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    ๋™๋ ฅ์›์„ ๊ณ ๋ คํ•œ ๊ตํ†ต๋ง์—์„œ ์—๋„ˆ์ง€ ์ตœ์ ํ™”๋ฅผ ์œ„ํ•œ ๋งํฌ ์‹œ๊ณ„์—ด๋กœ ์ด์‚ฐํ™” ๋œ ๋™์  ๊ตํ†ต ๋ฐฐ์ • ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2020. 8. ์ฐจ์„์›.Vehicle that provides convenience for mobility has been studied for more than 100 years. Recently, there has been a lot of research on the performance of a single-vehicle and interaction between other cars. For example, research on technologies such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and autonomous driver assistant system (ADAS) is actively studied. This change also extends the scope of the study, from a single vehicle to a vehicle fleet, and from micro-traffic to macro-traffic. In the case of vehicles subject to the main experiment, it is classified into internal combustion engine vehicle (ICEV), hybrid electric vehicle (HEV), electric vehicle (EV), and fuel-cell electric vehicle (FCEV) according to the electrification of the powertrain. Also, it can be divided into different categories depending on whether autonomous driving and communication are possible. This study focused on expanding the fuel consumption of vehicles, which has affected environmental pollution for a long time, to the transportation network level. Of course, these researches have been studied for more than a decade, but recent optimization studies using various powertrains have been hard to find. In particular, I decided to build a system that reflects the energy superiority of each road, based on the tendency to consume fuel by road type according to the powertrain. For several decades, the study of arranging the traffic situation of vehicles and determining the route of each vehicle has been mainly applied to traffic allocation for road planning, such as road construction. Therefore, the main content was to predict users' choices and to study from a macro perspective in hours or days. However, in the near future, it is expected to be able to control the route of vehicles in a specific unit of a transportation network, so based on these assumptions, researchers conducted many researches to optimize energy in the transportation network. Many studies on fuel consumption have advanced, but it is hard to find a study of many vehicles consisting of various powertrains. The main reason is that the fuel consumption itself is difficult to predict and calculate, and there is a significant variation for each vehicle. In this study, the average value of each variable for energy consumption was predicted using Vehicle Specific Power (VSP). It used to calculate the fuel consumption that matches the powertrain by each vehicle. Data on fuel consumption were taken from Autonomie, a forward simulator provided by Argonne National