In order to deal with dynamic traffic flow, adaptive traffic signal controls using reinforcement learning are being studied. However, most of the related studies are difficult to apply to the real field considering only mathematical optimization. In this study, we propose a reinforcement learning-based signal optimization model with constraints. The proposed model maintains the sequence of typical signal phases and considers the minimum green time. The model was trained using Simulation of Urban MObility (SUMO), a microscopic traffic simulator. The model was evaluated in the virtual environment similar to a real road with multiple intersections connected. The performance of the proposed model was analyzed by comparing the delay and number of stops with a reinforcement learning model that did not consider constraints and a fixed-time model. In a peak hour, the proposed model reduced the delay from 3 min 15 s to 2 min 15 s and the number of stops from 11 to 4.7 compared to the fixed-time model.

In the present study, we propose a methodology that predicts the number of passengers on new public transport lines based on smart card data and an optimal path finding algorithm. It employs a deterministic approach that assumes that, when a new line is added to the public transport network, passengers choose the fastest route to their destination. The proposed methodology is applied to actual lines (bus and subway lines) in Seoul, the capital of South Korea, and it is validated through the observed traffic volume of those lines recorded in the smart card data. The experiments are conducted using smart card data, with more than 100 million trips stored, extracted from about 1 million passengers who have check-in records in the catchment area of the new lines. The experimental results show that the proposed methodology predicts the daily average number of passengers very similar to the observed data.

A fire simulator and an evacuation simulator are generally used independently to diagnose the safety of a building in the case of the fire and evacuation. However, it is hard to provide highly accurate safety diagnosis with this method because it does not reflect the movement of pedestrians in the situation of a fire. Therefore, this study proposed a fire evacuation simulation technique that can describe the movement of pedestrians with considering the fire spread. The proposed simulation technique applies the fire spread data of the fire dynamics simulator (FDS) to the floor field model (FFM) and it models that pedestrians recognizes the fire and take a detour to a safe route. This study proposed a method to link the data between FDS and FFM and an improved FFM considering fire spread. Additionally, the proposed method was applied to a real building on a university campus. This study simulated evacuations under various scenarios. Simulation results showed that the number of evacuees escaping through each exit varied by the presence of fire. Moreover, it was found that the evacuation time was increased or decreased by the fire and bottleneck phenomenon was also worsened under fire situation.

Abnormal weather is occurring around the world, including the hottest weather in 174 years of observation records, the largest fire in Europe’s observation records, and approximately twice the average annual rainfall recorded in one day. This abnormal climate is highly related to greenhouse gases, and efforts to reduce emissions are required in various fields. This paper aims to reduce carbon dioxide emissions in the transportation sector, which accounts for a high proportion of emissions. A multi-agent reinforcement learning technique is used for adaptive traffic signal control, and especially a novel cooperative approach is introduced, when considering neighboring intersections. We consider not only the adjacent intersection’s last reward as a Q-function but also its state and action as state. This method has the advantage of considering only vehicles from adjacent intersections that enter an intersection. The proposed method was evaluated on roads in Icheon City, and the results show that it reduces waiting time and carbon dioxide emissions.

To analysis and mitigate the accelerated growth of urban complexity, dynamic urbar growth simulation modeling techniques have long been considered by planner and related stakeholder. However, most of the existing techniques to study dynamic urban growth have lack of accuracy at the level of household or land parcel and majority of them remain highly aggregate despite the use of disaggregate calibration methods and essentially static in nature. It is with this in mind, to anticipate the future changes of Seoul Metropolitan Area dynamic micro simulation model “UrbanSim” has undertaken into consideration or Korean perspective since it is operational and have been implemented and used for policy analyses. This paper provides an overview of UrbanSim model conception, its development, a list of application to date, as well as our experiment through Yongsan-Gu in Korea.