Construction and Evaluation of a Return
Prediction Model for One-Way Car Sharing
Ryota Saze1[0000−0002−7311−6966] , Manato Fujimoto2,3[0000−0002−6171−5697] ,Hirohiko Suwa1,3[0000−0002−8519−3352] , and Keiichi
Yasumoto1,3[0000−0003−1579−3237]
1

Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan{saze.ryota.sl4, h-suwa, yasumoto}@is.naist.jp
2
Osaka Metropolitan University, Osaka, Osaka 558-8585, Japan
manato@omu.ac.jp
3
RIKEN, Tokyo, Japan.

Abstract. One-way ECS (Electric Car Sharing Service) is attracting
attention as a new sustainable mobility option in urban areas. On theother hand, the vehicle uneven distribution problem occurs in one-way
ECSs due to their usage patterns. In this paper, we propose a vehicle
return prediction model for vehicle relocation to solve this problem. In
the proposed method, two machine learning models are created to predict
where and when a user will return a vehicle using static information suchas departure time and location, and dynamic information such as thevehicle’s current location and direction of movement. The model is used
to continually update the prediction results of vehicle returns duringuse, aiming for more accurate predictions. The proposed method has
been evaluated using actual data from a one-way ECS and has achieved
an accuracy of 0.93 for the prediction of stations to be returned. The
method also achieved a MAE of 42.3 min. and MAPE of 47% for theprediction of return times.
Keywords: Electric Car Sharing · Machine Learning · Return Prediction · Vehicle Uneven Distribution Problem.

1

Introduction

In recent years, one-way electric car sharing services (hereinafter referred to as“one-way ECS”) have become popular as a form of mobility in urban areas. In
fact, in Japan, TIMES MOBILITY CO., LTD. has launched a service that canbe used in Tokyo4 . In addition to being flexible for short-distance travel in urban
areas, one-way ECSs are expected to reduce CO2 emissions by reducing gasoline
consumption [1]. On the other hand, in one-way ECS, there is a “vehicle uneven
distribution problem” in which the demand for vehicles is unevenly distributedat certain times and places. If this situation occurs, the system’s ability to meetthe demand from users will be compromised, resulting in a decline in the rate4

https://share.timescar.jp/roadway/

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at which the system meets usage requirements. A solution to this problem is“vehicle relocation” in which vehicles are relocated by the system operator orby the users themselves [6]. In order to properly perform this vehicle relocation,
it is necessary to predict the future vehicle deployment and vehicle relocation inadvance in such a way as to prevent the occurrence of vehicle uneven distribution
problem.
There are some studies that predict vehicle demand for each station, but few
studies predict future vehicle deployment by predicting the return station andreturn time for each vehicle in use.
In this paper, we propose a method for constructing a return prediction
model that predicts the return station and return time of vehicles in real timeby capturing the movement direction vector of each vehicle during use as afeature, and evaluate the constructed model by using data obtained from a car
sharing service that is actually in operation. As a result, it is confirmed thatthe proposed method improves the return prediction results while the vehicle ismoving. The contribution of this study is three folds:
1. First, we propose a return prediction model to predict the return station andreturn time for a one-way ECS.
2. Second, we evaluate using data obtained from a one-way ECS in real world.
3. Finally, we confirm that the prediction results are improved over time byusing real-time vehicle movement direction vectors and other features as
explanatory variables.
The rest of this paper is organized as follows. In Section II, we briefly review
the existing studies related to our study. We then describe the challenges of aone-way ECS in Section III. Our proposed method for vehicle return prediction in
one-way ECS is described in Section IV. We then present the evaluation methodof our method and the results in Section V. Finally, Section VI concludes thispaper.