Laboratory in the United States. Based on the relationship between the simulated fuel consumption and the VSP as a variable, the deviation was optimized with Newton's method. However, after energy optimization, different vehicles have different travel times, resulting in wasted time due to relative superiority about the fuel consumption, which is a problem in terms of fairness for drivers. Therefore, based on the traffic time of each road, the first principle of Wardrop was applied to optimize the allocation of traffic. The first principle of Wardrop is Wardrop's User Equilibrium (UE) which means an optimal state with same travel cost in the same origin-destination. Based on UE, it was replaced by the question of distributing the allocated traffic flow depends on vehicle type. To this end, it is necessary to apply the traffic assignment based on the route, not the link unit, so that each vehicle can be distributed to the route. This distribution is also an optimization problem, which is a Linear Programming (LP) problem with equality constraint and inequality constraint with the fuel consumption per vehicle derived for each route as a factor. This problem can be resolved through the process of replacing the constraints with the Lagrange multiplier, and the simple conditions for optimization are met. In conclusion, the goal of this study is to allocate a path-based dynamic traffic assignment (DTA) so that it can be applied in real-time with minimal computation and to distribute them by vehicle type. First, under the current road conditions, each vehicle moves toward the intersection. The intersection at the end of the road that is currently running by time unit was organized by Origin-Destination (O-D). In DTA studies, intricate and detailed model like the cell transmission model (CTM) is used for modeling. The traffic flow is calculated as a fluid, which needs high calculating costs and many complex constraints to optimization. Therefore, link time-series was suggested to be modeled for each link and applied as a kind of historical information. This approach can be regarded as Discretized-DTA based on link time-series. It is possible to apply the time axis to the traffic network with a small computing cost and to allocate O-D traffic that changes with time. This optimization problem can be resolved by the Gradient Projection algorithm, which was widely used in path-based traffic allocation. Different delay equations were applied for the intersections by traffic lights for the modeling of the time delay. The actual transportation network flow was predicted as much as possible by the Discretized-DTA algorithm. The allocated traffic was divided by the route, and the fuel consumption per vehicle was derived for each route. In