Figure 2. Stock-Flow diagram.
Background
One of the greatest problems of large cities throughout the world is vehicular congestion. Government and public institutions in charge have tried to solve this issue, but have faced a significant and common obstacle: the lack of understanding of the complex interrelationships among the system components as well as their dynamic nature which results in the development of solutions that are impossible to successfully implement. One major reason is that vehicular congestion has not been studied as a whole but rather in separate parts. Examples of various policies the government have implemented to solve this problem and the obstacles they have faced are listed in Table 1.
| Traffic organization | Streets saturation. |
| Avenue width and reorganization | Extra cost, slow execution. |
| Public works on a large-scale | Slow execution, high cost. |
| Decentralization of activities | Problem extension, slowness, difficulty in planning, existing government economic policy. |
| "One Day No Drive" program | General attitude (public and private sectors). |
Secondary effects of these implemented policies include the increase
in: vehicular accidents, air pollution, gas consumption, operational
costs, government expenses, urban taxes and street damage. Among
other unintended effects are the diminishing health of the citizenry
and general property values.
Objective
The purpose of applying system dynamics to vehicular congestion is to obtain a holistic understanding and shared vision of the problem, to develop a training tool that focuses attention and efforts on the key drivers of the problem and to support policy-level decision making at the Secretariat of Transportation.
Another objective is the creation of a generative versus a reactive
vision by top management of the transportation department that
will allow for anticipating changes in the city and to proactively
identify preventive landmarks.
Current System Knowledge Model
The development of this model included:
Model Development
The following visual feedback diagrams showing the system's cause-effect structure were created as a result of the information sources mentioned above. (Figure 1 and 2).
| Figure 2. Stock-Flow diagram. |
The models were divided in blocks to facilitate the understanding of the relationships. Each block describes a part of the whole model (Table 2).
| Infrastructure block | Physical characteristics of Mexico City, such as street capacity, traffic lights, pavement and other construction. |
| Public and concession transportation block | Information about general transportation in Mexico City, including private cars, rail, public and concession transportation. |
| Pollution block | Public and private car pollution, including the effects of the "One Day No Drive" program. |
The next step in the process was creating scenarios with the working model and testing them in a flight simulator. In a macroeconomic problem, it is more difficult to make scenario plots because of the qualitative nature of many of the key driving forces, such as attitudes and centralization. In addition, when a strategy is implemented in the model, it can be very hard to measure the effect of this strategy upon these qualitative variables. It is also difficult to estimate the impact of these qualitative variables on the rest of the model.
Scenario plots in this case were developed by classifying them in three main areas: economic, political, social. In addition, the existing government plan, which was first implemented in Mexico City in1996 and is due to end in the year 2000, was included for policy testing purposes. Learning laboratories were designed on the basis of the causal model, the stock-flow model and the scenario table (figure 3 and 4).
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Conclusions
The 18-month study showed that transportation policy makers should pay the most attention to the issues listed in table 3.
| Private car owners' attitudes | The public, in general, does not obey traffic regulations. The public's lack of awareness of the consequences of their actions makes the problem worse. |
| Government's attitude | The effectiveness of the policies depends on the planning time frame the government uses as well as level of corruption and degree of centralization. |
| Public transportation drivers' attitudes | Public and concession transportation quality service is mainly determined by them. They don't follow traffic regulation ,either. These reasons increase the need for improvements in education and training in order to improve the vehicular congestion. |
| Short and long range planning | Short-range planning gives the model temporal solutions, long-range planning adds stability to the system. These variables must improve in order to control the system and ensure that implemented policies produce the intended results. |
| External factors | Educational level and economic levels are important drivers in the model. In the long-term, these variables significantly impact the behavior of the whole system. |
| Quality service | Today public and concession transportation is not a preferred option to people with cars in Mexico City because the level of quality service is low. This is low due to the gap between supply and demand for this service (overloading current capacity) as well as poor attitudes (drivers do not follow the traffic regulations, people ask for not allowed services and corruption permit the citizenry to brake the rules) ,which renders meeting the acceptable quality service level ineffective. |
The variables mentioned above are the high leverage change variables of the system. The analysis of these variables and their impact on the system as a whole led us to conclude that improvement in social education as well as planning are the key areas for policy development in order to slow the rate of increase in vehicular congestion in Mexico City.
The model also shows that, despite improvements in social education, the problem will not decrease even if the government applies the right policies, because of the extreme pressures placed on the system by the high, constant population growth rate in Mexico City as well as the delays inherent in successfully implementing broad educational programs.
Though the problem being studied is vehicular congestion in Mexico
City, the conclusions lead us to focus on issues of larger impact
for policy makers in Mexico at the national level, such as urban
development and decentralization of economic and industrial growth
for the nation.
References
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