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Machine Learning-based Wait-time Prediction for
Autonomous Mobility-on-Demand Systems
Trevor Hillsgrove
Florida Polytechnic University
4700 Research Way
Lakeland, FL, USA
thillsgrove@floridapoly.edu
Robert Steele
Florida Polytechnic University
4700 Research Way
Lakeland, FL, USA
rsteele@floridapoly.edu
Abstract— The development of more sophisticated autonomous
course-determination mechanisms for Autonomous Mobility-on-
Demand systems is an active area of research and development. In
the case of traditional ridesharing systems, there are various
factors to be considered such as efficient use of vehicular assets,
minimizing passenger wait times and selecting course of travel. In
this paper we consider the use of machine learning to aid the
selection of a destination that is predicted to result in a lower wait
time until the next rideshare ride request will occur in the vicinity
of a trip’s destination. We draw upon a real-world ridesharing
dataset to build and evaluate predictive machine learning models
to provide an exploratory analysis of the utility of this approach to
destination selection and demonstrate promising performance.
Keywords— data mining, mobility, mobility-on-demand,
autonomous vehicles
I. INTRODUCTION
In conventional vehicular systems, selection of the
destination or course of travel is the result of a complex
assessment by a human operator. Typically, this is based largely
on the current travel needs of a vehicle’s operator or users. In
the case of ridesharing systems, the selection of destination, or
course, or other ‘mission planning’, can be based upon the
system-wide needs of a population of users and the efficient
utilization goals of the rideshare service.
In this paper we consider the particular case of ridesharing
in an Autonomous Mobility-on-Demand (AMoD) system
[1][2]. Ridesharing to-date has typically involved vehicles
owned and operated by members of the public, but in the case
of AMoD systems it is anticipated a fleet of autonomous
vehicles can be utilized. An autonomous vehicle would travel
to a specific location requested by a client/passenger, and then
transport the passenger to a destination location indicated by the
passenger, potentially sharing the vehicle with other passengers
also using the service, along the way from source to destination.
In the case of AMoD, the approach to mission planning can be
sophisticated and make use of algorithmic optimization and
predictive approaches to improve local and system-wide
performance. AMoD systems are complex systems in which the
mission choices can be based upon invariant data, system-wide
parameters, real-time data and historical data.
There are a range of existing modeling and control
approaches [3], but none of these completely addresses the
complexity of such systems. In particular in this paper, we
consider the specific problem of being able to predict via
machine learning techniques, for a specific rideshare trip at the
time the trip starts and ends, how long it will be until there will
be another rideshare request in the vicinity of the endpoint of
the rideshare trip. In this case, vicinity is defined as having the
same ZIP code.
The information to know this deterministically is not within
the AMoD system, as passenger requests for a vehicle are not
known to the system until requested. For that reason we draw
upon a machine learning-based predictive approach. We
develop and evaluate this approach based upon a real-world
rideshare dataset generated from the usage activity of Ride
Austin [4] a non-profit ridesharing service located in Austin,
Texas.
MoD systems tend to lead to a build-up of vehicles
concentrated in certain areas due to the greater popularity of
some destinations, leading to the inefficiency of additional trips
needed to rebalance the vehicle locations [3]. One of the
benefits of being able to predict the wait time of autonomous
vehicles for a given location and point-in-time, is that it can aid
in the autonomous decision as to if, when and to where
autonomous vehicles should relocate to after completing a trip.
Specifically, it assists in two problems:
1) assisting a vehicle to decide at the point of pick-up of
a passenger, how long they may need to wait after
dropping the passenger off. This can be an input
variable to individual vehicle or system-wide pick-up
decision making algorithms
2) at the time that a passenger has been dropped off, this
provides a data input to decide whether a vehicle
should move to ‘rebalance’ the locations of the fleet
vehicles or wait for a next pickup in the current
vicinity
In this exploratory study of this machine learning-based
approach we demonstrate that this approach can generate good
predictive performance, and this mechanism for intelligent
decision making in relation to the vehicular resources has not
previously been addressed in the literature. The exploratory
study suggests that this approach can offer benefits in creating
AMoD systems able to incorporate predictive models into local
and globalized decision making. Such an addition should be
differentiated from course decision-making algorithms that are
hand-modeled based upon static data or real-time data inputs.