the Sioux Falls Network, the most commonly used example of a traffic allocation simulation, the total energy cost was reduced by about 2% when applying the vehicle distribution used in this study after static traffic assignment. This performance is the result of no time loss between the vehicles, as it is in a UE state. And if traffic simulation case is limited to O-D allocated on multiple paths, it is an effect of more than 3%. This improvement could be replaced by a reduction in fuel cost of about 20 million won for 360,600 vehicles daily. For evaluation of the performance as a navigating system, four navigating systems, as a comparison group, are modeled with algorithms that recommend the optimal route in real-time. The system proposed in this study was able to improve 20% in total traffic time and 15% in the energy aspect compared to the comparison group. It was also applied to Gangdong-gu, Seoul, to simulate a somewhat congested transportation network. At this time, the performance improvement was reduced by 10% in traffic time and 5% in the energy aspect. In the case of the navigating system, indeed, the effect of energy optimization for distributing by vehicle type is not substantial because allocation for each vehicle causes rarely distributed path. However, this improvement can be a significant impact if the effects are accumulated in the transportation network. In this study, energy optimization in the transportation network was achieved based on fuel consumption tendency by vehicle type, and the navigation system was developed for this. Nowadays, with the development of various communication and control technologies, the navigation system based on them can contribute to reducing the cost of transportation, both personally and socially.์‚ฌ๋žŒ๋“ค์˜ ์ด๋™์— ํŽธ์˜์„ฑ์„ ์ œ๊ณตํ•˜๋Š” ์ž๋™์ฐจ๋Š” 100๋…„ ๋„˜๋Š” ๊ธด ์‹œ๊ฐ„ ๋™์•ˆ ์—ฐ๊ตฌ๋˜์–ด์™”๋‹ค. ์ตœ๊ทผ์—๋Š” ๋‹จ์ผ ์ž๋™์ฐจ์˜ ์„ฑ๋Šฅ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์— ๋”๋ถˆ์–ด ๋‹ค๋ฅธ ์ž๋™์ฐจ์™€์˜ ์ƒํ˜ธ ์ž‘์šฉ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๋งŽ์ด ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ์˜ˆ๋กœ ์ฐจ๋Ÿ‰ ๊ฐ„ (vehicle-to-vehicle: V2V) ํ†ต์‹ , ์ฐจ๋Ÿ‰ ์ธํ”„๋ผ ๊ฐ„(vehicle-to-infrastructure: V2I) ํ†ต์‹ , ์ง€๋Šฅํ˜• ์šด์ „์ž ๋ณด์กฐ ์‹œ์Šคํ…œ(advanced driver assistance system: ADAS) ๋“ฑ์˜ ๊ธฐ์ˆ ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ๋“ค ์ˆ˜ ์žˆ๋‹ค. ์ด ๊ฐ™์€ ๋ณ€ํ™”๋Š” ์—ฐ๊ตฌ ๋Œ€์ƒ์˜ ๋ฒ”์œ„๋„ ๋‹จ์ผ ์ฐจ๋Ÿ‰์—์„œ ์ฐจ๋Ÿ‰ fleet, ๊ทธ๋ฆฌ๊ณ  micro-traffic๋ถ€ํ„ฐ macro-traffic๊นŒ์ง€ ๋„“์–ด์ง€๊ฒŒ ํ•˜๊ณ  ์žˆ๋‹ค. ์ฃผ ์‹คํ—˜ ๋Œ€์ƒ์ธ ์ž๋™์ฐจ์˜ ๊ฒฝ์šฐ์—๋„ ๋™๋ ฅ์ „๋‹ฌ๊ณ„์˜ ์ „๊ธฐํ™”์— ๋”ฐ๋ผ ๋‚ด์—ฐ๊ธฐ๊ด€์ž๋™์ฐจ, ํ•˜์ด๋ธŒ๋ฆฌ๋“œ์ž๋™์ฐจ, ์ „๊ธฐ์ž๋™์ฐจ, ์—ฐ๋ฃŒ์ „์ง€์ž๋™์ฐจ๋“ฑ์œผ๋กœ ๋ถ„๋ฅ˜๋˜๋ฉฐ, ์ž์œจ์ฃผํ–‰๊ณผ ํ†ต์‹  ๊ฐ€๋Šฅ ์—ฌ๋ถ€์— ๋”ฐ๋ผ ๋˜ ๋‹ค๋ฅธ ๋ถ„๋ฅ˜๋กœ ๋‚˜๋‰  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์˜ค๋žซ๋™์•ˆ ํ™˜๊ฒฝ์— ํฐ ์˜ํ–ฅ์„ ๋ผ์น˜๋Š” ์ž๋™์ฐจ์˜ ์—ฐ๋ฃŒ์†Œ๋ชจ๋Ÿ‰์„ ๊ตํ†ต๋ง ์ฐจ์›์œผ๋กœ ๋„“ํžˆ๋Š” ๊ฒƒ์— ์ฐฉ์•ˆํ•˜์˜€๋‹ค. ๋ฌผ๋ก  ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๋Š” ์‹ญ๋…„ ๋„˜๊ฒŒ ์ด๋ฃจ์–ด์ ธ์™”์ง€๋งŒ, ์ตœ๊ทผ ๋‹ค์–‘ํ•ด์ง„ ๋™๋ ฅ์ „๋‹ฌ๊ณ„์— ๋”ฐ๋ฅธ ์ตœ์ ํ™” ์—ฐ๊ตฌ๋Š” ์ฐพ๊ธฐ ํž˜๋“ค์—ˆ๋‹ค. ํŠนํžˆ ๋™๋ ฅ์ „๋‹ฌ๊ณ„์— ๋”ฐ๋ผ ๋„๋กœ ๋ณ„ ์—ฐ๋ฃŒ์†Œ๋ชจ ๊ฒฝํ–ฅ์ด ๋‹ฌ๋ผ์ง€๋Š” ๊ฒƒ์— ์ฐฉ์•ˆํ•˜์—ฌ, ๋„๋กœ ๋ณ„ ์—๋„ˆ์ง€์  ์šฐ์œ„๋ฅผ ๋ฐ˜์˜ํ•œ ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜๊ธฐ๋กœ ํ•˜์˜€๋‹ค. ์ˆ˜ ์‹ญ๋…„ ๋™์•ˆ ์ฐจ๋Ÿ‰๋“ค์˜ ๊ตํ†ต ์ƒํ™ฉ์„ ์ •๋ฆฌํ•˜์—ฌ ๊ฐ ์ฐจ๋Ÿ‰๋“ค์˜ ๋ฃจํŠธ๋ฅผ ์ •ํ•˜๋Š” ์—ฐ๊ตฌ๋Š”, ๋„๋กœ ๊ฑด์„ค ๋“ฑ์˜ ๋„๋กœ ๊ณ„ํš์„ ์œ„ํ•œ ํ†ตํ–‰ ๋ฐฐ์ •์— ์ฃผ๋กœ ์ ์šฉ๋˜์–ด์™”๋‹ค. ๋”ฐ๋ผ์„œ ์ด์šฉ์ž๋“ค์˜ ์„ ํƒ์„ ์˜ˆ์ธกํ•˜๊ณ , ์‹œ๊ฐ„ ๋‹จ์œ„ ๋˜๋Š” ์ผ ๋‹จ์œ„์˜ ๊ฑฐ์‹œ์ ์ธ ๊ด€์ ์—์„œ์˜ ์—ฐ๊ตฌ๊ฐ€ ์ฃผ ๋‚ด์šฉ์ด์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ทผ ์‹œ์ผ ๋‚ด์— ์ผ์ • ๋‹จ์œ„์˜ ๊ตํ†ต๋ง์—์„œ๋Š” ์ฐจ๋Ÿ‰๋“ค์˜ ๋ฃจํŠธ๋ฅผ ์ปจํŠธ๋กคํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋ผ ์˜ˆ์ƒ๋˜๊ธฐ์— ์ด๋Ÿฌํ•œ ๊ฐ€์ •์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ตํ†ต๋ง ๋‚ด์˜ ์—๋„ˆ์ง€๋ฅผ ์ตœ์ ํ™” ํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์—ฐ๋ฃŒ์†Œ๋ชจ๋Ÿ‰์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋งŽ์ด ์ง„ํ–‰๋˜์—ˆ์ง€๋งŒ, ๋‹ค์–‘ํ•œ ํŒŒ์›ŒํŠธ๋ ˆ์ธ์œผ๋กœ ๊ตฌ์„ฑ๋œ ๋‹ค์ˆ˜์˜ ์ฐจ๋Ÿ‰๋“ค์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์ฐพ์•„๋ณด๊ธฐ ํž˜๋“ค๋‹ค. ๊ทธ ๋Œ€ํ‘œ์ ์ธ ์ด์œ ๋Š” ์—ฐ๋ฃŒ์†Œ๋ชจ๋Ÿ‰์ž์ฒด๊ฐ€ ์˜ˆ์ธก ๋ฐ ๊ณ„์‚ฐํ•˜๊ธฐ ํž˜๋“ค๊ณ , ์ฐจ๋Ÿ‰๋งˆ๋‹ค ๊ทธ ํŽธ์ฐจ๊ฐ€ ํฌ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ฐจ๋Ÿ‰ ๋น„์ถœ๋ ฅ(Vehicle specific power: VSP)๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฐ ๋ณ€์ˆ˜๋“ค์˜ ํ‰๊ท ์น˜๋กœ ์˜ˆ์ธกํ•œ ํ›„์—, ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ์ฐจ์ข… ๋ณ„ ๋™๋ ฅ์ „๋‹ฌ๊ณ„์— ๋งž๋Š” ์—ฐ๋ฃŒ์†Œ๋ชจ๋Ÿ‰์„ ๊ณ„์‚ฐํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ์—ฐ๋ฃŒ์†Œ๋ชจ๋Ÿ‰์— ๋Œ€ํ•œ ๋ฐ์ดํ„ฐ๋Š” ๋ฏธ๊ตญ์˜ Argonne national laboratory์—์„œ ๊ณต๊ธ‰ํ•˜๋Š” ์ „๋ฐฉํ–ฅ ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ์ธ Autonomie์—์„œ ๊ฐ€์ ธ์™”๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋œ ์—ฐ๋ฃŒ์†Œ๋ชจ๋Ÿ‰๊ณผ VSP์™€์˜ ๊ด€๊ณ„๋ฅผ ๋ณ€์ˆ˜๋กœ ํ•˜์—ฌ ๋‰ดํ„ด๋ฒ•(Newtons method)๋กœ ํŽธ์ฐจ๋ฅผ ์ตœ์†Œํ™”ํ•˜๋„๋ก ์ตœ์ ํ™”ํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ๊ตํ†ต๋ง ๋‚ด์—์„œ ์—๋„ˆ์ง€ ์ตœ์ ํ™” ํ›„, ์ฐจ์ข…์— ๋”ฐ๋ผ ํ†ตํ–‰ ์‹œ๊ฐ„์ด ๋‹ฌ๋ผ์ ธ์„œ ์ƒ๋Œ€์  ์šฐ์œ„์— ๋”ฐ๋ฅธ ์‹œ๊ฐ„ ๋‚ญ๋น„๊ฐ€ ์ƒ๊ธฐ๋ฉด ์šด์ „์ž์˜ ๊ณต์ •์„ฑ ์ธก๋ฉด์—์„œ ๋ฌธ์ œ๊ฐ€ ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๊ฐ ๋„๋กœ์˜ ํ†ตํ–‰์‹œ๊ฐ„์„ ๊ธฐ์ค€์œผ๋กœ Wardrop์˜ ์ฒซ๋ฒˆ์งธ ์›์น™์„ ์ ์šฉํ•œ ์ตœ์  ํ†ตํ–‰ ๋ฐฐ์ •์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋ฅผ ๊ธฐ์ค€์œผ๋กœ ๋ฐฐ์ •๋œ ํ†ตํ–‰์˜ ์ฐจ๋Ÿ‰ ํ๋ฆ„์„ ์ฐจ์ข… ๋ณ„๋กœ ๋ถ„๋ฐฐํ•˜๋Š” ๋ฌธ์ œ๋กœ ์น˜ํ™˜ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ํ†ตํ–‰ ๋ฐฐ์ •์„ ๋งํฌ ๋‹จ์œ„๊ฐ€ ์•„๋‹ˆ๋ผ ๊ฒฝ๋กœ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ ์šฉํ•˜์—ฌ์•ผ ๊ฐ ์ฐจ์ข…์„ ๊ฒฝ๋กœ์— ๋ถ„๋ฐฐํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ถ„๋ฐฐ ๋˜ํ•œ ์ตœ์ ํ™” ๋ฌธ์ œ๋กœ, ์ด๋Š” ๊ฐ ๊ฒฝ๋กœ์— ๋Œ€ํ•ด ๋„์ถœ๋œ ์ฐจ๋Ÿ‰๋‹น ์—ฐ๋ฃŒ์†Œ๋ชจ๋Ÿ‰์„ ๊ณ„์ˆ˜๋กœ ํ•˜๊ณ , ๋“ฑ์‹ ์ œํ•œ ์กฐ๊ฑด๊ณผ ๋ถ€๋“ฑ์‹ ์ œํ•œ ์กฐ๊ฑด์„ ๊ฐ€์ง€๋Š” ์„ ํ˜•๊ณ„ํš๋ฒ•(Linear Programming: LP)๋ฌธ์ œ์ด๋‹ค. ์ด๋Š” ์ œํ•œ์กฐ๊ฑด์„ ๋ผ๊ทธ๋ž‘์ฃผ ์ƒ์ˆ˜(Lagrange Multiplier)๋กœ ์น˜ํ™˜ํ•˜๋Š” ๊ณผ์ •์„ ํ†ตํ•ด, ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ ์กฐ๊ฑด์ด ๋‹จ์ˆœํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์ตœ์ ํ™”๋ฅผ ์œ„ํ•œ ์กฐ๊ฑด์„ ๋งŒ์กฑ์‹œํ‚จ๋‹ค. ๋˜ํ•œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋ฃจํŠธ๋ฅผ ์ •ํ•ด์ฃผ๋Š” ์ผ์ข…์˜ ๋„ค๋น„๊ฒŒ์ด์…˜์„ ๋ชฉํ‘œ๋กœ ํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์—, Wardrop์˜ ์ด์šฉ์ž ํ‰ํ˜•(User Equilibrium: UE)์ƒํƒœ๋ฅผ ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋ณ€ํ•˜๋Š” ๋™์  ์ƒํƒœ๋กœ ์ ์šฉํ•ด์•ผ ํ–ˆ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, ๊ฒฝ๋กœ ๊ธฐ๋ฐ˜์˜ ๋™์  ํ†ตํ–‰ ๋ฐฐ์ •(Dynamic Traffic Assignment: DTA)์„ ์—ฐ์‚ฐ์„ ์ตœ์†Œํ™”ํ•˜์—ฌ ์‹ค์‹œ๊ฐ„์œผ๋กœ ์ ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๋ฐฐ์ •์„ ํ•˜๊ณ , ์ฐจ์ข… ๋ณ„๋กœ ๋ถ„๋ฐฐ๋ฅผ ํ•˜๋Š” ๊ฒƒ์ด ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉํ‘œ์ด๋‹ค. ๋จผ์ € ํ˜„์žฌ ์ฐจ๋Ÿ‰ ์ƒํ™ฉ์—์„œ ๊ฐ ๋„๋กœ๋Š” ๊ต์ฐจ๋กœ๋ฅผ ํ–ฅํ•ด์„œ ์ด๋™ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์‹œ๊ฐ„ ๋‹จ์œ„ ๋ณ„๋กœ ํ˜„์žฌ ๋‹ฌ๋ฆฌ๊ณ  ์žˆ๋Š” ๋„๋กœ์˜ ๋์˜ ๊ต์ฐจ๋กœ๋ฅผ ๊ธฐ์ ์œผ๋กœ ํ•˜๊ณ  ์›๋ž˜ ๊ฐ€๊ณ ์ž ํ•˜๋Š” ๋ชฉ์ ์ง€๋ฅผ ์ข…์ ์œผ๋กœ ๊ฐ€์ง€๋Š” ๊ธฐ ์ข…์ ์„ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ๋™์  ํ†ตํ–‰ ๋ฐฐ์ •์˜ ์—ฐ๊ตฌ์—์„œ๋Š” ์„ธํฌ ์ „์ด ๋ชจ๋ธ(Cell Transmission Model: CTM) ๋“ฑ์„ ์ด์šฉํ•˜์—ฌ, ๊ตํ†ต ํ๋ฆ„์„ ์œ ์ฒด์ฒ˜๋Ÿผ ๊ณ„์‚ฐํ•˜์—ฌ ์‹œ๊ฐ„ ์†Œ๋ชจ๊ฐ€ ๋งŽ๋‹ค. ๋”ฐ๋ผ์„œ ๋„๋กœ์— ์ง„์ž…ํ•˜๋Š” ๊ฐ ํ†ตํ–‰ ํ๋ฆ„์„ ๋„๋กœ์˜ ์‹œ๊ณ„์—ด์— ์ €์žฅํ•˜๋Š” ์ด์‚ฐํ™” ๋œ ๋™์  ํ†ตํ–‰ ๋ฐฐ์ •(Discretized-DTA)๋ฐฉ๋ฒ•์„ ๊ณ ์•ˆํ•˜์˜€๋‹ค. ์ด๋Š” ์ ์€ ๊ณ„์‚ฐ ๋น„์šฉ์œผ๋กœ ์‹ฌํ”Œํ•˜๊ฒŒ ์‹œ๊ฐ„ ์ถ•์„ ๊ตํ†ต๋ง์— ๋ถ€์—ฌํ•˜์˜€๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋ณ€ํ™”ํ•˜๋Š” ๊ธฐ ์ข…์ ์„ ํ†ตํ–‰์„ ๋ฐฐ์ •ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด ์ตœ์ ํ™” ๋ฌธ์ œ๋Š” ๊ฒฝ๋กœ ๊ธฐ๋ฐ˜ ํ†ตํ–‰๋ฐฐ์ •์—์„œ ๋งŽ์ด ์‚ฌ์šฉ๋œ ๊ฒฝ์‚ฌ ํˆฌ์˜๋ฒ•(Gradient Projection) ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ ์šฉํ•˜์˜€๋‹ค. ๊ฐ ๋„๋กœ์˜ ๊ตํ†ต ํ๋ฆ„์— ๋”ฐ๋ฅธ ์‹œ๊ฐ„ ์ง€์ฒด๋„ ์‹ ํ˜ธ๋“ฑ์ด ์žˆ๋Š” ๊ต์ฐจ๋กœ์˜ ๋‹จ์†๋ฅ˜์™€ ์‹ ํ˜ธ๋“ฑ์ด ์—†๋Š” ๋„๋กœ์˜ ์—ฐ์†๋ฅ˜์— ๋”ฐ๋ผ ๋‹ค๋ฅธ ์ง€์ฒด ์‹์„ ์ ์šฉํ•˜์—ฌ ์‹ค์ œ ํ˜„์‹ค์˜ ๊ตํ†ต๋ง์˜ ํ๋ฆ„์„ ์ตœ๋Œ€ํ•œ ์˜ˆ์ธกํ•˜์˜€๋‹ค. ์ด๋ ‡๊ฒŒ ๋ฐฐ์ •๋œ ํ†ตํ–‰๋Ÿ‰์„ ๊ฒฝ๋กœ ๋ณ„๋กœ ๋‚˜๋ˆ„์–ด, ๊ฐ ๊ฒฝ๋กœ์— ๋Œ€ํ•œ ์ฐจ๋Ÿ‰๋‹น ์—ฐ๋ฃŒ์†Œ๋ชจ๋Ÿ‰์„ ๋„์ถœํ•˜์˜€๋‹ค. ํ†ตํ–‰ ๋ฐฐ์ • ์‹œ๋ฎฌ๋ ˆ์ด์…˜์—์„œ ๊ฐ€์žฅ ๋งŽ์ด ์‚ฌ์šฉํ•˜๋Š” ์˜ˆ์ œ์ธ Sioux Falls ๋„คํŠธ์›Œํฌ์—์„œ๋Š” ์ •์  ํ†ตํ–‰ ๋ฐฐ์ • ์ดํ›„ ๋ณธ ์—ฐ๊ตฌ์— ์‚ฌ์šฉ๋œ ์ฐจ์ข… ๋ถ„๋ฐฐ๋ฅผ ์ ์šฉํ•  ๊ฒฝ์šฐ์—, ์ „์ฒด ์—๋„ˆ์ง€ ์ฝ”์ŠคํŠธ๊ฐ€ ์•ฝ 2% ๊ฐ์†Œํ•˜์˜€๋‹ค. ์ด๋Š” Wardrop์˜ ์ด์šฉ์ž ํ‰ํ˜• ์ƒํƒœ์ด๊ธฐ ๋•Œ๋ฌธ์—, ์ฐจ๋Ÿ‰๋“ค ๊ฐ„์˜ ์–ด๋–ค ์‹œ๊ฐ„ ์†ํ•ด๋„ ์—†๋Š” ๊ฒฐ๊ณผ์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋งŒ์•ฝ ํ†ตํ–‰๋Ÿ‰์ด ๋ณต์ˆ˜์˜ ๊ฒฝ๋กœ๋กœ ๋ฐฐ์ •๋œ O-D์— ํ•œ์ •ํ•  ๊ฒฝ์šฐ์—, ์•ฝ 3%๊ฐ€ ๋„˜๋Š” ํšจ๊ณผ๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Š” ํ•˜๋ฃจ ๊ธฐ์ค€์œผ๋กœ, 360,600๋Œ€์˜ ์ฐจ๋Ÿ‰์— ๋Œ€ํ•ด 2000๋งŒ์› ์ •๋„์˜ ์—ฐ๋ฃŒ๋น„ ๊ฐ์†Œ๋กœ ์น˜ํ™˜ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋„ค๋น„๊ฒŒ์ดํŒ… ์‹œ์Šคํ…œ์˜ ๊ฒฝ์šฐ์—๋Š”, ํ˜„์žฌ ๋„๋กœ์˜ ์ƒํƒœ๋ฅผ 4๊ฐ€์ง€ ์ •๋„๋กœ ๋‚˜๋ˆ„์–ด์„œ ์‹ค์‹œ๊ฐ„ ์ตœ์  ๊ฒฝ๋กœ๋กœ ์ถ”์ฒœํ•˜๋Š” ๊ฒฝ์šฐ๋ฅผ ๋น„๊ต๊ตฐ์œผ๋กœ ์ •ํ•˜์˜€๋‹ค. ๋น„๊ต๊ตฐ์— ๋น„ํ•ด ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•˜๋Š” ์‹œ์Šคํ…œ์€ ์ „์ฒด ํ†ตํ–‰์‹œ๊ฐ„๊ณผ ์—๋„ˆ์ง€์  ์ธก๋ฉด์„ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ์ด๋ฅผ ์„œ์šธ์‹œ ๊ฐ•๋™๊ตฌ์— ์ ์šฉํ•˜์—ฌ ์–ด๋Š ์ •๋„ ํ˜ผ์žกํ•œ ๊ตํ†ต๋ง์„ ๋ชจ์‚ฌํ•˜์˜€๋‹ค. ์ด ๋•Œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ์™€ ๊ธฐ์กด์˜ ์ตœ์†Œ ์‹œ๊ฐ„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ ์šฉํ•˜๋Š” ์ƒ์šฉ ๋„ค๋น„๊ฒŒ์ด์…˜ ์‹œ์Šคํ…œ ๊ฒฐ๊ณผ๋ฅผ ๋น„๊ตํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ์•ฝ 8000๋Œ€์˜ ์ฐจ๋Ÿ‰์ด ์ฃผํ–‰ํ•˜๋Š” ์‹œ๋‚˜๋ฆฌ์˜ค 1์˜ ๊ตํ†ต๋ง์„ ๊ธฐ์ค€์œผ๋กœ ์ „์ฒด ํ†ตํ–‰์‹œ๊ฐ„์€ 66%, ์—๋„ˆ์ง€ ์†Œ๋ชจ ๋น„์šฉ์€ 34%๋ฅผ ์ค„์ผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Š” ์ผ์ • ํ˜ผ์žก๋„๊นŒ์ง€๋Š” ํšจ๊ณผ๊ฐ€ ์ปค์กŒ์ง€๋งŒ, ์–ด๋Š ์ด์ƒ์—์„œ๋Š” ํšจ๊ณผ๊ฐ€ ๊ฐ์†Œํ•˜๊ธฐ๋„ ํ•˜์˜€๋‹ค. ๋ฌผ๋ก  ์ด์‚ฐํ™” ๋œ ๋™์  ๊ตํ†ต ๋ถ„๋ฐฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์ผ์ • ์‹œ๊ฐ„ ๋‹จ์œ„์˜ ์ฐจ๋Ÿ‰๋“ค์„ ํŽธ๋Œ€๋กœ ๋ฌถ์–ด ํ†ตํ–‰์„ ๋ฐฐ์ •ํ•˜๊ณ , ๊ทธ ๊ฒฐ๊ณผ ๊ฒฝ๋กœ๊ฐ€ ๋ถ„์‚ฐ๋˜๋Š” ๊ฒฝ์šฐ ์ž์ฒด๊ฐ€ ์ ๊ธฐ ๋•Œ๋ฌธ์— ๋ถ„์‚ฐ๋œ ๊ฒฝ๋กœ์— ์ฐจ์ข… ๋ณ„๋กœ ๋ถ„๋ฐฐํ•˜๋Š” ์—๋„ˆ์ง€ ์ตœ์ ํ™”์˜ ํšจ๊ณผ๋Š” ํฌ์ง€ ์•Š์€ ๊ฒƒ์ด ์‚ฌ์‹ค์ด๋‹ค. ํ•˜์ง€๋งŒ ์ด ๋˜ํ•œ ๊ตํ†ต๋ง ๋‚ด์—์„œ ๊ทธ ํšจ๊ณผ๋ฅผ ๋ˆ„์ ํ•˜๋ฉด ์˜ํ–ฅ์ด ํฌ๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ฒฐ๊ณผ์ ์œผ๋กœ ์ฐจ์ข… ๋ณ„ ์—ฐ๋ฃŒ ์†Œ๋ชจ ๊ฒฝํ–ฅ์— ๊ทผ๊ฑฐํ•˜์—ฌ ๊ตํ†ต๋ง ๋‚ด ํ†ตํ–‰ ์‹œ๊ฐ„ ๊ฐ์†Œ์— ๋”๋ถˆ์–ด ์—๋„ˆ์ง€ ์ตœ์ ํ™”๋ฅผ ์ด๋ฃจ์—ˆ๊ณ , ์ด๋ฅผ ์œ„ํ•œ ๋„ค๋น„๊ฒŒ์ดํŒ… ์‹œ์Šคํ…œ์„ ๊ฐœ๋ฐœํ–ˆ๋‹ค. ๊ฐ์ข… ํ†ต์‹ ๊ณผ ์ œ์–ด ๊ธฐ์ˆ ์ด ๋ฐœ์ „ํ•œ ์š”์ฆ˜, ๊ทธ์— ๊ธฐ๋ฐ˜ํ•œ ๋„ค๋น„๊ฒŒ์ดํŒ… ์‹œ์Šคํ…œ์€ ๊ฐœ์ธ์ , ์‚ฌํšŒ์ ์œผ๋กœ ๊ตํ†ต์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋น„์šฉ์„ ์ค„์ด๋Š” ๋ฐ ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ๋‹ค.Chapter 1. Introduction 1 1.1. Background 1 1.2. Research Scope and Contents 7 Chapter 2. Theory and Literature Review 11 2.1. Traffic Assignment