries. Source: Author.
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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Urban Sprawl and mobility
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Figure 4: Global urbanization trends. Source: UNFBA, 2010. Graphics: Paul Scruton.
Urban Sprawl and mobility
Walk and bicycle
Public (formal) Public (informal) Cars and motorcycles
Atlanta 2016
Los Angeles 2016
New York 2016
Stockholm 2015
Berlin 2015
Tokyo 2014
Hong Kong 2014
Beijing 2011
Cape Town 2013
Johannesburg 2013
Bogotá 2010
Mexico City 2017
São Paulo 2007
Addis Ababa 2015
Lima 2005
Delhi 2014
Nairobi 2005
Paris 2015 S T ABILIZIN G THRI V IN G ( G LOBAL NO RT H ) THRI V IN G ( G LOBAL S OUTH ) EMER G IN G S T RU GG LIN G
0% 20%
100%
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
40% 60% 80%
Dar es Salaam 2005
Figure 5: Mode shares of travelers across different categories of cities.Source: UITP, 2015a, 2015b; CAF, n.d.; Alliance for Biking & Walking, 2016.
Figure 6: Mode shares of travelers across different categories of cities. Source: UITP, 2015a, 2015b; CAF, n.d.; Alliance for Biking & Walking, 2016.
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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Definition
by providing individualised
without
“transportation service that supplements larger public transit systems
rides
fixed routes or time tables” (Merriam-Webster, 2022)
Paratransit as supply ,benefit and cost,operators, vehicles, route operation and boarding behavior.
Large-vehicle ’’Formal’’ transit Mini-bus Shared taxi Other Paratransit mobility
Figure 7: Percentage of Paratransit trips in developing countries. Source: Author based on Grieco, 2015.
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Abidjan
Capetown Delhi Jakarta
Manila Tehran
Dakar Mexico City Algiers Cairo
Paratransit as ,operators, vehicles, route operation and boarding behavior. supply,benefit and cost
Erratic scheduling
fewer vehciles in rush hours.
Meet mobility demand
“In-market” competition drivers compete around stops to make vehicles full.
“Cream Skimming” ride fares mayincreases at rush hours poor vehicles maintatince Safety
Benfits
Cost
Cheap operating fares
Lack of accountability due to absence of public policies
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Taxes and fees operatives avoid to pay taxes
Labour abuse age limites, and wages, and working hours. age limites, and wages, and working hours.
Lack of capacity
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Paratransit as , vehicles, route operation and boarding behavior. supply,benefit and cost,operators
One-car owner Franchisee route associations
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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behavior. supply,benefit and cost, operators,vehicles, Paratransit as
boarding
Class: Conventional Bus
Routes: Fixed
Schadule: Fixed Capacity: 25-60
Service niche: line-haul
Service cover: Region / Subregion
Class: Minibus/jitney
Routes: Fixed Schadule: Semi-Fixed Capacity: 12-24
Service niche: mixed
Service cover: Subregion
Class: Microbus/pick-up
Routes: Fixed Schadule: Semi-Fixed Capacity: 11-14
Service niche: Distribution
Service cover: Subregion
route operation and
Class: Motorcycle
Routes: Variable Schadule: Variable Capacity: 1-4
Service niche: Feeder
Service cover: Neighbourhood
Class: Cycle
Routes: Variable Schadule: Variable Capacity: 1-6
Service niche: Feeder
Service cover: Neighbourhood
Figure 8:
I: Macau Conventional bus. Source: WiNG
II: Matatu mininus in Nairobui. Source: Jociku
III: Minibus commuters from South Africa. Source: Nic Bothma/EPA, via Shutterstock
IV: Wheeler taxi in Indonesia. Source: Mackenzie Smith
V: Pedicab om Yangon. Source: David Stanley from Nanaimo, Canada
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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i. one-to-one iii. one-to-few iv. many-to-many
ii. corridor with mulitable stops
and boarding behavior. supply,benefit and cost, operators,vehicles, routes operation Paratransit as generator attractor link generator attractor link
Figure 9: Different route patterns. Source: Author based on Andreas, 2014, p. 23
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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boarding behavior. supply,benefit and cost, operators,vehicles, routes operation Paratransit as
and
Figure 10: Minibus stop in Damascus.Source: https://bit.ly/3taFOz0.
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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$
Challenges of public transport in the global south
Affordabilty Availability Accessibility Acceptability Safety Sustainability
The financial coast associated with transportation in relation to travelers’ paying abilities.
The route options, frequency and timing. The ways of using public transportation irrespective of age, physical ability or gender and the availability of informa tion on routes.
The extent to which modes of transporta tion are acceptable based on travelers’ standards.
The prevention of harm such being killed or injured to road users.
The environmental costs and impacts associated with transportation.
Figure 11: Public transport challanges in Global South. Source: Abdelaal et al., 2017, p. 4
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Chapter 2: Mobility data frameworks
What paratransit challenges in Global South?
Chapter 1 Introduction: -Unplanned Sprawl -Informal transport: paratransit -Mobility challenges
Chapter 4 data and methodology: - Alexandria unplanned sprawl - Alexandria mobility market - Minibus as transport supply
- Available data for mapping and analysing minibus supply
Could data map the informal mobility in Alexandria ?
How data could be contained?
Chapter 2 literature review: -Urban Mobility data -Urban Mobility models
Chapter 3 Good practice: -Matatu, Nairobi, Kenya. Chapas, Maputo, Mozambique
What could be learn from good practices?
Chapter 5 mapping and analyzing: - MATSim data framework - Executing the model - Scoring - Replanning
How could MATSim model address minibus inadequacy?
What is recommended to shift a traditional mobility Paradigm into a smart one? Kenya.
Chapter 6 conclusions: - Conclusions - Recommendations
Figure 12: Alexandria’s OSM public traces Source: Author based on OSM.Mobility data frameworks
Sensing the data
Building data sets
Modelling data Application
Collecting data from the feild Sorting and storing mobility data Test Urban mobility policy Real-time monitoring
Used for digital mapping
Forecast mobility demand Forecast mobility demand
Figure 13: The framework of mobility data based urban traffic monitoring. Source:Liu & Wu, 2011
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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Sensing,collecting,modelling the data,and application.
Sensors
Based on infrastructure Based on public vehicles Based on automatic fare collection records
Based on other sensors Based on crowdsourcing
Loop detector Taxis
AFC systems
Traffic camera
Buses
GPS Accelerometer Ambient temperature Gyroscope Light Proximity Pressure Relative humidity High-cost
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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Sensors low-cost Sensors
Sensing,collecting,modelling the data,and application.
p3 = {x, y, t}
GPS Point Trajectory
Characteristics Big data Small data
Volume Very large Limited to large Exhaustivity Entire population Samples
Resolution and indexicality Tight and strong
Coarse and weak to tight and strong
Relationality Strong Weak to strong
Velocity Fast Slow, freeze-framed
Variety Wide Limited to wide Flexible and scalable High Low to middling
Figure 14: GPS traces and trajectory. Source: Author.
