NATIONAL RESEARCH UNIVERSITY
HIGHER SCHOOL OF ECONOMICS
HSE Graduate School of Urbanism
Laboratory for Experimental Urban Design “Shukhov Lab”
Faculty of Urban and Regional Development
DESIGNING A TOOL FOR MAKING TRANSPORT MODE CHOICES
IN RURAL AND REMOTE AREAS:
A CASE OF NEW MOSCOW
Master’s Degree Final Project
in the field of study: Urban Development (07.04.04)
academic program: Prototyping Future Cities
Academic Supervisor
Guallart Vicente
Supervisor
Ryzhkov Alexander
Student
Smirnov Aleksei
Moscow 2020
Contents
Abstract ............................................................................................................................................. 3
List of Figures .................................................................................................................................... 4
List of Tables ..................................................................................................................................... 5
Project brief ....................................................................................................................................... 6
Research Study ................................................................................................................................. 8
Introduction ............................................................................................................................. 8
Literature review ..................................................................................................................... 9
Methodology ......................................................................................................................... 14
New Moscow Territory and Mobility analysis ....................................................................... 16
Key Findings ......................................................................................................................... 28
Project proposal .............................................................................................................................. 29
Prototype design .................................................................................................................. 29
Prototype functionality .......................................................................................................... 30
Coding features .................................................................................................................... 30
Data collection and calculations ........................................................................................... 32
Prototype work algorithm ...................................................................................................... 33
Prototype interface ............................................................................................................... 35
Recommendations ............................................................................................................... 42
Suggestions for future development..................................................................................... 43
Conclusions ..................................................................................................................................... 44
References ...................................................................................................................................... 45
Appendix 1. List of Settlements within area in public transport pedestrian accessibility area and
Calculation ...................................................................................................................................... 47
Appendix 2. List of Settlements without area in public transport pedestrian accessibility area and
Calculation ...................................................................................................................................... 51
Appendix 3. Number of Validations of tickets at New Moscow Routes by Mosgortrans, 2018 ....... 54
2
Abstract
This work raises the question of mobility in rural and remote areas and the choice of transport mode
for them. While notion of the mobility is being essential nowadays and more and more people do
rely on the means of transport in order to get from point A to point B, some of the territories do lack
the access to the transport and New Moscow is one of them. The question of the transport supply to
such territories could be a challenging one for Departments of Transport, Stakeholders and decisionmakers because it is hard to choose the correct mode of transport for the exact taken non-urban
territory. The objective of this paper is to suggest a solution to this problem. One of the possible
solutions for the mobility supply could be a demand-responsive transport system which is broadly
known as a Door-To-Door service; however, it has different operation strategies today. In this work,
the main method of the research is a spatial analysis, which is used in order to evaluate the current
situation (supply and estimated demand) in the mobility sphere of New Moscow. The results of the
analysis are used in order to make a Project Proposal – a prototype to solve the problem. The
prototype itself is a program, which helps to create a route between settlements, calculate the usage,
create a route and make a choice between conventional bus or demand-responsive transportation.
The results of this work could be used by transport planners and governmental bodies for a first-step
analysis of development possibilities for mobility services at the territory. In addition, transport
activists and citizens could use this prototype for creating a demand for mobility supply via
mechanism of official communication with authorities.
Keywords: demand-responsive transport, mobility, rural and remote areas, DRT
3
List of Figures
Figure 1. UITP Scheme of Public Transportation (UITP, 2019) ...................................................... 11
Figure 2. Classification of Demand Responsive Transport (Inturri G., 2017).................................. 12
Figure 3. The Demand Responsiveness of Public Transport (Nelson, Wright, Ambrosino, &
Naniopoulos, 2010) ......................................................................................................................... 12
Figure 4. Map of Moscow with Troitsky and Novomoskovskiy Administrative Okrug’s highlighted in
red (QGIS) ....................................................................................................................................... 16
Figure 5. New Moscow Public Transport Routes Network (QGIS and NextGIS) ............................ 17
Figure 6. Map of routes and settlements (QGIS and NextGIS) ....................................................... 18
Figure 7. Map of Settlements that do not have connection with public transport (QGIS and
NextGIS) ......................................................................................................................................... 19
Figure 8. Village of Marino, served with public transport (QGIS and NextGIS) .............................. 20
Figure 9. Village of Elino, not served with public transport (QGIS and NextGIS) ........................... 20
Figure 10. Hexagonal map of housing in New Moscow (QGIS and NextGIS) ................................ 22
Figure 11. Hexagonal map of housing with Public Transport Access Area (QGIS and NextGIS) .. 23
Figure 12. Visualization of Total Transport demand in settlements outside Public Transport
Accessibility Area (QGIS and NextGIS) .......................................................................................... 24
Figure 13. Grouping of Settlements ................................................................................................ 25
Figure 14. Mercedes-Benz Sprinter. Moscow Transport Livery (RIAMO, 2016) ............................ 27
Figure 15. Query for "population" data ................................... ................................................... 33
Figure 16. Route options calculation and Mode option choice ....................................................... 33
Figure 17. Prototype design. Map .................................................................................................. 35
Figure 18. Prototype design. Spreadsheet…………………………………………………………………… 35
Figure 19. Prototype design. Routing Map …………………………………………………………………. 36
Figure 20. Prototype design. Map and spreadsheet ………………………………………………………. 36
Figure 21. Prototype calculations. Group 1 ..................................................................................... 37
Figure 22. Prototype calculations. Group 2 ..................................................................................... 37
Figure 23. Prototype calculations. Group 3 ..................................................................................... 38
Figure 24. Prototype calculations. Group 4 ..................................................................................... 38
Figure 25. Prototype calculations. Group 5 ..................................................................................... 39
Figure 26. Prototype calculations. Group 7 - DRT Routing ............................................................. 39
Figure 27. Prototype calculations. Group 7 - Bus Routing ............................................................. 40
Figure 28. Prototype calculations. Group 7 - DRT&Bus Routing ................................................... 40
Figure 29. Prototype calculations. Group 8 .................................................................................... 41
4
List of Tables
Table 1. Trip Rate by Trip Purpose (Japan International Cooperation Agency, 2012) .................. 14
Table 2. Service design recommendations for choice of vehicle, (Wright, 2013) ........................... 15
Table 3. Calculations of Mobility Data in New Moscow................................................................... 21
Table 4. Grouping of Settlements ................................................................................................... 25
Table 5. Calculations for Suggested Mode of Transport ................................................................. 26
Table 6. Prototype algorithm ........................................................................................................... 34
Table 7. Comparison of results of mode choice (Wright & Prototype) ............................................ 41
5
Project brief
Modern transport systems are multifaceted and provide an extensive choice for both planners
and decision-makers. Currently, there are many districts, regions, and territories that, due to certain
features of their development, do not have access to transport systems or which are not enough to
meet the current demand for mobility. One of the viable solutions to this problem could be a demandresponsive transportation system.
A demand-responsive transport (DRT) system can be described as a system or a service
which provides mobility with the use of flexible modes and small means of transport like cars,
caravans or mini-buses (Engels, 2004; Inturri, 2017; Davison, 2014; Kim, 2016). Nowadays they are
not being considered as an option due to the development of high-speed means of transport and its
high costs. However, there are several examples of successful systems that are working to ensure
mobility for citizens. One of these examples is a city of Lincolnshire, the UK that works for more than
20 years. On the other hand, many DRT systems fail due to the economic reasons and close in oneor two-year’s time.
Today the territory of countries is dispersed: while having a growth in urbanization rates,
there are still numerous areas that are considered rural or remote. As an example, the European
Union possesses 75% of rural/non-urban areas (Finn & Nelson, 2019). For transport planners this
could be evaluated as a challenging question of mobility supply and potentially leading to using DRT
as a feasible/viable solution.
