THE COLLABORATION STRATEGY TO IMPROVE THE QUALITY OF A PUBLIC TRANSPORTATION SYSTEM: AN APPLICATION USING THE AGENT-BASED SIMULATION WITH NETLOGO

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1 THE COLLABORATION STRATEGY TO IMPROVE THE QUALITY OF A PUBLIC TRANSPORTATION SYSTEM: AN APPLICATION USING THE AGENT-BASED SIMULATION WITH NETLOGO Breno Barros Telles do Carmo Mohamed Jamal Eddine Pedro Furtado Bandeira de Melo

2 THE COLLABORATION STRATEGY TO IMPROVE THE QUALITY OF A PUBLIC TRANSPORTATION SYSTEM: AN APPLICATION USING THE AGENT- BASED SIMULATION WITH NETLOGO Breno Barros Telles do Carmo Département de Mathématiques et Génie Industriel École Polytechnique de Montréal Universidade Federal Rural do Semi-Arido Mohamed Jamal Eddine Pedro Furtado Bandeira de Melo Département de Mathématiques et Génie Industriel École Polytechnique de Montréal ABSTRACT: The urban mobility problem is related to the preference of people to use the car than the public transportation. The quality of the public transportation is important to ensure the utilization of this kind of service and, consequently, reduce the individual transportation. The research question of this paper is: How we can improve the public transportation system reliability, considering the high traffic in the routes? To answer this question, we will test a collaboration strategy between two bus drivers of different lines to reduce the delays in the depart of two bus lines. To analyze the efficacy of this strategy we used the multi-agents system simulation methodology. This model was implemented into a simulation platform using NetLogo. The experiments were done for a theoretical case, based on the routes of two bus lines. It is possible to observe that this kind of strategy can reduce the delays, a very important performance indicator related to the system reliability. In this research two indicators were evaluated: the number of path completed and the average delay. Looking the results obtained at each scenario, it is possible to conclude that the collaboration strategy improves these two indicators. RESUMO: O problema da mobilidade urbana está associado com a preferência pelo uso do transporte individual. A qualidade do sistema de transporte público é importante para assegurar a utilização deste serviço e, consequentemente, reduzir o número de veículos nas ruas. A questão de pesquisa deste trabalho é: como podemos melhorar a confiabilidade de um sistema de transporte, considerando a existência de tráfego nas vias? Para responder esta questão, foi testado o uso da estratégia de colaboração entre dois motoristas de linhas de ônibus com o intuito de reduzir os atrasos na estação de partida, melhorando assim a qualidade do serviço. Para a análise da eficácia desta estratégia, foi utilizada a simulação à base de agentes. Este modelo foi implementado na plataforma de simulação NetLogo. Os experimentos foram realizados em um caso teórico, baseado em um sistema com duas linhas de ônibus. Foi possível constatar que este tipo de estratégia tem potencial para reduzir os atrasos, um indicador de desempenho importante para a percepção da qualidade por parte dos usuários. Nesta pesquisa, dois indicadores foram avaliados: o percentual de trajetos previstos completados e a média de atraso em cada trajeto. Observado os resultados obtidos, constata-se que a estratégia de colaboração melhorou o desempenho nos dois indicadores utilizados. 1. INTRODUCTION The urban mobility problem is related to the preference of people to use the car than the public transportation. The travel time is increased by the size of the cities (people need more time to go from one point to other) and the traffic, that reduce the public transportation velocity. So, because of these factors, the public transportation is less attractive than the individual transportation (BANISTER, 2008). The quality of the public transportation is important to ensure the utilization of this kind of service and, consequently, reduce the individual transportation. However, to measure the satisfaction of the users is not simple because it means that it has to be well evaluated in different indicators. To break this vicious cycle, it is necessary to improve the quality of the public transportation and it is possible to get this by the improvement of the quality indicators. 1

