• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.70-77
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• 2012

Dial-a-ride is the most widely available transit service for disabled persons or seniors in the United States and Europe. This paper studies a static dial-a-ride problem considering multiple depots, heterogeneous vehicles, and soft time windows. In this paper, we apply a heuristic based on clustering first-routing second(HCR) to a real-world large dial-a-ride problem from Maryland Transit Administration(MTA). MTA's real operation is compared with the results of developed heuristic for 24 cases. The objective function of the proposed model is to minimize the total cost composed of the service provider's cost and the customers' inconvenience cost. For the comparison, the objective function values of HCR do not include waiting cost, delay cost, and excess ride cost. The objective function values from HCR are better than those from MTA's operation for all cases. This result shows that our heuristic method can make the real operation better and more efficient.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.118-133
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• 2020

Research interest in the Mobility-as-a-Service (MaaS) concept for enhancing users' mobility experience is increasing. In particular, dynamic pricing techniques based on reinforcement learning have emerged since adjusting prices based on the demand is expected to help mobility services, such as taxi and car-sharing services, to gain more profit. This paper provides a simulation framework that considers more practical factors, such as demand density per location, preferred prices, the distance between users and drivers, and distance to the destination that critically affect the probability of matching between the users and the mobility service providers (e.g., drivers). The aforementioned new practical features are reflected on a data structure referred to as the Matching Matrix. Using an efficient algorithm of computing the probability of matching between the users and drivers and given a set of precisely identified high-demand locations using HDBSCAN, this study developed a better reward function that can gear the reinforcement learning process towards finding more realistic dynamic pricing policies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.247-255
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• 2021

Individual passenger transfer information is not included in Seoul metropolitan subway Automatic Fare Collection (AFC) data. Currently, basic data such as travel time and distance are allocated based on the TagIn terminal ID data records of AFC data. As such, knowledge of the actual path taken by passengers is constrained by the fact that transfers are not applied, resulting in overestimation of the transport index. This research proposes a method by which a transit path that connects the TagIn and TagOut terminal IDs in AFC data is determined and applied to the transit index. The method embodies the concept that a passenger's line of travel also accounts for transfers, and can be applied to the transit index. The path selection model for the passenger calculates the line of transit based on travel time minimization, with in-vehicle time, transfer walking time, and vehicle intervals all incorporated into the travel time. Since the proposed method can take into account estimated passenger movement trajectories, transport-related data of each subway organization included in the trajectories can be accurately explained. The research results in a calculation of 1.47 times the values recorded, and this can be evaluated directly in its ability to better represent the transportation policy index.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.52-64
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• 2023

This paper concentrated on estimating the travel time value of individual regional bus passengers in various in-vehicle crowding conditions. In the analysis model, the traffic-selection data of individual transportation passengers based on smart-card data were used. Variables which reflect the level of in-vehicle crowding and the variables of in-vehicle travel time that reflect the level of in-vehicle crowding were included in the model using Box-Cox transformation. The result of this paper indicates that the travel time value experienced by individual users would increase as the in-vehicle crowding level increases. The smart card data used in this paper is considered to have significant implications in terms of conducting more sophisticated and realistic qualitative research to reflect the values of variables for in-vehicle traffic hours and in-vehicle crowding levels, which previously had limitations in observation and quantification. It is expected that the effects of improvement measures for reducing congestion on regional buses can be considered quantitatively by applying the estimation results of crowding multiplier.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.587-595
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• 2008

Many studies on bus scheduling optimization have been done from the 1960s to recent years for establishing rational bus schedule plan that can improve convenience of bus passengers and minimize unnecessary runs. After 2000, as part of the Intelligent Transport Systems (ITS), the importance of the schedule management and providing schedule information through bus schedule optimization has become a big issue, and much research is being done to develop optimization models that will increase bus passenger convenience and, on the side of bus management, minimize unnecessary bus operation. The purpose of this study is to calculate the optimal bus frequency and create a timetable for each bus stop by applying DTR or DTRC model that use data for each bus stop and route segment. Model verification process was implemented using data collected from bus management system (BMS) and integrated transit-fare card system for bus route of Seoul's No. 472 line. In order to evaluate the reliability and uncertainty of optimal solution, sensitivity analysis was implemented for the various parameters and assumptions used in the bus scheduling model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.1-11
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• 2008

As bus is realized by economical, environmental transportation that is available mass transport than car, various policy for improvement of services is achieved. As innovative public transportation systems like BMS (Bus Management System) have established, it is possible to manage bus service efficiently. However, the present bus service management system mainly focuses on enhancing service reliability represented by schedule adherence index. This study discusses the necessity of a special management for bus bunching phenomena at stops, and develops two kinds of bus bunching indices based on the Number of Berth and the Average Bus Arrival Rate. The bus bunching indices were measured by utilizing the bus operational information from BMS at the Seoul TOPIS(Transportation & Information Service). In order to evaluate the sensitivity of the Indices, the indices were applied to two different bus groups: buses on exclusive bus median lane, and regular (shared) lanes. As analysis result, is bunching as is near in downtown and is bunching to peak time morning than the afternoon. Compared with the schedule adherence index, the suggested indices were proved as an efficient complementary indices in the evaluation of the bus operational performance. The results of index comparison between exclusive bus median lane and shared lanes can promote the expansion of exclusive bus median lane. Moreover, it can also be used as a reference in deciding bus station scale including the Number of Berth and the route adjustment plan.

• Journal of Korea Port Economic Association
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• v.37 no.4
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• pp.33-40
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• 2021

Currently, the shipping and port industries are implementing strategies to improve port processing capabilities through the expansion and efficient operation of port logistics resources to survive fierce competition with rapidly changing trends. The calculation of the port's processing capacity is determined by the loading and unloading equipment installed at the dock, and the port's processing capacity can be improved through various methods, such as additional deployment of logistics resources or efficient operation of resources in use. However, it is difficult to expect an improvement effect in a short period of time because the additional deployment of logistics resources is clearly limited in time is clear. Therefore, it is a feasible way to find an efficient operation method for resources being used to improve processing capacity. Domestic ports are also actively promoting informatization and digitalization with the development of the 4th industrial revolution technology. However, the calculation of the number of Y/T (Yard Tractor) assignments in the current unloading process depends on expert experience, and related previous studies also focus on the allocations of Y/T or Calculation of the total number of Y/T required. Therefore, this study analyzed the factors affecting the number of Y/T allocations using the loading and unloading information of incoming ships, and based on this, cluster analysis, regression analysis, and deep neural network(DNN) model were used.