Mobile phone library service: seat management system based on WeChat

PurposeThis paper proposes a systematic method to manage students to use limited seat resources in Chinese university libraries, with the aid of mobile phone app, at the same time, its use is being investigated.Design/methodology/approachUse mixed research methods, quantitative and qualitative research. Through observation, questionnaire and interview to achieve research purpose. The survey was conducted in the library of Nanjing agricultural university.FindingsThe result shows system can offer convenient, accurate, more personalized, mobility service to each user. Actual average seat usage rate is over 51.7% in a day, most of users are satisfied with the seat management system, students' satisfaction degree are 94.8%. It is also an extension of mobile phone library service.Originality/valueSeat management system innovate traditional people-oriented service mode of study room into smart, readers can browse usage information of seats anytime and anywhere, get what they want, service become fast and convenient. In period of COVID-19, the seat system also plays an important role, it is easy for librarians to control the number of students to enter, the trajectory of readers in the library can be tracked and the possible epidemic risk can be accurately prevented and controlled.

Integration index for mobility as a service

The integration in transport informatics is facilitated by the rapid development of Information and Communication Technology. One of the realizations of the integration is Mobility as a Service (MaaS), which is proposed as a data-driven, user-centric, personalized mobility service. It integrates various forms of mobility services covering the entire travel chain. Qualitative methods have been applied in existing studies to analyse the integration of MaaS. However, a comprehensive quantitative method is still missing, which could be introduced as a supplementary tool to compare MaaS services. Therefore, we have developed a weighted elaboration method to calculate the complex integration index for MaaS systems. Three aspects are determined as variables, which are the functions of the MaaS application, involved transport modes as well as the tariff structure. Moreover, the organization as the backbone of such integration is considered as the fourth aspect. The integration phases of MaaS are introduced regarding these four aspects, then the calculation method of the complex index is developed by considering the weighted variables. Fourteen MaaS services are evaluated with the method and categorized by organization aspect. We found that public authority is proposed to be the inter-city MaaS operator, and the private company is proposed to be the MaaS operator in intra-city or national level. Our method may support decision-makers to have an abstract overview of MaaS and identify the possible development stage.

Clustered embedding using deep learning to analyze urban mobility based on complex transportation data

Urban mobility is a vital aspect of any city and often influences its physical shape as well as its level of economic and social development. A thorough analysis of mobility patterns in urban areas can provide various benefits, such as the prediction of traffic flow and public transportation usage. In particular, based on its exceptional ability to extract patterns from complex large-scale data, embedding based on deep learning is a promising method for analyzing the mobility patterns of urban residents. However, as urban mobility becomes increasingly complex, it becomes difficult to embed patterns into a single vector because of its limited capacity. In this paper, we propose a novel method for analyzing urban mobility based on deep learning. The proposed method involves clustering mobility patterns and embedding them to capture their implicit meaning. Clustering groups mobility patterns based on their spatiotemporal characteristics, and embedding provides meaningful information regarding both individual residents (i.e., personalized mobility) and all residents as a whole, enabling a more effective analysis of mobility patterns. Experiments were performed to predict the successive points of interest (POIs) based on transportation data collected from 1.5 million citizens in a large metropolitan city; the results demonstrate that the proposed method achieves top-1, 3, and 5 accuracies of 73.64%, 88.65%, and 91.54%, respectively, which are much higher than those of the conventional method (59.48%, 75.85%, and 80.1%, respectively). We also demonstrate that the proposed method facilitates the analysis of urban mobility through arithmetic operations between POI vectors.

Assessment of the DRT System Based on an Optimal Routing Strategy

Demand responsive transport (DRT) is operated according to flexible routes, dispatch intervals, and dynamic demand, is attracting a lot of attention. The biggest characteristic of the DRT service is that the vehicle routes and schedules are operated optimally based on real-time travel requests of using passengers without fixed operating schedules. This study analyzed the feasibility of implementing the DRT service by analyzing the benefits for the users and cost of the operator from the effects of increasing public transportation use and providing personalized mobility service based on DRT implementation by the introduction of DRT using multi-agent transport simulation (MATSim). Through the simulation, the DRT is expected to provide convenient, fast, and cost-effective mobility services to customers; provide an optimal vehicle scale to providers; and, ultimately, achieve a safe and efficient transportation system.