The purpose of this research is to identify where it is better to provide conventional Public
Transport and where – flexible solution (e.g. DRT) for mobility supply, based on the New Moscow
case. In the work, it is planned to produce an analysis of the current situation in mobility in New
Moscow, propose a solution for DRT with a model and a prototype. The model will include all the
necessary data to start operations: economic model, fleet. A prototype will be a digital system that
enables the developer or a researcher to understand which means of transport is better for the
territory to make a demand for transportation.
In the first part of the project a review of the current state of transport science in the sphere
of DRT will be provided. Then spatial analysis will be performed in order to understand the
distribution of inhabitants, the supply of mobility, and possible mobility demand. In the final part of
the work, a viable solution to the problem will be proposed.
Integrated 7 years ago, New Moscow has an average rate of supply for mobility, which is not
correlating with the demand – at least 60 % of households have no access to transportation. While
making some efforts in providing transportation to the Novomoskovskiy administrative district, which
is close to the “old city border” and has a historically developed connection with a capital, the Troitsky
administrative district is facing challenges – there are several transport routes, but a trip inside the
Moscow Central Ring Road (MKAD) could take at least one hour.
The question of mobility for this area nowadays is one of the most important for the
development of Moscow transportation – this territory is expected to grow dramatically. Statistics
6
show that today there are more than 360 thousand inhabitants (235 thousand for Novomoskovskiy
and 125 in Troitsky administrative districts) which is a growth in 1,5 times from 2012. The same
dynamic could be seen in the housing: from 20 to 30 million square meters are located here now
and more than 8 million will be built till 2021 (Kompleks gradostroitel'noy politiki i stroitel'stva goroda
Moskvy (Moscow City Building Policy Committee), 2020).
With such an extensive growth of official demography and real estate, a possibility that the
City will face a transport collapse in the near future exists. Nowadays New Moscow has 93 routes
that are operated by state companies Mosgortrans (Moscow City Ground Transport Operator –
operates Buses and Tramways), MosTransAvto (Moscow Region Bus Operator) and other private
companies, which provide connection inside the area. However, citizens living in distant areas do
not have access to these routes or have to make long trips to use this mobility option. One of the
potential solutions to this problem is the redevelopment of the whole transportation system in New
Moscow by organizing large transportation hubs that are connected to the places of settlement of
citizens with the DRT system.
This system should be an essential part of the New Moscow City Transport Complex, creating
a new side of (possible) MaaS network. I do propose an easy mechanism of this system – fixed
routes with the possibility to change it in case of demand. In order to make this system livable, it is
essential that the payment system should be connected with an existing Moscow Ticket Menu.
7
Research Study
Introduction
Modern mobility is a large interconnected sphere around the globe. This system includes air,
cargo, personal and public transport. However, with such an extensive growth of means of mobility,
we sometimes cannot manage the demand for transportation, especially in remote areas even in
developed urban agglomerations.
Moscow nowadays is an example of such an agglomeration. With more than 15 million people
inhabiting the city and many great numbers of the coming workforce from the region. In the year
2012, the border of Moscow city was changed – the extension to the south-west with the connection
of New or so-called Grand Moscow with the space of 1480 sq km.
The territory of New Moscow possesses 21 municipalities with more than 360 thousand
inhabitants. However, the existing model of transport supply with conventional routes and buses
badly serves the main purpose – providing mobility to this area – because it is not the best option
for such dispersed and not heterogeneously populate the territory.
Demand-responsive transport (DRT) could be a solution for providing transportation for
territories, where making a route with conventional buses is not the best option from economical and
other points of view. With the creation of a system that responses to the demand from end-users, it
is possible to create a sustainable mean of transport for distant, rural, or other areas.
The research objective of this thesis to identify where to provide conventional Public
Transport and where – flexible solution (e.g. DRT) for mobility supply, based on the New Moscow
case.
To achieve the objective of this study, the following questions will be asked:
1. What is the demand-responsive transport per se?
2. What is the current situation with mobility in New Moscow?
3. What is the estimated demand for mobility in New Moscow?
4. How can the settlements be provided with mobility and by what modes?
5. Which type of demand-responsive transport is suitable for the New Moscow?
In this work, the notion of DRT will be explained, several typologies, positive and negative
sides will be determined. In addition to that, the main aim of this work is to provide a new possible
solution for mobility for rural areas in the example of New Moscow. In the first part of the work there
is an observation of the existing literature about DRT, mentioning its different classification,
approaches, etc. In the second part there is an analysis of New Moscow territory in the sphere of
mobility and its supply. In the key findings there is a brief result of the work and the main proposition
for the prototype.
8
Literature review
Modern transportation science gives a special role to the DRT systems around the globe.
With numbers of articles and workshops, there are several ways of defining the term of DRT:
1. DRT services include such services as taxis, vehicle sharing, carpooling, and flexible
transit systems. (Inturri et al., 2017).
2. DRT is:
a. the service which is available to the general public (i.e. it is not restricted to
particular groups of users according to age or disability criteria or place of
employment);
b. the service which is provided by low capacity road vehicles such as small buses,
vans or taxis;
c. the service which responds to changes in demand by either altering its route
and/or its timetable;
d. the service where the fare is charged on a per passenger and not a per-vehicle
basis (Davison, Enoch, Riley, Quddus, & Wang, 2014).
3. DRT as a semi-public form of transportation that offers the carpool service of a
conventional bus and the door-to-door service of taxi (Kim, Moon, & Kim, 2016).
4. DRT is a user-oriented form of passenger transport and, unlike conventional public
transport, is characterized by flexible routes and/or timetables according to passenger
needs, with smaller vehicles operating between pick up and drop off locations (Perera,
Ho, & Hensher).
5. DRT as being a ‘flexible, intermediate’, transit mode which ‘fills the gap’ between
individual taxi type services and scheduled fixed-route conventional transit (Engels &
Ambrosino, 2004).
As can be seen from the above definitions, there are several approaches to the notion of
DRT in the literature; however, it is possible to define it as “a system or a service which provides
mobility with the use of flexible modes and small means of transport like cars, caravans or minibuses”.
The history of the notion comes from Dial-a-Ride services (DART), which were popular in the
1970s. Per se a door-to-door or Special Transport Services was a special, restricted way of moving
those individuals, who had a great demand for it – elderly and disabled - from one point to another.
However, not so many examples of such services survived due to the low level of profit of such
systems. One of the livable examples of DART is HandyDART in Metro Vancouver which us fully
subsidized by municipality.
It is crucial to understand that there are several systems of DRT. In general, they are defined
by the way of providing service but they are divided by the type of work:
1. System approach:
a. Interchange DRT – providing a link to the general public transport;
9
b. Network DRT – providing support to the existing services or replacing
economically inefficient routes;
c. Destination-Specific DRT – a special form of providing service to a certain
destination such as airports or employment zones;
d. Substitute DRT – total replacement of existing public transport infrastructure,
resulting in a reinvention of the whole system (Currie & Fournier, 2017).
2. Route approach:
a. Many-to-One Services – where vehicles-drivers is concentrated at one of the
two trip ends considerably reducing the complexity of operations;
b. Many-to-Few Services – where more than a single location at one trip end, but
few enough to remain manageable;
c. Many-to-many (Transfer) – where vehicles-drivers can travel to or from any
location, but the system may require vehicles-drivers to transfer vehicles to
complete their journey;
d. Many-to-Many – covering all trip origins and destinations with a direct service;
e. Shared Taxis – conventional taxis accept several individuals who can use the
same vehicle and it may involve route deviation (Currie & Fournier, 2017).
3. Route choice strategies:
a. Fully Random – FR – all vehicles drive at randomly chosen routes;
b. All vehicles drive on All Flexible routes – AVAR – all vehicles drive on flexible
routes;
c. Each vehicle is Assigned to a Flexible Route – EVAR – each vehicle drives on a
prefixed semi-flexible route (Inturri, et al., 2017).
