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Seungkyu Ryu is a post-doctorate researcher at the Korea Institute of Science and Technology Information, Korea. He received his Ph.D. from Utah State University, the USA in 2015. He has received many academic awards including the Best papers from the EASTS and ASCE. He has authored more than 20 articles
This study provides a gradient projection (GP) algorithm to solve the combined modal split and traffic assignment (CMSTA) problem. The nested logit (NL) model is used to consider the mode correlation under the user equilibrium (UE) route choice condition. Specifically, a two-phase GP algorithm is developed to handle the hierarchical structure of the NL model in the CMSTA problem. The Seoul transportation network in Korea is adopted to demonstrate an applicability in a large-scale multimodal transportation network. The results show that the proposed GP solution algorithm outperforms the method of the successive averages (MSA) algorithm and the classical Evan’s algorithm.
Seungkyu Ryu; Anthony Chen; Songyot Kitthamkesorn. A Two-Phase Gradient Projection Algorithm for Solving the Combined Modal Split and Traffic Assignment Problem with Nested Logit Function. Journal of Advanced Transportation 2021, 2021, 1 -18.
AMA StyleSeungkyu Ryu, Anthony Chen, Songyot Kitthamkesorn. A Two-Phase Gradient Projection Algorithm for Solving the Combined Modal Split and Traffic Assignment Problem with Nested Logit Function. Journal of Advanced Transportation. 2021; 2021 ():1-18.
Chicago/Turabian StyleSeungkyu Ryu; Anthony Chen; Songyot Kitthamkesorn. 2021. "A Two-Phase Gradient Projection Algorithm for Solving the Combined Modal Split and Traffic Assignment Problem with Nested Logit Function." Journal of Advanced Transportation 2021, no. : 1-18.
With the increasing level of air pollution and fine dust, many countries are trying to prevent further environmental damage, with various government legislations, such as the Kyoto Protocol and the Paris Agreement. In the transportation field, a variety of environmental protection schemes are also being considered (e.g., banning old diesel vehicles, alternate no-driving systems, electric car subsidies, and environmental cost charging by tax). Imposing environmental constraints is a good approach to reflect various environmental protections. The objective of this research was to analyze the mode-choice and route-choice changes based on imposing environmental constraints. For the objective, a combined modal split and traffic assignment (CMA) model with an environmental constraint model was developed. For the environmental constraint, carbon monoxide (CO) was adopted, because most of the CO emissions in the air are emitted by motorized vehicles. After a detailed description of the model, the validity and some properties of the model and algorithm are demonstrated with two numerical examples (e.g., a small and a real network in the city of Winnipeg, Canada). From the numerical results, we can observe that imposing the small restriction (or strict) value has more efficiency in mode change and reducing network emission.
Seungkyu Ryu. Mode Choice Change under Environmental Constraints in the Combined Modal Split and Traffic Assignment Model. Sustainability 2021, 13, 3780 .
AMA StyleSeungkyu Ryu. Mode Choice Change under Environmental Constraints in the Combined Modal Split and Traffic Assignment Model. Sustainability. 2021; 13 (7):3780.
Chicago/Turabian StyleSeungkyu Ryu. 2021. "Mode Choice Change under Environmental Constraints in the Combined Modal Split and Traffic Assignment Model." Sustainability 13, no. 7: 3780.
This study develops a mixed behavioural equilibrium model with explicit consideration of mode choice (MBE-MC) in a transportation system where fully automated vehicles (AV) coexist with conventional human-driven vehicles (HV). For the mode choice, travellers select among three options, following a logit modal split: driving their private HV, or taking an AV mobility service provided by either a firm or the government. For the route choice, the HV drivers follow the random utility maximisation principle while central agents route the AV passengers following the Cournot Nash (firm agent) or Social Optimal (government agent) principles. We consider two types of travel costs (i.e. travel time and monetary travel cost) to characterise the new features (e.g. expanded link capacity and reduced value of time) of the mixed AV–HV transportation system. We model the MBE-MC problem in a combined mode–route choice framework and formulate it as a route-based variational inequality (VI) problem. We show the equivalence between the VI formulation and the MBE-MC problem, and the existence of a solution to the MBE-MC problem. Then, we modify a partial linearisation algorithm for solving the proposed model. Numerical results validate the equilibrium conditions and show the efficacy of the new model in capturing the features of the mixed AV–HV transportation system. The impact patterns of different parameters on (1) the network performance in terms of AV share and system cost and (2) on the solution efficiency are analysed.
