Organizacija, Volume 49 Research Papers Number 3, August 2016 DOI: 10.1515/orga-2016-0013 Understanding the Structural Complexity of Induced Travel Demand in Decision-Making: A System Dynamics Approach Juan S. Angarita-Zapata1, Jorge A. Parra-Valencia2, Hugo H. Andrade-Sosa1 1 School of Systems Engineering and Informatics, Universidad Industrial de Santander, SIMON Research Group, Colombia juan.angarita1@correo.uis.edu.co (corresponding author) 2 School of Systems Engineering, Universidad Autónoma de Bucaramanga, Systems Thinking Research Group, Colombia Background and purpose: Induced travel demand (ITD) is a phenomenon where road construction increases vehicles' kilometers traveled. It has been approached with econometric models that use elasticities as measure to estimate how much travel demand can be induced by new roads. However, there is a lack of "white-box" models with causal hypotheses that explain the structural complexity underlying this phenomenon. We propose a system dynamics model based on a feedback mechanism to explain structurally ITD. Methodology: A system dynamics methodology was selected to model and simulate ITD. First, a causal loop diagram is proposed to describe the ITD structure in terms of feedback loops. Then a stock-flows diagram is formulated to allow computer simulation. Finally, simulations are run to show the quantitative temporal evolution of the model built. Results: The simulation results show how new roads in the short term induce more kilometers traveled by vehicles already in use; meanwhile, in the medium-term, new traffic is generated. These new car drivers appear when better flow conditions coming from new roads increase attractiveness of car use. More cars added to vehicles already in use produce new traffic congestion, and high travel speeds provided by roads built are absorbed by ITD effects. Conclusion: We concluded that approaching ITD with a systemic perspective allows for identifying leverage points that contribute to design comprehensive policies aimed to cope with ITD. In this sense, the model supports decision-making processes in urban contexts wherein it is still necessary for road construction to guarantee connectivity, such as the case of developing countries. Keywords: induced travel demand; system dynamics; decision-making; dynamic modeling. 1 Introduction Mobility is the necessity to travel that is derived from the desire to participate in economic and social activities in urban areas. Traveling between different locations involves an expenditure of time. However, negative implications appear when movements on roads are accomplished by spending more time than usual due to traffic congestion (Hills, 1996). In intuitive decision-making processes, road construction is a well-known policy that increases travel speeds and reduces travel delays (Hong et al., 2011; No-land and Lem, 2002). Nevertheless, evidence of a short-and medium-term correlation between road construction and travel demand has been found (Graham et al., 2014; Hanse, 1995). This phenomenon is known as induced travel demand (ITD) in which new roads, expressed as linear Received: May 6, 2016; revised: July 2, 2016; accepted: July 26, 2016 129 Organizacija, Volume 49 Research Papers Number 3, August 2016 kilometers, induce increases in the number of kilometers traveled by vehicles. ITD calls into question the effectiveness of road construction as a single and sufficient policy to address traffic congestion (Ladd, 2012). Several studies have approached ITD with econometric models that use elasticities as measure to estimate how much travel demand can be induced by new roads (Currie and Delbosc, 2010; Handy, 2014; Litman, 2015; Noland, 2004; Ozuysal and Tanyel, 2008). Those models are built with forecasting purposes to match sets of outputs between specified ranges of accuracy without claims of causality in their structure (Barlas, 1996). They do not focus on providing structural explanations of the counterintuitive behavior in which mobility tends to be saturated despite building new roads. Although econometric models corroborate the existence of ITD and quantify it, explaining this phenomenon structurally allows designing comprehensive policies that go beyond road construction to effectively address traffic congestion. However, to do this, ITD should be approached from a systemic perspective to suggest sizing up this phenomenon, placing it in a wide enough context, and thinking about it as a system in the same way that every social concern must be approached (Bunge, 2014). This implies recognizing and defining elements that interact between road construction and motorized travel demand as elements that are strongly linked and influence each other in a whole system and whose interactions determine ITD behavior. The latest avoids reductionist thinking in which