In addition to the system approach, there is a list of key factors identifying the success of
the DRT system:
1. Keep it simple: avoid complex systems unless you are confident in the revenue;
2. Ensure DRT Operator is confident with alternative services;
3. Ensure a High Level of Marketing to the Concept because users often don’t understand
how to use DRTs;
4. Raise Fares to Pay for Higher Quality Service;
5. Target Workable Catchments – under-developed areas and overly circuitous street
structures should be avoided since this can increase costs (Enoch, Potter, Parkhurst, &
Smith, 2004).
Davidson (2014) in his research discusses that the DRT transport solution was often chosen
for rural areas where there are few passengers spread among small settlements. One of the livable
examples is a system in Lincolnshire, the UK, which is many-to-many substitute DRT system, that
was implemented by the government in order to provide mobility to the region where conventional
public transport was too expensive.
10
Still, it is crucial that DRT systems are commercially unstable because it is very difficult to
understand the profitability of the enterprise. As it is defined by the Workshop 4 Report – Realizing
the potential benefits of DRT, the most surviving type of this system is a highly-subsidized company.
High costs of implementation do spoil the sustainability of the whole idea. The statistics show that
out of all DRT systems ever made, only 50% have survived (Currie & Fournier, 2017).
The system of DRT can operate using different scenarios: it could be an old-way of Dial-aRide or a new way of application or web-based platform that allows to an end-user to leave his or
her demand for a mobility service and a dispatcher (or a program) shares the data or a prepared
route to the drivers.
Figure 1. UITP Scheme of Public Transportation (UITP, 2019)
11
Figure 2. Classification of Demand Responsive Transport (Inturri G., 2017)
As shown in Figure 1 and Figure 2, the definition of on-demand services or flexible transit
services by researchers and International Union of Public Transport (UITP) is presented. This means
that they are being a part of the talk of modern research and decision-making of the sphere.
Technologically, DRT systems do need several points to be defined in order to make them
work: from the dispatcher system that collects those demands for mobility, to ticketing systems and
vehicles. Figure 3 represents the main parts of DRT System technology that allow it to be more
responsive to the demand. As for vehicles, it is suggested to use a maximum of 2 types of vehicles
in such systems, which could be small buses or mini-vans (Currie & Fournier, 2017).
Figure 3. The Demand Responsiveness of Public Transport (Nelson, Wright, Ambrosino, & Naniopoulos,
2010)
However, the systems of DRT face numerous challenges, that make them not a popular
decision when decision-makers start making choices in the sphere of public transport organization.
Currie shows that only 52% of DRT survived. It is obvious that the main challenge for them is the
economy: DRTs cannot survive without subsidy from the government because they face the highcost problem (Currie, Thredbo 16 - International Confernce Series on Competition and Ownership
12
in Land Passenger Transport, 2019; Bruni, Guerriero, & Berladi, 2014). In developing countries DRT
seeks profit, while in developed ones social objectives tend to dominate. Therefore subsidy is
essential – an example of DRT system which started in 1983 was able to survive in the 1990s only
because of the money from the central government (Davison, Enoch, Riley, Quddus, & Wang, 2014).
Unfortunately, governments of different countries underestimate the importance of mobility
for their citizens. For example, the European Union with 75% of rural/non-urban areas focuses on
the development of such a mean of transport. As the responsibility for mobility is located in different
levels of governmental structure from one to another Member State of the EU, the regional
framework of mobility is the most popular solution (17 out of 28 Member States). Still, Latvia is the
only country that pays attention to the rural mobility question, while Estonia, Hungary and Slovenia
do not have any end goals. Other countries have no policies at all. As a result, there is a “policy
vacuum” in the sphere of rural mobility in the EU (Finn & Nelson, 2019).
Still, examples of successful policy-making in the sphere of DRT exist. The regulatory base
in Great Britain has created numerous ways of providing such services to the public: first: being a
local bus service, which is registered through Traffic Commissioners for the Bus Service Operators
Grant and receives a subsidy; second: community transport organization provides services on a nonprofit base; third: having a vehicle, which is registered for hiring could have designated points of
pick-up (Davison, Enoch, Riley, Quddus, & Wang, 2014).
As it was mentioned above, DRT systems have several ways of interpretation. On the one
hand, this fact allows individuals, governments and actors to fit in the perfect solution for their specific
case and territory. On the other hand, no strict guidelines for implementing DRT systems, avoiding
risks, can be found in the literature.
13
Methodology
In this work the main focus is understanding the current transportation situation in New
Moscow, which will help to gain a deeper insight into the mobility needs of the residents and to
determine an approximate demand in general.
In order to understand the territory, spatial distribution and the current situation in mobility,
spatial analysis was performed using a dataset from the NextGIS repository, which contains all the
statistics on roads, public transport, housing, etc. For this purpose, the public transport and housing
layers were used.
For a better understanding of the spatial distribution of housing, a map was designed with a
hexagon of 500 m in length on each side. However, there was a need to transfer the data from
households into the number of inhabitants. A calculation of transport demand with the use of data
on the number of inhabitants in each settlement was performed.
For the calculation of transport demand per day, a methodology by Japan International
Cooperation Agency (Japan International Cooperation Agency, 2012) was used. In the methodology,
a simple formula to forecast the demand with a trip rate model was proposed:
𝐺! = 𝑃 ∗ 𝑟!
Where 𝐺
! - the number of person trips by trip purpose,
𝑃 - the quantity of population aged 5 years and more,
𝑟 ! - trip rate by trip purpose.
In addition, a trip rate by trip purpose data were also mentioned in the methodology.
Table 1. Trip Rate by Trip Purpose (Japan International Cooperation Agency, 2012)
Trip Purpose
Trip rate (trips per person)
To Work
0.31
To School
0.21
Business
0.02
Private
0.10
To Home
0.63
Total
1.27
With the use of calculated data, it was easier to understand the demand zones for
transportation in general and then to predict where to propose new modes of mobility.
After calculation of estimated mobility demand, methodology, suggested by Wright to suggest
the mean of transport for the supply (Wright, 2013) was used. His main proposition is to use the ratio
of passenger-km in order to predict a transportation mode. The methodology is shown in Table 2.
14
In order to calculate passenger-km, a simple formula of transport work parameter was used:
multiply the number of estimated passengers (trips per day) by their average trip length. As there is
a lack of access to the approximate trip length of each passenger, an average data from the
Transport Behavior research (Muleev, 2015) was. In the research Muleev mentioned that the
average trip length of minibus passengers is 14 km, while for a bus is 15,5 km. It is suggested to use
an average of these two parameters, which is 14,75 km.
To calculate passenger-km per hour, the quantity of 20 hours, which is an average working
time of conventional bus routes in Moscow (not including Night Buses) was used.
Table 2. Service design recommendations for choice of vehicle, (Wright, 2013)
Passenger - km / hour
Vehicle choice
Less than 10
Taxi
Between 10 and 20
Taxi(s) or a flexible minibus (DRT) could be used - the choice will
depend on availability and relative costs locally
Between 20 and 50
Flexible minibus should be provided with a lower degree of route
flexibility at the higher end of the range
Greater than 50
Largely fixed route bus service should be provided with limited
deviations
15
New Moscow Territory and Mobility analysis
Part 1. Territorial Context
New Moscow territory is a part of the City of Moscow, which occupies 1480 km2 of territory
and 21 municipalities, divided in two administrative districts (okrug’s): Troitsky and Novomoskovskiy.
Figure 4. Map of Moscow with Troitsky and Novomoskovskiy Administrative Okrug’s highlighted in red
(QGIS)
This territory is populated by 360000 individuals - 235 thousand for Novomoskovskiy and 125
in Troitsky administrative districts – which represent the growth in 1,5 times from 2012. The same
situation with housing – from 20 to 30 million square meters are located here now and more than 8
million will be built till 2021 (Kompleks gradostroitel'noy politiki i stroitel'stva goroda Moskvy (Moscow
City Building Policy Committee), 2020).
In order to provide a broad analysis of the chosen territory, a dataset, uploaded from the
NextGIS system, which includes all the data layers for GIS of Moscow such as streets, boundaries,
buildings, public transport, etc, is used. All the data will be focused on the territory of New Moscow
Administrative districts (Clip function in QGIS).
