Management system of urban transport market

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Transportation Research Procedia 36 (2018) 334–340 www.elsevier.com/locate/procedia

Thirteenth International Conference on Organization and Traffic Safety Management in Large Thirteenth International Conference on Organization and Traffic Safety Management in Large Cities (SPbOTSIC 2018) Cities (SPbOTSIC 2018)

Management system of uurban transport market Management system of uurban transport market * Mark Koryagin*, Alexei Dementiev, Victor Sokolov Mark Koryagin , Alexei Dementiev, Victor Sokolov Siberian Transport University, 191 Dusi KovalchukSt., Novosibirsk, 630049, Russia Siberian Transport University, 191 Dusi KovalchukSt., Novosibirsk, 630049, Russia

Abstract Abstract The article isdedicated dedicated to the urban transport management system. The game theory is used as mathematical model. model Classification of the management system is basedmanagement on the number of participants transport operators, municipal The article isdedicated dedicated to the urban transport system. The game(passengers, theory is used as mathematical model. model authorities). definitions of municipal monopoly, mixed market, free(passengers, market andtransport monopoly are given. Target ClassificationRespectively, of the management system is based on the number of participants operators, municipal functions andRespectively, strategies of the parties ties ofofthemunicipal transport system havemixed been defined: costsand and monopoly time spent in strategy authorities). definitions monopoly, market,financial free market aretransit given.((strategy Target ofchoice and of mode of ties travel) fortransport passengers, of frequency) for transport operators, , time((strategy loss of , profit functions of androute strategies the parties of the system have(strategy been defined: financial costs and time spent in transit strategy passengers transport costs of (strategy of, municipal transport for the authorities. . The, model of the ofchoice ofand route and mode travel) of forfrequency passengers, profit (strategy of traffic) frequency) for city transport operators, time loss of route network railway costs lines and bus routes has beenofpresented. example of a city model of 9. The districts (3 of 3). by the passengers andoftransport (strategy of frequency municipalAn transport traffic) for the cityconsists authorities. model Municipal transport (metro or and trambus in routes our model) has presented. short routes theofmain (networkofconsists of 6(3routes: model consists 9 districts route network of railway lines has )been Anlinking example a citydistricts by 3). 3 horizontaltransport and 3 vertical The in busour route network of 12 linking routes: 6the routes the North-East the East district ) hasconsists Municipal (metroones). or tram model) short routes mainconnecting districts (network consists of 6with routes: s connecting North-West South-West district, and 6ones). routes the North district with6the South-East district. Target arget of the 3 horizontal and 3 vertical The bus route network consists of 12 routes: routes connecting the North-East Eastfunctions district with participants beenand obtained for model.. Numerical example showswith how the the South-East market structure affects characteristics of the s the North-West South-West have district, 6 routes connecting the North district district. Target arget functions of transport system. of development of. the presented modelsshows have been participants have Directions been obtained for the model. Numerical example how defined. the market structure affects characteristics of the transport system. Directions of development of the presented models have been defined. © 2018 The Authors. Published by Elsevier B.V. © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ((https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. by Elsevier B.V. ND license This is an open accessPublished article under the CC BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) review under responsibility of the scientific committee of the Thirteenth Thirteenth International International Conference Conference on on Organization Organization and and Peer-review This is an open access article under the CC BY-NC-ND ND licenseof ((https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee the Traffic Safety Management in (SPbOTSIC 2018) 2018). Peer-review review responsibility of Cities the scientific committee Traffic Safetyunder Management in Large Large Cities (SPbOTSIC 2018).of the Thirteenth International Conference on Organization and Traffic Safety Management in Large Cities (SPbOTSIC 2018) 2018).

Keywords: management system; urban passenger transport route; route scheme; choice of travel method; game theory. Keywords: management system; urban passenger transport route; route scheme; choice of travel method; game theory.

