Accompanied with the promulgation of the slogan, “low-carbon life, green travel,” environmentally friendly vehicle sharing has been widely adopted by local citizens and advocated by the government due to its low cost for production, convenience, and cheap price. In the era of big data, the use of the Global Positioning System (GPS) information of shared vehicles can unveil the tracts and regions of users’ activities and gatherings. Therefore, studying shared bike usage has pivotal implications for understanding human behaviors and lifestyles and assessing city development.
At present, studies based on bike-sharing have mainly focused on the analysis of the mode and prospect of the shared economy [1
], spatial and temporal characteristics of trajectories [2
], and vehicle regulations [3
]. However, the existing studies about spatial and temporal characteristics based on origin–destination (O–D) points have shortcomings. First, studies concentrate on bike-sharing in large-scale cities [5
] and hinge on outdated data or less-than-a-month data, and few studies target small-scale cities, such as prefectures [6
], and employ current more-than-a-month data. Data that covers a time length greater than a month is more conducive in reflecting the daily changes in mobility behavior. Second, compared to shared bicycles (aimed at addressing last-kilometer problems for bus station and subway transit), shared e-bikes cover longer distances and are oriented towards recreational or work destinations, for example, rendering them typical subjects for comprehending citizens’ travel behaviors. Additionally, few contemporary studies have examined the spatial and temporal characteristics (and raw data cleansing method) based on e-bike GPS data.
Therefore, this paper analyzes the spatial and temporal travel characteristics of citizens in the central area of Tengzhou City based on real-time captured GPS data from May to July in 2018 in the central area of Tengzhou City, Shandong Province. This study is based on the station-free bike-sharing system (SFBSS), which is unique especially in China for its strong flexibility and convenience in usage [6
]. The objectives are: (1) to analyze the overall trip patterns of shared electric bikes (e-bikes); (2) to explore the characteristics of human travelling activities and changes in mobility behaviors at the daily temporal scale, hourly temporal scale, and spatial scale considering geographic hotspots and commuting; and (3) to assess the environmental and other possible factors that influence citizens’ mobility and suggest policy proposals. The study can reveal the routines, activities, and changing mobility behaviors of Tengzhou citizens and serve as a supplement for early spatial and temporal studies based on bike sharing. It also provides helpful guidance for future city planning in Tengzhou.
The paper is organized as follows. Section 2
presents a review of the relevant literature on bike sharing. Section 3
introduces the study area, data sources, data preprocessing, and modeling. Section 4
presents and analyzes patterns of e-bike usage, spatial and temporal travel characteristics of Tengzhou citizens, and their changes in mobility behaviors. Section 5
concludes the paper and makes plausible suggestions.
2. Literature Review
Understanding citizens’ mobility is important for developing knowledge on the structure of urban areas, the spatial distribution of hot areas, and the provision of transportation services [8
]. For instance, Cools et al., 2010a, 2010b identified the weather conditions that had significant impacts on the daily traffic intensities of Belgian highways and also reported that weather can influence travel demand, traffic flow, and traffic safety [9
]. In another research, Cools et al., 2013 assessed how weather forecasts trigger changes in Flemish activity-travel behavior and showed that the likelihood of changes in activity-travel behavior significantly depends on the weather forecasted [11
]. Besides, Liu et al., 2016 hinged on travel data in Sweden and pointed out that people in central and southern Sweden tend to walk less in abnormal temperature conditions in summer, while people in northern Sweden tend to walk more in these situations [12
]. Moreover, Luo et al., 2017 analyzed the taxi’s spatial–temporal distribution in Shanghai and reported that the highest activity moment of residents is 9–10:00 a.m., the second peak occurred in 7–8:00 p.m. by applying big data analysis on GPS data of taxies [13
With the development of environmentally friendly shared transit, research that involved using GPS trajectories to identify transportation modes and analyze ranges and tracts of human activities and gatherings from bike share data has become a focus recently. Current studies of various spatial and temporal characteristics are based on bike share data from the Bike Sharing System (BSS). For example, based on the O–D pairs from the station-free BSS in Shenzhen in six days, Yang et al., 2017 cleansed the data, preserved trips exceeding 150 m, and examined cycling flows [14
]. According to GPS data from the station-less BSS during 6:00–23:00 on 27 September 2017 in Shanghai, Sun et al., 2019 completed primary data cleansing and discovered that bike sharing is more intense at the urban center and less intense in the surroundings [15
]. With GPS data from the station-less BSS recorded per 5 min for half the month of April, 2017, in Nanjing, Zhou et al., 2018 performed simple data cleansing and revealed that citizens in the major urban zone tend to ride less than 1500 m, while those in secondary centers tend to travel longer distances, ranging from 1500 m to 7000 m [16
]. Wei et al., 2018 adopted 267 million pieces of GPS data from the station-less BSS on October 31, November 2, and November 12 in 2017 and researched the bike usage in Tianhe center, Guangzhou. Using coupling analysis, they concluded that, during weekdays, bike sharing was concentrated in transit hubs along the expressway and educational-and-research zones. On weekends, however, trajectories were spread along the periphery, particularly the business circle on Tianhe Road and Pearl River new town [17
In accordance with O–D pair data in Nanjing from the Ofo company, a dockless bike-sharing operator, Liu et al., 2018 preprocessed the data to exclude redundant information and errors, and they put forward a method for reallocating dockless bikes according to the spatial distribution patterns [6
]. Opting for the O–D data of bikes in station-based BSS from April 15 to May 27 in 2014 in Taipei, Cheng et al., 2014 deleted data with technical errors and discussed the spatial and temporal characteristics and influencing factors of bike station availability [18
]. With respect to the GPS data from a station-based BSS in Vienna from 2008 to 2009, Vogel et al., 2011 removed the trajectory data of broken bikes, utilizing data cleansing and mining. They discovered unequal distribution in bike usage and put forward regulation advice for bike sharing systems [19
]. Fernando Munoz-Mendez et al., 2018 used 1,459,945 ordered O–D pairs from the London station-based BSS from June to July in 2014 and showed that bike usage reflected rush hours. Compared to daytime trajectories, night trajectories were irregular and significantly decreased in number [20
]. Employing O–D data pairs from station-based BSS during several months in 2013 and 2014 in Chicago, Zhou et al., 2015 identified and verified clusters where shared bikes are densely used [21
]. Data from station-based BSS in eight cities in America are adopted by Kou et al., 2018 to identify the travel patterns, including the trip distance and distribution [22
]. In accordance with the station-based BSS data from a New York shared bike station in September, Faghih-Imani et al., 2016 notes that there is no statistically important relationship between bike usage and temperature [23
The existing spatial and temporal studies based on green transportation vehicles have various shortcomings. First, the studies adopt data that are concentrated on a time scale of less than one month (mainly several days), resulting in restrictions of time sensitivity and applicability. Second, the majority of studies on spatial and temporal characteristics (and data cleansing) are based on shared bikes instead of shared e-bikes, while e-bikes possess traits such as longer riding distance, faster speed, and destination orientation (directly targeting the ride destinations, such as work and entertainment districts, instead of transit stations). Third, past studies neglect small-scale areas, such as prefectures, as study areas and mostly focus on large-scale and metropolitan regions. Thus, this paper is conducted based on e-bike GPS data from May to July in 2018 in Tengzhou City and aims to shed light on the spatial and temporal characteristics of Tengzhou e-bike usage and human activities and gatherings.
5. Conclusions and Suggestions
This paper studied e-bike usage patterns and citizens’ spatial and temporal mobility characteristics, based on real-time-extracted e-bike data from station-less BSS with geographic coordinate system information from May to July 2018 in the central area of Tengzhou City, Shandong Province, China. The main conclusions are as follows.
(1) Overall, shared e-bike trips and citizens’ mobility patterns are within 5 km and 10 min, with a speed ranging from 5 km/h to 20 km/h. Accordingly, the spatial arrangement of large business and work districts can maintain 5 km- or 15-min rides from residential districts. In addition, the data show that there are still users exceeding the government speed limit of 20 km/h. To address safety concerns and maintain traffic order, the government needs to reinforce speeding penalties.
(2) Temporally: First, the daily shared e-bike usage and travelling behaviors display a trough on Tuesday and a peak on Friday, positively correlated with temperature in May and negatively correlated with temperature in July, and negatively correlated with the severity of air quality. To address air pollution, the government can advocate shared e-bike usage for commuting and transportation. Second, shared-bike usage is observed to be significantly reduced in number in rainy weather than on sunny days, indicating less travel behaviors, because of the potential safety hazard and traveling inconvenience; thus, speed limits for rainy and sunny weather need to be clearly and reasonably distinguished. Third, the hourly e-bike usage and citizens’ mobility are greater during the daytime than during the nighttime; local minima are at 2:00, 9:00, 14:00, and local maxima are at 7:00, 12:00, 18:00. Therefore, to avoid supply shortages that impede citizens’ travel, e-bikes can be relocated from 9:00 to 10:00, from 13:00 to 15:00, or at and after midnight when people display resting, sleeping, or indoor behaviors; the above-proposed three-mode Gaussian Function can further assist e-bike reallocation with quantified data support so that the changing demands in regard to changing mobility at different times of the day can be met. Moreover, hourly riding speed peaks at 5:00 (high travel efficiency) and is lower at 8:00 and 17:00 (low efficiency).
(3) Spatially: First, D-points (human gatherings) concentrate mainly in the mid-north and middle parts of the central area of Tengzhou City during the morning rush hour, and the trajectory paths (trips) diffuse from the center. During the evening rush hour, the trajectory paths radiate towards the mid-north, the middle, and the east, with shorter distances and more various traveling purposes such as recreational purpose. On account of the northward mobility patterns, this paper suggests that new residential districts can be built adjoining mid-north work zones, such as the city hall and the bus terminal. Second, e-bikes have a higher density in the middle and mid-north parts (areas with frequent human gatherings and activities) and are sparser in the outer periphery, rendering the city center an ideal place for the reallocation of shared e-bikes for the sake of its higher demands for travelling tools. Third, 9.4% of the total commuting is spillover commuting, and compared to metropolises, Tengzhou City, as a prefecture-level city, has an indistinctive separation between work and residential areas. In future city development and planning, the nonmetropolitan commuting advantage [33
] and work-residential balance can be preserved to ensure the convenience of citizens’ travel.
This paper contributes to the understanding of the spatial and temporal mobility characteristics and shared e-bike usage of citizens in the central area of Tengzhou City, and the findings build on the results of early spatial and temporal bike-sharing studies. However, this study has limitations. The partition and identification of geographic hotspots are simple, and future studies can adopt more complex methods. The reallocation function does not include complex machine-learning techniques, which can be incorporated into future work, that considers factors such as weather conditions [34
]. In addition, since the calculation in the data-processing section is based on simple Euclidean distance, future studies can be based on more precise methods like great circle distance. Moreover, more data can be added to develop quantified assessments of the influencing factors, such as human activities and housing types, to further enhance the accuracy of the results.