16
Part 2. Mobility Supply evaluation
Nowadays New Moscow has 93 routes that are operated by state companies Mosgortrans
(Moscow City Ground Transport Operator – operates Buses and Tramways), MosTransAvto
(Moscow Region Bus Operator) and other private companies, which provide connection inside the
area. However, citizens living in a distant area do not have access to these routes or have to make
long trips to this mobility option. Individuals who can get to the mobility, do spend at least one hour
commuting to the old-city border (inside Moscow City Ring Road – MKAD).
Figure 5. New Moscow Public Transport Routes Network (QGIS and NextGIS)
As shown in Figure 5, the main routes are distributed in the Novomoskovskiy district, and the
main highways - Kievskoe, Kaluzhskoe and Varshavskoe – are used to provide the connection to
the old Moscow. The routes get individuals mainly to the closest metro stations such as Tepliy Stan,
Salarievo, Yugo-Zapadnaya. Only one route – Night 11 – provides a connection to the city center.
17
Figure 6. Map of routes and settlements (QGIS and NextGIS)
As revealed in Figure 6, several settlements do not have any connection to the public
transport at all in both Administrative District – a detailed map of these settlements is shown in Figure
7. This implies that inhabitants of these territories are using other modes of transport to commute
to the working places - private cars, carpooling and carsharing – which is increasing the traffic and
creating congestion at the highways and in the city.
18
Figure 7. Map of Settlements that do not have connection with public transport (QGIS and NextGIS)
19
Figure 8. Village of Marino, served with public transport (QGIS and NextGIS)
Figure 9. Village of Elino, not served with public transport (QGIS and NextGIS)
20
Table 3. Calculations of Mobility Data in New Moscow
Total Routes
93
routes
80494
building
transport
29581
building
Blocks NOT in public transport
50913
building
Part 1. Housing
Total number of Blocks
Blocks
in
public
pedestrian accessibility area
pedestrian accessibility area
Percentage of Blocks in public
36,75%
transport pedestrian accessibility area
Percentage of Blocks NOT in
63,25%
public transport pedestrian accessibility
area
Part 2. People
Total number of Inhabitants
360000
people
Inhabitants
transport
132300
people
Inhabitants NOT in public transport
227700
people
197
villages
104
villages
93
villages
In
public
pedestrian accessibility area
pedestrian accessibility area
Part 3. Settlements
Total number of Villages
Villages
in
public
transport
pedestrian accessibility area
Villages NOT in public transport
pedestrian accessibility area
Percentage in public transport
53%
pedestrian accessibility area
Percentage
NOT
in
public
47%
transport pedestrian accessibility area
The data for Table 3 were collected using different functions of QGIS and Excel. For “Total
Routes” all the public transport routes which were not connected with the researched territory, e.g.
are operating inside the Moscow Ring Road, were manually deleted. Data for Part 1 were collected
with clipping from all the buildings in Moscow with New Moscow boundaries, while “Blocks in public
transport pedestrian accessibility area” was calculated with the use of 500 meters buffer, which is a
normative standard for Moscow Public Transport accessibility. Calculations of Part 2 were performed
with the use of proportion received after calculation of Housing in public transport pedestrian
21
accessibility area. Calculations of Part 3 were made by clipping all the settlements, located within
New Moscow boundaries with buffer form Part 1 of the Table 3.
As presented in Table 3, 104 from 197 settlements, located in New Moscow have access
to mobility: all of them are those, that are located close to the main axes of transportation and highdense mobility system close to the Old Moscow boundary. The list of settlements is determined in
Appendix 1. Figures 8 and 9 show us that not very dense village of Marino is supplied with
transportation, while Elino is not. As can be seen from the calculations in Appendix 1, while most of
the settlements are in the zone of mobility, approximately 26% of the population is somehow are
supplied with mobility. Appendix 2 includes a list of settlements, which have no access to public
transportation at all.
The data about routes validations for the year 2018 is included in Appendix 3. On average
each route carries 2537 passengers daily or in total 144535 (for 57 routes managed by Mosgortrans)
which is somehow correlating with the data from Table 3 on inhabitants living in public pedestrian
accessibility area. However, this is not a full picture because the other 36 routes are served by
Mostransavto and private companies and the data is not accessible.
Figure 10. Hexagonal map of housing in New Moscow (QGIS and NextGIS)
Figure 10 gives us a better understanding of the distribution of population on the researched
territory: the main zones of living are located closer to the Old Moscow border.
22
Figure 11. Hexagonal map of housing with Public Transport Access Area (QGIS and NextGIS)
Figure 11 allows us to see which parts of the New Moscow territory lack access to public
transport.
The discussion above demonstrates clearly that the mobility question in New Moscow is a
challenged one: while connecting almost a half of settlements to the city with several routes,
operated by Mosgortrans, Mostransavto and private companies, other half lacks any connection with
the Old Moscow. There are several territories that have no access to mobility, which could be
considered as a mistake in planning, that should be corrected in the future.
23
Part 3. Mobility demand prediction
In order to understand the demand in the settlements that have no access to the public
transport the calculation with the use of the methodology described before was performed. The total
number of trips will be enough to understand the current state of demand at the territory. The results
of the calculation are shown in Figure 12.
Figure 12. Visualization of Total Transport demand in settlements outside Public Transport Accessibility
Area (QGIS and NextGIS)
As can be seen in Figure 12, there are several groups of settlements with the demand for
transportation. These settlements are grouped in Figure 13.
Grouping of settlements was performed based on the close location to each other and being
distant from current transportation routes. The current groups with the list of settlements and
estimated transport demand are presented in Table 4.
24
Figure 13. Grouping of Settlements
Table 4. Grouping of Settlements
Settlements
Total Trips per day, estimated
Krucha, Spas-Kuplya, Teterinki, Gornevo,
Dmitrovka
66
Vasynino, Lopatino, Klenovka, Lykovka
120
Group 3
Khutora Gulyaevi, Zosimova Pustin’,
Shelomovo, Khmyrovo
293
Group 4
Devyatskoe, Tarasovo. Sal’kovo, Alkhimovo
306
Group 5
Kharino, Esenino, Staroselie, Serednevo.
Burtsevo, Golenishevo
3094
Konushkovo, Gorchakovo, Zhukovka,
Fominskoe, Klokovo, Uvarovo. Gubtsevo,
Khatminki, Roznovo, Pyatovskoe
Kuvekino, Vlasyevo, Evseevo, Raevo, Novinki,
Pyxchevo, Kiselevka
415
Nastasino, Elizarovo, Kamenka
342
Group 1
Group 2
Group 6
Group 7
Group 8
461
25
As shown in Table 5, there is a group with a high number of estimated trips – more than
3000, and groups with less than 500 estimated trips. With the use of methodology, described by
Wright it is possible to suggest a mode of transport for each group of settlements. In order to
understand the length of the route, a method of the shortest route between settlements to the nearest
existing public transport route is used.
Table 5. Calculations for Suggested Mode of Transport
Total Trips
per day,
estimated
Mode of
Length of
Passenger-
Passenger-
Route, km
km per day
km per hour
transport by
Wright
Group 1
66
38
973,5
49 DRT
Group 2
120
20
1770
89 Bus
Group 3
293
35
4321,75
216 Bus
Group 4
306
20
4513,5
226 Bus
Group 5
3094
14
45636,5
2282 Bus
Group 6
415
33
6121,25
306 Bus
Group 7
461
31
6799,75
340 Bus
Group 8
342
24
5044,5
252 Bus
Table 5 provides the results that there is only one zone – group 1 - that should be provided
with DRT, while others have enough demand (by Wright) in order to start a conventional bus route.
The discussion above clearly demonstrates that the territory of New Moscow possesses a
large number of settlements – some of them do have access to the mobility, some – do not have
this possibility. Spatial analysis was used to identify the isolated areas of the mobility, calculate the
trip demand and choose transport solution for each case.
