Procedia Computer Science

Procedia Computer Science 101 , 2016 , Pages 197 – 206

YSC 2016. 5th International Young Scientist Conference on Computational Science

Modeling accessibility with open data: Case study of St. Petersburg

Anastasia A. Lantseva1, Sergey V. Ivanov1 ITMO University, Saint-Petersburg, Russia [email protected], [email protected]

Abstract Transport accessibility is an important characteristic of a particular or territory, associated with the development of urban infrastructure and population density. In big cities, it is usually measured as the time required to get to the city center using with a focus on the network of subway stations. Other important types of movements include ground transportation and walking. In this study, we investigate the nature of the transport accessibility with various types of movement using open data and modeling approach. The aim of the study is to find the critical deficiencies in transport infrastructure and predict transport accessibility under changes of infrastructure and city growth. The proposed model can be the basis for optimization of public transport routes, schedules and periods of repairs. The calculation results can be updated at any time and apply to new territories as model input uses crawling of open data sources with high relevance. As the main example, we consider the data for St. Petersburg. The proposed model is quite general and can be applied to any big city.

Keywords: urban transportation, transport accessibility, open data, mobility

1 Introduction Nowadays transportation is an essential part of any modern city. Commercial and personal transportation are a daily necessity that tightly linked to economic activity. Every day each resident is forced to perform many transfers from home to work and back, visiting other places for education, shopping or leisure facilities. The standard distance, overcoming by residents during the day can exceed tens of kilometers (Elldér, 2014). The economics of regions and whole country depends on the efficiency of a transportation system which is constantly facing new challenges such as increasing housing density, uncontrolled growth of commercial property, urban planning mistakes, and others. For this reason, development of the transport and system is the most important and priority task of city government.

Peer-review under responsibility of organizing committee of the scientific committee of the 197 5th International Young Scientist Conference on Computational Science © 2016 The Authors. Published by Elsevier B.V. doi: 10.1016/j.procs.2016.11.024 Modeling transport accessibility with open data: Case study of St. Petersburg Anastasia A. Lantseva and Sergey V. Ivanov

The proportion of the urban population is known to be more than 70% in Russia, and comparable to this value in most developed countries. Any city is a complex system, which cannot function in the absence or the deficiency of transport provision. One of the ways to solve transport problem is the development of road network taking into account the needs of private cars. However, it is obviously insufficient, because personal transport is not accessible to everyone, it is less efficient, than, for example, metro from an economic point of view, and sometimes less rapid due to traffic jams. In this way, the public transport has high importance for city infrastructure, particularly for high-density cities, like most of the European and especially Asian megacities. Metro is the most preferred transport in the areas with over one million people because it provides the fastest and the safe transportation of large passenger flow. However, its accessibility is limited by the location of metro stations and efficiency of ground transport. Often many residents have to use ground transport that goes between the different districts of the city and to the nearest metro stations, and then they use the metro to get final destination (Slack, 2016). Now the availability of open data about roads, stops, locations of , the density of population and traffic gives us the ability to build a model, which can predict changes in a load of urban transportations system for planning its development. Here is a short description of the transportation system of St. Petersburg which is under study in this research. All information may be found in open sources (Public transport, 2016). The metro provides the largest share of all local passenger transportation: more than two million per day. St. Petersburg subway includes five lines with the interchange nodes in places of their intersections, the total number of stations is 67 and length of lines is 113 km. and minibus taxi are the preferable type of ground transport among citizen. The city has 40 lines, and the total length of the tram tracks is about 500 km. St. Petersburg transport up to 200 million people a year. The city has more than 300 municipal and more than 350 commercial routes that carry over 500 million passengers a year. Thus, despite the high level of development of transportation system, demand on the public ground transport remains extremely high, particularly during rush hours. It is supposed to be connected with the low flexibility of transport routes and the lack of along them. Using the open data on whole transportation system of the city, we have built a model of transportation accessibility detailed down to individual buildings. It can be used to assess the quality of the urban environment and as a basis for improving the transport system. The model is also suitable for the "if-then" analysis of transportation accessibility on a macro level, like changes in a particular area at the time repair works.

