Accessibility analysis of public transport networks in urban areas

ACCESSIBILITY
ANALYSIS OF PUBLIC
TRANSPORT NETWORKS
IN URBAN AREAS

ORDER OF PRESENTATION
 Introduction
 Objective of the Study
 Literature Review
 Impacts of Accessibility
 Case Study
 Conclusion and Future Scope
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 Public transport networks are the combination of several links and nodes connecting
various zones of an urban area.
INTRODUCTION
Accessibility of public transport system is the ease to
access a location using public transport mode.
 Last mile problem or accessibility problem has large
impact on choice of commuters to use a transit service.
 Accessibility being one of the considerable out comes of
public transport system, it’s improvement will result in
overall improvement in quality of public transport service.
Source: www.mapumental.com
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INTRODUCTION CONTD.
Destination
/Origin
Walk to
transit
stop
Wait for
transit
Board
transit
Travel
in
transit
Alight
Transit
Walk to
destinat
-ion
Accessibility journey
chain
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 To study the various methodologies used for the analysis of public transport
accessibility.
 Applicability of proposed methodologies in Indian context.
 To study the impacts of public transport accessibility on various parts of society.
 To identify future research scope based on the pros and cons of current
methodologies.
OBJECTIVE OF THE STUDY
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To determine accessibility transit level-of-service (TLOS) indicator which provides
accessibility measure is mainly used (Jay & Bhargav, 2016).
Likelihood of using public transport highly depends on distance of both origin and
destination from transit station (Keijer & Riefveld, 2000).
 Accessibility exponentially drops with increase in walking distance to transit stops
(Zhao, et al., 2015).
Accessibility to public transport is also affected by urban design (Bok & Kwon, 2016)
 GIS based software can be used for determining the distance between public transport
services and trip origins and destinations.
LITERATURE REVIEW
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LITERATURE REVIEW CONTD.
Methodology
To rate accessibility, UK approach which measures public transport accessibility levels
(PTAL) along with recent approaches is generally used.
 This approach uses a 6 level scale consisting of measurements such as waiting time, walk
time, service frequency.
Closest
Public
Transport
Stop
Walk Times
Average
Waiting
Time
POI
Total
Access
Time
Equivalent
Doorstep
Frequency
Weighted
Equivalent
Frequency
(WEF)
Accessibility
index
(PTAI)
Source: (Saghapour, et al., 2016)
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 POI is point of interest, which is the point for which PTAI is to be calculated.
 Walk Time (WT) is the time taken by pedestrian to walk from origin to transit stop.
 Average waiting time (AWT) is the time period from a commuter arrives at transit stop to
the arrival of desired service.
𝐴𝑊𝑇 = 0.5
60
𝑓
+ 𝑘
 Total Access Time, 𝑇𝐴𝑇 = 𝑊𝑇 + 𝐴𝑊𝑇
 Equivalent Doorstep frequency, 𝐸𝐷𝐹 = 30 𝑇 𝐴𝑇
 Weighted Equivalent Frequency, 𝑊𝐸𝐹 = 𝐸𝐷𝐹𝑚𝑎𝑥 + 0.5 𝑎𝑙𝑙 𝑜𝑡ℎ𝑒𝑟 𝑚𝑜𝑑𝑒𝑠 𝐸𝐷𝐹
 Public Transport Accessibility Index, 𝑃𝑇𝐴𝐼 = 𝑊𝐸𝐹
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 Public transport Accessibility levels discussed in UK approach are :
Source: Transport for London (2010), Table 3, p. 6
PTAL Range of AI Map Colour Description
1a (Low) 0.01 – 2.50 Very poor
1b 2.51 – 5.00 Very poor
2 5.01 – 10.00 Poor
3 10.01 – 15.00 Moderate
4 15.01 – 20.00 Good
5 20.01 – 25.00 Very good
6a 25.01 – 40.00 Excellent
6b (High) 40.01 + Excellent
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Accessibility
Employment Mobility
Social
Exclusion
Sustainability
IMPACTS OF ACCESSIBILITY
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The research paper “Jay, S. & Bhargav, A., 2016. Public Transport Accessibility Levels for
Ahmedabad, India. Journal of Public Transportation, 19(3), pp. 19-35” has been taken up for
the case study.
 Introduction
This study targets to measure public transport accessibility of several zones in the city of
Ahmedabad. It is based on PTAL calculation methodology (UK approach) after making some
essential alterations to fit for Ahmedabad data.
 Objective
Major objective is to measure PTAL, map it and discuss its importance and application in
transport planning process.
CASE STUDY
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 Data Collection
Ahmedabad Municipal Corporation Boundary is selected for the study. The data used in the study is listed as
follows:
CASE STUDY CONTD.
Sr. No. Data Type Source
1. Ahmedabad city base map (satellite image) Google Earth
2.
AMC boundary limit (GIS shape file) Prepared by authors from information
available on AMC website
3. AMTS bus stop locations (GIS shape file) CEPT University study
4. AMTS bus routes (GIS shape file)
Prepared by authors from route information
on AMTS website
5. AMTS peak hour bus frequency
Available from this report (AUDA,
Government of Gujarat 2011)
6. BRTS bus stops locations (GIS shape file) Prepared by authors from Google Earth
7. BRTS bus routes (GIS shape file)
Prepared by authors from the route
information on BRTS website
8. BRTS peak hour bus frequency
Available from this report (AUDA,
Government of Gujarat 2011)
9. MEGA metro station locations (GIS shape file) MEGA office (available in .kml file format)
10. MEGA metro routes (GIS shape file)
Prepared by authors from route information
on MEGA website
11. MEGA peak hour metro frequency MEGA office
Source: (Jay & Bhargav, 2016)16-02-2019 12