26
Part 4. DRT System proposal for New Moscow
Considering the results of the discussion above, a proposal of the DRT system will be based
on the requirements and definitions made by Currie (2017) and Inturri G. (2017) mentioned in the
literature review.
As far as there are several routes of conventional public transport of Moscow in the area,
there is no need to provide a DRT service to connect it to the Old Moscow, that is why the system
will be an Interchange DRT. While connecting different settlements with conventional routes, they
will be a many-to-many DRT with EVAR system because there is always a chance of new demand
at the taken time by an end-user from a point which has not been served with the route. Finally, this
is an Interchange many-to-many EVAR DRT system.
It is suggested to use existing automobiles such as Mercedes-Benz Sprinter (18 seats).
Figure 14. Mercedes-Benz Sprinter. Moscow Transport Livery (RIAMO, 2016)
One of the possible economic benefits of the system for the Moscow Transport System could
be the growth of users on the routes from and to New Moscow because new territories and
settlements will be connected. The promotion of mobility could also lead to the growth of inhabitants,
which could also lead to a benefit for the economy in prospective.
27
Key Findings
DRT systems could be described in several ways, which depend on their locations, type of
service, etc. However, it is possible to define it as “a system or a service which provides mobility with
the use of flexible modes and small means of transport like cars, caravans or mini-buses”. With
several approaches to organizing this mode of mobility, it is possible to fit it to any rural or remote
area, which has a demand for transportation.
As a result of the territory analysis, New Moscow territory has a demand for the DRT. This
territory possesses a big number of settlements which now are the parts of the City Tissue. While
being a part of it, some of its settlements are not connected to the core part by any means of public
transport.
The number of routes and carried passengers showed us that current capacities are not
enough to carry the individuals, who live in this territory: 93 lines of official Moscow Ground Transport
operator Mosgortrans, Mostransavto and private operators are not enough to cover the New
Moscow. This network provides only a half of the inhabitants with an approachable opportunity of
mobility, while other inhabitants are using other modes like unofficial and sometimes dangerous
minibuses, carsharing, carpooling and taxis. However, this is a part of the Capital and it is of high
importance for the Moscow Government to provide the mobility to these settlements, in order to give
individuals a possibility of connection with and within the City.
The methodology, used in the research, provided a data of the mobility demand on the
territory. As a result of a spatial analysis, eight groups of settlements with low level of supply and a
high level of demand were determined. With the use of methodology on mode prediction by Wright,
a mode of transport for each case was suggested. Only one group could be provided with DRT,
while other groups could be supplied with conventional buses.
The main limitation of the research was the lack of data. However, with the help of Center for
Moscow Traffic Organization, it was possible to obtain the data on validations, which is used in the
spatial analysis. In addition, the problem of no public gathered data on Moscow Region Ground
Transport carrier lead to the longtime of verification of routes with the use of open sources. The lack
of data on population in the NextGIS also lead to the several steps of data collection and verification.
The significance of the study is in raising the question of the mobility supply in the New
Moscow territory, which nowadays is not an often-raised problem by the decision-makers. The
analysis provided the accurate data on current situation, which could be used as a basis for round
of meeting and steps to the solution of the problem.
The results of the analysis will be used in the prototype use in order to determine routes and
find the best option for this territory - bus or DRT – and will help us to verify the solution, proposed
by Wright. In case the DRT option will be a solution, it is recommended to use the ideas of the DRT
System proposal for New Moscow.
28
Project proposal
In order to solve the problem, which was addressed in the Research Study of The Thesis –
understanding where to use fixed or flexible modes of mobility, in this part of the work a detailed
description of the solution – digital prototype for transport mode choice – is presented.
The prototype is designed to solve the problem of research and choice of transport supply
for the territory. As far as mobility is a global concept, which is up-to-date for any part of the globe,
the solution, presented in this part, could be used in any part of the world. As far as the software
works with open-source data, it could be used by the broad public. However, the main stakeholders
or users of this software could be decision-makers, transport engineers, state representatives and
active citizens.
The project deliverable is a software, compatible with private computers operating on
Windows 8 or 10 and connected to the Internet.
The prototype itself proposes a simple way of research of transport supply possibilities with
a choice between conventional bus routes and DRT systems. It is important to mention that all the
calculation now is using the data, which is relevant for Moscow. It is possible to make changes for
applying the software for other territories.
Specifications
1. Route data input
2. Route data calculation
3. Route options calculation
Prototype design
In order to address the problem correctly, there were several tryouts of the prototype. The
first one provided the simple calculations of costs and revenues for bus or DRT with the use of data,
entered by the user, and suggested, which mode of transport is to consider as a choice for the
specific route. However, this was not a useful solution for the problem, as far as the user had to
make several attempts in case, there was a need for a route system.
The second version of the prototype was developed in the way of making it easy-to-use for
anyone. With the use of this version of the software, the user is able to put the points, which are
necessary to connect and then to receive the result of calculations. In the end the software presents
a spreadsheet with the data on park (quantity of vehicle), costs, revenue and total profit (loss) for
the project with an interactive routing system. This version of the prototype is used in this project
proposal.
29
Prototype functionality
As it was mentioned before, the second version is currently being used as a solution for the
problem, mentioned in the Thesis. In this part the full way of prototype work will be described.
As the user opens the software, the starting screen represents the map of New Moscow,
which is the case for the current project. The user is invited to locate points on the map, which it is
necessary to connect with public transport modes. In this version the choice is between bus or DRT.
At the moment user ends the entering of routing points, the button “OK” is pressed, the program
starts the calculation of all possible routes between points and all economic parameters.
As a result of the calculation, the prototype presents the spreadsheet with an estimated
number of passengers, park of vehicles for Bus route and DRT route, the cost for Bus route and
DRT route, revenue for Bus route and DRT route, the total amount of money for Bus route and DRT
route. The last column of the spreadsheet represents the routing options: each point on the map has
its number and the program calculates routes between them. It could be route only with Bus usage,
only DRT usage or both: program proposes routing, following the economic parameters of the
project.
Coding features
In order to make a working prototype there was a choice between several coding languages:
Python, JavaScript and C#. In the end, the choice of C# was made because of the easy way of
creating forms. However, it is limited to the work of the system for only Windows OS.
There is no need to represent all the parts of code here. Still, we need to make clear several
parts of the calculations.
Here is the code for path calculations (S. 2):
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
namespace BestTransportSolver
{
public class PathInfo
{
public int[] Path;
public List<Route> Routes = new List<Route>();
public int PriceKm = 80;
public double CommercialSpeed = 33.0;
public int TicketPrice = 55;
public double RouteLength { get; set; }
30
public int Population { get; set; }
public double CostBus { get; set; }
public double CostDRT { get; set; }
public int BusPark { get; set; }
public int DRTPark { get; set; }
public double PAXBus { get; set; }
public double PAXDrt { get; set; }
public double TotalBus { get; set; }
public double TotalDRT { get; set; }
public double RevenueBus { get; set; }
public double RevenueDrt { get; set; }
public string Name
{
get
{
return string.Join(",", Path.Select(x => x + 1)) + " (" + WhatBest + ")";
}
}
public string WhatBest
{
get
{
return TotalBus > TotalDRT ? "BUS" : "DRT";
}
}
public PathInfo(int[] path, double routeLength, int population)
{
Path = path;
RouteLength = routeLength;
Population = population;
CalculateTotals();
}
public void CalculateTotals()
{
PAXBus = Population * 0.6;
PAXDrt = Population * 0.6;
var passengersAtPeakBus = PAXBus / 20.0 * 2;
var passengersAtPeakDRT = PAXDrt / 20.0 * 2;
BusPark = (int)Math.Ceiling((passengersAtPeakBus * ((2 * RouteLength) /
(double)CommercialSpeed)) / (18 * 60));
DRTPark = (int)Math.Ceiling((passengersAtPeakDRT * ((2 * (RouteLength + (RouteLength * 0.3)))
/ (double)CommercialSpeed)) / (18 * 60));
CostBus = BusPark * (20 / ((2 * RouteLength) / (double)CommercialSpeed)) * PriceKm * 2 *
RouteLength * 365;
31
CostDRT = DRTPark * (20 / ((2 * RouteLength) / (double)CommercialSpeed)) * PriceKm * 2 *
RouteLength * 365;
RevenueBus = PAXBus * (TicketPrice * 0.4) * 365;
RevenueDrt = PAXDrt * (TicketPrice * 0.4) * 365;
TotalBus = RevenueBus - CostBus;
TotalDRT = RevenueDrt - CostDRT;
}
}
In this code there are several parameters, which are making the whole program work and
calculate all necessary parameters:
PriceKm – which is an average price for 1 km of transport work. It is a constant parameter,
which is 80 (rubles) in the Moscow region.
