Research on the Traveling Characteristics and Comparison of Bike Sharing in College Campus-A Case Study in Hangzhou

Bike sharing emerging from college campus in China's Mainland has become a major part in the daily traveling of Chinese urban residents.It changes the traveling behavior of urban residents,and simultaneously,raises higher requirements on urban transportation facility construction and management.However,the return of bike sharing to college campus causes more troubles to schools.The fundamental cause is the closed peculiarity of campus traveling comparing with city traveling,and also the discrepancy between college campuses of different types.This paper investigates the traveling characteristics of bike sharing in college campus in three different locations in Hangzhou City,Zhejiang Province of China in the questionnaire,and compares the discrepancy with urban bike sharing traveling characteristics and the discrepancy in bike sharing use between college campuses of different types.From the perspective of parking,maintenance and operation,and hardware design,the paper eventually raises suggestions to optimize independent college campus bike sharing service facility and management consistent with urban system.The research may also offer beneficial reference to the release of bike sharing facilities consistent with urban system in all sorts of independent parks,especially college campus.

Applicability and Behavior Patterns of Road Space from the Perspective of Young Women:A Case Study of Wangjing South Station

In order to understand the travel characteristics and behavior patterns of women in Wangjing area and explore whether the existing situation can meet women's needs for the use of street space,the area around Wangjing South Station of Metro Line 14 was taken as an example for analysis and research.Wangjing area was classified to the following six use attributes:company enterprise,transportation hub,education and culture,residential area,municipal facilities,leisure and entertainment.The proportion of each use attribute was evaluated according to four levels:A 25%and above(including 25%),B 15%-25%,C 15%-5%,D 5%and below(including 5%).Finally,whether the plot had composite functions was judged,and the spatio-temporal laws and behavior patterns of surrounding women were analyzed from the perspectives of time and space.

Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis

Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems(ITS).According to previous studies,it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy.In order to provide persuasive passenger flow forecast data for ITS,a deep learning model considering the influencing factors is proposed in this paper.In view of the lack of objective analysis on the selection of influencing factors by predecessors,this paper uses analytic hierarchy processes(AHP)and one-way ANOVA analysis to scientifically select the factor of time characteristics,which classifies and gives weight to the hourly passenger flow through Duncan test.Then,combining the time weight,BILSTM based model considering the hourly travel characteristics factors is proposed.The model performance is verified through the inbound passenger flow of Ningbo rail transit.The proposed model is compared with many current mainstream deep learning algorithms,the effectiveness of the BILSTM model considering influencing factors is validated.Through comparison and analysis with various evaluation indicators and other deep learning models,the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968,and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%.

Future urban transport management

The incorporation of disruptive innovations into the transportation industry will inevitably cause major upheavals in the transportation sector.However,existing research lacks systematic theories and methodologies to represent the underlying characteristics of future urban transport systems.Furthermore,emerging modes in urban mobility have not been sufficiently studied.The National Natural Science Foundation of China(NSFC)officially approved the Basic Science Center project titled“Future Urban Transport Management”in 2022.The project members include leading scientists and engineers from Beijing Jiaotong University,Beihang University,and Beijing Transport Institute.Based on a wide range of previous projects by the consortium on urban mobility and sustainable cities,this project will encompass transdisciplinary and interdisciplinary research to explore critical issues affecting future urban traffic management.It aims to develop fundamental theories and methods based on social and technological developments in the near future and explores innovative solutions to implement alongside these emerging developments in urban mobility.