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Table 1: Big and small data charachteristics. Source: Kitchin & Lauriault, 2015, p. 464
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Sensing,collecting,modelling the data,and application.
Four steps model FSM
Activity-based travelling demand model
Landuse and Transportation interaction model (LUTI)
Land Use Inputs/ Forecasting Trip Generation Trip Distribution Modal Split/Choice Route Assignment
Planning Inputs Road and Public Transport Networks Trips arranged by Modal choice
Trips sorted by origin/ Destination zones Trips assigned to links On a network
Figure 2: Traditional four-step transport model (adapted from Button, 1977, p.117)
Figure 15: Traditional four-steps transport model.
Source: Button, 1977, p.117.
Trip based Model
Figure 16: Activity-based travel demand model.
Source:Joviić, 2001
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Transport & mobility input
LUTI model input input work at occupy regulate
Land use Policy & planning
occupy service adjust Output input
Socio-demographic & Economic features Household Industry Infrastructure Natural environment Scenarios of Land use patterns
Figure 17: Traditional four-step transport model.
Source: Button, 1977, p.117
Activity - based Model Landuse = Transportation based model
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Meeting Work Supermarket Home Cinema Work based tour Home based tour 1 Home based tour 2 Trip 1 Trip 2 Trip 3 Trip 4 Trip 5 Trip 6 Trip 7
Sensing,collecting,modelling the data,and application. Multi-agent Transport Simulation Model (MATSim)
initial demand mobsim scoring analysis re-planning
Figure 18: MATSim model. Soruce: Horni et al., 2016
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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Sensing,collecting,modelling the data,and application. Smart mobility services.
Navigating Monitoring Optimizing
Figure 19: Smart mobility survices.Source: Author based on Fourie et al., 2020
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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Chapter 3: Digitalizing self-organized mobility in Global South
What paratransit challenges in Global South?
Chapter 1 Introduction: -Unplanned Sprawl
-Informal transport: paratransit -Mobility challenges
Chapter 4 data and methodology:
- Alexandria unplanned sprawl
- Alexandria mobility market
- Minibus as transport supply
- Available data for mapping and analysing minibus supply
Could data map the informal mobility in Alexandria ?
How data could be contained?
Chapter 2 literature review: -Urban Mobility data -Urban Mobility models
What could be learn from good practices?
Chapter 5 mapping and analyzing:
- MATSim data framework
- Executing the model - Scoring - Replanning
What is recommended to shift a traditional mobility Paradigm into a smart one?
Chapter 3 Good practice: -Matatu, Nairobi, Kenya. Chapas, Maputo, Mozambique
How could MATSim model address minibus inadequacy?
Chapter 6 conclusions: - Conclusions - Recommendations
Figure 20: Paratransit in Ethopia. Source: Flickr risabelle chauvelAvailable Public transport data in Africa
Figure 21: Open source paratransit data in Africa. Source: Projects - DigitalTransport4Africa 2022
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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Digital Mapping Matatu in Nairobi
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Figure 22: Main mobility mean in Narobi. (Williams 2020)
Digital Mapping Matatu in Nairobi
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Figure 23: Students collects data via mobile phones and GPS devices. (Williams 2020)
Digital Mapping Matatu in Nairobi
Figure 24: Data visulization Williams 2020
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Digital Mapping Matatu in Nairobi
Figure 25: Raw GPS trajectories Williams 2020
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Digital Mapping Matatu in Nairobi
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Figure 26: Minibus routes simplified Williams 2020
Digital Mapping Matatu in Nairobi
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Figure 27: Minibus routes grouped Williams 2020
Matatu’s data findings
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Figure 29: Digital mapping outputs Williams 2020
Chapter 4: Paratranist supply in Alexandria Data and Methdology
What paratransit challenges in Global South?
Chapter 1 Introduction:
-Unplanned Sprawl
-Informal transport: paratransit -Mobility challenges
Chapter 4 data and methodology:
- Alexandria unplanned sprawl
- Alexandria mobility market
- Minibus as transport supply
- Available data for mapping and analysing minibus supply
Could data map the informal mobility in Alexandria ?
How data could be contained?
Chapter 2 literature review: -Urban Mobility data -Urban Mobility models
Chapter 3 Good practice: -Matatu, Nairobi, Kenya. Chapas, Maputo, Mozambique
What could be learn from good practices?
Chapter 5 mapping and analyzing:
- MATSim data framework
- Executing the model - Scoring - Replanning
What is recommended to shift a traditional mobility Paradigm into a smart one? Kenya.
How could MATSim model address minibus inadequacy?
Chapter 6 conclusions: - Conclusions - Recommendations
Figure 30: Al-manchya Square, Alexandria, Egypt from above. Source: Nour Jackson2021Upsurge Population growth
Increase in inhabitants
Population growth in Alexandria
0 100.000 200.000 300.000 400.000 500.000 600.000 700.000 800.000 900.000 1907-1917 1917-1927 1927-1937 1937-1947 1947-1960 1960-1976 1976-1986 1986-1996
Time period
Internal migration increase Natural increase
Figure 31: Natural and migratory increase of popultaion. Source: Lotfy Kamal Abdou Azaz, 2004, pp. 61–62
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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Upsurge Population growth
Coast line Built environment Lakes
1805 1855 1905 1955 1993
Figure 32: Alexandria - Characteristic stages of growth during the last 150 years. Source: Author based on Lotfy Kamal Abdou Azaz, 2004, p6
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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Earthstar
Source: Esri, Maxar, GeoEye, Earthstar Geographics, CNES/ Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community Maxar Maxar Maxar 0 5 2.5 Kilometers ¯ 0 0.55 0.28 Kilometers 0 0.2 0.4 0.6 0.8 0.1 Kilometers 0 0.2 0.4 0.6 0.8 0.1 Kilometers 0 0.09 0.17 0.26 0.34 Kilometers Alexandria’s Urban Ecologues 1 1 3 4 2 2 3 4 Borg El Arab Industrial District Borg El Arab Industrial District El Dekhila Port Informal Settlement The north coast The North coast summer resorts El Dekhila Port Informal settlements
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Geographics
Alexandria’s road network
Figure 33: Alexandria main street vehicles composition. Source: Author elaboration based on https://bit.ly/3KG1lY8
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Legend Alexandria street
Primar y
Secondar y Others
Trunk The Mediterranean Alexandria_coast_line
Figure 35: Alexandria Promanade
Source: https://bit.ly/3vpIwlo
Figure 34: Abu Quir Street Source: https://bit.ly/37qZdFg
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Facebook’s relative
Alexandria's Density ¹
KILOMETERS
Legend
Alexandria_coast_line Mediterranean Sea Population Density 0 - 9.0137915 9.0137915 - 13.67447 13.67447 - 22.688262 22.688262 - 40.121009
Water scapes
40.121009 - 73.836088 73.836088 - 139 04133 139 04133 - 265.14881 265.14881 - 509 04172 509 04172 - 980.73263 980.73263 - 1,892.9868 NoData
Figure 36: Alexandria's Facebook relative density.
