Journal Pre-proof
Demand-aware mobile bike-sharing service using collaborative computing and
information fusion in 5G IoT environment
Xiaoxian Yang, Yueshen Xu, Yishan Zhou, Shengli Song, Yinchen Wu
PII:
S2352-8648(22)00126-2
DOI:
https://doi.org/10.1016/j.dcan.2022.06.004
Reference:
DCAN 458
To appear in:
Digital Communications and Networks
Received Date: 11 July 2021
Revised Date:
4 June 2022
Accepted Date: 12 June 2022
Please cite this article as: X. Yang, Y. Xu, Y. Zhou, S. Song, Y. Wu, Demand-aware mobile bikesharing service using collaborative computing and information fusion in 5G IoT environment, Digital
Communications and Networks (2022), doi: https://doi.org/10.1016/j.dcan.2022.06.004.
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Digital Communications and Networks(DCN)
journal homepage: www.elsevier.com/locate/dcan
Demand-aware mobile bike-sharing
service using collaborative computing and
information fusion in 5G IoT environment
b
School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China
School of Computer Science and Technology, Xidian University, Xi’an, 710126, China
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Xiaoxian Yanga , Yueshen Xub,∗ , Yishan Zhoub , Shengli Songb , Yinchen Wub
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Abstract
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Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a
promising way to build smart cities, especially in the new era of 5G and Internet-of-Things (IoT) environments. A mobile
bike-sharing service makes commuting convenient for people and imparts new vitality to urban transportation systems. In the
real world, the problems of no docks or no bikes at bike-sharing stations often arise because of several inevitable reasons such
as the uncertainty of bike usage. In addition to pure manual rebalancing, in several works, attempts were made to predict the
demand for bikes. In this paper, we devised a bike-sharing service with highly accurate demand prediction using collaborative
computing and information fusion. We combined the information of bike demands at different time periods and the locations
between stations and proposed a dynamical clustering algorithm for station clustering. We carefully analyzed and discovered
the group of features that impact the demand of bikes, from historical bike-sharing records and 5G IoT environment data.
We combined the discovered information and proposed an XGBoost-based regression model to predict the rental and return
demand. We performed sufficient experiments on two real-world datasets. The results confirm that compared to some existing
methods, our method produces superior prediction results and performance and improves the availability of bike-sharing service
in 5G IoT environments.
c 2022 Published by Elsevier Ltd.
KEYWORDS:
Mobile bike-sharing service, Demand prediction, Collaborative computing, Information fusion, 5G IoT
1. Introduction
As a green and new mode of transportation, mobile bike-sharing services improve the diversity of the
city transportation and act as a convenient commuting service, helping solve the first-and-last mile problem in urban public transportation and effectively alleviating traffic jams. With mobile bike-sharing services, people do not need to buy or ride their own
∗ Corresponding author. The two authors Xiaoxian Yang and
Yueshen Xu contribute equally to this paper, so Yueshen Xu is also
the co-first author of this paper.
1 E-mail
addresses:
xxyang@sspu.edu.cn (X. Yang),
ysxu@xidian.edu.cn (Y. Xu),
yishanzhouh@hotmail.com
(Y.
Zhou),
shlsong@xidian.edu.cn
(S.
Song),
yinchenwu@stu.xidian.edu.cn (Y. Wu).
bikes but they can enjoy the sharing service. There are
two mainstream types of bike-sharing services. One
is the docked bike-sharing service that is usually managed by metropolitan transportation service bureaus,
and users follow a service procedure to pick up a bike
at one station, ride, and return the bike to another station. An an important way to build smart cities, the
docked mobile bike-sharing service has been deployed
in more than 700 cities [1] worldwide, including many
famous cities that serve tens of millions of people,
such as New York (Citi Bike)2 , London (Beryl)3 , Paris
2 https://www.citibikenyc.com/
3 https://beryl.cc/bikeshare/cities
2
Name of the first author, et al.
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However, we find that in reality, a station is not
isolated but is closely related to the nearby stations.
The following observations motivated us to introduce
clustering analysis and careful feature analysis in this
study. For a station that is near an office building but
has a limited number of available bikes, the bike demand cannot be satisfied until more bikes are transported to this station. In such a case, users are likely
to pick up bikes from nearby stations. Hence, the demands of a cluster of stations that are near each other
may better reflect the complete demand of users, and
the demand prediction for an individual station cannot account for the help that is sought from nearby
stations; hence, considering the bike demand for individual stations only results in low accuracy. The bike
demand of a cluster is more stable than that of a single
station. Moreover, we observe that the bike usage of
a bike station over a long time usually shows similar
patterns, which can be another useful feature. Besides,
the weather condition also has a clear impact on bike
demand [9]. Thus, taking full advantage of different
features of bike usage is important. We expect that the
study of different types of features can result in a new
breakthrough to enhance bike demand prediction.
Through the careful observation and investigation,
we find that the bike demand is impacted by the environmental factors, including temporal factors and
meteorological factors. We propose a demand prediction framework for mobile bike-sharing service in 5G
IoT environments using information fusion and collaborative computing. Therefore, we propose a dynamic location-aware clustering algorithm and introduce an XGBoost regression model [10] as the prediction model. Our contributions can be summarized as
follows.
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(Vélib’ Métropole)4 , Tokyo (Docomo)5 , Beijing6 and
Hangzhou7 . The other type is the non-docked bikesharing service that is usually managed by companies
such as Meituan Bike 8 (former known as Mobike) and
Hello Bike9 . In this second type of service, people do
not need to return bikes to stations, but the companies
and governments encourage them to return bikes in a
given area, and the running companies face the problem of bike scheduling. In this paper, we consider the
docked bike-sharing service as the main study case.
