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In many large cities in developing countries, investments in transportation infrastructure are insufficient for the growing population, resulting in chronic traffic congestion and overcrowding. The urban population of developing countries is expected to increase further toward the middle of this century, and urban planning and transportation policies that foresee future population changes and economic growth are necessary to make these cities more sustainable. Bangkok is one of the most congested metropolitan areas in the world, and transport projects such as the extension of the public transportation system are being implemented. However, due to the monocentric urban structure, both road and rail traffic is extremely congested during peak hours, which impedes some economic activities and personal interaction. In this study, we simulate the impact of urban and transportation measures in Bangkok from today to 2050. In addition to the expansion of the planned rail transit network, we evaluate the effects of a land use scenario in which sub-centers are established to develop a polycentric urban structure. The impact of alternative zoning and transportation policies and projects in Bangkok is discussed. Although this study is focused on Bangkok, the findings are assumed to be transferable to other large cities in developing countries. [ABSTRACT FROM AUTHOR]

Reducing congestion has been one of the critical targets of transportation policies, particularly in cities in developing countries suffering severe and chronic traffic congestions. Several traditional measures have been in place but seem not very successful. This paper applies the agent-based transportation model MATSim for a transportation analysis in Bangkok to assess the impact of spatiotemporal transportation demand management measures. We collect required data for the simulation from various data sources and apply maximum likelihood estimation with the limited data available. We investigate two demand management scenarios, peak time shift, and decentralization. As a result, we found that these spatiotemporal peak shift measures are effective for road transport to alleviate congestion and reduce travel time. However, the effect of those measures on public transport is not uniform but depends on the users' circumstances. On average, the simulated results indicate that those measures increase the average travel time and distance. These results suggest that demand management policies require considerations of more detailed conditions to improve usability. The study also confirms that microsimulation can be a tool for transport demand management assessment in developing countries. [ABSTRACT FROM AUTHOR]

The United Nations have developed Sustainable Development Goals (SDG) to guide countries' development in the next decades. In this paper, we first propose a set of measurable indicators that define the degree of achievement of SDG. Secondly, we use a microscopic integrated land use and transportation model to define future scenarios and measure SDG in the future with radical policies. The model is implemented in Munich and Kagawa. The results are not uniform across policies: while the core cities scenario limits urban sprawl and consumption of greenfield land, traffic conditions and GHG emissions worsened. Furthermore, the scenarios also show the relevance of testing policies in different study areas: the core city scenario and the draconic resettlement scenario showed some impact on vehicle-kilometers traveled in Munich, while the impact in the Kagawa region was almost negligible. In general, only strong (and perhaps implausible) relocation policies result in overall significant changes in the SDG indicators. [ABSTRACT FROM AUTHOR]

In this paper, we develop a synthetic population as the first step in implementing an integrated land use/transport model. The model is agent-based, where every household, person, dwelling, and job is treated as an individual object. Therefore, detailed socioeconomic and demographic attributes are required to support the model. The Iterative Proportional Updating (IPU) procedure is selected for the optimization phase. The original IPU algorithm has been improved to handle three geographical resolutions simultaneously with very little computational time. For the allocation phase, we use Monte Carlo sampling. We applied our approach to the greater Munich metropolitan area. Based on the available data in the control totals and microdata, we selected 47 attributes at the municipality level, 13 attributes at the county level, and 14 additional attributes at the borough level for the city of Munich. Attributes are aggregated at the household, dwelling, and person level. The algorithm is able to synthesize 4.5 million persons in 2.1 million households in less than 1.5 h. Directions regarding how to handle multiple geographical resolutions and how to balance the amount and order of attributes to avoid overfitting are presented. [ABSTRACT FROM AUTHOR]