The remainder of the paper is structured as follows. Section
II reviews the most recent relevant literature, Section III
describes the methodology used in evaluating this approach to
wait-time prediction. Section IV provides the results of the
evaluation. Section V discusses the technique and its
implications and applicability. Finally, this is followed by the
conclusion of the paper.
II. LITERATURE REVIEW
Our review of the literature indicates that there has yet to be
an approach based upon machine learning techniques to predict
time until next ride request in ridesharing or AMoD systems.
Many of the existing approaches to modeling such systems
are based upon queue-based system modeling, optimization and
also suffer the limitation of providing guidance based upon
static system parameters such as vehicle numbers.
Examples of existing works include predictive positioning
for Mobility on Demand (MoD) systems where a limited
number of autonomous vehicles are available to provide rides
to customers, so the objective as to improve customer quality of
service (QoS) in terms of minimizing customer wait times [5].
In this 2017 study based upon the MIT on-campus MoD system,
the authors identify the use of machine learning as a future
direction to improve customer QoS but do not utilize it within
that study.
Iglesias et al. [6] draw upon 2018 work at Stanford
University to formulate a Model Predictive Control (MPC)
approach to controlling autonomous vehicles within an MoD
system. This MPC approach predicts short-term future
customer demand utilizing an optimization algorithm rather
than a machine learning approach and does not do this in a
vicinity-specific way.
The proposed SAMoD system, Nov. 2018 [7] utilizes a
reinforcement learning approach to addressing shared
autonomous mobility-on-demand. In particular this work
considers a decentralized approach to determining vehicle
relocation in the case of a ridesharing scenario.
Another approach to vehicle balancing published in 2017
[8] utilizes dynamic region partitions and when evaluated on a
taxi ride dataset, demonstrates the average total idle driving
time is reduced by 30%.
Pendleton et al. (2017) [9], provide a survey paper
originating from the Singapore-MIT Alliance for Research and
Technology, in relation to perception, planning, control and
coordination of autonomous vehicles. In relation to planning,
the overarching area of this current work, the authors of this
2017 paper identify three sub-categories of planning, namely:
mission planning, behavioral planning and motion planning. In
particular, this current work tackles one problem in relation to
mission planning; mission planning deals with such high-level
tasks as pickup/ dropoff decision making. The Pendleton et al.
survey paper does not identify prior works applying machine
learning to mission planning.
The challenge of machine learning-based wait time
prediction for a given vicinity, the topic of this current paper, is
a valuable problem and yet to be addressed elsewhere in the
literature.
III. METHODOLOGY
In this exploratory study we are interested to evaluate
whether machine learning can be used to effectively predict
time until next ride in a given location and at a specific point in
time. While various ‘rules of thumb’ used by human drivers
may provide insights and judgements into this for given
circumstances, for a whole city or urban area, it would be
beneficial to be able to predict this systematically,
automatically and at any point in time for an AMoD service,
using real-time predictive model inference upon models, likely
trained off-line, to assist local vehicle or AMoD system-wide
decision making.
AMoD data log records are still in their early stages and in
some cases proprietary, with the widespread deployment of
AMoD systems yet to occur, and in particular large-scale
openly available AMoD datasets are currently not available. For
the purposes of this approach we require a large dataset that
represents real-world usage in a large urban environment.
Hence for this exploratory study, we utilize a historical dataset
of a real-world, ridesharing service that contains attributes that
would be minimally available to any future AMoD system as a
mechanism to evaluate the base effectiveness of this general
approach upon a specific urban area-rideshare service
combination to consider its potential broader applicability for
AMoD systems. We chose a dataset from the ridesharing
service RideAustin [3] as it had a richer attribute set than such
data as we examined made available by Uber or other current
commercial ridesharing services.
