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IMPOVERISHED DATA: EXPERIENCES AND LESSONS IN COLLECTING CAPE TOWN DATA FOR THE MILLENNIUM CITIES

IMPOVERISHED DATA: EXPERIENCES AND LESSONS IN COLLECTING CAPE TOWN DATA FOR THE MILLENNIUM CITIES

DATABASE
LISA KANE, VUSIMUZI BALENI1

and SEAN COOKE2
Centre for Transport Studies, University of Cape Town, Rondebosch 7708

Tel: 021 6715404; Email: lisakane@iafrica.com

1 Bigen Africa Services (Pty) Ltd, PO Box 29, The Innovation Hub, Pretoria, 0087

Tel: 012 842 8864, Email: vusi.baleni@bigenafrica.com

2 Centre for Transport Studies, University of Cape Town, Rondebosch 7708

Tel: 021 6502584; Email: ckxsea001@myuct.ac.za

ABSTRACT

The Millennium Cities Database for Sustainable Transport is a substantial database,
funded by the International Association of Public Transport, with over 200 indicators of
transport, demographic, economics and land-use data from about one hundred cities. This
dataset allows cities to benchmark against “best practice” or similarly positioned cities and it is a
valuable aid for better understanding of the status quo, and of the likely trajectories, of cities. In
1995/6 Cape Town and Johannesburg were included in the dataset, but until now the collection
of more recent South African data has not been possible. In 2012 a collaboration between three
University of Cape Town (UCT) final year undergraduate civil engineering students, staff at the
Centre for Transport Studies, UCT, and transport representatives from the City of Cape Town
was formed in an attempt to update the Cape Town data from 1995/6 to 2005/6 and 2010. This
paper describes the data collected, its quality, and the data gaps which were found.
Methodological lessons on this type of data collection are described. The paper ends with some
discussion on metropolitan transport data availability and quality, and the implications of this for
policy and decision making at a metropolitan level.
1 INTRODUCTION
The Millennium Cities Database (MCD) for Sustainable Transport is a substantial
database, funded by the International Association of Public Transport (UITP) in partnership with
Murdoch University (Australia) with over 200 data indicators from about one hundred cities. It
has been collated to allow mobility providers to evaluate the performance of their respective
cities, and includes data for transport, demographics, economics and land-use (Baleni, 2012).
The idea for the MCD began during Newman and Kenworthy’s well-cited study of
automobile dependence in major Australian cities (Newman, 1989). Newman and Kenworthy
found variables to describe automobile use which seemed more significant than the common,
widely accepted variables at that time of income, city size and fuel price. A decision was made
to broaden the perspective and to collect data for cities throughout the world to assess if these
automobile use relationships were specific to Australia. The data and related analyses derived
from it were compiled into a 1989 book: “Cities and Automobile Dependence: an International
Sourcebook”. (Cooke, 2012)

This sourcebook contained data from 1960, 1970 and 1980, although of varying reliability
and with some large gaps in the information for some cities. An upgrade of this initial database
became the Millennium Cities Database with a reference time period of 1995 to 1996
(Kenworthy & Laube, 2001) . The full list of the standardised indicators for the MCD is given in
Appendix A. In choosing cities to include, the authors attempted fair representation of every
continent, income bracket and city size (Cameron, 2004) and the MCD includes data from 35
Western European, 6 Eastern European, 15 North American, 10 Latin American, 8 African, 3
Middle Eastern, 18 Asian and 5 Australasian cities. The database is once again being updated,
this time to include 2005/6 data (Kenworthy and Laube, 2001; Vivier, 2001).
This paper is in two parts. The first part briefly outlines some work done by
undergraduate students at the University of Cape Town using the MCD to examine
characteristics of Cape Town with respect to other international cities of similar GDP. The
argument is made that even a preliminary international analysis such as this is valuable in
extending understanding of Cape Town’s transport. The main part of the paper describes the
outcome of attempts to update the existing 1995/6 data for Cape Town to 2005 (and 2010), and
the challenges encountered. The paper ends with some discussion on metropolitan transport
data availability and quality, and the implications of this for policy and decision making at a
metropolitan level.
2 USES OF THE MILLENIUM CITIES DATABASE
Using 1995/6 data from the MCD students explored three relationships of interest. The
first explored international relationships between urban density and modal split, and considered
the significance of this relationship to the city of Cape Town. A second student focused on the
degree to which the household travel patterns of Cape Town were distorted by apartheid spatial
planning, through an international comparative analysis. Although the analyses were relatively
contained, some interesting features emerged from the two studies, highlighting future possible
avenues for research.