Although mobile bike-sharing services bring much
convenience, they also face many challenges. For
many bike stations, the usage mode of bikes is random
and excessive demand often occurs because of diverse
factors, such as time, location and environment. There
are two typical cases in which the demand for bikes is
not satisfied and the quality-of-service (QoS) deteriorates, especially in 5G IoT environment. One is that no
bikes are available for users to pick up, and the other
is no docks are available to return bikes. Thus, it is
crucial to solve the task of demand prediction. Some
existing bike-sharing services try to monitor the status of bike stations, including the number of available bikes and docks [2]. In recent years, some researches attempted to design bike schedule strategies
for the event that a demand failure occurs. For example, some researchers designed an optimized route
for vehicles that transport bikes among stations [3][4].
Some researchers introduced the concept of inventory
and modeled the schedule as a non-stationary Markov
chain [5]. Others used programming tools (e.g., integer programming and non-linear programming) to design transportation route for bike scheduling [6]. Note
that, it is always a late measure to rebalance bikes after
a demand failure occurs, and it cannot guarantee bike
availability. Thus, in this paper, we aimed to predict
the demand for bikes at bike stations using information fusion and a collaborative approach in a 5G IoT
environment.
In the task of demand prediction for bike-sharing
services, some researchers define the task as a time
series problem and use historical records to predict future values. Researchers have proposed several prediction models. The autoregressive moving average
model (ARMA) and autoregressive integrated moving average model (ARIMA) are some representative
models [7]. Some researchers used classification or regression methods to better realize demand prediction.
Some researchers try to use classification method to
predict the bikes demand [8]. Furthermore, the regression model (e.g., k-nearest neighbor (KNN) regression) has been used to predict the hourly demand [6].
4 https://www.velib-metropole.fr/
5 https://docomo-cycle.jp/
6 http://bjggzxc.jtw.beijing.gov.cn/
7 https://www.hangzhou.com.cn/hzbike/
8 https://mobike.com/global/
9 https://www.helloglobal.com/
1. We performed a comprehensive study on a series
of potential features and fusion of information,
such as date, time, seasons, and weather conditions. We found that these factors have a clear
impact on bike demand in the mobile service.
Such information fusion is especially important
for machine learning-based prediction methods.
2. We proposed a collaborative framework for demand prediction in the bike-sharing service. We
constructed a weighted adjacency graph where
weighted edges represent similarities among stations.
We partitioned the adjacency graph
into small sub-graphs and dynamically identified
clusters hierarchically. Such a dynamic clustering approach can guarantee high similarity of the
demand in a cluster in 5G IoT environments.
3. We proposed a representation method for the discovered features. We employed XGBoost as the
regression model in the bike demand prediction
problem to predict hourly demand.
4. We evaluated the performance of our framework
using two real-world datasets containing the real
bike-sharing service data. The results show that
Paper Title (The title should be descriptive, not full sentence)
our method provides excellent results, and compared to the state-of-the-art methods, our model
has clearly lowered prediction error.
The rest of this paper is organized as follows. Section 2 presents the related works. Section 3 gives an
overview of the developed mobile bike-sharing service. Section 4 elaborates the analysis of diverse features and the method of information fusion. Section 5
explains the proposed collaborative demand prediction
approach in 5G IoT environments. Section 6 presents
the experimental results and analysis. Section 7 concludes the paper and discusses future work.
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The problems in bike-sharing services have received increasing attention among academia and
industry.
This study focuses on three aspects,
namely, service planning, demand prediction, and
bikes scheduling.
Collaborative Computing. Many cities currently
do not have bike-sharing services and aim to build
their own service. To build a bike-sharing service,
many factors, such as construction cost, geographical
locations and QoS, should be considered. These factors will determine the service scale and quality parameters, such as the number of stations, station deployment, and total number of bikes. In [11, 12], the
authors computed the total number of bikes necessary
in a bike-sharing service based on many factors. In
[13], the authors focused on the construction of bike
paths based on large-scale bike trajectory data. The
authors in [14] tried to compute the number and locations of bike stations, considering the interests of
users and investors and QoS constraints. The authors
in [15, 16] gave suggestions for the design of station
capacity, in addition to the deployment of station locations.
Information Fusion. Demand prediction is a crucial task that aims to predict the demand for bikes and
docks to avoid unnecessary bike scheduling and improve bike availability. Existing studies on demand
prediction can be summarized into two categories,
i.e., studies based on cluster-level and station-level
demand prediction. Bike usage is affected by many
factors, such as weather, time, and external events,
and the demand at one station has a strong connection with the neighboring stations. Therefore, stationlevel prediction involves many challenges [17]. Demand prediction was defined as a time series problem
in [18, 19], and an ARIMA model was employed to
predict the bike demand. Rixey et al. [20] built a linear regression model for predicting monthly rentals by
studying the network effect of the scale and geographical distribution of bike-sharing stations.
For cluster-level prediction, we assume that if people arrive at a bike station that does not have enough
bikes or docks, the people will go to another nearby
station. Researchers tried to group stations into clusters and predicted the demand of clusters. Li et al.
[21] used K-means twice as the clustering method, and
the first clustering was based on geographic distance
between two stations and the second clustering was
based on the clustering results of the first clustering
and bike usage patterns. Chen et al. [22] constructed
a weighted correlation network to model the relationship among stations and predicted the over-demand
probability of each cluster. Zhou et al. [23] clustered stations by the fast-greedy community detection
method, which identified the core and representative
clusters at certain time windows. In [24], K-means
clustering was applied to divide all stations into several clusters, and latent Dirichlet allocation (LDA) was
used to explore latent bike riding patterns.
Mobile Service Clustering. A bike-sharing service involves a large number of bikes and stations is
large, and hence clustering is a feasible way to reduce
the problem complexity [25, 26, 27]. In this paper,
we applied clustering and developed prediction models in a low complexity scenario. We constructed a
weighted adjacency graph, partitioned the graph into
small sub-clusters, and merged highly correlated subclusters into bigger clusters. The results testify that the
quality of clusters is clearly better than that achieved
by previous clustering methods. Hence, our clustering results provide a better basis for the subsequent
demand prediction.
