The behavior and the performance of firms, which are highlighted on small and medium-sized enterprise (SME) development, have been extensively discussed since few past decades among the scholars and researchers. Nonetheless, most of those studies only concentrate onto Western firms and less is known in South-East Asia firms, especially the firms located in Indonesia. Consistent with the lack of literature mentioned above, the most significant problem within the body of literature is there are also very little comprehensive findings of empirical research in examining the role of foreign enterprises which specialized in flying fish roe product, known as Tobiko or To Bio No Tamago in Japanese, on the development of Indonesian SMEs in general and the development of natives’ wealth and knowledge in particular. To this knowledge, this study thus aims to expound the role of Kanetoku Corporation and Kanematsu Corporation (formerly known as Kanematsu Gosho Ltd.) – as the representative of the foreign firms – on the development of Indonesian SMEs that are originated from South Sulawesi province, especially from Makassar, Takalar, and Mamuju region. Through the application of resource-based view (RBV) as the grounded theory and the emphasis on the underpinning concept of innovation and knowledge-transfer, this study is strongly expected come up with the result that innovation and knowledge transfer provided by Japanese firms intentionally or unintentionally have become the vital factors on the development of South Sulawesi SMEs that can be seen for example within (1) the successful internationalization of South Sulawesi SMEs and amusingly (2) the remarkable discovery of a new habitat of flying fish outside the South Sulawesi province by the South Sulawesi SMEs during the collaboration between Japanese firms.

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Estimating traffic volume at a link level is important to transportation planners, traffic engineers, and policy makers. More specifically, this vital parameter has been used in transportation planning, traffic operations, highway geometric design, pavement design, and resource allocation. However, traditional factor approach, regression-­‐based models, and artificial neural network models failed to present network-­‐wide traffic volume estimates because they rely on traffic counts for model development, and they all have inherent weaknesses. A review to previous research work and the state-­‐of-­‐practice clearly indicates that the Traditional Four-step Travel Demand Model (TFTDM) was generally based on large traffic analysis zones (TAZs) and networks consisting of high functional-class roads only. Consequently, this conventional modeling framework yielded a limited number of link traffic assignments with fairly high estimation errors. In the light of these facts and the obvious need of accurate network-wide traffic estimates, this review is conducted. In particular, this paper provides an extensive review of using traditional travel demand models for improved network-­‐wide traffic volume estimation. The paper then focuses on the challenges and opportunities in achieving high-fidelity travel demand model (HFTDM). This review has revealed that, opportunities in relation to both technological advances and intelligent data present a substantial potential in developing the proposed HFTDM for a much more accurate traffic estimation at a network-­‐wide level. Finally, the paper concludes with key findings from the review and provides a few recommendations for future research related to the topic.

The four-step travel demand model (FSTDM) is based on coarse rural traffic analysis zones (TAZs) which tends to exaggerate the intrazonal trips resulting in biased and unbalanced trip distribution over the roadway network with high estimation errors. These limitations have necessitated developing a geographic information systems (GIS)-based high-fidelity travel demand model framework (HFTDMF) capable of achieving network-wide traffic volume estimation with improved model accuracy. This requires using an all functional class roadway network and enhancing the census-based coarse TAZ structure with finer-grained spatial resolution TAZs by integrating the travel demand modeling software platform, remotely-sensed images, parcel-based digital property maps, the AZTool aggregation algorithm, and areal interpolation technique. Preliminary results from the Greater Fredericton Area (GFA) showed that increasing the GFA spatial resolution from the coarsest TAZ structure at census tract (CT) level (27 CT TAZs) to the finest TAZ structure at 4252 “fine” TAZs resulted in an improvement to modeling accuracy of R2, by 0.4092 (from 0.2490 to 0.6582) and an improvement in traffic assignment coverage by 46 percentage points (from 29% to 75%).

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