An international comparative analysis of urban density and mode split revealed that there
is a strong correlation between urban density and modal split in developed cities. As the urban
density increases, the motorised transport mode preference shifts from private vehicles to
public transport and a tipping point seems to be at 160 persons per hectare. Non-motorised
transport also increases with an increasing urban density but the correlation between these two

R2 = 0.6739

R2 = 0.4677

R2 = 0.7337

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%

0 50 100 150 200 250 300 350

Modal
split
(%)

Urban density (persons/ha)

Private vehicles
Non-motorised
transport
Public
transport

Figure 1: The different modal splits vs urban density for developed cities

factors is not as strong. In developing cities, the correlation between urban density and modal
split was weaker and instead, economic indicators appeared to be the dominant factor affecting
travel behaviour. The Cape Town context displayed how urbanisation since 1995 has increased
its average urban density and led to a higher proportion of trips being undertaken on public
transport. This follows the trend seen in Figure 1 and hints at a significant urban density-modal
split relationship that could be evident in the city.

Another project explored the effects of the apartheid system, and its accompanying
legislation, on the travel patterns of the residents of Cape Town. This research project set out to
benchmark Cape Town’s public and private transport systems against systems of other
developing cities, given the unique spatial configuration of the contemporary South African city.
The main patterns that emerged from the comparative analysis showed that the public transport
users of the apartheid South African cities travel longer distances in relation to other cities with
similar GDP per capita, and also by comparison with other cities of similar sized urban area. In
addition, the research showed that the private transport travel patterns of Cape Town were
similar to other comparable cities in the world.

CPT
JHB

SPL

CRT
R2 = 0.1375

0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0

ha 500 000 ha 1 000 000 ha 1 500 000 ha
Overall average trip distane by
public transport (km)

Surface area (ha)

BJN

CRT
KLP

TRN

GNZ
CRC
BGT
CPT
JHB

SPL
BDP
RYD
BKK

R2 = 0.1053

0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
Ratio of public transport trip distance to
$- $1 000 $2 000 $3 000 $4 000 $5 000 $6 000 $7 000 $8 000
private transport trip distance

GDP per capita (US$)

Figure 2: Overall average trip distance on public transport compared to metropolitan surface area

Figure 3: Trip distance ratios vs metropolitan GDP per capita

Having demonstrated the potential value of such comparative work, the students devoted
the rest of their time to updated data collection. This is the subject of the remainder of the
paper.

3 UPDATING THE MILLENIUM CITIES DATABASE – METHOD
A substantial part of the work by the students was the collection of this more recent
2005/2006 data and, where possible 2010/2011 data. This proved to be a time-consuming task,
revealing gaps in data compounded by the lack of a central agency that has all, or even a
majority, of the data which the MCD suggests is necessary. Apart from the indicators readily
available (but not centrally located) in published reports, there were efforts made to source data
from the City of Cape Town Departments of Transport Planning and Policy Development;
Transport Modeling and Systems Analysis; Project Planning and Conceptual Design; and
Transport Network Information. In addition non City bodies of the Passenger Rail Association of
South Africa (PRASA) and the Golden Arrow Bus Services (GABS), both of which are the major
agencies of public transportation in the Cape Town metropolitan area, were approached. Data
was also obtained from the University of Cape Town’s Household Travel Data of Cape Town.
(Cooke, 2012; Baleni, 2012)
Although some aggregate data measures (for example total vehicle kilometres, average
network speed) required use of the city’s emme/2 model, a major issue encountered during
data collection was the peak hour nature of the city’s transport models. Work commute trips do
not account for a large amount of the trips that are made at other times of the day and so
factoring from this peak model to the all day measures called for was problematic. Another
challenge was the lack of data on the informal minibus taxi industry and the unwillingness of the
private bus companies to share financial data.
4 RESULTS OF DATA COLLECTED: AVAILABILITY, QUALITY AND GAPS
In this section the availability, quality and gaps in the data are discussed in some detail,
under the categories:

 General, economic and demographic indicators
 Infrastructure indicators
 Transport impacts
 Private vehicle indicators
 Public transport indicators
 Household travel survey