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2. Related Work
3
3. Mobile Bike-sharing Service
In this section, we provide the definitions used in
this work, and present an overview of our framework.
3.1. Preliminary Information and Problem Definition
Definition 1. Riding record. A riding record rt =
(so , sd , τo , τd ) is a history record of bike usage from a
starting station so . τo denotes the rental time at station
so and τd denotes the return time at station sd .
Definition 2. Cluster. We define a group of nearby
stations with similar weights as cluster C. The weights
are computed using the bike demand in a given time
interval [t1 , t2 ] and geographic distance d.
Definition 3. Bike usage. We define the sum of
the absolute number of bikes rented Ui− (t) and bikes
returned Ui+ (t) at station S i during a given time [t, t+∆]
as the bike usage Ui (t) at station S i .
Definition 4. Rental demand and return demand.
We define the number of bikes rented Ui− (t) at station
S i in a time period t as the rental demand at station S i
. The number of bikes returned Ui+ (t) at station S i in a
time period t is defined as the return demand at station
S i.
Problem Definition. Cluster-level bike demand
prediction. Given a set of history riding records RT =
(rt1 , rt2 , ..., rtH ), the problem of cluster-level demand
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Name of the first author, et al.
prediction is to predict the bike rental and return demand for each cluster during a future period. The period in our work is set to 1h. In the real world, the bike
scheduling frequency is usually more than 1h [18],
so the hourly prediction can reach the requirement of
real-world applications and is more fine-grained.
4. Features Analysis and Information Fusion
The raw data contain much redundant information,
and hence simply using raw data usually leads to complex computation and noise. Hence, we comprehensively examined the potential factors for bike demand
prediction. We conducted empirical feature analyses
from various aspects using the bike trip records of
all New York bike-sharing stations and meteorological
data from 2014/04/01 to 2019/11/30. These two types
of data can be accessed publicly, and we crawled from
the Web pages of New York Citi Bike System [2] and
New York Weather [28].
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(b) Rest days
Fig. 1: Hourly bike demand analysis on working days and rest days
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4.1.1. Daily Temporal Analysis
Fig. 1 shows the bike usage of different hours in one
day and on different days in a week. The time period
is from 2019/04/01 to 2019/11/30. It can be observed
that the bike demands on working days and rest days
have different peak and low-peak periods. Note that
the rest days include weekends and holidays. The peak
demand of working days occurs in two time periods,
namely, the morning (7:00 to 9:00) and evening rush
hours (17:00 to 19:00). In contrast, the peak demand
of rest days is concentrated in the 10:00-20:00 period.
The usage pattern represents the behaviors of people using the bike-sharing service. The above observation indicates that there are working days and
rest days, which have different usage patterns. It also
shows that the daily peak distribution is different between the two usage patterns, but is stable on working
days and rest days.
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4.1. Temporal Information
(a) Working days
We observe that the changes in the average bike demand per hour are almost the same as the working
days in Section 4.1.1, which illustrates the necessity to
learn the features on working days and rest days separately. Especially, the bike demand on rest days cannot
be ignored. As shown in Fig. 2, the summer (green
line) has the highest average bike demand, while the
winter (peach line) has the lowest average demand,
which is probably caused by the weather conditions.
The same hours in different seasons can have different
demands. For example, the daytime demand in winter
is half less than that in spring. Based on the analysis, we consider the seasonal features as an influencing
factor.
4.1.2. Seasons
To study the impact of seasonal features on bike demand, we crawled the bike trip records of all Citi Bike
stations from 2018/1/1 to 2019/11/30. We define the
start and end dates of spring, summer, autumn, and
winter seasons according to the equinox and solstice
[29], as shown in Table 2.
Table 1: Dates of four seasons
season
Spring
Summer
Fall
Winter
Start and end dates
03/22-06/20
06/21-09/22
09/23-12/20
12/21-03/21
Fig. 2: Hourly bike demand in four seasons (spring, summer, fall
and winter)
4.2. Meteorological Information
Several previous studies gave an analysis of the effect of weather on bike riding [6][9]. The results indicate that bike demand can vary under different mete-
Paper Title (The title should be descriptive, not full sentence)
5
orological conditions. However, the existing study on
the weather features is not enough. In this section, we
will give a comprehensive analysis of meteorological
features, including weather condition, humidity, precipitation intensity, air temperature, and wind speed.
4.2.1. Weather Condition
We quantitatively analyzed the effects of weather
conditions on bike demand. Bike usage is different
at different hours on one day. Hence, we used the
morning and evening rush hours to study the impact of
different weather conditions on bike demand, and the
results are shown in Figure 3. We classified weather
conditions into four categories according to the impact
on bike demand. U1 = clear, partly − cloudy, U2 =
cloudy, U3 = f og, wind, rain and U4 = sleet, snow.
We used the four weather categories as meteorological features to understand the bike demand.
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(a) Humidity
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Fig. 3: The average hourly bike demand during rush hours in different weather conditions
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4.2.2. Humidity and Precipitation Intensity
As the effects of humidity and precipitation intensity on bike demand are similar, so we discuss these
factors together. We find that along with increasing
humidity, the demand to rent bikes reduces. Fig. 4
(a) shows that a higher humidity is related to a lower
hourly bike usage. The reason may be that under high
humidity conditions, people are easily uncomfortable,
thereby deteriorating the riding experience. Moreover,
the low bike demand at extremely low humidity (less
than 0.25%) is likely caused by cold temperature as
extremely low humidity often occurs in a cold environment.