2 Related works In the modeling of urban mobility, there is an inevitable problem of estimation of transport accessibility. Obviously, the transport accessibility of a particular area in the city depends on many factors the main of which is public : routes network, a holding capacity of transport units, frequency and periodicity of movement. A study that was realized in the paper (Wibowo S. S., Olszewski P., 2005) is dedicated to developing the method of evaluating of walking movement to a stop, which is an important part of transport accessibility on the whole. It is reasonable to assume, that index of the walking accessibility depends on the distance, which people need to overcome. However, the authors propose to count up the walking distance considering the different elements, for example, crossings at intersections, walkways, sidewalks, curb and others. The difficulty of walking equivalent is estimated by the set of some elements and distance that people need to pass to overcome each of these elements on the route. The authors claim that their method allows making a more accurate assessment of the accessibility of stops, than distances without taking into account characteristics of the route. The study described in the paper (Horner M. W., Downs J., 2014) is based on the time geographic density estimation (TGDE). The aim is the estimation of the level of transport accessibility. The more traditional approach is described in the paper (Yigitcanlar T. et al., 2007)

198 Modeling transport accessibility with open data: Case study of St. Petersburg Anastasia A. Lantseva and Sergey V. Ivanov

where the authors consider two options: walking movement and the public transport. The accessibility level was divided into four categories: high, medium, low and poor. If the is located in a radius of 300 m and frequency of transport at this stop does not exceed 10 min, then the transport accessibility of this stop is suggested to be high. The proposed method operates by the term "layer." Each layer represents one factor like, for example, distance to the bus stop or frequency of transport at the bus stop. The similar approach is discussed in the paper (Litman T., 2011) whose authors argue that only the factor of mobility can give the overall picture of what is happening in reality. The advantage of this approach is a combination of all factors which affect the people’s movement (i.e. mobility). Also, importance and significance of transport accessibility for the urban mobility assessment are referred in the paper (Jakimavičius M., Burinskiene M., 2009). In the paper (Trentini A., Mahléné N., 2010) the authors have divided the city into areas to analyze each area separately. They estimate some characteristics that are important for investigated area which is, for example, the density of residential and commercial buildings and population density. Also, authors made a comparative analysis of the average time, which takes to overcome each area using public transport or on foot. As a result, the authors described that problem with transport accessibility in areas, which have a large difference in the number of residential and commercial buildings, the central areas and sleeping quarters. Application of similar concept we can find in the paper (Jakimavičius M., Burinskiene M., 2009). The investigated city is divided into areas; the obtained characteristics of an area are recalculated with the use of data on traffic in the area. Thereby we obtain a square matrix, where rows and columns denote the analyzed areas and value in a cell is a time of moving from one area to another. This research is based on real data, and the one of goal was the forecasting the future transport situation in the city. The interesting results were obtained in the paper (Anderson P., Levinson D., Parthasarathi P., 2013), where the main goal was the study of the mobility of passengers in public transport. The main feature of this paper is the using of real data on the bus stop locations and contactless smart cards. This information promotes to estimate the fraction of people not using public transport. Also, popular areas of research include studies of subway networks. In the paper (Foell S. et al., 2014) authors explore the development of Seoul subway and the change on transport accessibility for different areas (districts). In addition to the evaluation of accessibility, authors consider the construction of the new lines and stations of metro and their influence on the characteristic such as people density distribution into areas. For each area, authors have estimated the average coefficient of transport accessibility; this research was conducted for some cities with different levels of metro development. In the paper (Song Y., Kim H., 2015) authors proposed an approach for the estimation of transport accessibility based on the splitting city into different concentric areas with their specific indexes of connectivity and availability. The similar research was made for Edinburgh (Karou S., Hull A., 2014), where authors analyzed the transport accessibility for each home and, as a result, showed the evaluation of accessibility for the whole region using a five- point scale. The authors in the paper (YU Wei, 2011) tried to solve the similar tasks for China, and their results allow to estimate the degree of improvement of the overall transport accessibility after the building of new metro lines and stations. As a result, we can distinguish the following features of existing solutions: x There is no common approach for evaluation of transport accessibility. The choice of the model depends on city size, transport types, and features of local culture. x Evaluation of transport accessibility is a very complex task, which depends on various types of data: transport infrastructure, labor market and people's activity, the of districts, and population density. x The key factors for transport accessibility evaluation are the mobility patterns that define the demand for transportation services.