3 There are some classes of indicators considered more important: service reliability (punctuality), treatment of employees, effective communication and design, related with the comfort, the safety and the cleanliness (FRIMAN, 2004). One of the most important indicators is the service reliability; because the user needs to be assured that he can arrive on time to his appointment. So, to improve a public transportation system, we must begin by improving this indicator. In this paper we will work with this problem. The delays in a public transportation system are caused, in most cases, by the traffic in the routes. The research question of this paper is: How we can improve the public transport system reliability, considering the high traffic in the routes? To answer this question, we will test a collaboration strategy between two bus drivers to reduce the delays in the depart station of two bus lines. To implement this strategy and get the results we used the multi-agents system methodology. The experiment was done for a theoretical case, based on the routes of two bus lines in Canada. More specifically, this paper aims answer this question: the collaboration strategy between the bus drivers of two different lines can mitigate the delay and improve the reliability of a transport system? To get this goal, the objective of this paper is "evaluate the capacity to reduce the delays by modeling the collaboration strategy between bus drivers of different lines. The drivers' decision system is modeled by multi-agent systems approach based on the drivers' perception of the traffic in the route. This researched is considered a theoretical case study, where we proposed different scenarios to evaluate the impact of the collaboration strategy. This paper is organized in four sections: 1) literature review, covering the topics of public transportation and application of multi-agent systems in this type of system; 2) the methodology adopted in this research; 3) the system modeling and the results and 4) Results analysis and conclusions. 2. LITERATURE REVIEW In this section, it is presented the concepts related with the urban mobility in the cities by an approach of public transportation. After this discussion, it will be presented the agent-based simulation systems in the transportation context. 2.1 The public transportation and the urban mobility Actually, in the big cities, we can observe a dependency of the people by cars and this characteristic influences the traffic (BANISTER, 2008; STEG et GIFFORD, 2005; SCHAFER, 1998). On the other hand, it is possible find the phenomenon of the decentralization of the services in the cities that increases the travel demand and consequently the number of cars in the streets (BANISTER, 2008). To solve this problem the governments must invest in the public transportation, because of the potential to reduce the number of cars in the streets. This idea is defended by Banister (2008), when he argues that policies for public transit should be a priority for governments. However, for those policies have practical effect, it is necessary to improve the costumer s satisfaction. So, the quality service can be defined as the set of attributes that can be 2

4 influenced by government policies and transport operators (PAULLEY et al., 2006). There are many indicators that can be used to evaluate the quality of the public transportation. The reliability of the service is considered the characteristic most important in many researches, as Paulley et al. (2006) and Friman (2004). This characteristic is related with the excessive waiting time due to delays, which may be caused, for example, by the traffic or by the problems in the system (Paulley et al., 2006). Because of this characteristic, it is possible to conclude a vicious circle: the amount of vehicles on the streets represents a traffic impact, increasing the travel time, which influences the migration of passenger from the public transportation system to the individual transportation, which influences the increase in the number of vehicles. The Figure 1 exemplifies this vicious circle. Figure 1 - Vicious circle To break this cycle, it is necessary that the organizations deploy strategies to reduce delays, even with the existence of trafficking. In this research we will work with this specific problem. The idea is to improve the quality of public transportation service using the collaboration strategy of two drivers of different bus lines. To evaluate this strategy, we used agent-based system methodology to simulate the application of this strategy. In the next section, we present the concepts agent-based simulation systems and its applications in the transportation domain. 2.2 Agent-based simulation systems and the transportation systems The agent-based simulation is an approach where the results are obtained by the interaction of the system's agents. This approach is already new in Brazil (AVANCINI, 2013). The agent-based simulation is used to model an environment when we want analyze the collective responses to the system's changes from the interaction between agents of an environment (HARE and DEADMAN, 2004). This kind of method is used to simulate the dynamics of complex systems by the interactions of agents and the virtual representation of the environment (FRAYRET, 2011). Based on Madejski (2007), the agent-based simulation can also be used as a heuristic technique to optimize decision problems, which are naturally distributed, complex, modular and heterogeneous. For the purposes of optimization, the agent-based simulation is used in 3