Precipitation intensity represents the degree of precipitation process on rainy or snowy days. For example, precipitation intensity larger than 0.3in/h indicates
heavy rain. It can be found from Fig. 4 (b) that as the
precipitation intensity increases, the bike demand decreases. When precipitation intensity is greater than
0.3 in/h, the bike demand drops to almost 0.
4.2.3. Temperature and Wind Speed
We examined the effect of temperature and wind
speed on bike rental demand in rush hours. Fig. 5(a)
shows that the bike rental demand per hour increases
(b) Precipitation Intensity
Fig. 4: The impact of humidity and precipitation intensity on bike
demand in rush hours
as the temperature increases. For wind speed, we observe that as the wind speed increases, the bike rental
demand first increases and then decreases, as shown
in Fig. 5(b). Hence, in this paper, temperature and
wind speed are also taken as meteorological features
that affect the bike demand.
5. Collaborative Demand Prediction in 5G IoT Environments
5.1. Similarity Computation
The demand in a cluster is more regular than that
in an individual station, and the prediction results of
clusters will be more stable. To produce a more accurate bike demand for the stations in a cluster, our
objective is to find the relationship between stations.
The records of renting and returning bikes at each station at any time are contained in historical trip records.
So we utilize the recorded bike demand to generate the
similarity between stations.
As shown in Fig.2, based on our observation, the
bike demands of all stations decreased dramatically
from 00:00 to 5:00 on working days and from 00:00
to 6:00 on rest days. In these two periods, the difference in bike demand between stations cannot be reflected, and so we remove the data of these two periods. We divided the remaining periods into four
6
Name of the first author, et al.
which is defined as

[Ui (TG1 ), Ui (TG2 ), Ui (TG3 ), Ui (TG4 )],







 tq = 1
ftq (si ) = 


[Ui (TG5 ), Ui (TG6 ), Ui (TG7 )],





tq = 0
(1)
where tq = 1 if the tq -th day is a working day and
tq = 0 if the tq -th day is a rest day. Ui (TG1 ) to Ui (TG7 )
denote the number of bike riding records in different
time groups (TG) of the day tq of station si . The complete bike-riding records vector of station si is
(a) Temperature
f(si ) = [ ft1 (si ), ft2 (si ), ..., ftK (si )]
(2)
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We compute the Pearson correlation coefficient [30]
to determine the similarity between station si and station s j , and this coefficient is denoted as sim(si , s j ).
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sim(si , s j ) =
cov(f(si ), f(s j ))
∂ f (si ) ∂ f (s j )
(3)
(b) Wind Speed
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where cov(f(si ), f(s j )) is the covariance of f(si ) and
f(s j ) and ∂ f (si ) is the standard deviation of f(si ). Finally, we normalize sim(si , s j ) to [0,1] to measure the
bike demand similarity between station si and station
s j.
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Fig. 5: The impact of temperature and wind speed on bike demand
in rush hours
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groups for working days and into three groups for rest
days, as shown in Table 2. The groups are denoted as
TGi (i = 1, . . . , 7). The usage pattern is stable within
a time group. For example, in the morning rush hours,
the usage pattern for bikes first increases and then decreases. The bike demand in a time group reflects the
correlation and similarity among stations.
Table 2: Dates of four seasons
Day type
Working days
Rest days
Time groups
morning rush hours (TG1 )
daytime (TG2 )
evening rush hours (TG3 )
nighttime (TG4 )
early morning hours(TG5 )
daytime(TG6 )
nighttime(TG7 )
Duration
6:00-9:00
10:00-15:00
16:00-20:00
21:00-24:00
7:00-10:00
11:00-20:00
21:00-24:00
We represent station si by the bike demand of different time groups. Specifically, for station si , we construct the bike riding records vector of the tq -th day,
5.2. Location-aware Stations Clustering
5.2.1. Adjacency Graph Construction
Our task was to dynamically cluster stations into
groups, where each cluster contains stations that have
similar bike demand and that are separated by small
geographical distances. We constructed an adjacency
graph G = (V, E) , where the set of nodes V =
{s1 , s2 , ..., sn } represents all n stations, and E is the set
of connected edges between two stations. The stations in a cluster should be similar in terms of both
geographic location and bike demand. So, for constructing the graph, we only needed to investigate the
neighboring nodes of the stations and did not need to
consider sites located far away. We select k nodes with
the top k edge weights for each node by the KNN approach to construct an adjacency graph G. We computed the edge weight using both bike usage similarity
and geographic distance as follows.
W(si , s j ) = sim(si , s j ) + log(
τ
)
dist(si , s j )
(4)
where dist(si , s j ) is the geographic distance between
station si and station s j and τ is the neighborhood distance threshold. The weight was computed as a combination of sim(si , s j ) and distance value. The distance value function rewards distances smaller than
the threshold τ and penalizes distances larger than τ.
The adjacency graph was divided into a group of
sub-clusters through graph partitioning. The subcluster pairs that will merge into clusters should have
Paper Title (The title should be descriptive, not full sentence)
7
high relative inter-connectivity and relative closeness.
The relative inter-connectivity and relative closeness
between two clusters are denoted as RI(Ci , C j ) and
RC(Ci , C j ), respectively. Relative inter-connectivity
represents the absolute inter-connectivity between Ci
and C j that is normalized by the inter-connectivity between the two clusters.
RI(Ci , C j ) =
|EC(Ci , C j )|
(5)
|ECCi |+|ECC j |
2
where EC(Ci , C j ) is the absolute inter-connectivity
between two clusters and represents the sum of the
weights of the edges connecting the nodes in cluster Ci
and the nodes in cluster C j . ECCi is the internal interconnectivity of a cluster Ci and is computed as the sum
of the weights of the truncated edges that partition Ci
roughly into halves. Relative closeness represents the
absolute closeness between Ci and C j normalized by
the internal closeness of the two clusters.
+
(6)
|C j |
|Ci |+|C j | S̄ ECC j
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where |Ci | is the number of nodes in cluster Ci .