CommercialSpeed – an average commercial speed for the chosen territory. It is a constant
parameter, which is 33 (km) for New Moscow.
TicketPrice – a price for a one-way ticket. It is a constant parameter, which is 55 (rubles) for
Moscow Transport.
RouteLength – a length of route in km. It is a dependent parameter. It is inserted into the
calculation after route creation.
Population – a number of individuals, living around each point. It is inserted into the
calculation after route creation. The number for this parameter comes from OpenStreetMap data.
The program looks for a polygon, in which the point is located, and takes a “population” parameter
from the polygon.
PAXBus and PAXDrt – an estimated number of passengers per day.
BusPark and DRTPark – an estimated number of vehicles.
CostBus and CostDRT – an estimated cost of starting a new route/routes in a year
perspective.
RevenueBus and RevenueDRT – an estimated revenue during operations in a year
perspective.
TotalBus and TotalDRT – an estimated net profit (net loss) of operations in a year
perspective.
Data collection and calculations
As far as the calculations need data, some of the numbers are already installed into the code
of calculations. However, there are two parameters, which are being calculated after the user locates
the routing points on the map: Population and RouteLength.
32
In order to receive the necessary data, the program creates a query to the Overpass-Turbo
system, which allows users to get the data from OpenStreetMap. Each point of the route is being
located on the polygon which, usually, represents a specific settlement. Each settlement has a
“population” parameter, which often has a data inside. However, there could be empty “population”
parameters. In this case, the program receives an ID of the polygon and makes a query to the WIKI
services, which could overlap this data gap and send us the necessary data. The way data is
collected is represented in Figure 15.
Figure 15. Query for "population" data
At the same time, the program starts to calculate RouteLength, which is different for each set
of options. The way the program calculates routing options and performs choice of mode is
presented in Figure 16.
Figure 16. Route options calculation and Mode option choice
Prototype work algorithm
In order to create a better understanding of the program workflow, an algorithm of the
prototype work is represented in Table 6.
33
Table 6. Prototype algorithm
User locates point on
map
Program creates
Program makes a
routes
query via
If there is no
Data
Check WIKI for data
OverpassTurbo to
No Data
in OSM for each
If there is Data
then 30 km
If route less, then 30 km
If route more,
checks “population”
polygon
If there is Data
Return 0
Divide
Write parameter
“Population” for
Write RouteLength
calculations
Perform calculations with
at all route parts
Bus
at all route parts
If Bus is cheaper
If DRT is cheaper
collected Data
DRT
If Bus is cheaper at
some route parts, and
DRT is cheaper at other
parts
Bus and DRT
Show spreadsheet
and visualize routes
on map
34
Prototype interface
In order to present the data in a way, that it is possible to percept, a choice of a simple
application design was made. The program features several buttons and two windows at maximum:
map with all the visual data and spreadsheet with all digit data. Figure 17 represents the state of the
first window after putting the points on the map: at each point there is a name of settlement and
number of individuals, who live there (S.1 & S.2).
Figure 17. Prototype design. Map
After performing all the calculations (S.2), the program creates a spreadsheet, with all the
data. The spreadsheet is represented in Figure 18.
As shown in Figure 18, there is a “Routing Name” column, which is an interactive part of the
spreadsheet. If the user clicks on a version of the route, the routing is being shown on the map (S.3).
An example of this interaction is represented in Figure 19. Figure 20 shows the maximum number
of possible windows in the program: map with routing and spreadsheet at the same time.
Figure 18. Prototype design. Spreadsheet
35
Figure 19. Prototype design. Routing map
Figure 20. Prototype design. Map and spreadsheet
Prototype work check
In the research part a grouping of settlements with no access to mobility was performed.
This groups were used to check the way the prototype works. Here the calculations of routing and
the results are provided.
36
Group 1: Red. Proposed mode by Wright: DRT.
Figure 21. Prototype calculations. Group 1
Group 2: Yellow. Proposed mode by Wright: Bus.
Figure 22. Prototype calculations. Group 2
37
Group 3: Blue. Proposed mode by Wright: Bus.
Figure 23. Prototype calculations. Group 3
Group 4: Brown. Proposed mode by Wright: Bus.
Figure 24. Prototype calculations. Group 4
38
Group 5: White. Proposed mode by Wright: Bus.
Figure 25. Prototype calculations. Group 5
Group 6: Green. Proposed mode by Wright: Bus.
This group had too many points, the PC could not finish the calculations.
Group 7: Orange. Proposed mode by Wright: Bus.
Figure 26. Prototype calculations. Group 7 - DRT Routing
39
Figure 27. Prototype calculations. Group 7 - Bus Routing
Figure 28. Prototype calculations. Group 7 - DRT&Bus Routing
40
Group 8: Pink. Proposed mode by Wright: Bus.
Figure 29. Prototype calculations. Group 8
Table 7. Comparison of results of mode choice (Wright & Prototype)
Mode of transport by
Mode of transport by
Wright
prototype
Group 1
DRT
DRT
Group 2
Bus
DRT
Group 3
Bus
DRT
Group 4
Bus
DRT
Group 5
Bus
DRT
Group 6
Bus
No data
Group 7
Bus
DRT / Bus / DRT&BUS
Group 8
Bus
DRT
As we can see from Table 7, the results of methodology by Wright and of prototype
calculations are different in 6 cases out of 8. This could be a result of different ways of calculating of
the parameters and different approaches to the question.
41
Limitations
The main part of the current prototype which is an advantage but still could be improved is
data collection from OpenStreetMap service via Overpass-Turbo: while gathering data on
population, it could receive no data at all for some parts of route or a false data, because of mistakes
in polygons in the information source. During tests, sometimes the same data of bigger
administrative entity was received instead of settlement “population” data. In order to partly solve
this problem, data on the population of all settlements in New Moscow was entered into the
database. However, it is crucial that the prototype is not an instrument for spatial analysis.
Furthermore, the current situation with polygons allows to user to make a connection only
between settlements, because cities are perceived as one polygon in OpenStreetMap, which leads
to writing population of the whole city for each point of the route. For example, if a user had an
intention to make a route in Moscow between four points, the program will write 12262741 to the
population four times. This may lead to miscalculations.
However, users can benefit from the prototype because of its easy-to-understand interface
and possibility to adjust all the calculations to the needs of the exact territory.
Practical implications
The prototype and the results of the calculations could be used by transport planners and
governmental bodies for a first-step analysis of development possibilities for mobility services at the
territory. In addition, transport activists and citizens could use this prototype for creating a demand
for mobility supply via mechanism of official communication with authorities.
Recommendations
In order to perform the calculation and receive accurate data, it is recommended to check
data on OpenStreetMap about the chosen for research and calculations territory. As far as
OpenStreetMap is an open-source platform, many users have already contributed to the
development and accuracy of the map.
It is recommended for Russian State Statistical Services to provide accurate and actualized
data for researchers in different forms – XLS, CSV, JSON, Shapefile, GeoJSON, etc. – in order to
allow individuals to perform an accurate analysis. As an example of such service could be a website
of
Canadian
government
agency
commissioned
with
producing
statistics
–
Statistics
Canada/Statistique Canada.