 Alterations in UK approach
• London PTAL assumptions regarding
peak hour factors, reliability and
walking speed are reconsidered.
• Study area is divided into 1 km2 grid
cells, which resulted into 675 grid
cells in total
CASE STUDY CONTD.
Parameters Units London Values Ahmedabad Values
Peak hour - 08:15-09:15 AM 09:30-10:30 AM
Walk speed Km/hr 4.8 3.6
Walk speed m/min 80 60
Bus - London Bus
AMTS
Bus
BRTS
Bus
Reliability (K) min 2 2.5 1
Max. walk time min 8
Not applicable
(calculated
Max. walk
distance
M 640
for each POI using
actual road network)
Rail -
Underground,
TRAM, DLR,
Overhead rail
MEGA metro rail
Reliability (K) min 0.75 0.75
Max. walk time min 12
Not applicable
(calculated
Max. walk
distance
m 960
for each POI using
actual road network)
Source: (Jay & Bhargav, 2016)
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13

 PTAI Calculations
CASE STUDY CONTD.
POI ID Mode SAP Name Route no.
Distance
(m)
Frequency
(per hr)
Weight
Walk
Time
(min.)
SWT
(min)
TAT
(min)
EDF AI
307
AMTS
Vijay Cross
Roads
40/3 338 1 0.5 5.63 32.5 38.13 0.78 0.39
nth route
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
Memnagar
Tube well Stop
200 427 5 1 7.11 8.5 15.61 1.92 1.92
nth route
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
nth SAP
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
BRTS
Vaalinath
Chouk
1 824 24 1 13.73 2.25 15.98 1.87 1.87
MEGA
Vijay Cross
Roads
1 300 20 1 5 2.25 7.25 4.13 4.13
Total (AI for a POI 307) 56.41
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 Final PTAL map
CASE STUDY CONTD.
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 Results
• Accessibility to public transport services is best in city centre area.
• Few scattered areas with excellent PTAL are also observed.
• Areas in outskirts of city are having poor PTALs, these are basically dominated by higher
income households.
 Recommendations
• Public transport services must propose high accessibility to all sections of society.
• PTAL maps can be used by planners for integrating accessibility with land use zoning.
• Govt. agencies can use PTAL maps to formulate housing policies for economically weaker
sections.
CASE STUDY CONTD.
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 Public transport accessibility drastically decreases with increase in walking distance.
 PTAL maps are easy and efficient representation of public transport accessibility.
 Improving public transport accessibility is necessary for achieving sustainable transport
system.
 Existing public transport systems can be improved using PTAL maps.
 Accessibility has considerable impacts on various sections of the society.
 Variation in transit services schedule is not incorporated in PTAL approach , studies in
future may consider this valid point while determining accessibility.
 In Indian conditions more studies on public transport accessibility are required.
CONCLUSION AND FUTURE SCOPE
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Bok, J. & Kwon, Y., 2016. Comparable measures of accessibility to public transport using
the general transit feed specification. Sustainability (Switzerland), 8(3), pp. 250-256
Jay, S. & Bhargav, A., 2016. Public Transport Accessibility Levels for Ahmedabad, India.
Journal of Public Transportation, 19(3), pp. 19-35.
Rastogi, R. & Krishna Rao, K. V., 2003. Defining transit accessibility with environmental
inputs. Transportation Research Part D: Transport and Environment, 8(5), pp. 383-396.
Saghapour, T., Moridpour, S. & Thompson, R. . G., 2016. Public transport accessibility in
metropolitan areas: A new approach incorporating population density. Journal of Transport
Geography, 54(4), pp. 273-285
Transport for London. 2010. “Measuring Public Transport Accessibility Level PTALS
Summary.” http://data.london.gov.uk/documents/PTAL-methodology.pdf, accessed
February 19, 2014.
Zhao, Y., Lu, J. & Qiu, H., 2015. Applicability of Multi-modal Public Transport System
Based on Accessibility Analysis. International Journal of Computer and Communication
Engineering, 4(3), pp. 211-218.
REFERENCES
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Accessibility analysis of public transport networks in urban areas

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