S̄ EC(Ci ,C j ) is the absolute closeness of the two clusters
and represents the average weight of the edges connecting the nodes in cluster Ci and the nodes in cluster
C j . S̄ ECCi is the internal closeness of cluster Ci and is
computed as the average weight of the truncated edges
that partition Ci into halves.
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S̄ EC(Ci ,C j )
|Ci |
|Ci |+|C j | S̄ ECCi
1. Construct a graph Gk . For a node si , if the weight
value from s j to si is one of the top k maximum
values among the weight values from all nodes
to si , we add a weighted edge between s j and
si (line 1). For each node, we use the KNN approach to find all top k weighted neighbor nodes
that are connected with weighted edges to construct a graph Gk . As the number of neighbor
nodes k is far smaller than the total number of
nodes, Gk is a sparse graph.
2. Partition the graph into sub-clusters. We use pway partitioning algorithm [31] to partition the
adjacency graph into M sub-clusters. M represents the number of sub-clusters (line 2).
3. Merge into clusters. The sub-clusters are composed into a set of sub-cluster pairs, and the subcluster pairs are iteratively selected from the set
of sub-cluster pairs. If all stations in a sub-cluster
pair satisfy the distance constraint, we calculate
the value function between sub-cluster pairs and
select the sub-cluster pair with the highest value
function to merge (line 5 to line 11). The merging
continues until only m sub-clusters are left. The
clustering result is m clusters.
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RC(Ci , C j ) =
Fig. 6: A toy example of LHC algorithm
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5.2.2. Dynamic Clustering Algorithm
To produce clusters with similar bike demand and
small geographic distance, the constructed graph is
partitioned into sub-clusters. We dynamically select
sub-cluster pairs and merge them into a new subcluster such that both the relative inter-connectivity
and relative closeness between the sub-cluster pairs
are high. The sub-clusters remaining after merging are
the final clusters. We propose a location-aware hierarchal clustering (LHC) algorithm for stations, to find
sub-cluster pairs with high relative inter-connectivity
and relative closeness. The edge weights in subcluster pairs are computed by Eq.4, and the value for
merging is computed as follows.
value(Ci , C j ) = RI(Ci , C j ) × RC(Ci , C j )α
(7)
where α is a constant that weighs the importance of
RI and RC. If α > 1, the relative closeness plays a
more important role, and if α < 1, the relative interconnectivity is dominant. We conducted preliminary
experiments, and the results show that the clustering
result is not sensitive to α, and α is set to 2 in all the
experiment cases.
The proposed LHC method has three steps and a
toy model is as shown in Fig. 6. The nodes represent
stations, and the nodes of the same color belong to the
same sub-cluster.
Each cluster in the generated m clusters has two
properties, namely high bike demand similarity and
close location. These two properties are crucial for demand prediction. The pseudocode of LHC algorithm
is shown in Algorithm 1.
5.3. Demand Prediction with XGBoost Regression
The demand prediction process involves two
phases, namely, feature representation and XGBoost
regression.
5.3.1. Feature Representation
The extracted features have two types of features,
i.e., temporal and meteorological features. More
specifically, temporal features contain isworkday,
isrestday, hours, months and seasons. Meteorological features contain weather conditions, temperature,
wind speed, humidity and precipitation intensity. All
8
Name of the first author, et al.
our work, we consider a data set with n examples and
m features.
Algorithm 1: The LHC Algorithm
Input: S = {S i }ni=0 , similarity set
{simi(S i , S j )}i, j=1...n , the number of
sub-clusters M, parameter m;
Output: m clusters: C1 , C2 , ..., Cm ;
n
1 Construct stations set S i i=0 to graph G k by KNN
approach based on {simi(S i , S j )}i, j=1...n and
locations;
2 Partition M sub-clusters C 1 , C 2 , ..., C m by k-way
partitioning algorithm;
3 Initialize k=0;
4 k = M − n, ln = M;
5 for i = 1 : k do
6
if ln < m then
7
return m1 clusters C1 , C2 , ..., Cm ;
10
11
ln = ln − 1
fk (xi ), fk ∈ F
(9)
k=1
where F is the space of regression trees and F is defined as
F = { f (x) = wq(x) }(q : Rm → T, w ∈ RT )
(10)
where q represents the structure of each tree with T
leaves in the tree. Each fk corresponds to an independent tree structure q and leaf weights w. For a
given sample, XGBoost follows the decision rules in
the trees to divide the features of the sample into leaf
nodes, and sum up the score of the leaf nodes to compute the final prediction result. The following regularized objective is minimized to learn the set of functions used in the model.
X
X
l(ŷi , yi ) +
Ω( fk )
L(φ) =
(11)
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ŷi = φ(xi ) =
K
X
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A tree ensemble model with K additive function to
predict the bike demand and ŷi denotes the predicted
result.
C2n
Generate
sub-cluster pairs by merging;
for j = 1 : C2n do
if Sub-cluster pairs satisfy distance
constraint then
Find a sub-cluster pair with highest
value by value function to merge;
(8)
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D = {(xi , yi )}(|D| = n, xi ∈ Rm , yi ∈ R)
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the features are extracted from the data of weather reports and bike trip history records [2][27]. To deal
with the problem that different features have different
original representations, we propose two ways of representing all features.
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1. One-hot encoding. Use 1 and 0 to represent the presence and absence of one feature. We divide the weather conditions in
the meteorological data into four weather categories, which are U1 ={clear, partly-cloudy},
U2 ={cloudy}, U3 ={fog, wind, rain} and U4
={sleet, snow}. We set a new feature daytype to indicate the day of a week, representing Monday to Sunday. We apply one-hot
encoding for isworkday, isrestday, day type,
weather categories, hours, months, and seasons.