4.1 General, economic and demographic indicators
The majority of these indicators were sourced through research into governmental
publications and correspondence with contacts from the City of Cape Town. The total land area
and population of the metropolitan area; urbanised area of the metropolitan area; number of
jobs (at place of work) in metropolitan area indicators were all reasonably readily available, from
a city statistics fact sheet for Cape Town that is compiled every few years and from officials in
the Strategic Development Information and GIS Department of the City of Cape Town (City of
Cape Town, 2011a; Sinclair-smith, 2012; Small, 2012).
The number of jobs (at place of work) in CBD, which describes the number of
employment opportunities that are situated within the Central Business District (CBD) of the city
of Cape Town was a disputed indicator since no agreed definition of the Cape Town CBD is
available. For this indicator, the CBD was assumed to be the four precincts of the Central City

Improvement District. Data on jobs available was derived from census data, employment growth
and development data by an official within the City GIS Department (Spotten, 2012).
Gross Domestic Product (GDP) of the metropolitan area is the economic value of goods
and services created within the metropolitan area by all of the inhabitants, in a one year period.
This information was sourced by reviewing various economic publications by the City of Cape
Town. Some disagreement as to the correct value of the City GDP emerged. WESGRO, the
official investment and trade promotion agency for the City of Cape Town contradicts the City
statistics fact sheet (from the City of Cape Town’s GIS Department), while the Economic
Development Strategy quotes a third value.
4.2 Infrastructure indicators
Total centreline length of the road network (all roads including residential) accounts for
the total length of all public roads within the road network of the city and includes any residential
road that falls within the responsibility of the metropolitan government. The information for this
indicator could not be found in any governmental publication for the reference years, although it
was available for 2008. The total length of express road network (all expressways, freeways,
tollways) was not available within any governmental publication.
The number of parking places in the CBD (off- and on-street) is an indicator inclusive of
all parking that exists in parking lots, parking structures and subterranean parking garages. This
data was supplied by the Cape Town City Improvement District from surveys and analysis done
in house, although this is unpublished data, and does not cover the whole of the central area,
only the area managed by the Improvement District.
4.3 Transport impacts
Total transport related deaths includes all of the deaths that were attributed to a transport
mode and occurred within the metropolitan area. The indicator was sourced from the Forensic
Pathology Laboratory Database via the Transport Network Information section of the Transport,
Roads and Stormwater department of the City of Cape Town. Air pollution inventory from
transport sources in the city is a record of all the airborne pollutants that each transport mode
has been assumed to have created during the reference year. The only data available is an
aggregate pollution estimate for the year of 2008, sourced from a city official.
Private passenger transport energy use is an indicator that can measure the average
efficiency of the private vehicles in a city. Contact with an official working in those areas
provided an aggregate estimate of Kton equivalent of carbon dioxide. Public transport energy
use data was made available in aggregate form for rail by a city official (Covary, 2012).
Subsequently citywide data was sourced from work on city energy modelling by Sustainable
Energy Africa (SEA, 2006).
4.4 Private vehicle indicators
The average road network speed indicator represents the average speed that motor
vehicles generally travel across an average road within the city’s network and would describe
the congestion that exists. The data for this indicator was sourced from the emme/2 transport
model for Cape Town. As mentioned, this model has been calibrated for the morning peak and
work commutes and therefore is not well suited for finding daily speeds of the traffic. It was
acknowledged that the model needs to be recalibrated to give more accurate results for this
indicator.
Total annual passenger and vehicle kilometres of travel in private cars (and motorcycles)
refers to the total number of kilometres travelled by all the private cars collectively, within the
metropolitan area. This alludes to the degree of utilisation of private cars in the city and the level

of automobilisation that has occurred. This data is typically calculated using city models, or
through analysis of household surveys, but was not available for Cape Town.
The number of private cars and motorcycle indicators includes all private vehicles owned
by inhabitants or companies that reside in the metropolitan area, not including any vehicle that
receives income from transporting passengers. The information was sourced from the Western
Cape vehicle population fact sheet, of which the city of Cape Town is a sub-division (Harris,
2012).

