Korea Advanced Institute of Science and Technology · 工学
Professor Hwasoo Yeo's research lab specializes in intelligent transportation systems, with a focus on traffic flow modeling, travel-time prediction, and the integration of emerging technologies such as autonomous vehicles and big data analytics. The lab conducts advanced research on dynamic traffic simulation, accessibility analysis for urban services like healthcare, and condition-based maintenance of transportation infrastructure. A key emphasis is placed on leveraging real-world trajectory data and machine learning techniques—particularly recurrent neural networks with attention mechanisms—for improving the accuracy of urban mobility forecasting and decision support systems.
Figures are computed from collected data and may differ slightly.
Near future travel-time information is one of the most critical factors that travellers consider before making trip decisions. In efforts to provide more reliable future travel-time estimations, transportation engineers have examined various techniques developed in the last three decades. However, there have not been sufficiently systematic and through reviews so far. In order to effectively support various transportation strategies and applications including Intelligent Transportation Systems (
Existing simulation models have difficulty in accurately modeling oversaturated traffic conditions on freeways. A new behavioral algorithm for oversaturated freeway flow can be used in microscopic simulation models; it was developed as part of the Next Generation Simulation (NGSIM) project, sponsored by FHWA. The proposed algorithm is an integrated car-following and lane-changing modeling framework that is consistent with the kinematic wave theory. The algorithm can explicitly model mandatory an
With recent aging demographic trends, the needs for enhancing geo-spatial analysis capabilities and monitoring the status of accessibilities of its citizens with healthcare services have increased. The accessibility to healthcare is determined not only by geographic distances to service locations, but also includes travel time, available modes of transportation, and departure time. Having access to the latest and accurate information regarding the healthcare accessibility allows the municipal go
The introduction of autonomous vehicles (AVs) in the near future will have a significant impact on road traffic. AVs may have advantages in efficiency and convenience, but safety can be compromised in mixed operations of manual vehicles and AVs. To deal with the issues associated with mixed traffic and to avoid its negative effects, a special purpose lane reserved for AVs can be proposed to segregate AVs from manual vehicles. In this research, we analyze the effect on efficiency and safety of AV
Cost-effective maintenance of infrastructure systems within an acceptable level of safety and performance is the major concern of managing agencies. Recent maintenance approaches have offered two distinct maintenance policies: time-based maintenance (TBM) and condition-based maintenance (CBM). This paper compares the two policies under different cost environments for stochastically deteriorating infrastructures. The performance of TBM and CBM is evaluated from the viewpoint of condition transiti
As the number of various positioning sensors and location-based devices increase, a huge amount of spatial and temporal information data is collected and accumulated. These data are expressed as trajectory data by connecting the data points in chronological sequence, and these data contain movement information of any moving object. Particularly, in this study, urban vehicle trajectory prediction is studied using trajectory data of vehicles in urban traffic network. In the previous work, Recurren
Capacity drop, which is defined as discharge flow drop after bottleneck activation, has been frequently observed on urban highways, especially in merging sections. Maintaining high capacity on roadways is a main concern for traffic operators, theorists, and transportation modelers. Accordingly, many researchers have investigated capacity drop, yet highway capacity and discharge flow measurement methods vary, and results are not comparable. A systematic methodology is introduced for finding capac
The charging infrastructure location problem is becoming more significant due to the extensive adoption of electric vehicles. Efficient charging station planning can solve deeply rooted problems, such as driving-range anxiety and the stagnation of new electric vehicle consumers. In the initial stage of introducing electric vehicles, the allocation of charging stations is difficult to determine due to the uncertainty of candidate sites and unidentified charging demands, which are determined by di
AbstractAssessing the probability of lane-changing (LC) is essential to traffic simulation for more realistic representation of complicated traffic phenomena in congested traffic. Discretionary lane changes (DLC), which are not required for reaching a destination, are decided by drivers for the purpose of faster travel. The probability of DLC is related to the speed difference and density difference between adjacent lanes. To reveal the characteristics of DLC, we aggregated Next Generation Simul
This paper presents a methodology for maintenance optimisation for heterogeneous infrastructure systems, i.e., systems composed of multiple facilities with different characteristics such as environments, materials, and deterioration processes. We present a bottom-up approach: facility-level optimal maintenance policies are first found; these policies are then combined with budget constraints in the system-level optimisation. In the first step, optimal and near-optimal maintenance policies for ea
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