A. Data source
We draw upon a dataset made public by the non-profit
Austin-based ridesharing service called RideAustin [3]. The
dataset initially consists of 911,057 ride records from the
RideAustin service from June 2016 to February 2017. The
dataset initially contains 28 data attributes for each rideshare
trip record, which include such information as the time the trip
was completed (day/hour/min/second), distance traveled, end
location latitude, end location longitude, when the trip started
(day/hour/min/second), the rating of the driver for the trip, the
rating of the rider for the trip, the trip start ZIP code, the trip end
ZIP code, the category of car requested, some billing attributes
such as free credit used, the surge factor, the start location
latitude, the start location longitude, various car-related
attributes such as color, make, model, year, and various
attributes related to the weather on that day such as amount of
precipitation, maximum temperature, minimum temperature,
average wind, wind gust speed, and whether there was fog,
heavy fog or thunder.
In its initial form, the dataset describes the rideshare trips
offered by this service during that period combined with a set
of attributes providing external contextual information, the
pertinent weather information in this case.
B. Data Preparation
From the initial dataset, a number of data cleaning and data
preparation activities were then carried out. First this involved
the removal of a number of duplicate ride records in the dataset.
Such removal was of records readily identifiable as duplicates
as they had identical start and end times to the second and all
other attributes also the same.
Additionally, as monthly ride frequency significantly rose
during the months of initial start-up of the service in mid-2016,
a subset representing a ‘steady-state’ month was chosen at
which point the ride volumes were high, and that month chosen
was January 2017.
As a prerequisite step towards model training and evaluation
a complex data engineering task was carried out, that involved
the creation of a new derived target attribute for the machine
learning process, that is:
● Wait time until next pickup in the vicinity (Wi): for
each row/ride Ri, that has a trip end ZIP code of Zi and
completes at time Ti, a new target attribute is created
for ‘wait time’, Wi, that is equal to the amount of time
after Ti before the next earliest ride departed from that
same destination zip code of the previous completed
trip, Zi
With this data engineering step, it facilitates the building of
predictive models for wait time prediction. Subsequently a
random subset of 50,000 rows of this newly engineered dataset
corresponding to a random sample of the January 2017 rides
was created. This size subset was chosen to decrease model
training and evaluation times.
A number of attributes were then removed as not applicable
to predicting wait time: these included car make, car model, car
year, car color, charity_id, free_credit_used, driver rating,
rating and rider rating. Additionally the median wait time in the
dataset was now calculated. Additionally, after wait times had
already been calculated to the second, time values such as time
of completion of ride and time of start of ride were converted
into a single hour value (in 24 hour time) to effect a binning of
ride records around hours of the day.
Finally two different versions of this dataset were created,
with all of the same input attributes but different versions of the
target attribute:
● Classification dataset (CD): the target attribute is a
binary nominal value, set to 1 for rows with a wait time
greater than or equal to the median wait time, and set
to 0 for rows with a wait time less than the median wait
time. This is a dataset of 50,000 rows.
● Regression dataset (RD): the target attribute is a
numerical value equal to the wait time in seconds. This
is a dataset of 10,000 rows, further chosen as a random
subset of the 50,000 rows to accommodate the greater
computational load and time needed for regression
model training
In these datasets there are 20 attributes, including the target
attribute as shown in Table I.
C. Data Exploration
An initial exploration of the 50,000 row dataset provides the
following simple descriptive statistics. The mean distance
travelled was 7,999 meters or approximately 8 kilometers, and
the mean wait time was 209 seconds, or approximately three
and a half minutes.
D. Model Development
An open source machine learning toolkit [10] was used to
develop numerous classifiers for the CD dataset and numerous
regressors for the RD dataset. These were created using 10-fold
cross validation on the 50,000 row CD dataset and similarly
using 10-fold cross validation upon a random subset of 10,000
rows from the RD dataset.
Numerous classifier base models of such types as Bayesian,
instance-based learners (such as k-Nearest Neighbors and
KStar), decision trees (such as C4.5) and rule-based (such as
Decision Table) were initially trialed for the CD dataset and
then based upon the best performing of these base models,
various ensemble model variants of the high performing base
models were also developed, based upon such meta approaches
as bagging, boosting and stacking [11]. The Bayesian models in
general performed significantly better in terms of predictive
performance.