4.5 Public transport indicators
The length of reserved public transport routes by each mode is an indicator which
describes routes that are legally reserved for a public transport mode only and effectively
policed to prevent other modes from utilising it. For rail, all routes are assumed to be reserved
so the total length of all rail lines was used for this indicator. Updates of the Integrated
Transport Plans (ITPs), and contacts at PRASA, provided this data. The dedicated bus and
minibus routes were found from direct measurement on Google Earth and the BRT route length
was taken from the ITP 2011 update. The length of public transport lines by mode includes the
length of all the routes used by a mode (regardless of whether the line was a dedicated route or
not). It alludes to the nature and extent of the network for each public transport mode and when
compared to urbanised area, will determine a degree of accessibility for the network. Again, the
only mode for which the data could be sourced was rail.
The average operating speed of each public transport mode was assumed to be the
average speed calculated by dividing the length of the route by the time taken to complete the
route. This includes the time taken to stop at each station but not the time spent waiting at
either end of the route. Rail data was available from a PRASA representative. The average
speed of minibus taxis was assumed to be equal to the average congested speed of traffic in
Cape Town, taken from the emme/2 model. According to the ITP 2011 update, the bus rapid
transport vehicles have an average operating speed of 30 km/h.
Annual revenue vehicle kilometre of service refers to the total number of kilometres that
every vehicle, in a specific mode, travelled while accepting payment from passengers. Only rail
data could be sourced for this indicator. Annual revenue seat kilometre of service describes the
total number of kilometres travelled by each seat on every vehicle for each public transport
mode. This illustrates the capacity of each transport mode through kilometres and gives an
indication of the coverage of the public transport network. Again, the only mode for which this
information could be sourced was rail.
The annual boardings indicator refers to the total number of times passengers board a
public transport mode in the reference year. This indicator can be used to create a modal split
for public transport modes, if a boarding is assumed to represent one trip. For rail, PRASA
made this data available. In the 2011 update of the ITP the number of bus boardings for GABS
buses was available. No taxi or other bus operator data was available for BRT at the time of
collection.
Annual passenger kilometres by each public transport mode refers to the total number of
kilometres travelled by every passenger on each transport mode. PRASA data was available.
Using the Emme/2 model estimates were made for bus and minibus although the reliability of
this data could be questioned.
The public transport vehicle fleet is the total number of vehicles operated in each mode
and is one description of the supply of public transport. Rail, bus and BRT data was found in the
ITP 2009 and 2011 updates (City of Cape Town, 2009a; City of Cape Town, 2011b). For

minibus taxis, the Current Public Transport Record summary 2009 update provided data (City of
Cape Town, 2009b; Western Cape Department of Transport and Public Works, 2011).
Annual total public transport farebox revenue is all of the money taken from the
passengers of each public transport mode in the reference year for services rendered. This
indicator was contentious as it requires companies to reveal financial details that competitors
could use to the detriment of the company. Only PRASA was willing to release data pertaining
to farebox revenue. (It is a parastatal company and therefore is forced to reveal all details to the
public under law.) The operating expenses summarise any financial requirement of a mode
during the reference year. This includes maintenance of vehicles, maintenance of routes,
salaries of staff and any other day to day expenses. None of the companies were willing to
supply data on their operating expenses.
4.6 Household travel survey
A statistically significant House Travel Survey (HTS) has not been performed in Cape
Town since the National Household Travel Survey (NHTS) was conducted in 2003, although
one is presently underway. In the absence of this, a privately funded HTS by the African Centre
of Excellence for Studies in Public and Non-motorised Transport (ACET) from the University of
Cape Town was used (ACET, 2011). The sample size of the UCT HTS was 16 231 (0.52% of
the study area population) which is too small to be truly representative. Nevertheless, as it was
the most recently available data, it was analysed and the results presented.
The number of daily trips indicator represents the number of trips made by the entire
metropolitan population on each mode during an average day and can be used to determine an
estimate for the modal split. The number of trips made by the sample group on an average day,
for each mode, was scaled up to approximate a number of trips for the City of Cape Town. The
average length of a trip for each mode is taken as the estimated average length of all the trips
utilising that mode on an average day, and was found from the HTS to be 10km. The average
time for a trip is calculated as the average amount of time taken for a trip to be completed on
each transport mode, and was found from the HTS 20 minutes for car and 38 minutes for public
transport.
Although the reliability of this HTS data to represent Cape Town as a whole is open to
question, it still gives some insight into the travel behaviour in Cape Town in 2010. However, it
also highlights the lack of household travel information and the great need for regular and
detailed Household Travel Surveys.
5 CONCLUDING REMARKS
Given the experience of the three students in collecting data over a brief two month
period, they were asked to judge whether they believed their data collected to be reasonably
reliable/complete; somewhat reliable/complete; or probably unreliable/incomplete. The
summary table of data found during the process (given in Appendix A) was thus labelled green
(for the most reliable data), amber and red (least reliable). Only eight indicators were deemed
reliable/complete; eighteen were judged somewhat reliable/partially complete and the remaining
sixteen as unreliable, or incomplete. The data found is available from the authors, on request.
The authors would like to acknowledge the generous gifts of time by various staff
members, especially at the City of Cape Town and PRASA, who provided data directly.
However, this style of data collection, relying on personal contacts and goodwill, raises
questions about risk, monitoring, and accountability. One risk is that as individuals leave the
City their knowledge and data memory is lost, unless a more systematic approach can be taken
on data collection, recording and publishing. Anecdotal evidence from the research process
suggests the availability or location of transport data not directly related to their work is not well

