Travel Behaviours of Sharing Bicycles in the Central Urban Area Based on Geographically Weighted Regression: The Case of Guangzhou, China

Mobile information and communication technologies(MICTs) have fully penetrated everyday life in smart societies;this has greatly compressed time, space, and distance, and consequently, reshaped residents’ travel behaviour patterns. As a new mode of shared mobility, the sharing bicycle offers a variety of options for the daily travel of urban residents. Extant studies have mainly examined the travel characteristics and influencing factors of public bicycles with piles, while the travel patterns for sharing bicycles and their driving mechanisms have been largely ignored. Using one week’s travel data for Mobike, this study investigated the spatial and temporal distribution patterns of sharing bicycle travel behaviours in the central urban area of Guangzhou, China;furthermore, it identified the influences of built environment density factors on sharing bicycle travel behaviours based on the geographically weighted regression method. Obvious morning and evening peaks were observed in the sharing bicycle travel patterns for both weekdays and weekends. The old urban area, which had a high degree of mixed function, dense road networks, and cycling-friendly built environments, was the main travel area that attracted sharing bicycles on both weekdays and weekends. Furthermore, factors including the point of interest(POI) for the density of public transport stations, the functional mixing degree, and the density of residential POIs significantly affected residents’ travel behaviours. These findings could enrich discourse regarding shared mobility with a Chinese case characterised by rapidly developing MICTs and also provide references to local authorities for improving slow traffic environments.

Clustering the Stations of Bicycle Sharing System

Bicycle sharing system has emerged as a new mode of transportation in many big cities over the past decade.Since the large number of bicycle stations distribute widely in the city,it is difficult to identify their unique attributes and characteristics directly.Oriented to the real bicycle hire dataset in Hangzhou,China,the clustering analysis for the bicycle stations based on the temporal flow data was carried out firstly.Then,based on the spatial distribution and temporal attributes of calculated clusters,visual diagram and map were used to vividly analyze the bicycle hire behavior related to station category and study the travel rules of citizens.The experimental results demonstrate the relation between human mobility,the time of day,day of week and the station location.

How do Users Evaluate Service Quality of the Dockless Bicycle Sharing System?

In order to improve the public transit’s accessibility and promote service quality of the public transit,it is necessary to alleviate the“last⁃mile”problem.The combination of the public transit and bicycle⁃sharing system is competitive compared with the private car.In 2016,the dockless bicycle⁃sharing system has been undergoing tremendous development.Because the dockless bicycle⁃sharing system is different from previous bicycle⁃sharing systems,components of service quality of the dockless bicycle⁃sharing system differ.However,there are few articles about the service quality of the dockless bicycle⁃sharing system.This paper examined the evaluation of service quality of the dockless bicycle⁃sharing system along with a measurement of users’satisfaction.A questionnaire about service quality was designed,which includes 15 factors.Through the analysis of travelers’evaluation of factors of the dockless bicycle⁃sharing system with Rasch Model,these original ordinal data(Likert data)was transformed to the interval data:the person parameter(ability)and the item parameter(difficulty).Through the analysis of interviewees’abilities,it was found that only education and monthly consumption have significant influence on interviewees’evaluation of service quality of the dockless bicycle⁃sharing system,while there is no significant influence of gender,weekly use of dockless shared⁃bicycle,private car ownership,or monthly income on the evaluation.Furthermore,it was found that users were dissatisfied with cycling under the adverse weather,disturbance from pedestrians and cars,continuity of the bicycle lane,complaint channel,and staff service the most.

Location Privacy in Device-Dependent Location-Based Services:Challenges and Solution

With the evolution of location-based services(LBS),a new type of LBS has already gain a lot of attention and implementation,we name this kind of LBS as the Device-Dependent LBS(DLBS).In DLBS,the service provider(SP)will not only send the information according to the user’s location,more significant,he also provides a service device which will be carried by the user.DLBS has been successfully practised in some of the large cities around the world,for example,the shared bicycle in Beijing and London.In this paper,we,for the first time,blow the whistle of the new location privacy challenges caused by DLBS,since the service device is enabled to perform the localization without the permission of the user.To conquer these threats,we design a service architecture along with a credit system between DLBS provider and the user.The credit system tie together the DLBS device usability with the curious behaviour upon user’s location privacy,DLBS provider has to sacrifice their revenue in order to gain extra location information of their device.We make the simulation of our proposed scheme and the result convince its effectiveness.