199 Modeling transport accessibility with open data: Case study of St. Petersburg Anastasia A. Lantseva and Sergey V. Ivanov

3 Open Data The use of open data is a popular approach in modern studies (Lantseva,2015). For this study, we have analyzed many resources that provide publicly available information including data on public transport, particularly, the routes and bus/tram stops. In the course of research, we have selected three resources that provide the most actual information: St. Petersburg public transport portal, St. Petersburg routes (Portal,2016) and OpenStreetMap (OSM, https://www.openstreetmap.org/). The first resource is created by St. Petersburg Government and provides the most relevant information on public transport; it also includes temporary route changes. Also, the site contains information about transport companies that provide transportation on regular bus routes and a transport map that shows current positions transport units in real time. In addition to web-interface, the resource has API, which provides an ability to obtain the information contained in a portal database. However, API is not entirely open, and the access with instruction for use is available upon request. In its turn OSM provides the ability to load large amounts of data in XML format, “St. Petersburg routes” resource does not allow to implement the same approach. During the work with this resource, we have to generate and send requests to web pages, which contain data about routes, and then we extract the required information by parsing. However, this source has a more complete and accurate information about bus, train and tram routes, then OSM edited by the community. In its turn, OSM provides additional opportunities for geospatial research, for instance, it contains detailed documentation about a plan of the city, various tools for working with map data, which is supported by a huge community, and OSM data format is a well-formalized. Moreover, OSM includes the data about buildings: their location and purpose (residential buildings or the construction of another type). Considering the specificity of the study, as a source of information about routs we have chosen “St. Petersburg routes”, and OSM is chosen for information about bus stops and metro locations. “St. Petersburg routes” source allows obtaining data about both forward and return (reversing) routes, the data is complete and contains full routes considering turns and ring roads. The data are encoded in GeoJSON format and contain all the segments of the route, the longitude, and latitude of the specific point, which can denote bus/, turn, and movement on a ring or crossroad. This data allows getting detailed information for each route for each type of public transport. Also, the data contain the type of public transport, its id and stops names. As mentioned above, OSM data contain information about buildings and stops locations; it allows to estimate the distance from building to the nearest bus/tram stop. Also, the information about building location allows making assumptions about the number of people using one or another bus/tram stop. Finally, we know the locations of bus stop/tram and building, and we can select the set of buildings tied to a particular bus/tram stop considering the distance between them. In the result we generate the full list of stops with associated nearest buildings. In this case, we make the assumption that the people living in any building will likely use the nearest bus stop. Obviously, this rule is pretty rude and not applied to buildings located in the vicinity of metro stations (closer than bus/tram stop), where people may cover the distance on foot. In this study, we consider metro (subway, underground) as the main type of urban transport. It is true for big cities like St. Petersburg, where surveys show the apparent desire of residents to live near the metro stations. Therefore, buses, trains, trams and other ground transport are used by people to get to the and to continue the movement by metro. Also, we use information about the location of metro stations for the matching houses and metro station. The buildings outside the vicinity of metro stations are tied to metro station using the following rule: 1. The bus/tram stop nearest to a particular building should be included in the route with one (or more) stops near the metro station. 2. The route may be associated with a metro station if one of its stops is close to this metro station. 3. If there is a choice between several stations, then we choose the closest in the course of the route from a building.

200 Modeling transport accessibility with open data: Case study of St. Petersburg Anastasia A. Lantseva and Sergey V. Ivanov

Figure 1 shows the buildings, tied to bus stops and the metro station “Victory Park” (“Park Pobedy”). The buildings are marked by squares with the corresponding color. The color indicates the specific metro station, to which the building is tied. In the figure green color denotes “Park Pobedy”, red “Moskovskaya” and blue “Elektrosila” station. The metro stations are marked with a circle. The gray stars denote the bus stops, and each house is tied to the nearest bus stop by the gray line. Also, there is the pink area showing the house that is located in walking distance to metro station, where people do not use ground transport. Bus routes are shown with solid lines with different colors. The interesting point is that the shortest distance from building to metro station, sometimes, does not match with a distance of transport route. Sometimes it is more profitable to go to a distant station because it is faster. Some buildings that are pretty close to “Park Pobedy” station, in reality, are tied to other stations.

