Analysis of Spatial and Temporal Characteristics of Citizens’ Mobility Based on E-Bike GPS Trajectory Data in Tengzhou City, China

Abstract

Environmentally friendly shared transit systems have become ubiquitous at present. As a result, analyzing the ranges and tracts of human activities and gatherings based on bike share data is scientifically useful. This paper investigates the spatial and temporal travel characteristics of citizens based on real-time-extracted electric bikes (e-bikes) Global Positioning System (GPS) data from May to July in 2018 in the central area of Tengzhou City, Shandong Province, China. The research is conducive for the exploration of citizens’ changes in mobility behaviors, for the analysis of relationships between mobility changes and environmental or other possible factors, and for advancing policy proposals. The main conclusions of the study are as follows. First, in general, citizens’ travelling is featured by rides that are less than 10 min, shorter than 5 km, and with a speed between 5 km/h and 20 km/h. Second, in terms of temporal characteristics, monthly e-bike usage and citizens’ mobility are positively correlated with temperature in May and negatively correlated with temperature in July; an overall negative correlation is also manifested between the e-bike usage (mobility) and air quality index; daily usage reaches a trough on Tuesday and a peak on Friday, indicating the extent of mobility on respective days; e-bike usage and human outdoor behaviors are significantly lowered in rainy weather than in sunny weather; hourly rides reach a peak at 18:00 (more human activities) and a trough at 2:00 (less activities), and average hourly riding speed maximizes at 5:00 and minimizes around 8:00 and 17:00. Third, for spatial characteristics, destinations (D points) during morning rush hour and regions where e-bikes are densely employed are concentrated mainly in mid-north and middle parts of the central area (major human gatherings), and the rides have a diffusing pattern; e-bike origin–destination (O–D) trajectories radiate mostly towards the mid-north and the east during evening rush hour. In addition, 9.4% of the total trips to work areas during morning rush hour represent spillover commuting, indicating that separations between jobs and residential are not severe in the central area of Tengzhou City and commuting is relatively convenient.

Exploring Travel Mobility in Integrated Usage of Dockless Bike-Sharing and the Metro Based on Multisource Data

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Geographic Information System and Atomized Transportation Modes

“…Many micromobility issues have been addressed using GIS, such as micromobility equity [43], potentiality for micromobility to replace cars [44], spatiotemporal characteristics [45], the role of micromobility in first/last mile problem [46], whether micromobility complements or competes with public transit [47], and changes in micromobility usage before and after COVID-19 lockdowns [48]. Improper micromobility parking causing blocked access is one of the most common concerns of micromobility and has been addressed by many authors [49,50].…”

Parking Behaviour Analysis of Shared E-Bike Users Based on a Real-World Dataset -- A Case Study in Dublin, Ireland

“…In [10], a Mixed-Path Size logit model was devised to analyse the route choice decisions of E-bike users in the Netherlands. The work in [9] carried out analysis of both spatial and temporal characteristics of E-bike user' mobility in Tengzhou City, China. In [11], a trip purpose imputation framework was developed to assist in trip purpose prediction based on the trip records of different micro-mobility tools before and during the COVID-19 pandemic period in Zurich, Switzerland, and finally in [12], a hierarchical clustering method was employed to address the issue of free-floating bike-sharing parking in Beijing, China.…”