• Journal of Navigation and Port Research
• /
• v.46 no.1
• /
• pp.33-41
• /
• 2022

An important operational goal of a container terminal is to maximize the efficiency of the operation of quay cranes (QCs) that load and/or unload containers onto and from vessels. While the maximization of the efficiency of the QC operation requires minimizing the delay of yard tractors (YT) that transport containers between the storage yard and QCs, the delay is often inevitable because of traffic congestion. In this paper, we propose a method for learning a model that predicts traffic speed in a terminal using only YT operation data, even though the YT traffic is mixed with that of external trucks. Without any information on external truck traffic, we could still make a reasonable traffic forecast because the YT operation data contains information on the YT routes in the near future. The results of simulation experiments showed that the model learned by the proposed method could predict traffic speed with significant accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.9
• /
• pp.1811-1818
• /
• 2009

This paper is about the research to maintain and enhance the flow of data of the wireless traffic control. Various types of burst traffic that were found at TCP window flow control have been removed or mitigated using the two-way traffic control. Currently, TCP ACK Compression problem appears during the transmission of the wireless communication control channel because the queues are mostly located at the end system. Therefore, in this paper, the periodic bursty characterist of the source IP queue wilt be analyzed to predict the maximum value of queues. And then the prediction tool will be applied to wireless communication traffic control to handle symmetric traffic as to increase the throughput and improve the performance.

• Journal of Korean Society of Transportation
• /
• v.25 no.6
• /
• pp.33-42
• /
• 2007

This research proposes strategies about providing detour route information and traffic management for flood disasters. Suggested strategies are based on prevention and preparation concepts including prediction, optimization, and simulation in order to minimize damage. Specifically, this study shows the possibility that average travel speed is increased by proper signal progression during downpours or heavy snowfalls. In addition, in order to protect the drivers and vehicles from dangerous situations, this study proposes a route guidance strategy based on variational inequalities such as flooding. However, other roads can have traffic congestion by the suggested strategies. Thus, this study also shows the possibility to solve traffic congestion of other roads in networks with emergency signal modes.

• Proceedings of the IEEK Conference
• /
• 1998.06a
• /
• pp.126-129
• /
• 1998

In order to control the flow of traffics in ATM networks and optimize the usage of network resources, an efficient control mechanism is necessary to cope with congestion and prevent the degradation of network performance caused by congestion. This paper proposes a new UPC(Usage Parameter Control) mechanism that varies the token generation rate and the buffer threshold of leaky bucket by using a Neural Network controller observing input buffers and token pools, thus achieving the improvement of performance. Simulation results show that the proposed adaptive algorithm uses of network resources efficiently and satisfies QoS for the various kinds of traffics.

• ETRI Journal
• /
• v.22 no.4
• /
• pp.40-50
• /
• 2000

We apply a "sliding-window" Maximum Likelihood(ML) estimator to estimate traffic parameters On-Off source and develop a method for estimating stochastic predicted individual cell arrival rates. Based on these results, we propose a simple Connection Admission Control(CAC)scheme for delay sensitive services in broadband onboard packet switching satellite systems. The algorithms are motivated by the limited onboard satellite buffer, the large propagation delay, and low computational capabilities inherent in satellite communication systems. We develop an algorithm using the predicted individual cell loss ratio instead of using steady state cell loss ratios. We demonstrate the CAC benefits of this approach over using steady state cell loss ratios as well as predicted total cell loss ratios. We also derive the predictive saturation probability and the predictive cell loss ratio and use them to control the total number of connections. Predictive congestion control mechanisms allow a satellite network to operate in the optimum region of low delay and high throughput. This is different from the traditional reactive congestion control mechanism that allows the network to recover from the congested state. Numerical and simulation results obtained suggest that the proposed predictive scheme is a promising approach for real time CAC.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.10
• /
• pp.3195-3200
• /
• 2000

this paper is a stucy onthe preductionof multi-media traffic flow for the realizationof optimum ATM congestion control. In ATM network it is expected that the characteristic of multi-media traffic flow is varied slowly with a time. Fjor the simulation, time-variable multi-media traffic is penerated using possion distribution(connect calls per process time).\, gamma distribution(transmission rate per a call) and exponential distribution(holding time per a call). And using back-propagation neural netwok and proposed tripple neural network, the simulation to predict generaed traffic is executed. From the result,it's capability is shown that the proposed neural network model can be used in the predictionof ATM traffic flow.