linear cause-effect relationships are studied in isolation. In this sense, "white-box" models can propose representations about the structural complexity of ITD. Through system dynamics (SD) models, which are "causal-descriptive" models, it is possible to formulate statements of how ITD actually works. These models are built to understand why certain phenomena occur. This question is answered using a feedback structure that explains the occurrence of ITD over time (Sterman, 2000); this means that the feedback structure produces a behavior that can be similar to ITD behavior. Therefore, the feedback mechanism is conceived as a dynamic hypothesis of ITD based on a fundamental premise of the systems dynamics paradigm: to similar causal structures correspond similar behaviors (Andrade et al., 2001; Forrester, 1971). In this paper, we highlight the structural complexity underlying ITD. These insights are based on an SD model whose feedback structure is a dynamic hypothesis that explains and simulates ITD by road construction. The SD model represents a complex mobility phenomenon that is corroborated and measured through econometric models using a systemic approach. This means including the linear relationship between kilometers traveled and kilometers built within a structure of cyclical influence from which ITD dynamically emerges using SD modeling tools. 2 Bibliographic review The phenomenon of induced travel demand (ITD) was recognized even before the automobile age (Ladd, 2012). However, serious attention began only in the 1980s, especially in the UK (Goodwin, 1992). During that time, scholars in the USA carried out statistical works to discuss and corroborate this phenomenon (Cervero, 2001; Noland, 2001). Since the 1990s, several studies using econometric models have produced more solid evidence about the existence of ITD (Duranton and Turner, 2011; Handy, 2014; Hymel et al., 2010; Litman, 2010; Noland, 2004; Ozuysal and Tanyel, 2008). These confirmations contradict the long-term benefits of road construction on mobility. As a result, road construction in developed countries is no longer an exclusive policy to reduce traffic congestion. However, in developing countries, rapid urban sprawl, high population growth, raised motorization rates and great traffic congestion have promoted a perceived need of more roads among transport policy-makers. Currently, these countries invest huge budgets for new and better roads to solve the issues described above. Nevertheless, despite available evidence about ITD in countries in Europe and North America, we have not found works that discuss ITD and evaluate its possible implications in developing countries under their current road construction scenarios. It is probable that if econometric models were used to assess how much travel demand can be induced by road construction projects, the results would show how the building policy increases the quantity of motorized travel in a long run time horizon. Assuming as a fact the increment of motorized travel after road construction, regardless of the precise quantity of such increases, a representation with a system dynamics (SD) model of ITD provides a modeling tool that improves the decision-making process in developing countries. The SD model enhances the level of understating about the structural complexity of ITD. The better this phenomenon is known this phenomenon, the better comprehensive policies in mobility would be designed, taking into account that road facilities are still necessary to guarantee connectivity in developing urban cities. We performed a bibliographic review that covers the period between 1990 and 2015. All papers reviewed were made under an econometric approach wherein elasticities are the primary measure to corroborate and quantify ITD. However, we did not find papers with a "causal-descriptive" or system dynamics approach. This supports the statement that there is a lack of "white-box" models with causal hypothesis to represent the structural complexity of ITD, based on available statistical evidence provided by econometric models. 130 Organizacija, Volume 49 Research Papers Number 3, August 2016 3 Materials and Method System dynamics (SD) is a methodology based on feedback control theory equipped with mathematical simulation models by computer, which uses linear and non-linear differential equations. Jay Forrester at Massachusetts Institute of Technology developed this approach in the 1960s. Since then, it has been employed to address complex issues in various fields such as urban dynamics (Forrester, 1969), business and management (Sterman, 2000), education and learning (Andrade et al., 2014; Forrester, 1994), and economy and environment (Ford, 1999). The purpose of SD in these areas has focused on explaining structure and modelling complex phenomena that are represented