5 conjunction with a "physical" or "functional" decomposition of the problem to address. In the first case, agents represent physical entities (e.g., workers, machine tools, resources, vehicles) involved in a specific problem to be solved. This general modeling philosophy is used in the specific modeling of the transportation systems. The agents are autonomous systems that can realize the environment and make decisions about it. So, an intelligent agent can realize the environment from a set of perceptions, objectives, the intensions and a set of plans to answer the system's events (DIA, 2002). The artificial intelligence techniques are given to solve transportation problems. According to Sadek (2007): (1) this problem has quantitative and qualitative variables; (2) it is necessary to model the interactions between the system components; (3) it is hard to model the interactions between agents by traditional optimization techniques. The agent-based simulation is a technique based on artificial intelligence concepts. In this approach, interactions between agents are simulated to generate the behavior of the system (BERNHARDT, 2007). In the case of a transport system, an agent must be able to collect information on the environment (traffic, for example) and make a decision from these observations. In this study, we consider each driver as an agent. So, the interactions between drivers of two bus lines represent an action of the agents (take or not to take passengers on the other line) as a response to change from one state (not traffic / traffic). The purpose of the simulation is to model this type of behavior to achieve a goal. There are some applications of this approach at the transportation systems, as the model developed by Armendáriz et al. (2011) that proposes a model to the carpooling system based on a model at library of NetLogo. Bernhardt (2007) et al. presents an example of use of agentbased simulation to model the behavior of drivers from their perception of the environment. From the perception of traffic, drivers can take action in response to the observed state. In this case, they are passive agents, since the decision is made from a perception (this is a response to a stimulus). In the case presented in this study, the agents must interact (intelligent agents). The idea of this approach is the same as this research. There are some software based on the multi-agent system (MAS) approach. In the case of this research, we used the NetLogo, an open source software that can be used at platforms Mac, Windows, Linux, etc. It allows "the simulation of multi-agent systems that involve a large number of agents" (Sakellariou et al., 2011). This software was developed by Uri Wilensky in 1999 and is a "programmable modeling environment for simulating natural and social phenomena" (NetLogo, 2014). 3. METHODOLOGY This research is considered as a case study, where we proposed different scenarios to evaluate the different strategies used to solve the problem. A case study aims to increase knowledge on individuals, groups and organizations, and social phenomena, political and related services (RUNESON et HÖST, 2009). In our case, we will analyze a public transportation system based on the interactions between the different agents of the system. We want understand the collaboration phenomenon and its impacts in the quality of the public transportation. 4

6 To get the goal of this study, we will use the sequence of tasks proposed by Frayret (2011) to apply simulation-based agents. The Figure 2 shows the tasks that will be followed in this research. Figure 2 - Methodology. Source: Adapted from Frayret (2011) According to the Figure 2, the output of the first step is the definition of the agents of the studied system and their tasks at the simulation. In the second step we defined the interaction diagram and the performance indicators to evaluate the different scenarios. Finally, in the third step, it is presented the results and the analysis of the simulation developed. In the next section, we will describe the application of the method presented in the Figure 2 at the system studied. 4. METHODOLOGY APPLICATION This section is divided in three steps according to the method proposed in the last section: Problem analyses, problem modeling and simulation. 4.1 Problem analyses Two lines of bus compose the system studied. The passengers arrive at the "Departure station" and they can choose between two possibilities of bus lines (blue and orange). The blue line has a distance of 10km and the orange line a distance of 5km. There are an alternative route at the end of service of the blue line that allows the blue driver to go directly to the "Transfer Station the Figure 3 shows the theoretical system studied. In this case studied, we considered that the bus speed is reduced by the traffic level that means that the travel time will be increased when there is traffic in the route. The buses' lines share a stretch in common and the passengers can be transported by the other line at the common section. This means that the drivers have the possibility of collaboration between them. If the driver of the blue line observe that there is traffic in the route, he can call to the driver of the orange line to ask help. If there is no traffic in the orange line, the orange driver can take the 5