2

Related WorkOne-way ECSs have been the subject of various studies, including comparisonswith the conventional round-trip car-sharing method, analysis of user usage characteristics, and research to gain insight into actual operations [3, 10, 14].
Huang et al. [4] compared two relocation methods, operator-based and userbased. Senju et al. [11] proposed a method of user-based vehicle relocation andconfirmed that it improves the utilization demand fulfillment rate in simulations
assuming a small-scale one-way ECS. Yang et al. [19] proposed an integrated
model for operator-based vehicle relocation to minimize daily operational costsby optimizing request acceptance, relocation tasks, and relocation staff travel
routes. Wang et al. [17] proposed a combined user-based and operator-based
vehicle relocation method that considers the impact of dynamic constraints suchas user demand, vehicle state of charge, and operating profit. Wang et al. [18]also proposed an adaptive co-location model that combines ECS and cycle share

Return Prediction Model for One-Way Car Sharing3

to increase ECS utilization. In this research, they achieved a 70% relocation cost
reduction compared to staff-based relocation. Luo et al. [9] used multi-agent
reinforcement learning to model and treat vehicle relocation tasks, and confirm
that demand fulfillment and net benefits are improved using actual operationaldata.
In order to implement vehicle relocation, some studies have been made to
predict the demand from users, and determine where to relocate. Luo et al. [8]extracted features from a graph representing various correlations (distance, pointof interest, etc.) between stations of a one-way ECS using multi-graph convolution, and used them to predict the demand for vehicles per station. Yu et al. [20]performed LSTM-based vehicle demand forecasting using temporal features suchas time of day, day of week, and weather. Huo et al. [5] used real-time data as
input and built a data-driven optimization model that combines a stochastic
expectation model and a linear programming problem to deal with the vehicle
relocation problem. Wang et al. [16] used application log data of ECS users,
historical usage data, as well as real-time station data and user’s personal data.
Wang et al. [13] proposed a method to improve the operational efficiency of ECS
by dynamically changing the frequency of demand forecasting in ECS according
to the amount of demand at each time of day.
On the other hand, there are several previous studies on predicting vehicle returns, which is another necessary element for vehicle relocation. Wang et al. [15]constructed a model to predict which stations are likely to return vehicles by
calculating trajectories with high similarity by matching real-time GPS trajectory data with historical trajectory data. Liu et al. [7] utilized real-time vehicle
trajectory data and application log data in addition to historical usage data tocalculate the probability that a user will return a vehicle within 15 minutes andthe probability of returning a vehicle to stations that can be reached within 15
minutes. The method proposed in their paper calculates the probability that auser will return a vehicle within 15 minutes and the probability of returning avehicle to each station that can be reached within 15 minutes.
However, while there have been studies on vehicle return prediction based onreal-time vehicle data, to the best of the authors’ knowledge, there have been
no studies that predict the return station and return time from the time a user
starts using a vehicle, and that continue to improve the prediction accuracy overtime by updating the prediction results during the user’s use of the vehicle. In
this study, we propose a method for predicting vehicle returns at regular intervals
using static information such as the start time of use and the starting station,and dynamic information such as the real-time movement direction vector andlocation of the vehicle.

3

Challenges of a one-way ECS

3.1

One-way ECS

A one-way ECS is a type of ECS that allows vehicles to be returned to a stationdifferent from where they were rented. As described in Section 1, the vehicle

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Demand Prediction Model
Deployment Prediction Model

DemandDifference
Vehicle Relocation
Usage Data
Demand Data

Date, Positioning
Vehicle Data

The Past

Deployment

Present

FutureFig. 1. Conceptual diagram of vehicle relocation in a one-way ECS.

uneven distribution problem occurs in one-way ECSs. This is due to the factthat the demand for vehicles is unbalanced temporally and spatially, resulting
in stations with no vehicles or overflowing with vehicles. There are two ways
to solve this uneven distribution problem: operator-based vehicle relocation, in
which personnel hired by the system operator relocate vehicles, and user-based
vehicle relocation, in which users are requested to reallocate their vehicles.
3.2

Vehicle Relocation

In order to perform appropriate vehicle relocation, it is necessary to predict the
future utilization demand and vehicle deployment in the ECS. Fig. 1 shows therelationship between vehicle relocation and the prediction of utilization demandand vehicle deployment in a one-way ECS. As shown in the figure, vehicle relocation is possible by predicting both vehicle demand and vehicle deployment.
The demand prediction is necessary to determine in advance which stations
will have a high concentration of demand in the future and to determine thenecessity of relocation. On the other hand, vehicle deployment prediction isnecessary to reduce unnecessary relocation of vehicles in combination with future
demand predictions. For example, suppose a situation where a large demand ispredicted for a certain station in the future. If the future vehicle deployment is
not predicted, it is not known how many vehicles currently in use will be returnedto that station, and unnecessary relocation of vehicles may occur. This would
result in extra operational costs, and thus reduce the operational efficiency of
the ECS.
3.3