as systems for understanding their behavior over time. Building an SD model involves an iterative process. In the progression from one step to the next, the modeler moves backward and forward through each methodological tool that SD offers to create a model as an abstraction of a real phenomenon (Sterman, 2000). For this paper, we assumed these methodological tools as a set of languages that each represents a particular view of the model (An-drade et al., 2001). This methodological assumption corresponds to the modeling methodology of "five languages" that was proposed by Hugo Andrade et al. (2001) and is shown in Figure 1. The model was built with Evolución 4.51, a software platform developed by the SIMON2 research group at Universidad Industrial de Santander (Colombia) to build SD models. 4 Results In this section, we expose the model that was built using each language of Figure 1. The purpose of this model is to propose a dynamic explanation in terms of circular causality of induced travel demand as emerging phenomenon between road construction and motorized travel demand. We did not take into account alternative means of transport, and the benefit of road construction inducing more travel demand is travel speed. Moreover, a specific urban context with mobility features is used to calibrate the basic model parameters. 4.1 System verbalization The Metropolitan Area of Bucaramanga (MAB) is a metropolitan zone located in the department of Santander, Colombia, with an estimated population of 1,113,522 people. It is composed of four cities: Bucaramanga (capital city of Santander), Floridablanca, San Juan de Girón and Piede-cuesta. They are linked geographically and commercially, and transportation is a key element that influences the way Figure 1: The methodology of "five languages" used to build the SD model. Source: adapted from (Andrade et al., 2001) 1 More information about Evolución software is available at: Andrade, H. H., Lince, E., Hernandez, A. E., Monsalve, A. J. (2010). Evolución: herramienta software para modelado y simulación con dinámica de sistemas. Revista de Dinámica de Sistemas Vol. 4, Núm. 1, ISSN: 0718-1884. 2 For more information about SIMON research group, please visit www.simon.uis.edu.co Organizacija, Volume 49 Research Papers Number 3, August 2016 in which people do their daily activities along the MAB. MAB'S fleet consists mostly of private vehicles (cars, vans and campers) that represent 38% of the total fleet. Additionally, there is a motorization rate of 442 vehicles per 1,000 people (Observatorio Metropolitano de Bucar-amanga, 2014); meanwhile, road supply has built approximately 1,300 kilometers of road, and several road construction projects are underway that require great budgets to increase the number of kilometers available (Secretaría de Infraestructura de Bucaramanga, 2001). However, according to the evidence reviewed of induced travel demand in cities abroad, we suggest that this one-side policy will generate, at best, modest results in MAB. 4.2 Causal Loop Diagram (CLD) The proposed CLD can be seen in Figure 2, and a brief description of each variable is shown below. According to Figure 2, the CLD includes two types of travel conditions on roads: the first type corresponds to potential conditions. They represent the level of service on roads calculated on the basis of vehicles that the kilometers built can hold at average flow conditions, including the whole fleet of private vehicles; this includes cars in use and cars that do not travel because of traffic congestion3. The second type corresponds to real conditions that represent the level of service on roads only based on cars traveling and the road capacity in terms of vehicles that the kilometers built can support. • Kilometers Built: This is the available road infrastructure expressed as linear kilometers. • Road Congestion Index: This represents the state of mobility as a ratio between kilometers traveled by cars and kilometers built. This index takes values between zero and one. Values closer to zero represent uncongested mobility. Values closer to one correspond to traffic congestion on the available road infrastructure. • Fleet Growth: This represents the average growth of private vehicles in the Metropolitan Area of Bucaramanga. The flow rate at which the fleet increases is influenced by a motorization rate of 442 vehicles per 1.000 people. This growth rate includes social and economic elements that also increment travel demand growth and that are not specified within this model. • Potential Level of Service (LoS): Thus represents the potential flow conditions on roads that change depending on values of a ratio, a dimensionless load factor, between fleet growth and vehicles that the kilometers built can hold at an average flow of 3,200 vehicles/hour and a service travel speed of 60 kilometers/hour. The scale of LoS has six discrete values ranging from A to F, which can be seen in Table 1. Each discrete range is associated with an average range of potential travel speeds. Figure 2: Causal loop diagram: feedback mechanism that structurally explains induced travel demand by road construction 3 Those cars that do not travel because of traffic congestion correspond to discretionary riders who have the option of traveling in more than one means of transport. When mobility is congested, discretionary riders do not use private vehicles; they tend to use other means of transport, such as public transport. 