Figure 1: Linking buildings to the metro station in the area of "Park Pobedy" metro station. Some characteristics of estimated distances for the whole city are presented in Table 1. Here we can notice that maximum distance from the house to the bus stop is about 1.5 km. This is because the region of St. Petersburg includes remote areas, which are individual towns, and routes covering central regions of St. Petersburg does not cover all of the stops in these areas. Therefore, residents, probably, have to get to the right stop by local public transport. Also, we can see characteristics of the linear distance from home to the nearest metro station and the ratio of the direct path to the real path, which is calculated with the curvature coefficient.

max min average Distance from the house to the 1.494 0.0077 0.273 bus stop(km) Direct distance from house to 38.79 0.0063 7.328 metro station(km) The curvature of the path 5.991 0.2786 1.867 Table 1: Characteristics of different distances in the city.

201 Modeling transport accessibility with open data: Case study of St. Petersburg Anastasia A. Lantseva and Sergey V. Ivanov

4 Evaluation of transport accessibility For the buildings located in walking distance to metro station, we calculate the linear distance from the house to a station and multiply it by a coefficient of curvature of the route. For other buildings, we add up the distance to the nearest bus stop considering the coefficient of curvature and the shortest path from the bus stop to metro station taking into account all routes on this bus stop. Some routes at the stop determine the time needed to wait for the next transport unit. In this way, we have all distances from the buildings to the nearest metro stations considering the house to stop walking distance and distance to the nearest bus stop and the path of public transport to the metro station. It is important that we consider all public transports (bus, train, and tram) for each bus stop, which are passing through it. Thereby we have three distances from house to metro and choose the best one. Finally, we can calculate the time needed to get from a particular house to the nearest metro station. For the houses within walking distance, the equation is: ஽௜௦௧ሺுǡெௌሻሾ௞௠ሿ (͸Ͳሾ݉݅݊ሿ (1 כ ݐ݅݉݁ுሺ݉݅݊ሻ ൌ ௞௠ ௌ௛ൣ ൗ௛௢௨௥൧ For other building ஽௜௦௧ሺுǡ஻௦ሻሾ௞௠ሿ ஽௜௦௧௉௔௧௛ሺ஻ௌǡெ௦ሻሾ௞௠ሿ ଶ଴ሾ௠௜௡ሿ ͸Ͳሾ݉݅݊ሿ ൅ቀ ቁǡכݐ݅݉݁ுሺ݉݅݊ሻ ൌ൬ ௞௠ ൅ ௞௠ ൰ ௌ௛ൣ ൗ௛௢௨௥൧ ௌ௧ൣ ൗ௛௢௨௥൧ ே௥ሺ஻௦ሻ (2) where H is a house, Bs is the nearest bus stop for H, Ms is the nearest metro station, Sh is average speed of human (≈4 km/hour), St is average speed of public transport (≈ 32 km/hour), Nr is the number of routes on a bus stop. These values are taken from online routing services. For the building, where movement from building to metro includes public transport, besides the calculating of time needed to get to a bus stop and the metro station, we need to take into account waiting time of public transport. Obviously, that if the bus stop contains a lot of routes, the waiting time is less. However, absolute time is suitable for evaluation of transport accessibility, because it has not predefined min and max values and scale. Thus, we suggested to calculate the coefficient of transport accessibility as follows:

୲୧୫ୣ  ൌͳǦ ౞ , (3) ୌ ୫ୟ୶ሼ୲୧୫ୣ୤୭୰ୟ୪୪୦୭୳ୣୱୣୱሽ

where AH is an index of transport accessibility for house H, timeH is calculated by formulas (1) or (2). Using the formula (3) we make the normalization for getting the index of transport accessibility in the range from 0 to 1 for each house. The situation with the houses having low accessibility index requires further explanation. There are the areas (districts), included officially into the city, but located far from the city center. For example, Sestroretsk is a municipal town in “Kurortny District” of the federal city of St. Petersburg, located 36 kilometers away from the main city. Also, this district has poorly developed public transport that goes to St. Petersburg, and it includes fewer people than any commuter town in the city. Thereby the importance of this district is less than the interest to the commuter towns (sleeping quarters) with a large number of residents. To emphasize the importance of separate zones, we may take into account density of people in in the city. For that, we have the data provided by the Office of Federal Migration Service in St. Petersburg and Leningrad region about an official number of people registered in each house. These data may not be referred as ‘open’, but it is very fruitful for the analysis of the importance of zones with low transport accessibility. Due to the large difference in the sizes, the number of people living in these houses may vary in big range, especially in neighboring houses. Tus, we have divided a map of city into areas, where each area is the square [5 x 5] km2. For each area, we calculated the total number of people, and, thus, we had obtained comparable population density for the whole city. When we know the density and transport