*

Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000 000-0000 Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000 000-0000 E-mail:[email protected] 2352-1465© 2018 The Authors. Published by Elsevier B.V. https://creativecommons.org/licenses/by-nc-nd/4.0/)Peer-review under This is an open access under the CC by BY-NC-ND license (https://creativecommons.org/licenses/by 2352-1465© 2018 Thearticle Authors. Published Elsevier B.V. responsibility the scientific committee the Thirteenth International Conference on Organization and Traffic Safety Mana Management https://creativecommons.org/licenses/by-nc-nd/4.0/)Peer-review under in This is an openofaccess article under the CCof BY-NC-ND license (https://creativecommons.org/licenses/by Large Cities (SPbOTSIC 2018). responsibility of the scientific committee of the Thirteenth International Conference on Organization and Traffic Safety Mana Management in Large Cities (SPbOTSIC 2018). * E-mail:[email protected]

2352-1465  2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the Thirteenth International Conference on Organization and Traffic Safety Management in Large Cities (SPbOTSIC 2018). 10.1016/j.trpro.2018.12.104

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Mark Koryagin et al. / Transportation Research Procedia 36 (2018) 334–340 Mark Koryagin, Alexei Dementiev, Victor Sokolov / Transportation Research Procedia 0000 (2018) 000000-000000

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1. Introduction Developing countries encounter substantial transportation problems due to increasing level of vehicle-topopulation ratio (Hovavko, 2014). Infrastructure cannot be developed with the same rate. The method proposed in article (Agureev, 2017) enables to perform a multidimensional modelling of the dynamics of development of the transport system in the region with assessment of the cost of development of the road network. Development of new solutions to change transport system requires a large amount of source data and specialized software (Levashov, 2017). Land allotted for road construction is less than 10% against 20-35% in developed countries (Cervero, 2013). On the other hand, cities urgently need to implement the transport strategy (Russian Federation, 2011) to reduce the adverse environmental effect of transport (Cervero, 2013; Khovavko, 2014). The main goal of research is to find a model of sustainable urban development or a model of sustainable mobility of citizens. Vuchic(Vuchic, 1999) selects four levels of transport planning, where the first level describes the relationship between the city and the transport system: interaction of transport systems with other features of the city (economy, environment protection, housing, social processes, etc.). The city is a complex subject of research, since it integrates diverse interests of people and organizations. The work (Evans, 1987) presents the participants, including “potential passengers”, “operators” and “public bodies”, but the problem is presented as a non-game model. To resolve the conflict of interests between the parties better, the best suited concept is that of non-cooperative games. The interest of scientists to the game theory in the field of transport increases significantly (Hollander et al., 2006). Many authors have studied competition between transport operators (Evans, 1987), (Johnson and Katsoulacos, 1988; Koryagin, 2015), or between operators and municipal authorities (Hollanderet al., 2006). 2. Management system This article reviews the following participants of urban passenger transport system: passenger flows, municipal authorities and public transport operators. Their strategy and target functions are presented in table 1. Table 1. Participants of urban transportation system. Participants

Strategies

Purposes

Passengers

Choiceof route and mode of transport

Based on logit model

Authority

Frequency of municipal transport traffic

Minimization of transportation costs and time loss for population

Transport operators

Frequency of commercial public transport traffic

Profit

Multiple options of management system of urban passenger transport have been reviewed. The proposed classification is featured mainly by the presence of players. That is, depending on the participants, we obtain various urban transport management systems (Table 2). Table 2. Management system structure. No. 1 2 3 4 5 6 7

Participants State monopoly Mixed market Free market Monopoly

Passengers + + + +

Municipality

Transport operators

+

-

+

+

-

+

-

+

The number of participants 1 1+N 2 2+N K K+N 1+N

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Only one of seven models has one participant, while others must be reviewed from the point of view of conflict of interest, and solution of this task is referred to the game theory. Such models were addressed in several works of the authors. 3. Routes The model consists of 9 districts (3 by 3). Municipal transport (metro or tram in our model) has short routes linking the main districts. The high cost of infrastructure prevents building a large number of routes. Therefore, passengers are provided with free transfer. Thus, passengers can get to their destination for a single fare. The network consists of 6 routes: 3 horizontal and 3 vertical ones (Fig. 1).

Fig. 1. Direct routes (type 0).