Source: Author based on Facebook data for good and OSM
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
31°14'N 31°12'N 31°6'N 31°N
31°16'N 31°10'N 31°8'N 31°6'N 31°4'N 31°2'N 31°N 30°6'E 30°4'E 30°2'E
29°50'E 29°48'E 29°46'E 29°44'E 29°42'E 29°40'E 29°38'E 30°6'E 30°4'E 30°2'E 30°E 29°58'E 29°56'E 29°54'E 29°52'E 29°50'E 29°48'E 29°46'E 29°44'E 29°42'E 29°40'E 29°38'E 0 2 4 6 8 10 12 1
desnity 31°22'N 31°20'N 31°18'N 31°16'N
31°22'N
30°E 29°58'E 29°56'E 29°54'E 29°52'E
Alexandria's Density ¹
KILOMETERS
Legend
Figure 37: Alexandria's Facebook relative density.
Source: Author based on Facebook data for good and OSM
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
31°16'N 31°14'N 31°12'N 31°6'N 31°N 31°22'N 31°16'N 31°10'N 31°8'N 31°6'N 31°4'N 31°2'N 31°N 30°6'E 30°4'E 30°2'E 30°E
29°56'E 29°54'E 29°52'E 29°50'E 29°48'E 29°46'E 29°44'E 29°42'E 29°40'E 29°38'E 30°6'E 30°4'E 30°2'E 30°E 29°58'E 29°56'E 29°54'E 29°52'E 29°50'E 29°48'E 29°46'E 29°44'E 29°42'E 29°40'E 29°38'E 0 2 4 6 8 10 12 1
Facebook’s relative desnity 31°22'N 31°20'N 31°18'N
29°58'E
Alexandria_coast_line Mediterranean Sea Population Density 0 - 9 0137915 9 0137915 - 13.67447 13.67447 - 22.688262 22.688262 - 40.121009 40.121009 - 73.836088 73.836088 - 139 04133 139 04133 - 265.14881 265.14881 - 509 04172 509 04172 - 980.73263 980.73263 - 1,892.9868 NoData Water scapes
Transport Supply in Alexandria
Figure 38: Alexandria main street vehicles composition. Source: Author elaboration based on https://bit.ly/3KG1lY8
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus m.bus bus https://i1.trekearth.com/photos/5043/corniche.jpg taxi taxi taxi taxi taxi car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car car Private cars Taxi Mini buses Bus
Transportation Market
Ownership
Public Semi-public Private companies
Private ownership Operated by companies. 1 Lines 15 Line 49 Line 76Line
No data available. Data retrieved.
Private Individuals
Service: Primary and secondary Roads. Operation: Fixed schedule.
Service: Operates in primary and secondary roads on demand. Operation: On demand
Service: Operates in primary and secondary roads. Operation: On demand
Service: All roads except primary and secondary roads. Operation: On demand
Reachness
Figure 39: Alexandria mobility market. Source: Author.
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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Kilometers
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Legend
tram station
Figure 41: Tram operates within cars in western Alexandria. Source: Heike
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Alexandria’s tram network 31°20'N 31°19'N 31°18'N 31°17'N 31°16'N 31°15'N 31°12'N 31°11'N 31°10'N 31°9'N 31°8'N 31°7'N 31°6'N 30°2'E 30°1'E 30°E 29°59'E 29°58'E 29°57'E 29°56'E 29°55'E 29°54'E 29°53'E 29°52'E 29°51'E 29°50'E 29°49'E 29°48'E 29°47'E 29°46'E 29°45'E 30°2'E 30°1'E 30°E 29°59'E 29°58'E 29°57'E 29°56'E 29°55'E 29°54'E 29°53'E 29°52'E 29°51'E 29°50'E 29°49'E 29°48'E 29°47'E 29°46'E 29°45'E 0 2 4 6 1
tram lines Alexandria_coast_line The Mediterranean Alexandria street network Alexandria street network Alexandria's tram lines ¹
Figure 40: Alexandria tram network. Source: Author elaboration based on OSM
Alexandria's tram lines permeability
Kilometers
Legend
Alexandria street network
Alexandria coast line The Mediterranean
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
29°48'E
0
1
Alexandria’s tram network 31°20'N 31°19'N 31°18'N 31°17'N 31°16'N 31°15'N 31°12'N 31°11'N 31°10'N 31°9'N 31°8'N 31°7'N 31°6'N 30°2'E 30°1'E 30°E 29°59'E 29°58'E 29°57'E 29°56'E 29°55'E 29°54'E 29°53'E 29°52'E 29°51'E 29°50'E 29°49'E
29°47'E 29°46'E 29°45'E 30°2'E 30°1'E 30°E 29°59'E 29°58'E 29°57'E 29°56'E 29°55'E 29°54'E 29°53'E 29°52'E 29°51'E 29°50'E 29°49'E 29°48'E 29°47'E 29°46'E 29°45'E
2 4 6
low permeability high permeability
¹
Tram permeability Figure 42: Alexandria tram network. Source: Author elaboration based on https://bit.ly/3KG1lY8
Figure 43: Isolated tram in Alexandria. Source: APTA
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trains
31°22'N 31°20'N 31°18'N 31°12'N 31°6'N 31°24'N 31°20'N 31°18'N 31°14'N 31°12'N 31°10'N 31°8'N 31°6'N 30°8'E 30°6'E 30°4'E 30°2'E 30°E 29°58'E 29°56'E 29°54'E 29°52'E 29°50'E 29°48'E 29°46'E 30°8'E 30°6'E 30°4'E 30°2'E 30°E 29°58'E 29°56'E 29°54'E 29°52'E 29°50'E 29°48'E 29°46'E 0 2 4 6 8 10 1 Kilometers
Train stops Alexandria Abu Kir railway coast line the mediterranean sea Alexandria street network Alexandria domestic train ¹
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility. Alexandria’s domestic
31°24'N
Legend
Figure 44: Alexandria domestic train. Source: Author elaboration based on https://bit.ly/3KG1lY8
Figure 45: Train in Alexandria. Source: https://bit.ly/3wAQryH
Figure 46: Alexandria domestic train. Source: Author elaboration based on https://bit.ly/3KG1lY8
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Figure 48: Natural Gas bus in Alexandria
Source: https://bit.ly/3v06QeP
Figure 47: Double deck bus in Alexandria Source: https://bit.ly/3uVTW13
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0
Alexandria’s bus network 31°22'N 31°20'N 31°18'N 31°16'N 31°14'N 31°8'N 31°20'N 31°18'N 31°14'N 31°12'N 31°10'N 31°8'N 31°6'N 30°10'E 30°8'E 30°6'E 30°4'E 30°2'E 30°E 29°58'E 29°56'E 29°54'E 29°52'E 29°50'E 30°10'E 30°8'E 30°6'E 30°4'E 30°2'E 30°E 29°58'E 29°56'E 29°54'E 29°52'E 29°50'E
2 4 6 8 1 Kilometers
Legend Bus routes 2013 behiera_streets Alexandria_coast_line The Mediterranean Alexandria street network APTA bus network
Minibus as a transport supply
Transportation Model Share in Alexandria 1985
Transportation Model Share in Alexandria 1985
Transportation Model Share in Alexandria 2005
Transportation Model Share in Alexandria 2005
Public transport Public transport Private cars Minibuses (8%) Minibuses (25%)
Private cars
Private cars
Private cars
Public transport Public transport Private cars Minibuses (8%) Minibuses (25%)
Taxi Others Taxi
Taxi Others Taxi
Private cars
Figure 49: Transport mode share in Alexandria. Source: Abdel-Monem Hassan, 2008, p. 123
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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Minibus vehicles, stops and fares
Figure 50: Minibus vehicles in Alexandria.