known, and so data collation is resource inefficient and onerous. This also raises questions
about cross-disciplinary understanding about the transport sector. Also concerning are
inconsistencies within data sets. The City of Cape Town, for example, report approximately 650
transport related deaths in the period, while the National Injury Mortality Surveillance System
attributed 1021 deaths to transport in 2004.
This data collection experience, and other data collection experiences of the main author
also suggests that, for the most part, up-to-date aggregate transport-related data for the City of
Cape Town is not available in a form which enables meaningful international comparison within
the Millennium Cities Database. This is problematic. It means that bench-marking of Cape Town
(and likely other metropolitan areas of South Africa) is not possible in any extensive way, and
so learning which could come from genuinely comparable international cities is not possible.
This is a loss for research and learning.
Lack of suitable data also compromises moves in the City towards integrated transport
services, which will require a more holistic perspective on matters of transport policy and
practice. A protocol for comprehensive data collection, which clearly defines scope, measuring
criteria and methods will be essential to ensure data which can be compared year-on-year and
so identify genuine trends and shifts due to newer public transport investments. In this regard, a
Household Travel Survey for Cape Town which is statistically significant is long overdue (and is
now underway). Also overdue is some agreement (or requirement) for the submission of data
by public and paratransit operators to a central body.
The process also highlighted a lack of all day data. The spatially comprehensive data
(such as vehicle kilometres) which does exist tends to be for the morning peak only. It has been
argued elsewhere that transport planning which bases its insights on modal splits for a morning
peak period are systemically biased towards the employed; professionals; males; and such data
is likely to seriously overestimate the importance of the motorized commuter trip (Behrens,
2001). Until reliable and regularly updated all-day data for the whole transport system is

available, such perspective bias likely continues to be propagated in policy and decision-
making.

Just more pressing, and more concerning, are the implications that an absence of
reliable data, in combination with large expenditures on transport, have for matters of
transparency and accountability. Even the data which is available is not collated in a format
which makes it easily accessible. For the lay-person such data would be very difficult – perhaps
even impossible – to access. The claims made for transport expenditure are not often called to
account in the public realm, but there is no reason why they should not be. Evidence based
policy claims would be a key part of this.
Important in this regard are the implications of good data for a sustainable transport
agenda. The City has for some years now had a vision for sustainable transport, but the City
data is truly impoverished in its ability to reflect on matters of sustainability. There is no regular
or reliable measure of vehicle or passenger kilometres, and so no good measure of energy use
or emissions. This means that any attempt to manage car use, or to shift mode and its
attendant emissions on a citywide basis simply cannot be monitored or evaluated. Recent work
by Venter and Mohammed (2013) using Household Survey Data from Nelson Mandela
Municipality provides a template and shows how valuable such analysis can be for
understanding the energy and emissions profile of urban areas.
It is important to mention that at the time of writing some of the Cape Town indicators
were being updated, for the Integrated Transport Plan process, but this will not significantly
impact on the Conclusions here. Finally, the authors understand that the situation in Cape Town
is not unusual, rather, it reflects a typical pattern of impoverished transport data surveys, data-

handling systems, data publicity, and data monitoring across South Africa, which is seriously
out of step with international best practice.