Vehicle Deployment Prediction

Vehicle deployment prediction can be achieved by predicting when and wherea vehicle in use will be returned to a station based on historical and real-timeReturn Prediction Model for One-Way Car Sharing

5

vehicle usage data. In this paper, we define this as “Vehicle Return Prediction”,
and in particular, we define the task of predicting where a vehicle in use will
be returned as “Return Station Prediction”, and the task of predicting when avehicle will be returned as “Return Time Prediction”. Even if the return station
prediction can be done with high accuracy, if the time of return is not known
accurately, the future deployment of vehicles will be ambiguous. In the opposite
case that the place of return is unclear and the time of return is accurate, it will
be assumed that the future placement of vehicles will be incorrect. Therefore,both of these prediction tasks must be performed with high accuracy for proper
vehicle return prediction.

4

Proposed method

We assume that for accurate vehicle return prediction, it is necessary to take
into account the real-time direction of vehicle movement and the time since
the vehicle was first boarded. Thus, we propose a return prediction model thatpredicts vehicle returns using real-time vehicle data, aiming to realize optimalvehicle relocation for solving the vehicle uneven distribution problem.
4.1

Summary of Proposed Methodology

The proposed method predicts the return of a user’s vehicle every 5 minutes
from the start of the user’s use of a vehicle. The vehicle return prediction isbased on static information such as the user’s departure station and departuretime, and dynamic information such as the vehicle’s location and direction of
movement, using machine learning. Note that the return station prediction andreturn time prediction are performed using different models, and therefore twomachine learning models are used in the proposed method.
4.2

Features Used in The Model

Table 1 shows the information used to create the return prediction model. The
Start slot indicates the time slot in which the user used the vehicle. In this study,
one day is divided into 20-minutes time slots, and 72 slots are assumed per day.
The User ID is an ID that identifies each user of the ECS. The Ori station andDst station are the stations where the user started and finished using the ECS
vehicle. In this study, each station is encoded as a number. The Day of week isthe day of the week when used. We encoded Day of week as integers from 0 to6, e.g., Monday is encoded as 0. The Elapsed time indicates the time elapsed
since the start of use. The unit of Elapsed time is minutes. The Latitude andLongitude are the GPS position information at the time the data was saved. The
Movement direction is the calculated azimuth angle moved from 5 minutes ago
when the north direction is 0[°]. The Total usage time is the total time a user
uses the ECS.

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Table 1. Variables in return prediction models.

ColumnVariable

Unit

Total usage time
Objective Minutes
Dst station
Objective
IntegerStart slot
Explanatory Integer
User ID
Explanatory Integer
Ori station
Explanatory Integer
Day of week
Explanatory Integer
Elapsed time
Explanatory Minutes
LatitudeExplanatory Fractional
Longitude
Explanatory Fractional
Movement direction Explanatory Fractional

4.3

Return Station Prediction Model

The return station prediction model was created by training XGBoost [2] on aclassification task. For the return station prediction, the Start slot, User ID, Ori
station, Day of week, Elapsed time, Latitude, Longitude, and Movement directionin the collected data were set as explanatory variables, and the Dst station was
set as the objective variable.
4.4

Return Time Prediction Model

The return time prediction model was created by training XGBoost on a regression task. The return station prediction used the same explanatory variables as
the return station prediction, but the objective variable was changed to Total
usage time. It is assumed that the actual time of return is calculated using thetotal usage time and the start time of usage predicted by the model