Source: https://bit.ly/369Mvdh
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Figure 51: Minibus vehicles in Alexandria.
Source: https://bit.ly/369Mvdh
Figure 52: A sticker declares the fare in front of a vehicle.
Source: https://bit.ly/3wxCp0D
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Critiques about the service offered by minibuses
Affordability
Fares inflate based on fuel prices.
inadequity
Availability Accessibility
Passengers que for long time to get into a vehicle during rush hours.
Acceptability
People with disabilities can not access the minibus.
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Safety
Seats are too close and not comfortable enough.
Not safe for women, specially in late night, And some drivers do not have a licence.
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Neoliberal competitors
Figure 53: Banner on an Alexandrian taxi "Uber and Kareem, they destroyed our busi ness" during a rally against Uber,Careem on March 30, 2016
Source: Photo: Al-Ahram
Figure 54: SWVL bus.
Source: https://bit.ly/37AFDpT
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Mapping Minibus
Open street map
Public GPS traces Generators Attractors
Street Network
Google Open-building project ESRI street map premiun
Personal
Data base
Volunteer
mini bus routes
GPS Minibus traces
Figure 55: Data sources classification based on type. Source: Author.
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Bus routes Relative movment
Alexandria Passengers transportation Agency
Relative Density
Meta formerly Facebook Data for good portal
Big data Small
data
51
Mapping Minibus
Open street map
Public GPS traces Generators Attractors
Google Open-building project
Street Network
ESRI StreetMap Premium
Personal
Data base
Volunteer
mini bus routes
GPS Minibus traces
Figure 56: Data sources classification based on privacy. Source: Author.
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Bus routes Relative movment
Alexandria Passengers transportation Agency
Relative Density
Meta formerly Facebook Data for good portal Public data Private data
52
Minibus stops
¹
Minibus stops
0 10 20 5
29°50'E
29°52'E
29°54'E
31°18'N 31°12'N 31°10'N 31°8'N 31°6'N
29°56'E
Kilometers
Legend
Alexandria_coast_line Alexandria street network
Alexandria street network
The Mediterranean minibus stops Alexandria_coast_line Orgin Destination
30°4'E
30°6'E
30°8'E
31°22'N 31°20'N 31°18'N 31°16'N 31°12'N 31°10'N 31°6'N
30°2'E 30°E 29°58'E 29°56'E 29°54'E 29°52'E 29°50'E 30°8'E 30°6'E 30°4'E 30°2'E 30°E 29°58'E
Figure 57: Minibuses stops in eastern Alexandria.
Source: Author based on OSM
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
53
Networks incompatability
31°12'5"N
29°54'20"E 29°54'15"E 29°54'10"E 29°54'5"E 29°54'E 29°53'55"E 29°53'50"E 29°53'45"E 29°53'40"E 29°53'35"E 29°53'30"E 29°53'25"E
Network differences
0 1 0.5
Kilometers
31°12'N 31°11'55"N 31°11'50"N 31°11'45"N 31°11'40"N 31°11'35"N 31°11'30"N
Legend
Alexandria_coast_line
Alexandria street network
The Mediterranean minibus stops Alexandria_coast_line ESRI_Mahattetmasr_man Mahattet_masr Manchya
31°12'5"N
29°54'20"E 29°54'15"E 29°54'10"E 29°54'5"E 29°54'E 29°53'55"E 29°53'50"E 29°53'45"E 29°53'40"E 29°53'35"E 29°53'30"E 29°53'25"E
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
31°12'N 31°11'55"N 31°11'50"N 31°11'45"N 31°11'40"N 31°11'35"N 31°11'30"N
Figure 58: ESRI Network and OSM incompatibility.
Source: Author based on ESRI Premium maps, OSM maps
54
¹
Minibus routes
Minibus network
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Kilometers
Legend
Alexandria_coast_line Alexandria street network
Alexandria street network The Mediterranean Minibus routes minibus stops Alexandria_coast_line
Mini-bus hubs KMs of Minibus routes.
Figure 59: Minibus Network in eastern Alexandria.
Source: Author based on OSM
55
31°22'N 31°20'N 31°18'N 31°16'N 31°12'N 31°10'N 31°6'N 31°18'N 31°12'N 31°10'N 31°8'N 31°6'N 30°8'E 30°6'E 30°4'E 30°2'E 30°E 29°58'E 29°56'E 29°54'E 29°52'E 29°50'E 30°8'E 30°6'E 30°4'E 30°2'E 30°E 29°58'E 29°56'E 29°54'E 29°52'E 29°50'E 0 10 20 5
¹
Minibus route 23 934 62
Ground-truthing the data
Figure 60: Shared minibus routes with vounteers with corrections. Source: Author based on Google maps.