6 BIBLIOGRAPHY
African Centre of Excellence for Studies in Public and Non-motorised Transport (ACET) 2011.
Cape Town Study. Cape Town: ACET
Baleni, V. 2012. Effects of apartheid legislation on transport in Cape Town. Unpublished
undergraduate thesis. Department of Civil Engineering, University of Cape Town.
Behrens, R. 2003. Looking beyond commuter travel in Cape Town: Methodological lessons
from the application of an activity-based travel survey, in Stopher P and Jones P (eds),
Transport survey quality and innovation. Amsterdam: Pergamon.
Cameron, I. 2004. Understanding, modelling and predicting transport mobility in urban
environments. Perth: Murdoch University.
City of Cape Town 2009a. Integrated Transport Plan Update. Cape Town: City of Cape Town.
City of Cape Town 2009b. Current Public Transport Record Summary. Cape Town: City of
Cape Town.
City of Cape Town 2011a. City Statistics. Cape Town: City of Cape Town.
City of Cape Town 2011b. Integrated Transport Plan Update. Cape Town: City of Cape Town.
Cooke, S. 2012. An analysis of the urban density-modal split relationship and its significance in
Cape Town. Unpublished undergraduate thesis. Department of Civil Engineering, University
of Cape Town.
Kenworthy, J. & Laube, F. 2001. The Millennium Cities Database for Sustainable Transport.
Brussels: International Union (Association) of Public Transport, (UITP).
Medical Research Council/ UNISA. 2013. Crime, Violence and Injury Lead Programme. A
profile of fatal injuries in South Africa. 6th Annual Report of the National Injury Mortality
Surveillance System 2004. Section 4. Cape Town Fatal Injury Profile. Available online at
http://www.sahealthinfo.org/violence/capetown2004.pdf. Accessed 10 May 2013.
Newman, P.W.G. 1989. Cities and automobile dependence: a sourcebook. Aldershot, UK:
Gower.
Newman, P. 1999. Sustainability and cities: overcoming automobile dependence. Washington
DC: Island Press.
Sustainable Energy Africa (SEA) 2006. The Energy Book. Cape Town: Sustainable Energy
Africa.
Venter, C.J. & Mohammed, S.O. 2013, “Estimating car ownership and transport energy
consumption: a disaggregate study in Nelson Mandela Bay”, Journal of the South African
Institution of Civil Engineering, vol. 55, no. 1, pp. 2-10

Vivier, J. 2001. Millennium Cities Database for Sustainable Transport: Analyses and
recommendations. Brussels: International Union of Public Transport (UITP).
Western Cape Department of Transport and Public Works 2011. Provincial Land Transport
Framework. Cape Town: City of Cape Town.

APPENDIX A: MILLENIUM CITIES DATABASE INDICATORS
The table below summarised the data within the MCD, and the analysis of the data
collected for the process. (Data collected available on request from the authors). Green
represents data that is complete, considered accurate and reliable; yellow highlights data
that is questionable and may need further investigation, and red refers to data that is
missing or that is perceived to be inaccurate and should not be utilised for analysis without
further study.
1 Total land area of the metropolitan area
2 Urbanised area of the metropolitan area
3 Total population of the metropolitan area
4 Number of jobs (at place of work) in metropolitan area
5 Number of jobs (at place of work) in CBD
6 Gross domestic product of the metropolitan area
7 Number of private cars and station wagons, RVs, company cars (not taxis)
8 Total annual vehicle kilometres of travel in private cars
9 Total annual passenger kilometres in private cars
10 Average road network speed (7day/24hour)
11 Total centreline length of the road network (all roads including residential)
12 Total length of express road network (ALL expressways, freeways, tollways)
13 Number of parking places in CBD (off-street)
14 Number of parking places in CBD (on-street)
15 Length of reserved (policed) public transport routes by each mode
16 Average operating speed of each public transport mode
17 Annual revenue vehicle kilometer of service by each public transport mode
18 Annual revenue seat kilometer of service by each public transport mode
19 Annual boardings by each public transport mode
20 Annual passenger kilometres by each public transport mode
21 Private passenger transport energy use
22 Public transport energy use
23 Total transport related deaths
24 Number of two-wheeled motor vehicles (motorcycles)
25 Vehicle kilometres of travel on two-wheeled motor vehicles (motorcycles)
26 Passenger kilometres on two-wheeled vehicles (motorcycles)
27 Public transport vehicle fleet by mode
28 Length of public transport lines by mode
29 Annual total public transport farebox revenue
30 Annual operating expenses of public transport
31 Air pollution inventory from transport sources in the city
32 Number of daily walking trips
33 Number of daily mechanized, non-motorised trips
34 Number of daily motorised trips on public modes
35 Number of daily motorised trips on private modes
36 Average length of a trip (all modes)
37 Average length of a trip (mechanized modes)
38 Average length of a car trip
39 Average length of a home-work commute (all modes)
40 Average length of a home-work commute (mechanized modes)
41 Average time for a trip by car
42 Average time for a trip by public transport

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