• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.70-81
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• 2020

Point-by-point data, which is abundantly collected by vehicles with embedded GPS (Global Positioning System), generate useful information. These data facilitate decisions by transportation jurisdictions, and private vendors can monitor and investigate micro-scale driver behavior, traffic flow, and roadway movements. The information is applied to develop app-based route guidance and business models. Of these, speed data play a vital role in developing key parameters and applying agent-based information and services. Nevertheless, link speed values require different levels of physical storage and fidelity, depending on both collecting and reporting intervals. Given these circumstances, this study aimed to establish an appropriate collection interval to efficiently utilize Space Mean Speed information by vehicles with embedded GPS. We conducted a comparison of Probe-vehicle data and Image-based vehicle data to understand PE(Percentage Error). According to the study results, the PE of the Probe-vehicle data showed a 95% confidence level within an 8-second interval, which was chosen as the appropriate collection interval for Probe-vehicle data. It is our hope that the developed guidelines facilitate C-ITS, and autonomous driving service providers will use more reliable Space Mean Speed data to develop better related C-ITS and autonomous driving services.

• Proceedings of the KOR-KST Conference
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• 1995.12a
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• pp.45-73
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• 1995

The current paper presents a system dynamics model which can generate the land use anq transportation system performance simultaneously is proposed. The model system consists of 7 submodels (population, migration of population, household, job growth-employment-land availability, housing development, travel demand, and traffic congestion level), and each of them is designed based on the causality functions and feedback loop structure between a large number of physical, socio-economic, and policy variables. The important advantages of the system dynamics model are as follows. First, the model can address the complex interactions between land use and transportation system performance dynamically. Therefore, it can be an effective tool for evaluating the time-by-time effect of a policy over time horizons. Secondly, the system dynamics model is not relied on the assumption of equilibrium state of urban systems as in conventional models since it determines the state of model components directly through dynamic system simulation. Thirdly, the system dynamics model is very flexible in reflecting new features, such as a policy, a new phenomenon which has not existed in the past, a special event, or a useful concept from other methodology, since it consists of a lots of separated equations. In Chapter I, II, and III, overall approach and structure of the model system are discussed with causal-loop diagrams and major equations. In Chapter V _, the performance of the developed model is applied to the analysis of the impact of highway capacity expansion on land use for the area of Montgomery County, MD. The year-by-year impacts of highway capacity expansion on congestion level and land use are analyzed with some possible scenarios for the highway capacity expansion. This is a first comprehensive attempt to use dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions. The model structure is not very elaborate mainly due to the problem of the availability of behavioral data, but the model performance results indicate that the proposed approach can be a promising one in dealing comprehensively with complicated urban land use/transportation system.