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22 results on '"Yang Hongtai"'

20. Optimal Design for Demand Responsive Connector Service Considering Elastic Demand.

Author

Yang, Hongtai, Zhang, Zhaolin, Fan, Wenbo, and Xiao, Feng

Abstract

Differing from the conventional fixed-route transit, Demand Responsive Connector (DRC) is scheduled to pick up passengers based on requests, and delivers them to a common destination (e.g., a metro station). The demand for DRC is affected by not only the fare and travel time, but also time reliability. The latter’ impact is non-negligible because of the uncertainty nature of DRC on collecting the temporally and spatially distributed passengers. This paper explicitly introduces an elastic demand function embedded with a time deviation penalty model. Based on that, one-vehicle and two-vehicle DRC models are formulated to maximize the social welfare. The decision variables include fare, service area, and operating cycle time. Augmented Lagrange Multiplier Method is applied to solve the optimization problem. Simulation is performed to verify the validity of the models. In numerical studies, sensitivity analysis unveils the influences of several key parameters, e.g., potential demand density, vehicle capacity, line-haul distance and elasticity factors. By comparison, threshold values of demand density are revealed for the applicability of the one-vehicle and two-vehicle schemes. The proposed model provides a decision-making tool for DRC operators considering local conditions. [ABSTRACT FROM AUTHOR]

Published
2021
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22. Development of Dynamic Platoon Dispersion Models for Predictive Traffic Signal Control.

Author

Shen, Luou, Liu, Ronghui, Yao, Zhihong, Wu, Weitiao, and Yang, Hongtai

Abstract

As the development of traffic detection technology, recent research is directed to a new generation of signal control systems supported by new traffic data. One of these directions is dynamic predictive control by incorporating short-term prediction capability. This paper focuses on investigating dynamic platoon dispersion models which could capture the variability of traffic flow in a cross-sectional traffic detection environment. The dynamic models are applied to predict the evolution of traffic flow, and further used to produce signal timing plans that account not only for the current state of the system but also for the expected short-term changes in traffic flows. We investigate factors affecting model accuracy, including time-zone length, position of upstream traffic detection equipment, road section length, traffic volume, turning percentages, and computation time. The impact of these factors on the model’s performance is illustrated through a simulation analysis, and the computation performance of models is discussed. The results show that both the dynamic speed-truncated normal distribution model and dynamic Robertson model with dynamics outperform their respective static versions, and that they can be further applied for dynamic control. [ABSTRACT FROM AUTHOR]

Published
2019
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