In this model, the bus route network consists of 12 bus routes: 6 routes connecting the North-East district with the South-West district, and 6 routes connecting the North-West district with the South-East district. Thus, all possible routes are presented in opposite corners. Routes are grouped under the same form of the route: with one turn (Fig. 2); with two turns (Fig. 3); with three turns. (Fig. 4).

Figure. 2. Routes with one turn (type 1).

Figure. 3. Routes with two turns (type 2).

Figure. 4. Routes with three turns (type 3).

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Possibility for using the routes is shown in Table 3. The first line and the second column of the table describe a city district (the vertical and horizontal ones), the first column specifies district code. Inside thetable, Ki,jmmeans the number of each type of routes (number of routes of m-type provides direct access between districts i and j). For example, a horizontal urban route (Figure 1), 2 routes of type 1 (Fig. 2), 1 route of type 2 (Fig. 3) and 2 routes of type 3 (Fig. 4) are available between districts 1 and 2. Table 3. Number of routes providing direct transit between districts. (i) 1 2 3 4 5 6 7 8 9

(1.1)

(1.1) (1.2) (1.3) (2.1) (2.2) (2.3) (3.1) (3.2) (3.3)

1 1 1 0 0 1 0 0

2 2 2 0 1 2 1 2

1 0 1 4 1 0 1 2

2 0 2 4 1 0 1 2

(1.2) 1 2 1 2 1 1 1 2 1 2 0 1 1 0 0 1 0 2 2 0 0 1 1 0 1 0 1 1 1 0 1 0 0 2 0 0 1 1 1 1

(1.3) 2 0 0 1 2 1 2 0 0 1 1 1 0 4 4 1 2 1 2 1 2 2 2 1 1 1 1 0 2 0 0 0

(2.1) (2.2) 2 1 2 0 0 4 1 1 0 1 0 2 1 1 1 0 0 4 1 0 2 0 2 2 0 0 2 1 1 1 2 1 2 0 0 4 1 1 0 1 0 2 1 1 1 1 2 1

4 2 4 2

0 0 1 1 1

(2.3) 1 1 1 1 2 1 0 0 1 1

1 0 2 2 0

1 0 0 1 0 0

(3.1) 2 0 1 1 2 2 2 1 0 4 1 1

0 1 2 2 4 1

0 1 0 0 1 0 1

(3.2) 1 1 0 0 1 1 1 1 0 2 1 1 2 1

1 2 1 0 2 0 2

0 4 0 1 1 1 2 0 1 1 0 1 2 1 2 2 1 2 1 2 1 2 0 0 1 2 1 2

0 0 1 0 0 1 1 1

(3.3) 2 2 1 1 2 0 1 1 0 4 2 1 2 0 2 1

2 1 0 1 4 2 0 2

4. Mathematical model It is worth to assume that each type of the routes is controlled by independent transport operator. As a result of the symmetry of cities and routes of each type, the similar frequency of traffic is defined for routes of each type: µmfrequency of traffic for routes of m type. Let us assume that the passenger traffic between each pair of districts has the same intensity λ, i.e., the total passenger traffic intensity is 72λ (since we do not consider the passenger traffic within each district). Probability for use of urban transport betweeniandj – pi,j, hence, λi.j= λpi,j intensity of passenger flow usingpublic traffic between districts i and j. Then the waiting time for public transport:

 1    3 K m   2 , 0 i j m   m1



 Ki0, j  1 K 0  i, j  3 3  m 2 K     m0Kim, j m  m1 i, j m 0  2



(1)

It should be noted that (1) assumes the maximum time for waiting for a deterministic flow (or the average value of Poisson flow). The last summand describes the case where the districts are located on the same railway line. In other cases, passengers can choose 2 lines (a vertical or a horizontal one). In this case (the second summand) the passenger has to change the line. This model assumes that passengers choosethe first arrived vehicle. This is possible in two cases: 1) stations are located near bus stops; 2) information system indicates the arrival time for all buses on route. The probability of passengers choosingmunicipal transport (2) and commercial operators of type l (3) is equal to