5

Evaluation

The evaluation experiment was conducted using the one-way ECS service data5
operated by the Nara Institute of Science and Technology (NAIST). We usedthe data between June 2020 and May 2021 to train two machine learning modelsof the proposed method and used the data from June 2021 to test the models.
The return station prediction model, which is a classification task, was evaluatedusing the confusion matrix and the F value for each class. The regression task,
return time prediction model, was evaluated using Mean Absolute Error (MAE)and Mean Absolute Percent Error (MAPE), which was introduced to correctly
evaluate the importance of the same error for long and short total usage.
5

https://naist-carshare.github.io/

Return Prediction Model for One-Way Car Sharing

7

Number of usage

200
150
100
50

202

0.0
202 6
0.0
202 7
0.0
202 8
0.0
202 9
0.1
202 0
0.1
202 1
0.1
202 2
1.0
202 1
1.0
202 2
1.0
202 3
1.0
202 4
1.0
202 5
1.06

0

PeriodFig. 2. Usage amount for each month.
Table 2. Number of times used at each original/destination station.
Dst
School Station Facility
Ori
School 1208
202
28
Station 215
33
13
Facility 16
21
24

5.1

Data Collection

The data for this evaluation was collected from a one-way ECS that is in operation at NAIST. This is operated on a scale of three vehicles and three stations in
total operation. The three stations are located in a school, near a railroad station, and near a research facility, respectively, and are referred to as “School”,“Station”, and “Facility”. Each vehicle operated by the ECS is equipped with a
GPS module, and GPS positioning data is stored every minute when the vehicle
is in operation and available. The ECS is managed with a time slot similar to theone assumed in Section 4.1. Users bid for the time slots they wish to use using
their own tokens. The user who has bid with the most tokens at the time of thestart of the time slot wins the right to use the service. the number of usage foreach month is shown in Fig. 2, and the number of times each station is used as
an original and a destination is shown in Table 2.
5.2

Evaluation of Return Station Prediction

Prediction Result of Each Stations Table 3 shows the results of the return
station prediction model for the test data, evaluated separately for each station.

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R. Saze et al.
Table 3. Prediction results of the return station prediction model.
Precision Recall F-value
SchoolStationFacility

0.94
0.77
0.96

0.98
0.58
0.84

0.96
0.66
0.90

True Label

SchoolStationFacility

SchoolStationPredicted Label

Facility

Fig. 3. Confusion matrix of return station prediction model’s precision.

This table shows that the station prediction model is able to predict stations
with a high degree of accuracy. In fact, the percentage of correct predictions forthe entire test data was as high as 0.93. The reason for this high accuracy canbe attributed to the large number of usage for School in the test data, and thehigh accuracy of the prediction for the use where the destination station is a
School, as can be seen from the table.
Also, Fig. 3 shows a confusion matrix. Figure shows that the predictions werehighly accurate when the target station was a Facility. The reason for this is that
only a small number of users used the Facility as their destination station in theusage data, and the high accuracy is thought to be due to the fact that theUser ID features were used as input. However, when the destination station is
the Station, the prediction accuracy is lower than in the other cases. The reason
for this is yet to be clarified, and future efforts should be made to improve theaccuracy by adding distance information to each station as a feature value.
Time-series Change of Station Prediction Results The proposed method
updates time-series information, such as the Movement direction from the startof use to the end of use, and makes predictions. Intuitively, the closer the endof use is, the closer the predicted return station is to the target station, so
the prediction accuracy should improve. In fact, about half of the incorrectReturn Prediction Model for One-Way Car Sharing

100

MAE
MAPE

140
120
100

60

80
40

60

MAPE[%]

MAE[min.]

80

9

40

20

20

0

0
0-60

60-120 120-180 180-240 240-300 300-360
Time range[min.]Fig. 4. Results of the return time prediction model for each time-range.

predictions, that were made at beginning of the use, actually became correct
near the end of the use. Therefore, it is possible to improve prediction accuracy
by continuing to update features and make predictions in a time-series manner.
In the future, it is necessary to confirm what characteristics (travel route, userID, etc.) are observed in these cases in which the predicted results did not change
to correct ones.
5.3