7 passengers of the two lines and leave the passengers of the blue line at the station at the end of the common path. In this study, we prose some assumptions: The bus of the orange line has a fixed capacity. It was not considered in this study the transfer time between the two bus at the "Transfer Station". It was considered the existence of an alternative route that allows the blue driver get directly at the transfer station at the end of the route. Figure 3 - Theoretical system studied. In our system, we supposed the drivers as the endogenous elements and they are the candidates to be agents. In this case, we want to control the interaction between then. The Table 1 shows the responsibilities of the agents of the system. KIND OF AGENT Reactive comportment Reactive Comportment AGENT Driver of the blue line Driver of the orange line RESPONSABILITY Take a look the traffic in the route. Evaluate the traffic at the route. Ask for help to the driver of the orange line. Take a look the traffic of the route. Evaluate the traffic at the route. Evaluate the number of passengers at the "Departure station" and the capacity of the bus. Answer the demand of the driver blue. Table 1 - Agents responsibilities. 4.2 Problem modeling To model the system, it is necessary to describe the states of the agents and their interactions. The interaction (orange driver - blue driver) follows these steps: (1) the driver of the blue line looks the state of the traffic on the blue line and evaluates it. Two options are possible: traffic and "no traffic". If there is traffic, he asks for help to the driver of the orange line. (2) The orange driver looks the traffic level on the orange line and evaluates it. Two options are 6

8 possible: traffic and "no traffic". If there is traffic, the orange driver gives a negative response to the request of the blue driver. If there is no traffic, the orange driver does the second analysis (capacity). To do this second analysis, he looks the probability to the number of passengers of the two lines. If the sun of the demand of the two lines is smaller than the bus capacity, that means the use of the collaboration strategy between the drivers. Then, the driver of the blue line takes the alternative route and the driver of the orange line takes passengers from the blue line at the "Departure Station" and deposited than at the "Transfer Station". These interactions are presented in the Figure 4. Figure 4 - Interaction diagram. Based on the interaction diagram presented in the Figure 4, it was defined the performance indicators to compare the different scenarios proposed in this study: (1) the number of routes completed relative to the number planned in a week "type" (no traffic) and (2) the average delay in the transfer station. From these measurements, we can determine if the system is able to regulate itself in the presence of traffic. The Table 2 presents these indicators and their notation. PERFORMANCE INDICATORS NOTATION PTC : Percentage of paths completed =!"# over the predicted paths eq.1!"# NTC : Number of complete paths NTP : Number of predicted paths!! =.2 NTC : Number of complete paths Table 2 - Performance indicators. 7

9 To model this problem, we used the software NetLogo. The advantage of the use of this software is that it is open source software. The traffic was defined as a probabilistic law, represented by an uniform distribution (0, 1). The uniform distribution (0, 20) was used to represent the number of passengers at the "Departure Station" for each line. Every time that the blue driver arrive at the "Transfer Station", he evaluates the traffic and the number of passengers at the "Departure "Station", that means: the value of the traffic and the amount of people at this stations change. The interaction was modeled by the rules defined in the Table 3. INTERACTION Ask for help (Blue Driver) Answer the demand (Orange Driver) RULE If traffic > 0,5 The blue driver ask for help to orange driver traffic <= 0,5 The blue driver take de usual route If traffic > 0,5 The orange driver gives a negative answer traffic <= 0,5 The orange driver does the second analysis If passengers blues + passengers oranges <= orange bus capacity The orange driver takes the passengers of the blue line at the Departure Station Table 3 - Interaction rules. The blue driver is programmed to maintain its usual route if there is no traffic on the route. If there is traffic, he requires assistance to Orange Driver that, before issuing a response, evaluates the traffic at the Orange Line and the capacity available of his bus. The speeds of the two drivers are modeled by a uniform law and the parameters of this law change depending on the level of traffic. In order to make performance measures has been programmed two functions ("Time" and "Account"). The "Account" function is used to count the number of passages made by the blue driver and allows us to calculate our measure of performance PTC. The value is increased by one for each passing at the transfer station. For "Time" function, it count the number of ticks used to complete the route. It allows us to calculate the average delay during the simulation period. The Figure 5 presents the interface page developed at NetLogo. 8