1  K  2  K   K   2  K  0 i, j

3

m 1

m i, j

0 i, j

0

3

m

0

m0

1  K  K   K  K   2  l i, j

0 i, j

3

m 1

m i, j

m

0 i, j 3

l

0

0

m i, j

(2) m

Kil, j l

Km  m0 i , j m

(3)

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Thus, municipal (railway) transport (4) and route operator of type1(5) gain profit:

 1  K i0, j  K il, j l K i0, j K il, j l    3 K m   2  3 K m    pi , j   4Cl l i , j 1    m 0 i , j m  0  m 1 i , j m

(4)

 1  K i0, j  2 0 K i0, j 0     pi , j   C0 0 ,   3 K m   2 3 m  K i , j 1    m0 i, j m  0  m 1 i , j m

(5)

9

9

wherein Cl means transportation costs for 1 trip via the route of type 1, β is the tariff charged at the municipal transport. The criterion of efficiency of the transport system consists of the total loss of time for passengers and transportation costs.

  K i0, j 3 0    pi , j C p  ] , 1   K  i, j   3 3 m m i , j 1    m0Ki, j m   m 1K i , j  m  2 0 9

3

9

m 1

i , j 1

C0 0  4Cm m   1  pi , j  C c Li , j wherein Cp means the cost of time, Li, j is the path length between districts i and j, Ccis the cost per unit of the route length. The passenger’s choice depends on the speed of transport: bus speed Vb, train speed Vt, car speed Vc. Time of travel by car:

Ti ,cj 

Li , j Vc

Average time of travel by public transport: 0 l Li , j  1  K i , j  K i , j l K i0, j K il, j l     3 T  3 1  K   3 m  V  3 m m m  m1Ki, j m  20  m0Ki, j m b   m1Ki, j m  20  m0Ki , j m  p i, j

3

0 i, j

K i0, j

 1  K i0, j  2  0 K i0, j  0  Li , j   3  3   K im, j  m  2  0  K im, j  m  Vt m0  m 1  The theory of choice of the method of travel describes the decision-making process, which depends on many parameters. For example, the logit and probit distribution are normally used for such models (Horowitz et al., 1986; Litman, 2011; Bravo et al., 2009). Logit model describes the decision-making according to passenger survey data.

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Logit function can be represented by the following equation (Litman, 2013; Koryagin and Dekina, 2014; Levashev, 2017):

pi , j 



1  exp A  C

p

T

1

p i, j

 Ti ,cj     C c Li , j



,

wherein A is a constant. Researchers often use the game theory to solve problems of urban transport (Chen and Cheng, 2010). Interaction between transport operators, passengers and authorities has been described (Koryagin, 2015). This approach allows to prove the existence of Nash equilibrium for each management system (Table 2). The following section studies characteristics of the transport system in situation of Nash equilibrium. 5. Results and discussion Let us use the following values in the numeric example: the distance between adjacent districts is 3 km; Cp = 100 is the cost of time; Vb=Vt = 20 km/h is the speed of municipal transport; Vc = 40 km/h is the average speed of a car; CI= 1000 are the costs of trip of type1,β = 20 is the tariff applicable at municipal transport; Cc= 10 are the costs per unit of the route length for travel by car; passenger traffic intensity is λ = 200, A = 0. It is worth emphasizing that municipal lines are twice shorter than the bus routes, so the cost of 1 km is twice higher. Table 3 summarizes the results obtained for models 2, 4, 6 (including choice of route by passengers). Table 4. Characteristics of routes. Municipal transport

Operator 1

Operator 2

Model

frequency

Profit

frequen cy

Profit

frequen cy

Operator 3 profit

frequen cy

Municipal monopoly

15.5

88,588

0

0

0

0

0

0

Free market

0

0

6.2

43,165

8.2

31,369

5.9

25,935

Mixed market

13.3

28,315

5.5

8,887

3.9

2,898

4.6

6,402

profit

Routes of the first type are the most competitive (with the exception of municipal transport). The second type of routes is more efficient in the free market, while the third type of routes is more efficient in the mixed market (less competition with municipal routes). Municipal transport significantly reduces profits of bus routes and their frequency. Table 5. Characteristics of transport operators (per passenger). Model

