Kaohsiung Mass Rapid Transit Research Articles

Optimizing Headways for Mass Rapid Transit Services

Mass rapid transit (MRT) operators need to balance the total system costs with the level of service. The primary service of MRT systems is to provide cost-efficient mobility. Governments are responsible for establishing and regulating minimum standards to ensure that the MRT service indicators meet a specific level. Thus, approaches to optimizing MRT system service frequencies are critical concerns for MRT system operators. Previous studies on MRT operations have focused primarily on delays, energy conservation, route design, and general system operations. Conversely, few studies have focused on optimizing headway problems. This study adopts a mathematical programming method to develop a headway-oriented model for the Kaohsiung MRT (KMRT) system aiming to minimize system costs while maintaining an acceptable level of train services. The developed model systematically adjusts train headways based on time-series passengers’ spatio-temporal distribution data. A numerical case study and sensitivity analysis were conducted to test the feasibility and effectiveness of the proposed models and solution algorithms. The proposed model framework provided the KMRT operator with a flexible tool to evaluate the effects of service frequency on both operating cost and passengers’ waiting cost, which facilitates the operator to prepare a cost-efficient train service plan.

Behavioral intentions of public transit passengers—The roles of service quality, perceived value, satisfaction and involvement

Understanding the behavioral intentions of public transit passengers is important, because customer loyalty is seen as a prime determinant of long-term financial performance. This study highlights such behavioral intentions and explores the relationships between passenger behavioral intentions and the various factors that affect them. Apart from the factors recognized by past studies, such as service quality, perceived value, and satisfaction, this study addresses the importance of the involvement of public transit services in passenger behavioral intentions. By using passenger survey data from the Kaohsiung Mass Rapid Transit (KMRT), a newly operating public transit system in Taiwan, we apply the structural equation modeling technique to analyze the conceptualized relationship model. The findings reveal that all causal relationships are statistically significant. Managerial implications are discussed.

Design of Optimal Coasting Speed for MRT Systems Using ANN Models

An artificial neural network (ANN) has been proposed in this paper to determine the optimal coasting speed of train operation for the Kaohsiung Mass Rapid Transit (KMRT) system to achieve the cost minimization of energy consumption and passenger traveling time. A train performance simulation (TPS) is applied to solve the energy consumption and the traveling time required to complete the journey between stations with various riderships to create the data set for ANN training. The ANN model for the determination of the optimal coasting speed is then derived by performing the ANN training. To demonstrate the effectiveness of the proposed ANN model, the annual ridership forecast of the KMRT system over the project concession period from 2007 to 2035 has been used to determine the optimal coasting speed of train sets for each study year according to the distance between stations and the passenger ridership. The power consumption profile of train sets and the traveling time of passengers have been solved by TPS to verify the reduction of social cost for KMRT system operation with the optimal coasting speed derived.

Optimization of an MRT Train Schedule: Reducing Maximum Traction Power by Using Genetic Algorithms

Because of the traction power load characteristics of mass rapid transit (MRT) systems, a significant difference exists between the peak and valley of the traction power load curve. Furthermore, an extremely high traction power peak occurs if numerous trains are accelerating simultaneously. Careful train scheduling can avoid the simultaneous acceleration of too many trains, thus reducing maximum traction power. This paper employs genetic algorithms to optimize train scheduling and uses the Kaohsiung MRT system as an example. Simulation results show that the proposed method can significantly reduce the maximum traction power.

Estimation of consolidation settlement caused by groundwater drawdown using artificial neural networks

The method of back-propagation neural networks was employed in this study to develop a model for estimating the consolidation settlements caused by transient or long-term groundwater drawdown along the main Red line sections of Kaohsiung mass rapid transit, Taiwan. The available on-site boring test data including soil void ratio, groundwater drawdown depth and total unit weight of soil were taken as the input parameters. Three neural networks models with different combinations of these inputs were examined, which showed that the groundwater drawdown depth was the dominating factor to affect the consolidation settlement. The estimated results were compared with theoretical results, and statistical t-tests were performed to enhance the reliability of neural networks model. From the overall estimated results, the potential hazardous regions were identified along the Red line sections. It was found that there exists either a higher initial groundwater level or a thicker low compressibility clay layer, which might be the major reasons to cause the severely settlements, and must be carefully evaluated during the construction in these regions.

Neural networks approach and microtremor measurements in estimating peak ground acceleration due to strong motion

Peak ground acceleration is a very important factor that must be considered in construction site for examining the potential damage resulting from earthquake. The actual records by seismometer at stations related to the site may be taken as a basis, but a reliable estimating method may be useful for providing more detailed information of the strong motion characteristics. Therefore, the purpose of this study was by using back-propagation neural networks to develop a model for estimating peak ground acceleration at two main line sections of Kaohsiung Mass Rapid Transit in Taiwan. Additionally, the microtremor measurements with Nakamura transformation technique were taken to further validate the estimations. Three neural networks models with different inputs including epicentral distance, focal depth and magnitude of the earthquake records were trained and the output results were compared with available nonlinear regression analysis. The comparisons exhibited that the present neural networks model did have a better performance than that of the other methods, as the calculation results were more reasonable and closer to the actual seismic records. Besides, the distributions of estimating peak ground acceleration from both of computations and measurements might provide valuable information from theoretical and practical standpoints.

Multiobjective Transportation Planning for Waste Hauling

In the transportation planning for some industrial wastes, in addition to hauling cost, environmental impact must frequently be considered. A notable example is transporting waste soil generated by major construction projects. Adequate transportation planning is particularly important for construction in a metropolitan area. In this study, we present a novel two-phase approach to address the multiple-criteria decision problem. The first phase applies the fuzzy analytic hierarchy process to obtain a “composite impedance” for each road sector where transportation costs, environmental impact, and traffic congestion are considered in the evaluation. The second phase employs fuzzy mathematical programming to find the optimal transportation network based on the fuzzy impedance. An illustrative example is provided for the transportation planning for waste soil of the Kaohsiung mass rapid transit system construction project. The optimal solutions using the proposed approach are compared with the solutions using the conventional shortest-path approach where minimizing the transportation cost is the only objective.