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
56
57
GPS traces for minibuses in Alexandria 31°20'N 31°19'N 31°18'N 31°17'N 31°16'N 31°15'N 31°13'N 31°10'N 31°19'N 31°16'N 31°13'N 31°12'N 31°11'N 31°10'N 31°9'N 31°8'N 30°6'E 30°5'E 30°4'E 30°3'E 30°2'E 30°1'E 30°E 29°59'E 29°58'E 29°57'E 29°56'E 29°55'E 29°54'E 29°53'E 29°52'E 30°6'E 30°5'E 30°4'E 30°3'E 30°2'E 30°1'E 30°E 29°59'E 29°58'E 29°57'E 29°56'E 29°55'E 29°54'E 29°53'E 29°52'E 0 2 4 6 1 Kilometers Legend Alexandria_coast_line GPS traces The Mediterranean Alexandria street network background GPS traces for minibuses in Alexandria ¹ Figure 61: Minibus GPS traces. Source: Author based on vounteers data and OSM 11 Volunteers. GPS Point 53 747 246 Minibus stops 62 GPS trajectory
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
31°15'50"N
GPS traces for minibuses in Alexandria
29°59'50"E 29°59'40"E 29°59'30"E 29°59'20"E 29°59'10"E 29°59'E 29°58'50"E 29°58'40"E 29°58'30"E 29°58'20"E
31°15'40"N 31°15'30"N 31°15'20"N 31°15'10"N 31°15'N
Legend
Vactoria
31°15'50"N 31°15'40"N 31°15'30"N 31°15'20"N 31°15'10"N 31°15'N
Figure 62: GPS traces Properties. Source: Author based on voulnteers and OSM
29°59'50"E 29°59'40"E 29°59'30"E 29°59'20"E 29°59'10"E 29°59'E 29°58'50"E 29°58'40"E 29°58'30"E 29°58'20"E
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
58
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0.1
¹
Alexandria_coast_line Alexandria street network Mandarah
GPS stops The Mediterranean Minibus network SpeedKMH 54 0.0 Manddarah Vactoria GPS trace 0
Kilometers
Transportation
Tram Way
Trains
Train Station
Minibus station
Minibus Sea road corridor
Minibus abuquir corridor
Minibus agricultural road
Minibus regional road
Minibus connecting road
The Promanade route: Average speed: 40km/h
The Promanade route: Average speed: 30km/h
International and regional route: Average speed: 70km/h
Minibus route start
Minibus end
Transportation hub
m01-m02-m03-m13-m15-m17 m21-m01r-m28-m02r-m30-m10r m33-m38-m42-m28r-m54-m55
m01-m02-m03-m04-m17m02r-m36-m52-m55
LEGEND
Landuses
The mediterranean Parks Lakes Built Environment
AboQuir Minibus stop
m01-m02-m03-m10-m17-m32 m01r-m28-m02r-m30-m10r-m33 m34-m38-m42-m28r-m54-m59 m26-m44-m45-m51-m57
m07-m08-m09-m11
23
From # To
#
30 M
Assafra Minibus stop
From # # To
07 08 09 Assafra Elmawqaf Eljadid 10 10 Assafra 45 kilo 21 Assafra 45
Manchya 12 12 Assafra 45 Manchya 13 13 Assafra Sidi beshr 13 13
Bahari Minbus stop
42 M
Manchya Minbus stop
#
From # # To From # # To
37 M
Kilo 21 Minbus stop
45 M
Maamoura Minbus stop
From # # To
Mahmett Masr Minbus stop
From # # To
53 M
01r M.masr Abu quir M.masr Assafra 23
01r
24 M.masr Elmawqaf Eljadid 24
25 M.masr Kilo 21 25
26 M.masr Mandrah 26
27 M.masr Manchya 27
28 M.masr Sedi beshr 28
59 M
56 M
30
02r M Manchya Abuquir Manchya Elsa’aa 10r M Manchya Assafra Manchya M.Masr
33 M Manchya Sedi bishr 34 M Manchya Vactoria 27r M Manchya Kilo 21 35 M
02r 10r 33 34 27r 35
Sidi Beshr Minbus stop
Agriculture From # To From # # To
41 43 28r 49
42
41 M Sidi beshr Assafra Quibly Sidi beshr Manchya 43 M Sidi beshr Victoria Sidi beshr M.Masr 28r M Sidi beshr El sa’aa 49 M
Mandrah Minbus stop
36 38 39 40
37
36 M Mandarah Abuquir Mandarah Assafrah 38 M Mandarah Manchya Mandarah Tabyah 39 M Mandarah Victoria 40 M
Sidi Gaber Minbus stop
From # # To From # # To
44 46 47 28r
45
44 M Sidi Gaber Abbis Sidi Gaber Al haddrah 46 M Sidi Gaber Green Plaza Sidi Gaber Elmawqaf Eljadid 47 M Sidi Gaber M.Masr 28r M
Tabyaa Minbus stop
52 54
52 M Tabyaa Mandrah Tabyaa Ma’mmoura 54 M Tabyaa Manchya 53
Victoria Minbus stop
From # # To From # # To
55 M Victoria Abuquir Victoria El sa’aa 57 M Victoria M.Masr 56
55 57
58 M Victoria Manchya Victoria Sidi gaber 39r M Victoria Mandrah 59
58 39r
N
m04-m19 m04-m05-m19-
m12-m12r m12-m12r-m25-m35
Eltabyah ElMa’moura ElMandarah Assafrah Assafrahst45 SidiBeshr Elsaa Awayed Vactoria SidiGaber SidiGaber M.Masr Manchya Airport’s lake Almazah airport ElMahmoudi’aCanal ElMontazah Palace Marriout’s lake M.Eljadid Karmouz GreenPlaza Bahari ToElAmryia,Kilo21 M 01 02 03 04 M 17 M 17 M 30 M 10 M 33 M 38 M 42 M 27 M 02 M 19 M 20 52 M 53 54 M 53 20 M 36 19 05 14 36 M 37 M 40 M 14 40 08 M 11 41 M 43 49 49 56 43 43 55 M 56 M 57 58 M 59 M 42 39 M 11 24 M 26 M 26 M 54 M 26 51 M 57 M 24 47 M 45 09 M 12 M 06 M 07 09 08 M 07 M 44 46 M 44 M 45 M 46 M 51 47 07 05 M 04 M 13 M 35 M 34 03 03 15 15 M 10 12 26 M 52 M 39 37 41 M 02 10 38 13 M 10 M 58 M 59 01 01 02 55 01 M 28 27 M 28 M 21 32 21 30 M 43 28 28 M 33 m24m25 m12-m12r-m44
Abuquir
Alexandria Minibus Transportation map 02 01 Abuquir
03
04 02 01 03 04 06 05
07
06 05
Mahattet_masr Abuquir Manchya
Abuquir Bahari Abuquir Awayed
Assafra Awayed Assafra 45 Karmous
Assafra Amerya Assafra El Sa’aa 08 Assafra Hadarah 09
03r 15 Bahari Assafra Bahari Abu Quir 03r 15
03r
Kilo 21 Manchya Kilo 21 Assafra 45 03r 17
20
19 Maamoura Awayed Maamoura Tabya 20 19
Chapter 5: Analysing Minibus in MATSim model
What paratransit challenges in Global South?
Chapter 1 Introduction: -Unplanned Sprawl -Informal transport: paratransit -Mobility challenges
Chapter 4 data and methodology: - Alexandria unplanned sprawl - Alexandria mobility market
- Minibus as transport supply
- Available data for mapping and analysing minibus supply
Could data map the informal mobility in Alexandria ?
How data could be contained?
Chapter 2 literature review: -Urban Mobility data -Urban Mobility models
Chapter 3 Good practice: -Matatu, Nairobi, Kenya. Chapas, Maputo, Mozambique
What could be learn from good practices?
Chapter 5 mapping and analyzing: - MATSim data framework
- Executing the model - Scoring - Replanning
What is recommended to shift a traditional mobility Paradigm into a smart one? Kenya.
How could MATSim model address minibus inadequacy?