132 Organizacija, Volume 49 Research Papers Number 3, August 2016 • Potential Travel Speed: This is the potential speed at which vehicles could travel depending on the Potential Level of Service. • Attractiveness of Car Use: This variable represents people's satisfaction with respect to Potential Travel Speed that decreases or increases the number of cars in use. • Car use: This represents the number of vehicles traveling on roads. • Real Level of Service (LoS): This is used to assess real flow conditions on road infrastructure that change depending on a dimensionless ratio between cars already in use and the vehicles that the kilometers built can hold at an average flow of 3,200 vehicles/hour and a service travel speed of 60 kilometers/hour. The scale of LoS has six discrete values ranging from A to F, which can be seen in Table 1. • Real Travel Speed: This is the real speed at which vehicles are traveling on roads depending on the Real Level of Service. • Kilometers Traveled: This is the total kilometers traveled by cars traveling on roads. According to Figure 2, there are four causal loops. Two of them are reinforcing loops, and the other two are balancing loops. They are described below: • "Intuitive Building Policy" balancing loop: This loop represents the intuitive decision-making process in which more roads are built to reduce traffic congestion. When the road congestion index (RCI) increases, more kilometers are built to supply more road space, and therefore, traffic congestion is released. • "Travel Speed Decreasing" balancing loop: This loop depicts how higher travel speeds and benefit of new roads are absorbed by car use. When there are more cars on roads, flow conditions decrease, which results in low travel speed. Lower travel speeds decrease car use until new roads are built again. When this happens, higher travel speeds come back because more road space is available. • "Generated Traffic" reinforcing loop: In this causal loop, if more roads are built, then the potential level of service increases. Then, potential travel speed increments and attractiveness of car use grows. Consequently, more cars are used, and new traffic is generated, which would happen if new roads were not built. Therefore, there are more cars traveling on new roads, and kilometers traveled increases. As a result, the RCI increases, which induces more road construction. Level of Service Operating Conditions Load Factor Average Travel Speed A Individual users are virtually unaffected by others in the traffic stream. Freedom to select desired speeds is extremely high. 0.00 to 0.60 70 km/h > 50 km/h B This represents the range of stable flow, but the presence of other users in the traffic stream begins to be noticeable. 0.61 to 0.70 49 km/h > 40 km/h C This represents the range of stable flow, but the selection of speed is affected by the presence of others. 0.71 to 0.80 39 km/h > 36 km/h D This represents high-density but stable flow. Speed is severely restricted. 0.81 to 0.90 35 km/h > 30 km/h E All speeds are reduced to a low but relatively uniform value. The freedom to drive within the traffic stream is extremely difficult. 0.91 to 1.00 29 km/h > 26 km/h F This represents forced or breakdown flow. Greater than 1.00 25 km/h Table 1. Level of Service and travel speeds. Source: adapted from (Cerquera, 2007) 133 Organizacija, Volume 49 Research Papers Number 3, August 2016 • "Induced Travel Demand" reinforcing loop: In this loop, when more kilometers are built, more road space is provided. This increases the real level of service and improves real travel speeds. Then, flow conditions for cars that are already in use are improved and vehicles tend to use more new routes and spend more time on them, which means that more kilometers are traveled. Finally, the total numbers of kilometers traveled grow, and RCI increases again. Consequently, more road construction is influenced by higher RCI values. 