2. Numerical encoding. The values of the features
are used directly. We applied numerical encoding to temperature, wind speed, humidity, and
precipitation intensity.
After feature extraction, we fully utilized all features by combining the temporal and meteorological
features into a matrix, where the columns represent
each feature and the rows represent combination vectors of all types of features.
5.3.2. XGBoost-based Regression Model
We employed XGBoost as the regression model to
realize demand prediction in the bike-sharing service.
XGBoost is a tree boosting model [10] based on the
ensemble of decision trees and widely used in regression tasks and provides competitive performance. In
i
k
where l is a differentiable convex loss function that
measures the difference between the prediction ŷi and
the real value yi . To minimize (11), in each iteration,
the fk that improves the model the most will be greedily added in (11). Formally, ŷ(t)
i represents the prediction of the i-th instance at the t-th iteration, and the
objective function to be minimized in the t-th iteration
is
L(t) =
n
X
l(yi , ŷ(t−1)
+ ft (xi )) + Ω( fk )
i
(12)
i=1
To accelerate the optimization of (12), we introduce
Taylor’s second-order approximation, and (12) can be
transformed to a new objective function.
L(t) =
n
X
l(yi , ŷ(t−1)
+ ft (xi )) + Ω( fk )
i
(13)
i=1
where gi = ∂ŷ(t−i) l(yi , ŷ(t−1) ) and hi = ∂2ŷ(t−1) l(yi , ŷ(t−1) )
are the first and second order partial gradients of the
loss function. Finally, the XGBoost model minimizes
the objective function (13) and outputs the bike demand prediction results.
6. Experiments and Evaluations
6.1. Datasets
We performed experiments on two real-world bikesharing service datasets obtained from the database
Paper Title (The title should be descriptive, not full sentence)
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1. Citi Bike data. Bike-sharing trip records are
publicly available from the platform of Citi Bike
System of New York City [2]. We crawled 5.35
million records from 1st April to 30th September
in 2014 to form the first dataset, and 23.75 million records from 1st November in 2018 to 30th
November in 2019 to form the second dataset.
The records format is (trip duration, start time,
stop time, start station ID, end station ID).
For the first dataset, we used all stations that appeared in the bike-sharing riding records during
the corresponding period. For the second dataset,
new stations that were built within the corresponding period were included along with the already existing stations. As training data, the data
for the periods from 1st April to 10th September in 2014, and from 1st November in 2018 to
31th July in 2019 were used. The remaining data
from 11th to 30th September in 2014, and from
1st August and 30th November in 2019 were used
as test data.
2. Meteorological data. We collected the hourly
meteorological data of New York City from April
2014 to November 2019. Hence, the time period
of the meteorological data successfully covered
both bike-sharing datasets. The format of the meteorological data was (timestamp, weather condition, temperature, wind speed, humidity, precipitation intensity). A small proportion of the meteorological data was missing, and these missing
data were supplemented according to the hourly
data in the previous hour.
1. HA. HA is short for Historical Average, which
predicts the rental and return number by using the
average number of historical rental and return demand in each time period [8][19]. For example,
for 10:00 am to 11:00 am on work days, the time
periods are all the historical times in the training
set from 10:00 am to 11:00 am on all work days.
2. ARMA. ARMA considers the rental and return
demands as time series and predicts the demand
in time periods. The time periods are set identical
to those in HA [8][32].
3. ARIMA. The ARIMA model is designed to handle unstable data and considers the bike riding
data as a time series [30]. The difference between ARIMA and ARMA is that the raw data
in ARIMA are processed by the first-order difference and transformed into more stable data.
4. HP-KNN (BC). HP is short for hierarchical prediction and BC is short for bi-partite clustering.
HP-KNN (BC) uses BC clustering to complete
station clustering, predicts the entire bike-sharing
demand of the city and allocates the entire trip
records to each cluster based on the capacity proportion of each cluster [8]. The proportion is
learned by KNN.
5. HP-MSI (BC). MSI is short for Multi-similaritybased Inference, which also uses a BC method to
complete station clustering, and predicts the entire bike-sharing demand of the city and allocates
the entire demand to stations based on the capacity proportion of each cluster. The proportion is
learned by MSI [8].
6. MFR-ARMA. MFR is short for Multiple Factor Regression, which uses a weighted k-means
clustering method to complete station clustering
and predicts the rental and return demands with a
multi-factor regression model with ARMA [32].
7. GBRT. GBRT is short for Gradient Boosted Regression Tree, which uses the negative gradient of
the loss function in the current model value as an
approximation of the residuals to fit a regression
tree. The rental and return demands are predicted
individually by GBRT [30].
8. RF. RF is short for Random Forest, which is a
powerful tool to solve the multivariate classification and regression problems. A RF model is
composed of multiple random trees and the average value of the output of random trees is used as
the predicted value [33].
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of New York City. We collected bike-sharing riding
records and meteorological data from 1st April to 30th
September in 2014, and from 1st November in 2018
to 30th November in 2019. The details of the two
datasets are presented in Table 3.
9
Table 3: Details of the datasets
Data source
Time period
Bike-sharing
data
Meteorological
data
#Stations
#Bikes
#Riding
Records
#Temporal
frequency
New York City
1st November
in 2018 to 30th
November in
2019
331
706
6,800
19,800
1st April to
30th September
in 2014
5,359,995
23,752,004
hourly
hourly
6.2. Compared Methods and Metrics
Our proposed method of predicting the hourly rental
and return demand is named as LHC-XGBoost, as it
involves the LHC and XGBoost regression. To better
evaluate our method, we compare our method with the
following well-known baselines.
Although neural network-based methods can also
be used in prediction tasks, those methods usually requires a lot of computation resource [31]. Furthermore, neural network-based methods are not as representative as the typical prediction methods for time
series data. In contrast, ARMA and ARIMA are
widely-used prediction methods for time series data.