Evaluation of Return Time Prediction

Accuracy of Return Time Prediction The prediction results of the return
time prediction model for all test data were 42.3 min. for MAE and 47% for
MAPE. Fig. 4 shows the results of comparing MAE and MAPE for the usage
divided by the Total usage time. The bar graph in the figure shows MAE and theline graph shows MAPE. From the results of Fig. 4, MAPE was high at 146%
for use within 60 minutes. This is a reasonable value considering that the MAE
is 42.6 min, but considering the application to a real system, the value needs
further improvement.
On the other hand, for the use between 60 and 180 minutes, the MAE wasabout 31 min. in both cases, and the MAPE was 34% and 22% lower than in theprevious time period. In June 2021, the median of the total time of use 6 was
1.6 hours, and in nearly half of the cases, the Total usage time was between 60
and 180 minutes. This indicates that a practical model for predicting the return
time has been obtained.
For data with a Total usage time of 180 minutes or more, the MAE is considered to be larger than in the above case because there was less training data.
On the other hand, the MAPE was about 40%, which is thought to be due to6https://naist-carshare.github.io/logs/2021-07-02-log-202106/

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400
350
300

True Value

250
200
150
100
50
0

0

50

100

0

50

100

150

200

250

300

350

400

150

200

250

300

350

400

Predicted Value
Fig. 5. Result for usage within 15 minutes from the start of use. This result shows themodel’s Total usage time prediction results for the input data within 15 minutes of thestart.
400
350

True Value

300
250
200
150
100
50
0

Predicted Value

Fig. 6. Result for 15 minutes after the start - 15 minutes before the end. This resultshows the model’s Total usage time prediction results for the input data from 15 minutes after the start to 15 minutes before the end.
400
350
300

True Value

250
200
150
100
50
0

0

50

100

150

200

250

Predicted Value

300

350

400

Fig. 7. Result for within 15 minutes from the end of use. This result shows the model’s
Total usage time prediction results for the input data within 15 minutes of the end.

Return Prediction Model for One-Way Car Sharing

11

the fact that the increase in Total usage time was larger than the increase inprediction error.

Time-series Changes in Time Prediction Results Figs. 5, 6, 7 shows theprediction results of the return time prediction model within 15 minutes of theuser’s start of use, from 15 minutes after the start to 15 minutes before the end,
and within 15 minutes of the user’s end of use. The closer to the diagonal linefrom the lower left to the upper right of the graph, the more accurate the prediction results are. In the Fig. 5, the model predict shorter use times than thetrue value in many cases. Fig. 6 show an increase in density around the diagonalcompared to Fig. 5. Furthermore, Fig. 7 were clearly more concentrated aroundthe diagonal than those for the earlier time periods. This indicates that the prediction accuracy of the actual return time improves with the Elapsed time of use.
Therefore, it is shown that the proposed method improves prediction accuracy
over time. On the other hand, there were cases where large prediction errors
occurred even within the last 15 minutes of use, so it is necessary to improve thereturn time prediction model by analyzing the characteristics common to these
uses.

6

Conclusion

In this study, we propose a vehicle return prediction model that is necessary forvehicle relocation to solve the vehicle uneven distribution problem in a one-way
ECS. The proposed method performs vehicle return prediction by adding realtime vehicle location and direction information in addition to the user’s past
usage history, and keeps updating the prediction results while the vehicle is inuse. The evaluation results showed that the proposed method achieved a highaccuracy rate of 0.93 for the entire test data in the return station prediction.
MAE and MAPE were smaller for the 60 to 180 minute time period than for
the earlier time period. Furthermore, we confirmed that the prediction results
became more accurate as the time of use increased.
Future plans include adding features such as the relative distance to eachstation to solve the problem that the return station prediction model is inaccurate for specific station. In addition, to solve the problem that the prediction
accuracy of the return time prediction model does not improve even at the endof use for some users, it is necessary to analyze the characteristics of these usage. Furthermore, since the data used in this evaluation is for a small ECS, it isnecessary to investigate whether similar results can be obtained for a larger ECS
with a different station configuration. In addition, we would like to study vehicle relocation methods that use the proposed method, focusing on user-based
vehicle relocation with incentives as described in the literature [12].

12R. Saze et al.

Acknowledgment
This work was supported in part by the Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research number JP21H03431 and JP21K11879.

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