Guangchao Wang; Hang Qi; Huiling Xu; Seungkyu Ryu. A mixed behaviour equilibrium model with mode choice and its application to the endogenous demand of automated vehicles. Journal of Management Science and Engineering 2020, 5, 227 -248.
AMA StyleGuangchao Wang, Hang Qi, Huiling Xu, Seungkyu Ryu. A mixed behaviour equilibrium model with mode choice and its application to the endogenous demand of automated vehicles. Journal of Management Science and Engineering. 2020; 5 (4):227-248.
Chicago/Turabian StyleGuangchao Wang; Hang Qi; Huiling Xu; Seungkyu Ryu. 2020. "A mixed behaviour equilibrium model with mode choice and its application to the endogenous demand of automated vehicles." Journal of Management Science and Engineering 5, no. 4: 227-248.
Cycling is gaining popularity both as a mode of travel in urban communities and as an alternative mode to private motorized vehicles due to its wide range of benefits (health, environmental, and economical). However, this change in modal share is not reflected in current transportation planning and travel demand forecasting modeling processes. The existing practices to model bicycle trips in a network are not sophisticated enough to describe the full cyclist experience in route decision-making. This is evident in the existing practices’ methodology: the all-or-nothing assignment uses single attributes such as distance, safety, or a composite measure of safety multiplied by distance. The purpose of this article is to develop a multi-class and multi-criteria bicycle traffic assignment model that not only accounts for multiple user classes by acknowledging that there are different types of cyclists with varying levels of biking experience, but also for relevant factors that may affect each user classes behavior in route choice decisions. The multi-class, multi-criteria bicycle traffic assignment model is developed in a two-stage process. The first stage examines key criteria to generate the set of non-dominated (or efficient) routes for each user class, and the second stage determines the flow allocation to efficient routes by user class. Numerical experiments are then conducted to demonstrate the two-stage approach for the multi-class, multi-criteria bicycle traffic assignment model.
Seungkyu Ryu; Anthony Chen; Jacqueline Su; Keechoo Choi. A multi-class, multi-criteria bicycle traffic assignment model. International Journal of Sustainable Transportation 2020, 15, 524 -540.
AMA StyleSeungkyu Ryu, Anthony Chen, Jacqueline Su, Keechoo Choi. A multi-class, multi-criteria bicycle traffic assignment model. International Journal of Sustainable Transportation. 2020; 15 (7):524-540.
Chicago/Turabian StyleSeungkyu Ryu; Anthony Chen; Jacqueline Su; Keechoo Choi. 2020. "A multi-class, multi-criteria bicycle traffic assignment model." International Journal of Sustainable Transportation 15, no. 7: 524-540.
As more people choose to travel by bicycle, transportation planners are beginning to recognize the need to rethink the way they evaluate and plan transportation facilities to meet local mobility needs. A modal shift towards bicycles motivates a change in transportation planning to accommodate more bicycles. However, the current methods to estimate bicycle volumes on a transportation network are limited. The purpose of this research is to address those limitations through the development of a two-stage bicycle origin–destination (O–D) matrix estimation process that would provide a different perspective on bicycle modeling. From the first stage, a primary O–D matrix is produced by a gravity model, and the second stage refines that primary matrix generated in the first stage using a Path Flow Estimator (PFE) to build the finalized O–D demand. After a detailed description of the methodology, the paper demonstrates the capability of the proposed model for a bicycle demand matrix estimation tool with a real network case study.
Seungkyu Ryu. A Bicycle Origin–Destination Matrix Estimation Based on a Two-Stage Procedure. Sustainability 2020, 12, 2951 .
AMA StyleSeungkyu Ryu. A Bicycle Origin–Destination Matrix Estimation Based on a Two-Stage Procedure. Sustainability. 2020; 12 (7):2951.
Chicago/Turabian StyleSeungkyu Ryu. 2020. "A Bicycle Origin–Destination Matrix Estimation Based on a Two-Stage Procedure." Sustainability 12, no. 7: 2951.