4.3 Reference mode Based on the causal loop diagram (CLD) proposed, the stocks-flows diagram is formulated to run dynamic simulations of ITD behavior. Qualitative analysis of interactions between CLD's feedback loops allows for a discussion of the expected behavior for simulations of the stocks-slows diagram. This reference pattern, known as reference mode, provides a point of reference during the modeling process, enabling us to stay on track of the model validation and its quantitative results (Ford, 1999). The reference mode (RM) for the causal loop diagram of section 4.2 can be seen in Figure 3. It is proposed by Litman (2015), and it depicts the generated traffic caused by road construction. According to the RM, traffic grows when roads are uncongested (projected traffic growth line), but the growth rate declines as congestion appears (blue curve), which means that discretionary riders stop using their vehicles. If more roads were built, car use would increase, and traffic would grow again. This additional traffic is called generated traffic (red curve). 4.4 Simulation model The causal loop diagram (CLD) gives a qualitative representation of the model that is useful for describing the ITD structure in terms of the feedback loops formulated. However, decision-making processes require formulating and testing policies in the model to think about their possible effects on ITD. The stocks-flows diagram is the mathematical representation of the CLD using a graphical language of accumulators and pipes, which allows for computer simulation. The stocks-flows diagram can be seen in Figure 4. In addition, types, units and formulas of each variable are shown in Table 2. The approach here is based on linking differential equations, which is presented in terms of a graphical language of 'stocks' and 'flows' that keeps the model transparent and easy to understand. Stocks are depicted by rectangles, suggesting a box that holds the content. Flows can be inflow to a stock or outflow from a stock. They are represented with valves that control the rate of flow into or out of the stock. Undergirding the notation of 'stocks' and 'flows' is the mathematical notation that shows how the stock is the integral of inflow minus outflow starting with an initial level of stock. As a stock with inflows and outflows is linked to other stocks and flows, the system structure is described by a set of linked linear and non-linear differential equations. Figure 3: The reference mode for the causal loop diagram proposed in section 4.2. Source: adapted from (Litman, 2015) Organizacija, Volume 49 Research Papers Number 3, August 2016 4.5 Model simulations Having formulated both a causal loop diagram and a stocks-flows diagram, this section presents model simulations. These are the quantitative temporal evolution of the model that we have built. The behaviors observed in the graphs below emerge from dynamic relationships between the feedback loops that are described in section 4.2. Before running simulations, we assumed a congested mobility; with this condition, we evaluated two simulation scenarios: a road construction scenario to analyze how new roads induce more motorized travel demand, and a not construction scenario to depict normal travel demand growth without ITD. These hypothetical scenarios are necessary because ITD cannot be evaluated simply by looking Figure 4: The stocks-flows diagram built on the basis of the feedback structure proposed in section 4.2 135 Organizacija, Volume 49 Research Papers Number 3, August 2016 Table 2: Equations of the stocks-flows diagram Type Variable names Units Formulas Population People Initial_Popul Fleet Vehicles Initial fleet HJ Roads Kilometers 1319 DeterioraedRoads Kilometers 100 Birth flow People/year Birth rate*Population Construction Kilometers/year MIN(Possible_kmBuüd,(Delay_1/CostPerKilometer)) m Dead flow People/year Population*(Death rate) ž o Deterioration Vehicles/year Fleet/average lifetime Maintenance Kilometers/year DeterioratedRoad/MaintenanceRate RoadDeterioratio Kilometers/year Roads/Lifetime roads Purchase flow Vehicles/year Population*MotorizationRate ATT_per_Vehicle Hour 0.53 Available budget US dollars 510638297 Aver Investment Percentage fraction 1 Average Cars Cap Vehicles/Kilometer 53 Birth rate Dimensionless/year 0.1911 CostPerKilometer US dollars 1200 ^ Death rate Dimensionless/year 0.0556 1 CM Initial Popul People 1113522 Initial fleet Vehicles 492299 Lifetime roads Years 15 MainteanceRate Years 1.1 Maximum roads Kilometers 500000 MotorizationRate Vehicles/people 0.023 Serv_TravelSpeed Kilometers/hour 60 average lifetime Years 12 AKT_per vehicle Kilometers (ATT_per Vehicle*Serv TravelSpeed) Attract Car Use Dimensionless NR_Pot_TravSpeed Attract moretrav Dimensionless NR RealTravSpeed Budget_allocated Percentage fraction NR RCI*Aver Investment Cars in use Vehicles Fleet*Delay 2 Coverage Dimensionless Real TravelSpeed/Serv TravelSpeed KT_per_vehicle Kilometers/vehicle (AKT_per_vehicle*Attract_moretrav) m Kilom Traveled Kilometers (Cars in use* KT_per vehicle) Possible_kmBuild Kilometers IF(Maximum_roads-Total_roads<=0,0,Maximum_ roads-Total roads) £ Pot Travel speed Kilometers/hour Pot LevelService Potential Load F Dimensionless Fleet/Roads_Capacity B < RealLoadFactor Dimensionless Cars in use/Roads Capacity Real TravelSpeed Kilometers/hour Real LevelServic Road Congest Ind Dimensionless (Kilom Traveled/Roads built)/8045 Roadslnvestment US dollars Available budget*Budget allocated Roads_Capacity Vehicles (Roads_built*Average_Cars_Cap) Roads built Kilometers (Roads+(0.5*DeterioratedRoad)) Speed coverage Dimensionless Pot Travel speed/Serv TravelSpeed Total_roads Kilometers DeterioratedRoad+Roads 136 Organizacija, Volume 49 Research Papers Number 3, August 2016 Table 2: Equations of the stocks-flows diagram (continued) M ft m Ö O NR_Pot_TravSpeed Dimensionless INTSPLINE (2,0,0.05,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0. 5,0.5,0.5,0.7,0.7,0.8,0.85,0.9,0.9,0.9,0.95,1,1,1,1,1,1,1, 1,1,1,1,1) NR_RCI Dimensionless INTSPLINE (2,0,0.01,1,1,1,1,1,1,1,1.1,1.15,1.2,1.3,1.3 30416,1.483599,1.5,1.5,1.5) .S u is 3.0.C0;2-4 Bleijenberg, A. 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Business Dynamics: Systems Thinking and Modeling for a Complex World. USA: Irwin McGraw-Hill. Juan S. Angarita-Zapata, systems engineer graduated from Universidad Industrial de Santander (UIS), Colombia. Currently, he is doing his master degree in systems engineering at UIS. Member of both the Colombian and the Latin American communities of System Dynamics (SD). Active member of System Dynamics Society. Researcher in the area of mathematical modeling and simulation with SD. Author of national and international academic event publications related to urban transport, environment, education and production systems approached from systems thinking and SD. Hugo H. Andrade-Sosa, full professor and researcher at Universidad Industrial de Santander (UIS), Colombia, in the areas of systems thinking, and mathematical modeling and simulation with System Dynamics (SD). Author of publications in national and international academic events, as well as academic journals. He is the director and founder of SIMON Research Group at UIS, member of the System Dynamics Society, and member of the Colombian and Latin American community of System Dynamics. Jorge A. Parra-Valencia, professor and researcher at Universidad Autónoma de Bucaramanga (UNAB), Colombia. Member of the Research Group on Systems Thinking at UNAB. Currently, he is President of the Colombian Community of System Dynamics. His research areas are focused on systems thinking, system dynamics and systems engineering. In recent years, his professional and research work has focused on the formulation and implementation of research projects, development of simulation experiments, and designing models to understand and improve social systems. 143 Organizacija, Volume 49 Research Papers Number 3, August 2016 Analiza strukturne kompleksnosti povpraševanja po povzročenih potovanjih pri odločanju: pristop sistemske dinamike Ozadje in namen: Povpraševanje po povzročenih potovanjih (ang: induced travel demand, ITD) je pojav, kjer se izgradnjo cest povečuje prevoženih kilometrov na vozilo. ITD navadno analizirajo z ekonometričnimi modeli, ki uporabljajo elastičnost za oceno koliko povpraševanja po povzročenih potovanjih lahko povzroči gradnja novih cest. V literaturi ne najdemo modelov »bele škratlje« z vzročno hipotezo, ki bi pojasnjevali strukturno kompleksnost tega pojava. V članku predlagamo model sistemske dinamike, ki temelji na mehanizmu povratne informacije, da pojasni strukturo ITD. Metodologija: Za modeliranje in simulacijo ITD smo uporabili metodologijo sistemske dinamike. Najprej smo izdelali diagram strukture ITD v smislu povratnih zank. Nato smo oblikovali diagram zalog in tokov, da smo lahko uporabili računalniško simulacijo. Na koncu smo izvedli simulacijo kvantitativno časovnega razvoja modela. Rezultati: Rezultati simulacije kažejo, kako nove ceste v kratkem času povzročajo več prevoženih kilometrov pri vozilih, ki so že v uporabi; v srednjeročnem obdobju pa povzročijo nastanek novega prometa. Pojavljajo se novi vozniki avtomobilov se pojavijo, ker boljši pogoji pretoka zaradi novih cest povečajo privlačnost uporabe avtomobila. Več novih avtomobilov skupaj z vozili, ki so že v uporabi, povzročijo prometne zastoje. Povečana hitrost potovanja, ki jo omogočajo zgrajene ceste, je omejena zaradi ITD učinkov. Zaključek: Pristop k analizi ITD s sistemskega vidika sistemskega omogoča ugotavljate finančno ravnovesje in prispeva k oblikovanju celovite politike obvladovanja ITD. V tem smislu je model podpira procese odločanja v urbanih okoljih, kjer se odloča o gradnji cest z namenom, da se zagotovi povezljivost znotraj države, na primer v državah v razvoju. Ključne besede: povpraševanje po povzročenih potovanjih; sistemska dinamika; odločanje; dinamično modeliranje 144