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Modeling Transport Accessibility with Open Data: Case Study of St Procedia Computer Science Procedia Computer Science 101 , 2016 , Pages 197 – 206 YSC 2016. 5th International Young Scientist Conference on Computational Science Modeling transport 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 building 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 public transport 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 buildings, 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. Buses and minibus taxi are the preferable type of ground transport among citizen. The city has 40 tram lines, and the total length of the tram tracks is about 500 km. St. Petersburg trams 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 vehicles 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 transports: 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 bus 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 bus stop 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.
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