Time of travel

Cost of travel

Probability

Total expenses

Municipal monopoly

0.33

14.6

0.66

47.9

Free market

0.31

13.7

0.67

44.8

Mixed market

0.33

17.5

0.66

50.5

Change of line by a passenger (for municipal transport) takes a lot of time, and therefore the overall efficiency of the lines is low. The best result in this model has been demonstrated in the free market, which enabled to reduce transportation costs and the time of travel. 6. Conclusions The models proposed in the article have showed that multiple schemes for management of urban passenger transport may be implemented in urban conditions. Therefore, a cycle transition from one scheme to another

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sometimes occurs (Wright and Hook, 2007). Such schemes are also changed in Russian cities. In the 1990s, a trend emerged towards the development of free market, which is currently a mixed one. The presented models can be refined by increasing the area of the city (which will significantly increase the number of districts and routes). It is also required to take into account parking time and car speed, which depends on the intensity of traffic and degree of development of the transport infrastructure. The practical implementation of this approach makes it possible to determine the optimal scheme for management of urban transport in cities. References Agureev, I., Elagin, M., Pyshnyi, V., Khmelev, R., 2017. Methodology of substantiation of the city transport system structure and integration of intelligent elements into it. Transportation Research Procedia 20, 8–13. https://doi.org/10.1016/j.trpro.2017.01.003. Bravo, M., Briceño, L., Cominetti, R., Cortés, C. E., Martίnez, F. J., 2009. An integrated behavioral model of the land-use and transport systems with network congestion and location externalities. Transportation Research Part B 44 (4), 584–596. https://doi.org/10.1016/j.trb.2009.08.002. Cervero, R., 2013. Linking urban transport and land use in developing countries. Journal of transport and land use 6 (1), 7–24. http://dx.doi.org/10.5198/jtlu.v6i1.425. Chen, B., Cheng, H. H., 2010. A review of the applications of agent technology in traffic and transportation systems. IEEE Transactions on intelligent transportation systems 11 (2), 485–497. https://doi.org/10.1109/TITS.2010.2048313. Evans, A. W., 1987. A theoretical comparison of competition with other economic care regimes for bus services. Journal of Transport Economics and Policy 21, 7–36. Hollander, Y., Prashker, J., Mahalel, D., 2006. Determining the desired amount of parking using game theory. Journal of Urban Planning and Development 132 (1), 53–61. Horowitz, J. L. Koppelman, F. S, Lerman, S. R., 1986. A Self –Instructing Course in Disaggregate Mode Choice Modeling. Technology Sharing Program. U. S. Department of Transportation, Washington. Khovavko, I. Yu., 2014. Economic analysis of traffic jams in Moscow. State department Electronic bulletin 43, 121–134. Koryagin, M. E., 2015. An agent-based model for optimization of road width and public transport frequency. Promet – Traffic & Transportation 27 (2), 147–153. https://doi.org/10.7307/ptt.v27i2.1559. Koryagin, M. E., Dekina, A. I., 2014. Optimization of the road capacity and the public transportation frequency, which are based on logit-model of travel mode choice. Communications in computer and information science 487, 214–222. https://doi.org/10.1007/978-3-319-13671-4_26. Levashev, A., 2017. Application of Geoinformation Technologies for the Transportation Demand Estimation. Transportation Research Procedia 20, 406–411. https://doi.org/10.1016/j.trpro.2017.01.066. Litman, T., 2011. Why and how to reduce the amount of land paved for roads and parking facilities. Environmental practice 13 (1), 38–46. https://doi.org/10.10170S1466046610000530. Litman, T., 2013. Understanding transport demands and elasticities. How prices and other factors affect travel behavior. Victoria Transport Policy Institute, Victoria. Vuchic, V. R., 1999. Transportation for livable cities. Rutgers University, Brunswick, N.J. Wright, L., Hook, W., 2007. Bus rapid transit planning guide. Institute of Transportation & Development, New York.