10 Figure 5 - NetoLogo: Interface page. 4.3 Simulation To test our model, we proposed four scenarios. The first one, we defined the speed variation by an uniform distribution and we did not consider the traffic. This is the best case, where there is no possibility of delays. This scenario is the base of comparison with the other scenarios. In the second scenario, we added the traffic to our environment. The traffic is represents by a uniform distribution too with different parameters. In the third scenario we implemented the collaboration strategy. Finally, at the fourth scenario, we added a second element to be evaluated: the capacity of the orange bus. The Table 4 presents the resume of the characteristics of the four scenarios. SCENARIOS Scenario 1 Scenario 2 Scenario 3 Scenario 4 CHARACTERISTICS There is no traffic The speed is represented by an uniform distribution There is no collaboration between the two drivers There is traffic The speed is represented by an uniform distribution There is no collaboration between the two drivers There is traffic The speed is represented by an uniform distribution There is collaboration between the two drivers There is traffic The speed is represented by an uniform distribution There is collaboration between the two drivers It is evaluated the capacity versus the demand Table 4 - Scenarios of the simulation. A qualitative validation of the model was done. To get it, we tested each element in our model to know if it works how was planned. The next section presents the results obtained by this simulation. The experiences were done for each scenario. We found that that the minimal number of experiences (simulations) is 100 to get a result statistically valid. 5. RESULTS AND DISCUSSION In the first analyze, it was evaluated the number of completed paths during la simulation. The basis scenario is the scenario 1 (represents 100%), which means that the Blue Driver completed all the number of paths planned. The Figure 6 presents the results obtained to the others three scenarios. 9

11 105% 100% 95% 90% 85% 80% 100% 97% 92% 88% Scenario 1 Scenario 2 Scenario 3 Scenario 4 Figure 6 - Percentage of paths completed by the blue driver It is possible to see that when we added the traffic to our model (Scenario 2), the efficiency decreases 12%, considering a small reduction of the speed because of the traffic (reduction of +- 25%), that means a reduction of the number of paths completed. In the third scenario, it was added the possibility of collaboration between the two drivers that means that the blue driver can take the alternative route if he receives the help of the orange driver. In this case, it is possible to observe that the collaboration strategy improve this indicator and the blue driver can overcome the traffic problem. In the fourth scenario, however, we added one second constraint (capacity) and it is possible to conclude that this constraint reduces de efficacy of this strategy. On the other hand, if we use a bus with a bigger capacity, it is possible to obtain the same results of the third scenario. In the second analyze, it was evaluated the accumulated delay for each scenario. The total delay was divided by the number of passages at the transfer station to observe the average delay in the two cases: without collaboration (Scenario 2), with collaboration (Scenario 3) and with collaboration and capacity evaluation. The Figure 7 shows the results ,34 7,02 7, Scenario 1 Scenario 2 Scenario 3 Scenario 4 Figure 7 - Average delay (in minutes). By the Figure 7, it is possible to conclude that the collaboration strategy reduced the average delay in 26% even if there is the capacity evaluation that means the improvement at the efficiency of the system. Even if we add the capacity constraint, the collaboration model reduces around 24%. Looking these analyses that were done, we can conclude that the collaboration strategy represents a great possibility to improve the system. The last analyze that was done is related to the delay comportment at the transfer station. In our simulation model, considering the traffic, the drivers can complete 46 times the route. The Figures 7, 8 and 9 show the delays at each passage at Transfer Station in the case of the scenario 2, scenario 3 and scenario 4. 10