Thus, neural network-based methods are not competitive enough for the studied task of this paper, and
10
Name of the first author, et al.
hence they were not selected as methods for comparison.
Metrics. To evaluate the performance of different methods, we employed Root Mean Squared Logarithmic Error (RMLSE) and Error Rate (ER) as the
metrics, because they have been widely used in bikesharing service evaluation [8][33].
of clusters. As shown in Algorithm 1 in Section
5.2.2, if the LHC algorithm modifies the stop criteria when no sub-cluster pairs merge, we can automatically obtain a more reasonable number of
clusters. By running the LHC algorithm, we obtained 16 clusters, which are shown in Fig. 7(c).
With the generated 16 clusters, our method yields
the best prediction performance. The RMLSE is
0.303 and ER is 0.234 for rental demand prediction, and RMLSE is 0.289 and ER is 0.227 for
return demand prediction.
RMLS E =
r
1 Xn
1 XT
(log(ȲCi ,t + 1) − log(YCi ,t + 1))2
t=1
i=1
T
n
(14)
Pn
1 XT
i=1 |ȲCi ,t − YCi ,t |
Pn
ER =
t=1
T
i=1 YCi ,t
(15)
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where YCi ,t denotes the ground truth of the rental and
return demand of cluster Ci during t time. ȲCi ,t is the
predicted value.
(a) LHC algorithm with 23 clusters
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Based on our observation, a larger number of clusters probably lead to a lower accuracy of predicting the
demand. If the number of clusters is equal to the number of stations, each cluster is equal to an individual
station. Then, it is difficult to generate accurate predictions because the bike demand fluctuates randomly. If
there is only one cluster, all bike stations are contained
in that cluster, and the demand prediction accuracy for
the entire city can be high, but the demand prediction
results of the entire city are not useful. Thus, the selection of an appropriate number of clusters that can capture the demand correlation between stations and meet
the distance constraints requires knowledge and experience. A previous study [8] proposed a BC method to
cluster stations. Clustering was performed on the first
bike-sharing dataset (1st April to 30st September in
2014). As shown in Table 3, the first dataset contained
331 stations, 6800 bikes and 5,359,995 bike-sharing
riding records.
From the clustering results, the following observations can be made.
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6.3. Evaluation of Clustering Analysis
1. To compare the clustering results obtained by our
LHC algorithm and BC, following the setting in
[8], we set the same number of clusters m as 23.
The clustering results are shown in Fig. 7, where
Fig. 7(a) shows the clustering results of the LHC
algorithm and Fig. 7(b) show the clustering results of the BC method. It can be seen that our
dynamic clustering algorithm LHC can generate
more intra-connected clusters. LHC can generate clusters with more similar demand and tighter
station locations. In some clusters that are generated by the BC method, the stations are more
scattered, and a typical example is the stations
colored sky-blue in Fig. 7(b).
2. As our LHC method can dynamically generate
clusters, it is not necessary to set a fixed number
(b) BC algorithm with 23 clusters
(c) LHC algorithm with 16 clusters
Fig. 7: Clustering comparison. The same colors denote the stations
that are contained in the same cluster.
We also investigated the clustering performance of
Paper Title (The title should be descriptive, not full sentence)
11
In the GC algorithm, a city is divided into uniform grids and the stations that are in the same
grid form a cluster. For the clustering results,
the HA (historical average) is employed to yield
the demand prediction results. It can be seen that
HA, HA (GC) and HA (BC) perform worse than
our methods, such as LHC-GBRT, LHC-RF and
LHC-XGBoost.
2. We compared our methods with the following
widely-used or state-of-the-art methods, namely
ARMA, HP-KNN (BC), HP-MSI (BC), MFRARMA, GBRT and RF. For fairness, we used
the same features for the machine learning-based
methods, including GBRT and RF, and the parameters were all set according to the setting of
original papers. We carefully fine-tuned each
model. From Table 4, it can be seen that
our methods (LHC-GBRT, LHC-RF and LHCXGBoost) yield the highest prediction accuracy
on both evaluation measures. For example, compared to HP-MSI(BC) and MFR-ARMA, the
rental demand prediction of LHC-XGBoost can
reduce RMSLE by 0.028 and by 0.011, respectively, and can reduce ER by 0.024 and 0.019,
respectively. Furthermore, compared to HPMSI(BC) and MFR-ARMA, for return demand
prediction of LHC-XGBoost, RMSLE is reduced
by 0.036 and by 0.02, respectively and ER is reduced by 0.036 and 0.026, respectively.
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the LHC algorithm on the second dataset, for which
the time period ranges from 1st November in 2018
to 30th November in 2019. The LHC algorithm also
yields superior clustering results, which are shown in
Fig. 9. Because of the newly constructed stations,
more stations are included in the second dataset. So
from Table 3, we can see that the numbers of stations
(706), bikes (19,800) and riding records (23,752,004)
in the second dataset are all clearly more than those
in the first dataset. More stations result in more subcluster pairs, and the LHC algorithm can choose the
most appropriate sub-cluster pairs to merge and produce better clustering results. We clustered 600 stations from 1st November in 2018 to 30th November
in 2019 in New York City using the LHC algorithm.
Thus, we obtained 31 clusters, as shown in Fig. 8.
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(a) The geographic map of clustering
Table 5 shows the average RMSLE and ER over
all hours in the test set from the second dataset,
corresponding to time period from 1st November,
2018 to 30th November, 2019. The experimental results demonstrate that the three methods (LHC-GBRT,
LHC-RF, LHC-XGBoost) with our LHC clustering algorithm all produce superior prediction accuracy. The
method LHC-XGBoost produces the best prediction
accuracy.
Table 4: Prediction errors comparison of rental and return demands
for the first dataset
(b) Clustering over New York City Map
Model
Fig. 8: Clustering results from 1st November in 2018 to 30th
November in 2019. The same colors denote the stations that are
contained in the same cluster.