202 Modeling transport accessibility with open data: Case study of St. Petersburg Anastasia A. Lantseva and Sergey V. Ivanov

accessibility, we can select the areas that have high density and low accessibility also with high density and good accessibility. The figure 2 (a) shows the combination both of these estimations, where the color denotes population density. We can notice that regions with high density are located on the periphery of the city. Different figures mark the transport accessibility divided into five categories from low to high. On histogram (Figure 2 (b)), we can notice that buildings with low density and accessibility take about 10%. Those a districts like Sestroretsk, which is the separate town. Therefore, in this study, these regions are not important. However, the areas with high density and low accessibility are interesting for the study of city mobility on the whole.

Figure 2: Evaluation of transport accessibility for St. Petersburg with consideration of population density a) The whole map of the city; b) 3d histogram with joint characteristics.

Distance(km) Transport max min average accessibility low 86 20 35 medium 6 2.5 4 high 0.8 0.06 0.4 Table 2: Distances from the house to the metro station according transport accessibility. Table 2 demonstrates max, min and average distances to metro station for the house having low, medium and high transport accessibility. We can notice that houses with high accessibility are located in a radius of less than 1km from metro station, i.e. within walking distance. There is an opposite situation with low accessibility; these houses are usually located far away from the city. Most areas of low accessibility, fortunately, have a low density. Here we do not analyze a time, which is used in the evaluation of accessibility index because may vary due to traffic factor, while distance is a constant characteristic. Particular interest are the zones with low accessibility and high density (see Figure 3). Percentage of such areas in the whole city is quite low, and we can make an obvious conclusion that these areas are located far from the nearest metro station. Unfortunately, some of these zones are pretty large.

203 Modeling transport accessibility with open data: Case study of St. Petersburg Anastasia A. Lantseva and Sergey V. Ivanov

Figure 3: The areas with high density and low transport accessibility.

5 Contingency analysis For contingency analysis, we have chosen two scenarios: one with a new station and one with the station under repair. For the first model experiment, we have chosen “Yugo-Zapadnaya” station, which is supposed to be built in nearest time. It will be located in the north of the city in Krasnoselsky region and the South-West district. This station will help to unload the difficult situation in this district and, it is located not far from areas having high density and low accessibility. Figure 4 demonstrates the area in the current situation (before), and changes after adding a new station. Some part of the houses improved their accessibility than previously, however, the adding of that station did not bring significant improvements for the other areas, because there are two are stations not far from a new one.

Figure 4: Transport accessibility before the addition of the new "Yugo - Zapadnaya" metro station and after. The similar situation is observed for the “Yelizarovskaya” station. This station has been closed for repairs since February of 2016 to January of 2017. In Figure 5 we can see the accessibility before the closing station and after with taking into account the temporary routes, such as “8b” and “8v”, and extended route “31”. The accessibility in this area did not change considerably because there is

204 Modeling transport accessibility with open data: Case study of St. Petersburg Anastasia A. Lantseva and Sergey V. Ivanov

“Lomonosovskaya” station near and the new routes are covered well the selected area around the closed metro station. It shows the right measures to extend temporary routes.

Figure 5: Transport accessibility before the closing of the "Yelizarovskaya" metro station and after.

6 Discussion and conclusions In this work, we have presented a pretty simple approach to modeling transport accessibility with open data. We carried out some numerical experiments with open data for St. Petersburg and we got reasonable results. The proposed model may be a basis of useful tools for city planning, transport optimization, and other urban challenges. Approaches for modeling transport accessibility is closely related to mobility patterns and thus should be verified on these data. Unfortunately, right now we do not have a clear picture of the mobility for the whole city, but some incomplete data are available for partial verification of the model. Another important thing is the influence of traffic on the mobility, and consequently on transport accessibility. Traffic data are highly volatile and may distort the real picture. As far as we know, traffic roughly equally affects all areas of St. Petersburg, and cannot greatly change the static evaluation which is easier to understand and analyze. The study of these two factors (measurements on mobility and traffic) is the subject of future research. This work was financially supported by Russian Foundation for Basic Research (project 15-29- 07034\16)

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