• Journal of Korean Society of Transportation
• /
• v.33 no.5
• /
• pp.497-507
• /
• 2015

Nonrecurrent congestion of freeway was primarily caused by incident. The main cause of incident was known as a traffic accident. Therefore, accurate prediction of traffic incident clearance time is very important in accident management. Traffic accident data on freeway during year 2008 to year 2014 period were analyzed for this study. KNN(K-Nearest Neighbor) algorithm was hired for developing incident clearance time prediction model with the historical traffic accident data. Analysis result of accident data explains the level of accident significantly affect on the incident clearance time. For this reason, incident clearance time was categorized by accident level. Data were sorted by classification of traffic volume, number of lanes and time periods to consider traffic conditions and roadway geometry. Factors affecting incident clearance time were analyzed from the extracted data for identifying similar types of accident. Lastly, weight of detail factors was calculated in order to measure distance metric. Weight was calculated with applying standard method of normal distribution, then incident clearance time was predicted. Prediction result of model showed a lower prediction error(MAPE) than models of previous studies. The improve model developed in this study is expected to contribute to the efficient highway operation management when incident occurs.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.3D
• /
• pp.215-222
• /
• 2010

Average speed denotes a travel speed based on the average travel time of vehicles to traverse a segment of roadway, and average travel speed is used as a measure of effectiveness (MOE) suggested in the highway capacity manual (HCM) for evaluating the level of service (LOS) of roadway. Most of the urban freeways in our country are having congestion problem regardless of the rush hours as a high-speed highway with a speed limit of 80km/h or less. Especially traffic congestion within the ramp junction areas is becoming worse by the increased traffic and lack of links with the arterials around the urban freeway. So, the purpose in this study is to identify the traffic characteristics within the ramp junction areas of urban freeway, predict the average speed within the ramp junction areas based on the traffic characteristics identified, and finally prove the validity of the average speed predicted.

• Journal of the Korea Convergence Society
• /
• v.13 no.1
• /
• pp.51-62
• /
• 2022

AI-based speed prediction studies have been conducted quite actively. However, while the importance of explainable AI is emerging, the study of interpreting and reasoning the AI-based speed predictions has not been carried out much. Therefore, in this paper, 'Explainable Deep Graph Neural Network (GNN)' is devised to analyze the speed prediction and assess the nearby road influence for reasoning the critical contributions to a given road situation. The model's output was explained by comparing the differences in output before and after masking the input values of the GNN model. Using TOPIS traffic speed data, we applied our GNN models for the major congested roads in Seoul. We verified our approach through a traffic flow simulation by adjusting the most influential nearby roads' speed and observing the congestion's relief on the road of interest accordingly. This is meaningful in that our approach can be applied to the transportation network and traffic flow can be improved by controlling specific nearby roads based on the inference results.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 1999.10a
• /
• pp.131-140
• /
• 1999

Today freeway is experiencing a severe congestion with incoming or outgoing traffic through freeway ramps during the peak periods. Thus, the objectives of this study is to identify the traffic characteristics, analyze the relationships between the traffic characteristics and finally construct the delay predictive models on the ramp junctions of freeway with 70mph speed limit. From the traffic analyses, and model constructions and verifications for delay prediction on the ramp junctions of freeway, the following results were obtained: ⅰ) Traffic flow showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy also showed a big difference depending on the time periods, and the downstream occupancy(Od) was especially shown to have a higher explanatory power for the delay predictive model construction on the ramp junction of freeway. ⅲ) The speed-occupancy curve showed a remarkable shift based on the occupancies observed ; Od < 9% and Od$\geq$9%. Especially, volume and occupancy were shown to be highly explanatory for delay prediction on the ramp junctions of freeway under Od$\geq$9%, but lowly for delay predicion on the ramp junctions of freeway under Od<9%. Rather, the driver characteristics or transportation conditions around the freeway were through to be a little higher explanatory for the delay perdiction under Od<9%. ⅳ) Integrated delay predictive models showed a higher explanatory power in the morning peak period, but a lower explanatory power in the non-peak periods.