Abstract
This paper proposes an evaluation method based on a T-S fuzzy neural network for evaluating the speed grade of public-transport lines in the context of large-scale rail-transit planning and construction in Hangzhou. The six-dimensional data of morning peak/evening peak average speed, average speed at peak, average station distance, proportion of dedicated lanes, and nonlinear coefficients were selected as input data for the neural network to output the operating speed grade of bus lines. Improving and optimizing the membership function of the Takagi–Sugeno (T-S) model improves its predicted result accuracy compared to a traditional T-S model. The line data of 28 typical trunk lines or expressways in Hangzhou were used as an example; the results demonstrate that the speed grade evaluation method based on an improved T-S fuzzy neural network can effectively and quickly evaluate the speed grade of Hangzhou public-transportation lines. This paper presents a novel analysis and method for large-scale rail-transit planning and evaluation of urban public-transport lines. The aim is to provide practical instruction for the subsequent optimization of public-transportation lines in Hangzhou.
Highlights
In recent years, urban road traffic pressure has increased with the rapid growth in the number of motor vehicles in Hangzhou
Li and Sun [3] used system, science, objectivity, and practicality as their objective based on the evaluation system of a general public-transport service level and used the gray clustering method to comprehensively evaluate the bus rapid transit (BRT) service level
Based on the situation of large-scale urban rail-transit planning and construction, this study mainly considers the morning/evening peak average speed and peak average speed, which involves the bus speed level, and three vital indicators affecting the bus speed: the average station distance, proportion of dedicated lanes, and nonlinear coefficient: (1) Average speed of morning peak/evening peak/average peak, V1/V2/V3 e average speed of morning peak/evening peak/ average peak refers to the average bus operation speed of all bus lines during the morning peak/ evening peak/average peak time. e calculation formula is as follows:
Summary
Urban road traffic pressure has increased with the rapid growth in the number of motor vehicles in Hangzhou. There are limited studies in the field of busline speed grades, rail-transit planning and construction are being rapidly carried out in many new first-tier cities, such as Hangzhou, Nanjing, Chengdu, Chongqing, Wuhan, Suzhou, and Ningbo, and this is critically impacting the operating speed of conventional bus lines. Erefore, against the background of large-scale rail-transit planning and construction, the T-S fuzzy neural network (based on MATLAB) is applied in this study to evaluate the bus-line speed grade [12]. An improved T-S fuzzy neural-network model was established to evaluate the speed grade of publictransportation lines in Hangzhou. E results show that the model can accurately evaluate the bus-line speed grade, offer a theoretical foundation for the optimization of Hangzhou public-transportation lines, and provide a new idea and method for evaluating the speed level of public-transport lines in large-scale rail-transit planning and construction cities An improved T-S fuzzy neural-network model was established to evaluate the speed grade of publictransportation lines in Hangzhou. e results show that the model can accurately evaluate the bus-line speed grade, offer a theoretical foundation for the optimization of Hangzhou public-transportation lines, and provide a new idea and method for evaluating the speed level of public-transport lines in large-scale rail-transit planning and construction cities
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