Chapter 6 conclusions: - Conclusions - Recommendations
Figure 63: Alexandria’s MATSim model. Source: AuthorFigure 64: MATSim model. Soruce: Horni et al., 2016
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
61
initial demand mobsim scoring analysis re-planning
MATSim
62
Route profile Departure times Vehicle type Vehicle length Standing number Seats number Change transportation mode Change routes Change departure time Scoring Constant money per transportation factor Constant money per transportation factor distance spend on vehicle time spend on vehicle Transport type waiting score performance score late arrival score early departure score Strategy Transport scoring Activity scoring Vehicle type Transit Vehicles Re-planning Initial Demand Network Transit schedule Street intersection Street geometry Streets geometry Speed limits Street lanes Street intersections Network type (Walking, cars, public transport) Street capacity Activity location Activity type Transportation Type Activity time Model input Mobility Policy input
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
model inputs flow
65: Alexandria’s MATSim model inputs flow.
Figure
Source: Author
Initial demand
-Origin - destination of movement -Mobility Network -Transport Schedule
initial demand mobsim scoring analysis re-planning
Figure 66: MATSim model. Soruce: Horni et al., 2016
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
63
Figure 67: Movement between tiles (Origin-Destination).
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
31°13'30"N 31°13'N 31°12'30"N 31°12'N 31°11'30"N 31°11'N 31°10'30"N
Disaggregating Facebook movement between tiles map
0 2 1 Kilometers
Legend
The Mediterranean Starting_trips Ending_trips end_quad_ke start_quad fb_tiles
¹ Figure 68: The aggregated syntactic population. Source: researcher based on PostGIS and OSM 2.5km 2.5km
E Ib ah m a S a on ﺔ ﻤ ﻫ ر ﻰ ﻃ ﺸ ك ﻤﺠ ﻢﺴ
Abu E Abbas ﻪﻛﺪ مﻮﻛ قرﺷ ﻲ ﻏرﻤ ﻳ ﻮﺼ
ﺶﻴﺠﻟاﻖﻳرﻃ
ﻟ ا ع ﺎر ﺪﻴﻌﺳرﻮ عرﺎﺷ ﺐﺟرﻰﻛ عرﺎﺷ
ﺶﺠﻟاﻖﻳرﻃ ﻦﻛ ﺴﻣ ط ﻀﻟ ﻦﻛﺎﺴﻣ ت و ﻌ ﻪﺣﻮﻤﺳ
مﻮﻛ ﻰ رﻤﺸﻟا ﺔﺷﻮﺷ ﻮ ﻞ ﻣ ﻤﻬ ﺔ ﻴﻨﺠ ةر ﻜ ﺰ ﻌﻤ قﻮﺳو برﻏ ﻦ ر ﻄﻌ ﻦ ر ﻄﻌ رﺻﺎﻨﻟاﺪﺒعلﺎﻤﺟعرﺎﺷ ذﺎﻌﻣﻳﺰﻮﻓﺪﻤﺤﻣءاﻮﻠﻟاعرﺎﺷ
ﻦﺑاعرﺎﺷ ﻞﻴﻨﻟ ا ع رﺎ ﺷ ةﺪﻳ ﺪﺠ ﻟ ا ﺔ ﻜﺴ ﻟ ا عر ﺎﺷ ر نﻮﻣاﺦﻨع تﻮﺗعرﺎﺷ
عر ﺎﺷ ﻞ ﻳ ﻮﻧﺎ ﻤﻳا رﻮﺘﻜﻓ ع رﺎ ﺷ ﻮﻻاترﺒﻟاعرﺎﺷ
و برﻏ ﻪ ﺴﻤ ﺷﺎ ﻒ رﺷ برﻏ ﻪﻛﺪ مﻮﻛ ﺪ ﺪﺠ ب ﺸ ﻣ و برﻏ ر ﻮﻧ ﺷ ﺐﻏ ر ةﺪﻫ رﻔ ةر ﺣ دﻮﻣ ﻌ ﻮ ﺎﻣ 14عرﺎﺷ
ﺔﻫﺰ ﻟا ﻖ ﺪﺣ هرﻀﺤ ﻰﻠ ﻗ ﻊﻣﺎﺠﻟ ﺔ ﺰع ﺔﻫﺰ ﻄﻣ ﺪ ﺼﻟ ﻳ ﺪ ﺔ ﺰع Map data © OpenStreetMap contributors, Microsoft, Esri Community Maps contributors, Map layer by Esri
29°57'E 29°56'30"E 29°56'E 29°55'30"E 29°55'E 29°54'30"E 29°54'E 29°53'30"E 29°53'E 29°57'E 29°56'30"E 29°56'E 29°55'30"E 29°55'E 29°54'30"E 29°54'E 29°53'30"E 29°53'E
ﺔﺣﻮﻤﺳ ﺪ ﻲﺿ ﻳرﻟا ﺪ ﻧ ﺞ رﻮ ﺳ ﺔ رﺪ ﻜﺳﻻا ىر ﻟﺤﺒ ا ﺔﻳ دﻮ ﻤ ﺤﻤ ﻟ الﺎﻨﻗعرﺎﺷ يرﺋاﺪﻟاﻖرﻄﻟا
21 27
31°13'30"N 31°13'N 31°12'30"N 31°12'N 31°11'30"N 31°11'N 31°10'30"N
64
Initial demand
ﻦ د ﺼ ﻪﻛر E An oush نﺎﻣﻻاعرﺎﺷ ش ﺎﻄﻴ ﻟا عر ﺎﺷ ﻦ ﺳ أر عرﺎﺷ ﺲﻜﻤﻟاعرﺎﺷ رﻔﺻ ةروﺪﻣ ﻳ ر ﻘ ﺪﻴﻌﺳرﻮﺑعرﺎﺷ A Sha by Park ﻪ ﻤ ﻫ ر ﻻ ىرﺤﺑ ﺔع رﺰ ﺔﻴ ﻛ ﺔع ر ﻟ ﺔ ﻛ ﺊﻃ ﺷ ﻲ ﻃﺎﺸ E Shah d Mus a a Zayan S a on E Aza a S at on E Shoban E Mus men S a on E Gamaa E Sha by S
نﻮﻟﻮﻃ
ﻀﺧﻻاب ﺎ
ﺷ لﻮﻠﻏزﻪﻔﺻع رﺎﺷ ﻚ مرﺤﻣ عر ﺷ ز ﻮ رﻛ ع رﺎ ﺷ دﺎﻗﻮﻟاﻞﺒﻧ عرﺎﺷ ﻪﻳرﺤﻟاﻖرﻃ ﻪ ﻇﺎﺑ انﺎﻤﺜع
ﻊرﺴﻟا رﺎﺒﻘﻟاﻖرﻃ ﺲﻳﻮﺴﻟاهﺎﻨﻗﻖﻳرﻃ 10 ﻳ رﺎﻘﻟاىرﺑﻮﻛ ﻚ ﺑ مرﺤﻣ ىرﺑﻮﻛ تﻻﻼﺸ طﻮ رﻣ ةر ﺤ قرﺷ ﻪ ﺴﻤ ﺪﻳﺪﺠﻟا ب ﻰﻗرﺷ ﻪﺻرﻮ و ﻫر ﻜ و ﻪ ﺠ ﻮﻄﻟا ﺲﻃ ﻐ رﻔﻛ و ﻮ ﻮﺑ ﻲ رﺪ ﻜﺳﻻ ﺔ ر ﻐﻤﻟ كر قﻮﺳو ﺔ ﺸ ﻤﻟا ير ﻜ
Source: Author elaboration based on Facebook data for good ﻪﻓﺎﻘﺸ
at on Kahwet Farouk قرﺷ
Networking
OSM Network
0 2 1 Kilometers
Legend
OSM Network
Alexandria_coast_line
The Mediterranean
MATSim Network
Legend
Matsim network
Alexandria_coast_line
The Mediterranean
Figure 69: Transforming OSM network to MATSim. Source: Author based on OSM,MATSim & JOSM
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
65
¹
Networking Matsim network
MATSim Network
0 2 1 Kilometers
Legend
OSM Network
OSM Network ¹
Alexandria_coast_line
The Mediterranean
Legend
Matsim network Alexandria_coast_line
The Mediterranean
Figure 70: Transforming OSM network to MATSim. Source: Author based on OSM,MATSim & JOSM
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Alexandria_coast_line The Mediterranean
66
Networking
0 2 1 Kilometers
MATSim Network
Legend
OSM Network Alexandria_coast_line The Mediterranean
Figure 71: OSM to MATSim conversion settings. Source: Author based on OSM,MATSim and JOSM
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Legend
OSM Street network Metadata MATSim Street network Metadata
Matsim network Alexandria_coast_line The Mediterranean
67
OSM Network ¹
Figure 73: Major Minibus stops in Eastern Alexandria. Source:Authors elaboration based on OSM
68
31°10'N 31°9'N 31°20'N 31°19'N 31°18'N 31°17'N 31°16'N 31°15'N 31°14'N 31°13'N 31°12'N 31°11'N 31°10'N 31°9'N 30°5'E 30°4'E 30°3'E 30°2'E 30°1'E 30°E 29°59'E 29°58'E 29°57'E 29°56'E 29°55'E 29°54'E 29°53'E 29°52'E 29°51'E 30°5'E 30°4'E 30°3'E 30°2'E 30°1'E 30°E 29°59'E 29°58'E 29°57'E 29°56'E 29°55'E 29°54'E 29°53'E 29°52'E 29°51'E 0 2 4 6 1 Kilometers Legend Alexandria_coast_line car pt MATSim simulation network ¹ Mediterranean sea