42
Suggestions for future development
As this project is a prototype, there could be several ways of development. In order to achieve
accurate calculations, the code could be developed, the source of data could be changed (from
OpenStreetMap to other accurate databases) and the data input function could be developed. The
last part could be created as a csv-file input to the program, which reads the encoded digits and
includes them into the calculations. The current design of the prototype could be developed in order
to make it more user-friendly.
43
Conclusions
The main objective of the study was to identify where it is better to provide conventional
Public Transport and where – flexible solution (e.g. DRT) for mobility supply, based on the New
Moscow case. In the text the notion of DRT was evaluated, spatial analysis with identification of
mobility supply and demand was conducted with the use of GIS systems and databases.
As a result of the spatial analysis, it could be mentioned that New Moscow is undersupplied
with mobility. With 93 routes, served by state transport operators and private companies, the territory
is not covered with estimated level of transport service: at least half of the settlements have no
access to the transportation at all. In order to provide a solution, eight groups of settlements were
created and a proposal of modes of transport by Wright was made.
In order to reach the main objective of the study, a prototype was proposed. This is an
application which allows user (decision-maker, transport planner or an activist) to make an easy firststep analysis of possible solution for a transport supply question for a territory. As a result, it creates
a several routing options with a choice between conventional bus or DRT and calculates all the
necessary economic data.
The basic choice between conventional bus routes and DRT was made on purpose: in order
to provide New Moscow with a new mobility mode and Moscow City Government and Department
of Transport and Road Infrastructure Development with a new possible solution for this rural and
remote territory. DRT at this territory could become the option because it allows to provide small,
dispersed groups of people (which are located in New Moscow) with a convenient mode of transport.
City of Moscow should consider this as a possible solution for this territory, because there are no
other options now for citizens.
The system of DRT in New Moscow should be developed in way, that allows to make it
commercially viable. First, a system of subsidies should be created, because 90% DRT systems fail
in first two years because of the lack of funds. Second, it is important to create routes, that are useful
both for the City and for the citizens. Third, it is necessary to use small buses for the system because
they are cheaper than the big one and all DRT systems do use this type of vehicle.
44
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46
Appendix 1. List of Settlements within area in public transport pedestrian accessibility area
and Calculation
Approximate
Russian
English
public
population
transport
within public
pedestrian
transport
accessibility
Proportion
Area in
Total Area
Name in
Population
Name in
pedestrian
Абабурово
Ababurovo
236
55,56
6,88
0,12
29
Александрово
Aleksandrovo
67
46,72
4,01
0,09
6
Анкудиново
Ankudinovo
6
14,27
6,11
0,43
3
Армейский
Armeiskii
156
8,97
8,97
1,00
156
Бабенки
Babenki
105
39,44
35,19
0,89
94
Белоусово
Belousovo
24
55,74
11,62
0,21
5
Богородское
Bogorodskoe
5
59,50
10,56
0,18
1
Богоявление
Bogoiavlenie
38
49,89
1,12
0,02
1
Большое
Bolshoe
27
142,31
78,70
0,55
15
Свинорье
Svinore
Ботаково
Botakovo
49
10,72
10,72
1,00
49
Брёхово
Brekhovo
10
153,12
1,87
0,01
0
Бунчиха
Bunchikha
24
72,22
54,15
0,75
18
Былово
Bylovo
422
73,04
31,93
0,44
184
Валуево
Valuevo
435
32,58
22,79
0,70
304
Ватутинки
Vatutinki
11081
123,04
54,05
0,44
4868
Верхнее
Verkhnee
53
59,85
41,72
0,70
37
Валуево
Valuevo
Власово
Vlasovo
78
120,71
20,40
0,17
13
Внуково
Vnukovo
7815
83,32
61,95
0,74
5810
Ворсино
Vorsino
22
31,60
11,04
0,35
8
Газопровод
Gazoprovod
2443
61,88
16,30
0,26
644
Голохвастово
Golokhvastovo
43
20,62
20,62
1,00
43
Десна
Desna
22413
100,36
46,74
0,47
10437
Дешино
Deshino
59
33,83
14,69
0,43
26
Дровнино
Drovnino
25
30,54
8,36
0,27
7
Зверево
Zverevo
50
97,80
65,75
0,67
34
Зимёнки
Zimenki
97
13,45
13,11
0,97
95
area
accessibility
area
47
Знамя Октября
Znamia
7394
164,70
44,19
0,27
1984
Oktiabria
Изварино
Izvarino
179
23,68
5,22
0,22
39
Ильино
Ilino
47
19,84
19,44
0,98
46
Ильичёвка
Ilichevka
467
18,71
3,13
0,17
78
Института
Instituta
248
13,84
6,79
0,49
122
Полиомиелита
Poliomielita
Каменка
Kamenka
151
86,98
39,02
0,45
68
Картмазово
Kartmazovo
308
78,72
19,61
0,25
77
Климовка
Klimovka
2
13,67
10,67
0,78
2
Кнутово
Knutovo
10
32,97
0,34
0,01
0
Кокошкино
Kokoshkino
19045
338,89
83,13
0,25
4672
Коммунарка
Kommunarka
5226
243,18
84,74
0,35
1821
Конаково
Konakovo
20
20,97
0,10
0,00
0
Красная Пахра
Krasnaia
2434
115,44
25,27
0,22
533
Pakhra
Красное
Krasnoe
95
45,61
25,78
0,57
54
Крёкшино
Krekshino
63
437,04
105,57
0,24
15
Кресты
Kresty
125
54,76
50,26
0,92
115
Кузнецово
Kuznetcovo
774
226,00
0,70
0,00
2
Кузовлево
Kuzovlevo
13
20,95
16,69
0,80
10
Лапшинка
Lapshinka
224
69,13
1,13
0,02
4
Ларёво
Larevo
77
22,36
15,54
0,69
54
Летово
Letovo
178
41,69
4,16
0,10
18
Ликова
Likova
122
16,21
11,93
0,74
90
ЛМС
LMS
5238
157,09
12,74
0,08
425
Львово
Lvovo
168
14,80
14,15
0,96
161
Мамыри
Mamyri
414
12,29
3,34
0,27
112
Марушкино
Marushkino
7082
272,08
18,87
0,07
491
Марьино
Marino
2301
144,98
20,45
0,14
325
Мешково
Meshkovo
504
117,61
1,25
0,01
5
Михайловское
Mikhailovskoe
100
42,15
23,13
0,55
55
Московский
Moskovskii
61224
465,38
98,22
0,21
12922
Мосрентген
Mosrentgen
20736
210,41
57,85
0,27
5702
Мостовское
Mostovskoe
227
58,85
27,90
0,47
108
Ознобишино
Oznobishino
298
88,65
63,47
0,72
213
Остафьево
Ostafevo
1149
56,42
0,56
0,01
11
48
Пенино
Penino
172
18,18
18,18
1,00
172
Первомайское
Pervomaiskoe
8481
37,07
13,18
0,36
3016
Поповка
Popovka
102
218,15
78,75
0,36
37
Посёлок
Poselok
12
10,20
0,99
0,10
1
кирпичного
kirpichnogo