Problem 11 2.1.1. Wardrops Principle 11 2.1.2. Dynamic Traffic Assignment (DTA) 16 2.1.3. Volume-Delay Function (VDF) 18 2.2. Vehicle Fuel Consumption 20 2.2.1. Tendency Based on Driving Cycle 21 2.2.2. Tendency Based on Powertrain 22 2.3. Vehicle Specific Power (VSP) 24 2.4. Route Guidance System 26 2.4.1. Optimal Routing System Based on Fuel Economy 27 Chapter 3. Target Model Development 29 3.1. Vehicle Model Development 29 3.2. Fuel Consumption Trend Depends on Vehicle Model 32 3.3. Introduction of Vehicle Specific Power 35 3.4. Calibration of VSP Parameters 36 3.5. Regression of VSP Variables 38 3.5.1.. VSP Variables from General Vehicles 39 3.5.2. Regression of VSP Variables by Travel Time 40 Chapter 4. Traffic Assignment based on Energy Consumption 46 4.1. Model for Static Traffic Assignment 46 4.1.1. Sioux Falls Network 46 4.2. Gradient Projection (GP) Algorithm 48 4.3. Distribution of Vehicles to Energy Optimization 51 4.3.1. Problem Formulation for Vehicle Distribution 51 4.3.2. Linear Programming 53 4.4. Simulation Result in Test Network 54 Chapter 5. Navigating System using Discretized Dynamic Traffic Assignment 57 5.1. Modeling of Discretized Dynamic Traffic Assignment 57 5.1.1. Discretized-DTA with Vehicle Fleets 57 5.1.2. Discretized-DTA with Link Time-Series 60 5.1.3. Target Network 62 5.2. Navigating System 65 5.2.1. Structure of the Navigating System 65 5.2.2. Algorithm of the Navigating System 65 5.2.3. Assumption of the Navigating System 69 5.3. Result of Navigating System 70 5.3.1. Results of the Travel Time Prediction 70 5.3.2. Results in Scenario 1 71 5.3.3. Results in Scenario 2 79 5.3.4. Results in Scenario 3 81 Chapter 6. Conclusion and Future Works 85 6.1. Conclusion 85 6.2. Future Work 87 Bibliography 88 Abstract in Korean 100Docto

    The relationship between biological and psychosocial risk factors and restingโ€state functional connectivity in 2โ€monthold Bangladeshi infants: A feasibility and pilot study

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    Childhood poverty has been associated with structural and functional alterations in the developing brain. However, poverty does not alter brain development directly, but acts through associated biological or psychosocial risk factors (e.g. malnutrition, family conflict). Yet few studies have investigated risk factors in the context of infant neurodevelopment, and none have done so in lowโ€resource settings such as Bangladesh, where children are exposed to multiple, severe biological and psychosocial hazards. In this feasibility and pilot study, usable restingโ€state fMRI data were acquired in infants from extremely poor (n = 16) and (relatively) more affluent (n = 16) families in Dhaka, Bangladesh. Wholeโ€brain intrinsic functional connectivity (iFC) was estimated using bilateral seeds in the amygdala, where iFC has shown susceptibility to early life stress, and in sensory areas, which have exhibited less susceptibility to early life hazards. Biological and psychosocial risk factors were examined for associations with iFC. Three restingโ€state networks were identified in withinโ€group brain maps: medial temporal/striatal, visual, and auditory networks. Infants from extremely poor families compared with those from more affluent families exhibited greater (i.e. less negative) iFC in precuneus for amygdala seeds; however, no group differences in iFC were observed for sensory area seeds. Heightโ€forโ€age, a proxy for malnutrition/infection, was not associated with amygdala/precuneus iFC, whereas prenatal family conflict was positively correlated. Findings suggest that it is feasible to conduct infant fMRI studies in lowโ€resource settings. Challenges and practical steps for successful implementations are discussed

    A Survey on Cache Management Mechanisms for Real-Time Embedded Systems

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    ยฉ ACM, 2015. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys, {48, 2, (November 2015)} http://doi.acm.org/10.1145/2830555Multicore processors are being extensively used by real-time systems, mainly because of their demand for increased computing power. However, multicore processors have shared resources that affect the predictability of real-time systems, which is the key to correctly estimate the worst-case execution time of tasks. One of the main factors for unpredictability in a multicore processor is the cache memory hierarchy. Recently, many research works have proposed different techniques to deal with caches in multicore processors in the context of real-time systems. Nevertheless, a review and categorization of these techniques is still an open topic and would be very useful for the real-time community. In this article, we present a survey of cache management techniques for real-time embedded systems, from the first studies of the field in 1990 up to the latest research published in 2014. We categorize the main research works and provide a detailed comparison in terms of similarities and differences. We also identify key challenges and discuss future research directions.King Saud University NSER

    Annual report of the selectmen and treasurer of the town of Allenstown, New Hampshire together with the reports of the road agent and other officers of the town for the fiscal year ending December 31, 1975.

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    This is an annual report containing vital statistics for a town/city in the state of New Hampshire
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