31°19'N 31°18'N 31°17'N 31°16'N 31°15'N 31°14'N 31°13'N 31°12'N 31°11'N 31°10'N 31°20'N 31°19'N 31°18'N 31°17'N 31°16'N 31°15'N 31°14'N 31°13'N 31°12'N 31°11'N 31°10'N 30°5'E 30°4'E 30°3'E 30°2'E 30°1'E 30°E 29°59'E 29°58'E 29°57'E 29°56'E 29°55'E 29°54'E 29°53'E 30°5'E 30°4'E 30°3'E 30°2'E 30°1'E 30°E 29°59'E 29°58'E 29°57'E 29°56'E 29°55'E 29°54'E 29°53'E 0 2 4 6 1 Kilometers Legend Alexandria s ree ne work minibus s ops Alexandria_coast_line Mininbus hub connectivity Mediterranean sea Minibus rou es OD Minibus major stations in eastern
¹
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility. Transit schedule 31°20'N 31°19'N 31°18'N 31°17'N 31°16'N 31°15'N 31°14'N 31°13'N 31°12'N 31°11'N
31°20'N
Alexandria
Figure 72: MATSim Minibus segmentation network. Source: Author based on OSM and MATSim plugin for JOSM
Mobsim (mobility Simulation)
Figure 74: MATSim model. Soruce: Horni et al., 2016
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Overall Mobility Simulation Stuck Mobility
Demand on minibus route Activities related to minibus route
initial demand mobsim scoring analysis re-planning
69
Mobility Simulation
Figure 75: Traffic simulation in MATSim.
Source: Author elaboration based on MATSim
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
70
Stuck agents
Figure 76: Most Frequent minibus stops.
Source: Author based on MATSim Simunto Via
Figure 77: Stuck agents statistics. Source: MATSim
71
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Minibuse routes
Most stucked agents at Minibus stops
Passengers demand on minibus routes
Transit Route r09 19 departures between 00:00:00 and 24:00:00
Figure 78: Passengers entering and leaving along Assafrah Haddarah route
Source: Author based on MATSim and Simunto Via
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
31°17'N 31°16'N 31°15'N 31°14'N 31°13'N 31°12'N
0 5 2.5
Legend Alexandria street network
The Mediterranean Minibus routes Assafrah Haddarah route number of passengers 0 1 2 3 4 5 6 7
Proposed stops Minibus routes
30°E 29°59'E 29°58'E 29°57'E 29°56'E 30°E 29°59'E 29°58'E 29°57'E 29°56'E
Kilometers
Passengers drop in and off along route Assafrah - Haddarah ¹
31°17'N 31°16'N 31°15'N 31°14'N 31°13'N 31°12'N
72
Stops -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0 1 2 3 4 5 6 7
Activites on single Minibus route
73
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Minibus route Routes agents
Scoring
Scoring activity behaviour
Scoring travel behaviour
initial demand mobsim scoring analysis re-planning
Figure 79: MATSim model. Soruce: Horni et al., 2016
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
74
Scoring
Evaluating one person’s overall movement based on a desired urban policy
(Encourging public transport over cars... )
Mobility Score = Activity Score + Travelling Score
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
75
Scoring
Evaluating
(Encourging public transport over cars... )
Mobility Score = Activity Score + Travelling Score
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
leaving home at 7:30 am (+)
being at work at 8:30 am (+) having lunch break at 3 pm (-)
76
one person’s overall movement based on a desired urban policy
Scoring
Evaluating one person’s overall movement based on a desired urban policy (Encourging public transport over cars... )
Mobility Score = Activity Score + Travelling Score
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Walking to bus stop (-)
Taking tram to work (-) Go to mall by car (-)
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Scoring
@home car walk
Activity + TravelActivity + TravelActivity + 0
time @lunch @workplace workplace opening time
Figure 80: MATSim scoring criteria.
Source:Axhausen, 2016, p. 6
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score accum.score
Adapting minibus policy measures
MATSim Scoring Values
Score
10 5 0 -5 -10 -15 -20
6 -0.9 - 6
-18
-0.5
Figure 81: MATSim Policy measures Source: Author.
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Late Arrival On time trips Waiting for minibua Travel constant Travel per Hour Travel per KM Travel constant Travel per Hour Travel per KM Travel constant Travel per Hour Travel per KM
Activity Car Minibus Walking
-0.01 -0.456 -1 -.0001 --0.5 --0.5 -0.001 Positive score Negative score
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Replanning
initial demand mobsim scoring analysis re-planning
Figure 82: MATSim model. Soruce: Horni et al., 2016
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Replanning
is a MATSim component that trigger a mutational change in travelling behaviour to reach an optimim score.