завода
zavoda
посёлок
poselok
196
79,40
19,56
0,25
48
станции
stantcii
Крёкшино
Krekshino
Постниково
Postnikovo
157
55,27
1,38
0,03
4
Птичное
Ptichnoe
4262
296,72
81,92
0,28
1177
Пучково
Puchkovo
350
48,92
21,16
0,43
151
Пыхтино
Pykhtino
155
25,97
3,44
0,13
21
рабочий
rabochii
13714
99,97
21,21
0,21
2909
посёлок
poselok
Киевский
Kievskii
Рассказовка
Rasskazovka
408
53,00
31,13
0,59
240
Рассудово
Rassudovo
269
163,70
59,58
0,36
98
Рогозинино
Rogozinino
24
46,52
21,57
0,46
11
Рождественно
Rozhdestvenn
2
42,00
5,25
0,13
0
o
Руднёво
Rudnevo
187
140,91
47,88
0,34
64
Румянцево
Rumiantcevo
673
51,85
17,92
0,35
233
Саларьево
Salarevo
369
58,82
1,36
0,02
9
Свитино
Svitino
16
24,64
24,56
1,00
16
Семенково
Semenkovo
26
113,25
7,60
0,07
2
совхоза
sovkhoza
1460
148,97
51,03
0,34
500
Крёкшино
Krekshino
Сосенки
Sosenki
30651
119,47
71,11
0,60
18244
Страдань
Stradan
35
59,07
38,09
0,64
23
Терехово
Terekhovo
23
41,45
10,92
0,26
6
Товарищево
Tovarishchevo
2
23,00
18,56
0,81
2
Троица
Troitca
23
23,58
0,24
0,01
0
Троицк
Troitck
61079
1632,25
197,02
0,12
7372
Ульяновского
Ulianovskogo
24
39,96
22,74
0,57
14
лесопарка
lesoparka
49
Фабрики им. 1
Fabriki im. 1
3103
31,64
4,03
0,13
395
мая
maia
Филимонки
Filimonki
7026
46,09
10,01
0,22
1525
Черепово
CHerepovo
50
15,55
1,53
0,10
5
Чириково
CHirikovo
25
15,47
13,27
0,86
21
Ширяево
SHiriaevo
73
39,22
4,67
0,12
9
Щапово
SHCHapovo
9572
197,16
67,19
0,34
3262
Щербинка
SHCHerbinka
53281
762,33
80,08
0,11
5597
Юдановка
IUdanovka
174
85,02
69,42
0,82
142
Юрьевка
IUrevka
23
45,76
38,57
0,84
19
Яковлево
IAkovlevo
1048
102,51
12,49
0,12
128
Яковлевское
IAkovlevskoe
4138
105,56
62,59
0,59
2454
Ясенки
IAsenki
80
44,14
43,09
0,98
78
10499,805
2867,93
Sum 383891
101219
50
Appendix 2. List of Settlements without area in public transport pedestrian accessibility
area and Calculation
Name in Russian
Name in English
Population
Акулово
Akulovo
33
Алхимово
Alkhimovo
11
Алымовка
Alymovka
11
Бакланово
Baklanovo
51
Бурцево
Burtsevo
15
Варварино
Varavrino
25
Васюнино
Vasynino
73
Власьево
Vlas’yevo
94
Говорово
Govorovo
192
Голенищево
Golenishevo
10
Голохвастово
Golohvastovo
43
Горнево
Gorneevo
1
Городище
Gorodishche
124
Горчаково
Gorchakovo
32
Губкино
Gubkino
23
Губцево
Gubtsevo
15
Девятское
Devyatskoye
301
Дмитровка
Dmitrovka
1
Долгино
Dolgino
0
Евсеево
Evseevo
63
Елизарово
Elizarovo
48
Ерино
Erino
2692
Жуковка
Zhukovka
204
Заболотье
Zabolotie
1
Зайцево
Zaytsevo
10
Зосимова Пустынь
Zosimova Pustin
23
Каменка
Kamenka
151
Каракашево
Karakashevo
22
Киселёвка
Kiselevka
26
Кленовка
Klenovka
16
Клоково
Klokovo
20
Конаково
Konakovo
20
Конюшково
Konyushkovo
6
Костишово
Kostishovo
12
51
Красные Горки
Krasnie Gorki
61
Круча
Krucha
4
Кувекино
Kuvekino
97
Кукшево
Kukshevo
4
Лапшинка
Lapshinka
224
Лопатино
Lopatino
5
Лужки
Luzhki
5
Лукино
Lukino
0
Лыковка
Lykovka
1
Макарово
Makarovo
46
Малеевка
Maleevka
1
Марьино
Marino
247
Мачихино
Machikhino
1
Настасьино
Nastasiino
70
Никольское
Nikolskoe
1
Новиково
Novikovo
10
Новинки
Novinki
34
Овечкино
Ovechkino
19
Пёсье
Pesie
94
Писково
Piskovo
103
Поляны
Polyani
4
посёлок Станции Мачихино
Machihino
50
Прокшино
Prokshino
32
Пудово-Сипягино
Pudovo-Sypyagino
1
Пыхчево
Pykhchevo
26
Пятовское
Pyatovskoye
0
Рабочий посёлок № 1
Rabochiy Poselok 1
8
Раево
Raevo
34
Рожново
Rozhnovo
0
Сальково
Salkovo
13
Сахарово
Sakharovo
74
Середнёво
Serednevo
16
Спас-Купля
Spas-Kuplya
29
Староселье
Staroselie
21
Талызина
Talyzina
11
Тарасово
Tarasovo
13
Тетеринки
Teterinki
17
52
Троица
Troitsa
23
Уварово
Uvarovo
20
Фёдоровское
Fedorovskoe
3
Филино
Filino
15
Фоминское
Fominskoye
20
Харьино
Harino
73
Хатминки
Khatminki
10
Хмырово
Hmirovo
0
Хутора Гуляевы
Hutora Gulyaevi
6
Шеломово
Shelomovo
158
Ямищево
Yamisheco
128
Ярцево
Yartsevo
22
Total 6223
53
Appendix 3. Number of Validations of tickets at New Moscow Routes by Mosgortrans, 2018
Route
Annual
Discounted
Payed
Total
Per day
863
1005711
927021
1932732
5892
858
970101
785450
1755551
5352
737
996855
748043
1744898
5320
374
868548
816775
1685323
5138
108
956988
724967
1681955
5128
767
887135
763230
1650365
5032
32
947124
672017
1619141
4936
398
954742
575813
1530555
4666
33
818619
697493
1516112
4622
848
839548
671714
1511262
4608
288
831268
650058
1481326
4516
420
740289
627098
1367387
4169
781
696712
604250
1300962
3966
895
620439
670035
1290474
3934
911
552521
674197
1226718
3740
891
551553
618476
1170029
3567
531
576990
585916
1162906
3545
753
637376
498994
1136370
3465
577
496905
575734
1072639
3270
611
504929
553276
1058205
3226
882
442899
563536
1006435
3068
433
561958
434627
996585
3038
343
491796
381058
872854
2661
707
472448
355999
828447
2526
600
493882
330635
824517
2514
876
482114
341241
823355
2510
864
470549
347985
818534
2496
272
378189
368000
746189
2275
804
374814
357098
731912
2231
1002
367110
318822
685932
2091
982
313848
367767
681615
2078
734
360462
238265
598727
1825
497
357110
230232
587342
1791
508
316325
264055
580380
1769
54
333
295705
273102
568807
1734
750
327955
236652
564607
1721
779
273282
269854
543136
1656
249
271444
258599
530043
1616
802
304644
221490
526134
1604
512
269924
235508
505432
1541
878
275070
214971
490041
1494
881
274257
183094
457351
1394
526
237054
218244
455298
1388
819
233622
103555
337177
1028
579
154404
121530
275934
841
515
132116
125168
257284
784
874
164670
80817
245487
748
514
133644
111341
244985
747
866
130387
113888
244275
745
890
111311
105666
216977
662
889
137169
75780
212949
649
1001
124698
76055
200753
612
17
147429
48886
196315
599
Н11
78741
32424
111165
339
860
70764
21057
91821
280
550
213825
177573
391398
193
513
33270
29049
62319
190
305
No Data
No Data
No Data
No Data
307
No Data
No Data
No Data
No Data
308
No Data
No Data
No Data
No Data
485
No Data
No Data
No Data
No Data
554
No Data
No Data
No Data
No Data
575
No Data
No Data
No Data
No Data
30
No Data
No Data
No Data
No Data
32к
No Data
No Data
No Data
No Data
1003
No Data
No Data
No Data
No Data
1038
No Data
No Data
No Data
No Data
1043
No Data
No Data
No Data
No Data
1055
No Data
No Data
No Data
No Data
707к
No Data
No Data
No Data
No Data
55
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