12 Figure 6 - Delay path in scenario 2 (minutes) Figure 7 - Delay path in scenario 3 (minutes) Figure 8 - Delay path in Scenario 4 (minutes) The ordinate axis represents the delay (in minutes) and the abscissa represents the passage through the transfer station. By analyzing these figures, it is possible to observe that the implementation of the collaboration strategy reduces the delays, which means an improvement of the efficiency of the system studied. In the next section, it is presented the conclusions obtained by this study and the limitations. 6. CONCLUSIONS Improve the quality of public transportation system is important to reduce the number of cars at the streets and, consequently, improve the problems of urban mobility. However, there are many characteristics that influence the quality. Among these, the most important is the service reliability (punctuality). To guarantee the punctuality it is possible to use different strategies. In this paper, it was tested a collaboration strategy between the drivers of two different bus lines. This paper aims to evaluate if the collaboration strategy has the capacity to improve the service quality of the bus line in a public transportation system, observing the effects of this strategy in the service reliability. The results showed us that that this kind of strategy can reduce the delays and improve it. In this research it was defined two indicators to evaluate this strategy: the number of paths completed and the average delay. Looking the results obtained at each scenario, it is possible to conclude that the collaboration strategy improved the two indicators. However, if we consider a limited capacity to the bus used in the orange line, the gains associated to the use of 11

13 the collaboration strategy can be reduced. Looking this aspect, the size of the buses used in the orange line must have sufficient capacity to transport the passengers of the two bus lines. In our case, we simplified the system, and the data used are theoretical. To next step, we must collect real data to evaluate the benefits of this strategy in a real system. In the continuation of this research we can include a new type of agent to coordinate the collaboration between the drivers, because in this simplified case, we considered just one bus driver for each route and in real system, there are many drivers in each line, that improves the complexity of this problem. Acknowledgement Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES. REFERENCES ARMENDÁRIZ, M., ARNOULD, G., KHADRAOUI, D., BURGUILLO, J. C. and PELETEIRO, A. (2011). Carpooling: a multi-agent simulation in NetLogo. Proceedings 25th European Conference on Modelling and Simulation, Poland. AVANCINI, D. P. (2013) Demanda por transporte rodoviário urbano: um modelo computacional baseado em agentes. Monografia de conclusão de curso. Universidade Federal de Santa Catarina. Florianópolis. BANISTER, D. (2008) The sustainable mobility paradigm. Transport Policy, Vol. 15, pp BERNHARDT, K. L. S. (2007) Agent-based modeling in transportation. In Transportation Research Board. Available in the site : DIA, H. (2002) An agent-based approach to modeling driver route choice behavior under the influence of realtime information. Transportation Research - Part C. Vol. 10. pp FRAYET, J. M. (2011) Multi-agent system applications in the forest product industry. Journal of Science & Technology for Forest Products and Processes. Vol.1, n. 2, pp FRIMAN, M. (2004) Implement quality improvements in public transport. Journal of Public Transportation, Vol. 7, No. 4, pp HARE, M. et DEADMAN, P. (2004) Further towards a taxonomy of agent-based simulation models in environmental management. Mathematics and Computers in Simulation. Vol. 64. pp MADEJSKI, J. (2007) Survey of the agent-based approach to intelligent manufacturing. Journal of Achievements in Materials and Manufacturing Engineering. Vol 21. (1): 4. NETLOGO. (2014) What is NetLogo? Available at the site: PAULLEY, N. BALCOMBE, R., MACKETT, R., TITHERIDGE, H., PRESTON, J., WARDMAN, M., SHIRES, J. et WHITE, P. (2006) The demand for public transportation : the effect of fares, quality of service, income and car ownership. Transport Policy. Vol. 13, pp RUNESON, P. et HÖST, M. (2009) Guidelines for conducting and reporting case study research in software engineering. Empir Software Eng. Vol. 14. pp SADEK, A. W. (2007) Artificial intelligence applications in transportation. In Transportation Research Board. Available at the site: SCHAFER, A. (1998) The global demand for motorized mobility. Transportation Research, Part A, Vol. 32, No. 6, pp SAKELLARIOU, I., KEFALAS, P. and STAMATOPOULOU, I. (2011) An intelligent agents and multi-agents systems course involving NetLogo. In: Beer, M., Fasli, M. and Richards, D. Multi agent systems for educational and entertainment: desing, use and experience. Hershey, New York, STEG, L. et GIFFORD, R. (2005) Sustainable transportation and quality of life. Journal of Transport Geography, Vol. 13, pp