Score Reroute
Change transportation mode Change time allocation
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Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Replanning
Figure 83: MATSim Strategy componenet. Source: Author.
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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Scoring Value 70% 20% 5% 5% Change mode Rerouting Change Time Allocation Re-planning strategy components
Change Trasnportation mode
Time profile mobility profiles Scores statistics
initial demand mobsim scoring analysis re-planning
Passengers drop in and off per route
Figure 84: MATSim model. Soruce: Horni et al., 2016
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Analysis
Figure 85: Mode Statisistcs.
Source: Author elaboration based on MATSim
Transportation mode statistics
Minibus Cars Number of iterations
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
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12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57
63 66 69 72 75 78 81 84 87 90 93
Analysis 0 20 40 60 80 100 0 3 6 9
60
96 100
Model Selection Percentage Testing improvments trends Mobility policy effectiveness: increase of public transport share Mobility policy ineffectivness: 60% of passengers are using minibuses, 40 % of passengers are using cars
Equilibrium between minibuses and cars
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Analysis 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 101 Car Travel time
travel time
35000
25000 20000 15000 10000 5000 0
Minibus
Minibus waiting time Walking travel time Passenger Hours travelled per mode Hours
30000
Iteration Testing improvments trends Mobility policy effectiveness: decrease in car travel time Mobility policy ineffectivness Minibus waiting time is relatively long (1/3) comparing to the minibus travel houes Figure 86: Passenger hours travel per mode. Source: Author elaboration based on MATSim
100
0 3 6 9 12 15 18
24
30
36 39 42
48
54
60
66 69
78 81 84
86
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility. 21
-300 27
-250 33
-200 45
-150 51
-100 57
-50 63
0 72 75
50 87
90 93 96 99 100
Average Score
Score Iteration
Score Statistics
Fluctuating results Score improves (effective policy) Mobility policy ineffectivness Simulation ends with possitve score Score indiactes postive values
Figure 87: MATSim Simulation Score Statistics.
Source: Author elaboration based on MATSim
Analysis
31°16'N 31°15'30"N 31°15'N 31°14'30"N 31°14'N 31°13'30"N 31°13'N 31°12'30"N 31°12'N
30°2'E 30°1'30"E 30°1'E 30°0'30"E 30°E 29°59'30"E 29°59'E 29°58'30"E 29°58'E 29°57'30"E 29°57'E 29°56'30"E 29°56'E 29°55'30"E 29°55'E
¹
Passengers
drop in and off along route Assafrah - Haddarah
Kilometers
0 5 2.5
Legend Alexandria street network
The Mediterranean
Minibus routes
Assafrah Haddarah route The change of number of passengers comparing to the business-as-usual model 3 Passengers less 2 Passengers less 1 Passenger less No change 1 Passenger more 2 Passengers more 3 Passengers more 4 Passengers more Minibus routes
30°2'E 30°1'30"E 30°1'E 30°0'30"E 30°E 29°59'30"E 29°59'E 29°58'30"E 29°58'E 29°57'30"E 29°57'E 29°56'30"E 29°56'E 29°55'30"E 29°55'E
Figure 88: The change of bording behviour on route Assafrah - Haddarah after the iterations.
Source: Author elaboration based on MATSim and OSM
Mohamed El Gohary l M. Sc. Thesis: Mutations of self-organized mobility.
Number of passengers
12
10
8
6
4
2
31°16'N 31°15'30"N 31°15'N 31°14'30"N 31°14'N 31°13'30"N 31°13'N 31°12'30"N 31°12'N
14 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88
0
The change of passengers numbers after and before Replanning Stops along the route
Business as usual passenger number along the minibus route
Number of passengers after applying replanning component
Figure 89: The change of bording behviour on route Assafrah - Haddarah before and after the iterations.
Source: Author elaboration based on MATSim and OSM
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Analysis
Chapter 6: Conclusions
What paratransit challenges in Global South?
Chapter 1 Introduction:
-Unplanned Sprawl
-Informal transport: paratransit -Mobility challenges
Chapter 4 data and methodology:
- Alexandria unplanned sprawl
- Alexandria mobility market
- Minibus as transport supply
- Available data for mapping and analysing minibus supply
Could data map the informal mobility in Alexandria ?
How data could be contained?
Chapter 2 literature review: -Urban Mobility data -Urban Mobility models
Chapter 3 Good practice: -Matatu, Nairobi, Kenya. Chapas, Maputo, Mozambique
What could be learn from good practices?
Chapter 5 mapping and analyzing:
- MATSim data framework
- Executing the model - Scoring - Replanning
What is recommended to shift a traditional mobility Paradigm into a smart one? Kenya.
How could MATSim model address minibus inadequacy?
Chapter 6 conclusions: - Conclusions - Recommendations
Figure 90: Traffic Jam in promandate avune. Source: ESMEE https://bit.ly/36KJfpb.
Conclusion
The rule of data in mapping minibuses
Alexandria:
The Rule of MATSim Model
• Tracking the minibuses via primitive GPS devices helped understate the primitive properties of minibus in the early stage of the research.
• Big data sources helped create a synthetic demand for mobility on the city scale, which were used to measure minibus performance.
• It is crucial to rely on different data sources (Big-Small) to verify the collected data and gap the incompatibility between (private and public) data sources.
• Mobility data is more powerful when it standardized and open to re-use. In addition, it gives the data reliability and validity of different adaptations.
• Digitizing Alexandria minibuses were only helpful in creating the schematic map and a structured data that enables testing the urban policy in the MATSim model.
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in
Planning a paratransit system is difficult due to its complex nature and data scarcity.
Conclusion
Planning a paratransit system is difficult due to its complex nature and data scarcity.
The rule of data in mapping minibuses in Alexandria:
MATSim Model helped construct different mobility scenarios for urban mobility due to
Detailed visuals >
Sophisticated modular >
Accurate measures >
The Rule of MATSim Model:
Comprehensive analysis >
MATSim simulates the business-as-usual movement patterns and spots overall mobility performance (stuck areas, overall traffic on streets, average actual speed…)
In Alexandria, the model was asked to measure the impact of making public transport five times cheaper than cars.
The model stated a 6.25% increase in the total number of passengers who use minibuses due to the cheaper fares.
Despite the rise of total minibus passengers, the model shows a long queuing that could be (1/3) of total trip time.
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Recommendations
1- Alexandria Minibuses:
• Government should encourage minibus drivers to form an organizational structure to facilitate communication between officials, drivers, and passengers.
• In append, Government should develop a strategy to constantly collects mobility data that supplement urban mobility policy.
2- Collecting GPS data via volunteers:
• The objective of tracking minibus should be clear to volunteers, as they avoid tracking mistakes and give recommendations on the methodology.
• Data should be public; many resources were consumed to regenerate data that was kept private.
3- MATSim model:
•Facebook population census is bais, as the collected data represents only 52% of all Alexandrian populations actively using the Facebook app.
•It is essential to establish a GTFS feed for all mobility data on navigation maps and different applications.
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