6.4. Evaluation of Demand Prediction
We compared the demand prediction accuracy of
our methods with well-known or state-of-the-art methods for the two datasets. Table 4 shows the average
RMSLE and ER over all hours in the test set of the
first dataset.
1. We first compared our methods with HA plus geographical grid clustering (GC) and HA plus BC.
HA(GC)
HA(BC)
HA
ARMA
ARIMA
HP-KNN (BC)
HP-MSI (BC)
MFR-ARMA
LHC-GBRT
LHC-RF
LHC-XGBoost
Rental demand
RMSLE
ER
0.387
0.353
0.372
0.355
0.367
0.354
0.380
0.366
0.359
0.350
0.358
0.299
0.349
0.282
0.332
0.277
0.328
0.268
0.329
0.267
0.321
0.258
Return demand
RMSLE
ER
0.377
0.347
0.365
0.352
0.356
0.352
0.369
0.363
0.351
0.348
0.360
0.295
0.350
0.290
0.334
0.280
0.319
0.261
0.318
0.258
0.314
0.254
12
Name of the first author, et al.
Table 5: Prediction errors comparison of rental and return demands
for the second dataset
Model
HA
ARMA
ARIMA
LHC-GBRT
LHC-RF
LHC-XGBoost
Rental demand
RMSLE
ER
0.500
0.450
0.515
0.527
0.447
0.498
0.347
0.267
0.362
0.278
0.341
0.262
Return demand
RMSLE
ER
0.501
0.449
0.510
0.529
0.444
0.500
0.341
0.262
0.355
0.271
0.335
0.256
(a) Silhouette coefficient score
6.5. Sensitivity Analysis of Parameters
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We examined the impact of parameter k on clustering results. We see k plays an important role in the
construction of the adjacency graph and represents the
number of neighboring stations of a target station. The
value of k is studied from 6 to 14, and we employed the
clustering evaluation indexes, namely, silhouette coefficient score and Davies-Bouldin index (DBI) to evaluate the clustering result. The silhouette coefficient
score is a metric for clustering analysis that jointly
evaluates the cohesion and separation of clusters, the
value of which ranges from -1 to 1. A larger score denotes that clusters are separated better and have high
cohesion, which indicates better clustering [34]. The
DBI is defined as an index that is defined as the ratio
of the intra-cluster scatter to the inter-cluster separation. A small DBI score means that the scatter within
clusters is smaller than the separation among clusters,
thereby indicating a better clustering result [34]. Fig.9
shows the values of the two indexes along with the
change in k.
It can be seen that a too small value of k (k=6) results in a lower silhouette coefficient score. The reason is that there are very few neighboring stations in
the adjacency graph construction, and such a small
number of neighboring stations is insufficient for successfully mining the bike demand correlations among
stations. A large value of k, (e.g., k is equal to 12
or 14) also negatively lowers the silhouette coefficient
score. The reason can be inferred that a large k leads to
the increase of neighbor stations in clusters and further
leads to the complexity of the adjacency graph, which
harms the effectiveness of partitioning the graph into
sub-clusters.
As shown in Fig.9, a higher silhouette coefficient
score is obtained when k is 8 or 10, which indicates
a better clustering result. Moreover, the bike demand
fluctuates frequently at an individual station, and the
other stations in the same cluster can provide more
valuable information for the demand prediction accuracy of the target station. In our experiment, k is set to
10 as the default value.
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6.5.1. Discussion of Parameter k
(b) DBI Score
Fig. 9: Cluster evaluation indexes with different k values
6.5.2. Discussion of Edge Weight Computation and
Parameter τ
In this section, we study the impact of the neighborhood distance threshold τ on cluster results. As τ
is a parameter in edge weight computation (see (4) in
Section 5.2), we also discuss the edge weight computation. The results are shown in Fig.10. Fig. 10(a)
shows the number of station pairs with different Pearson Coefficient Correlation (PCC) values. PCC is employed to compute the similarity between two stations
in bike riding records, and is one of two parts in edge
weight computation. It can be found that the PCC values of the most station pairs are at 0.6 to 0.9.
In edge weight computation, the other part is the
distance between two stations, and the distance exceeding τ will be punished (Section 5.2). We study
the clustering performance under different values of
neighborhood distance threshold τ from 0.5km to
2km, which is evaluated by Davies-Bouldin index
(DBI) and A small DBI value means a better clustering result. Fig. 10(b) shows DBI results and it can be
found that when τ is 1.5km, DBI reaches the smallest
value. So the τ is set to 1.5km as the default value.
7. Conclusion and Future Work
In this paper, we propose a holistic framework
for demand prediction for bike-sharing service. Our
framework contains collaborative computing, information fusion and demand prediction, where we have
13
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novel contributions.
We successfully mine the relations between stations
via comprehensively features analysis and the utilized
information fusion include bike riding records, temporal features, meteorological features and geographical
locations in 5G IoT. We propose a new similarity computation method that successfully supports the further
proposed dynamic clustering algorithm. XGBoost is
employed to work with our clustering algorithm as
the regression model finishing the final demand prediction. The experiments are performed on two large
real-world datasets and the results demonstrate that
our clustering algorithm is effective, the studied features are indeed useful and the prediction model yields
the superior demand prediction performance.
In future, we intend to mine more features from
other types of data, especially the social data, such as
city events and activities. We plan to study whether
such social data have impact on the QoS and demand
of bike-sharing service.
Acknowledgment
This paper is supported by National Natural Science
Foundation of China (No. 61902236) and Fundamental Research Funds for the Central Universities (No.
JB210311).
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Declaration of interests
☒ The authors declare that they have no known competing financial interests or personal relationships
that could have appeared to influence the work reported in this paper.
☒The authors declare the following financial interests/personal relationships which may be considered
as potential competing interests:
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None.