For boosting we utilized the Ada Boost (adaptive boosting)
meta-algorithm [12]. While this is often utilized with a decision
tree base model, however the boosted decision tree models that
we trialled in this case performed significantly worse than the
Bayesian models.
For the RD dataset, various base regression models were
trialed including Support Vector Machine (SVM) regression,
instance-based approaches such a K-Nearest Neighbors and
KStar, linear regression, decision tables and decision trees such
as C4.5 and REPTree. Again for the best performing of these
base models, additional ensemble model variants were also
trialed. In terms of regression models, a wider range of base
models demonstrated higher performance levels.
TABLE I. MODEL ATTRIBUTES
ATTRIBUTE NAME
DESCRIPTION
completed_on
Hour of day completed, in 24
hour time
distance_travelled
Distance in meters
end_location_lat
Latitude of end of trip
end_location_lon
Longitude of end of trip
started_on
Hour of day trip started, in 24
hour time
start_zip_code
ZIP code where trip started
end_zip_code
ZIP code where trip ended
requested_car_category
Category of car requested,
REGULAR, SUV, LUX,
PREMIUM
surge_factor
15 distinct surge factor values
start_location_lat
Latitude of where trip started
start_location_lon
Longitude of where the trip
started
PRCP
Precipitation in millimeters
TMAX
Maximum temperature of the
day
TMIN
Minimum temperature of the
day
AWND
Average wind speed
Gustspeed2
Speed of wind gusts
Fog
Fog level described by 9
distinct values
HeavyFog
Binary indicating heavy fog or
not
Thunder
Binary indicating presence of
thunder during day or not
WAIT
For CD: binary, indicating
either above or below median
wait time
For RD: exact wait time in
seconds
E. Evaluation
A number of different performance evaluation metrics were
captured for each type of model trained. For each classifier the
Area Under the Receiver Operator Characteristic curve (AUC),
the True Positive (TP) rate (weighted between the two classes)
and the Area Under the Precision-Recall Curve (AUPRC) were
noted.
For the regression models Correlation Coefficient and Mean
Absolute Error (MAE) were captured.
IV. RESULTS
The performance of the best performing classifiers
developed and evaluated with 10-fold cross validation, using
the 50,000 row CD dataset, are summarized in Table II.
TABLE II. CLASSIFIER PERFORMANCE - 10-FOLD CROSS VALIDATION ON
50,000 ROW CD DATASET
MODEL
AUC
TP
WEIGHTED
AUPRC
BAYES NETWORK
0.821
0.744
0.812
BOOSTED BAYES NETWORK (ADA
BOOST)
0.825
0.752
0.810
BAGGED BAYES NETWORK
0.821
0.744
0.812
NAIVE BAYES
0.804
0.726
0.785
BOOSTED NAIVE BAYES (ADA
BOOST)
0.824
0.754
0.809
BAGGED NAIVE BAYES
0.804
0.726
0.786
STACKING - META: NAIVE BAYES,
STACKED MODELS: NAIVE BAYES,
BAYES NET, ONER
0.827
0.750
0.815
Fig. 1 and Fig 2. provide the AUC curves (in terms of the
low delay class and high delay class respectively) for the best
performing classifier, the Stacking model listed in Table II. The
OneR model referred to as one of the base models used by the
Stacking model is a simple rule-based model that uses a rule
based upon just one input attribute that best predicts the target.
FIG. 1: ROC THRESHOLD CURVE FOR STACKED MODEL - META: NAIVE
BAYES, STACKED: NAIVE BAYES, BAYES NET, ONER - LOW DELAY TIME
FIG. 2: ROC THRESHOLD CURVE FOR STACKED MODEL - META: NAIVE
BAYES, STACKED: NAIVE BAYES, BAYES NET, ONER - HIGH DELAY TIME
The best performing regression models developed and
evaluated using 10-fold cross-validation, on the 10,000 row RD
dataset are shown in Table III.
The MAE value is given in seconds.
V. DISCUSSION
The best performing classification models achieve an AUC
of over 0.82, with the stacking approach (see Table II)
achieving the highest AUC of 0.827. This would be considered
a good level of discriminative performance for a model, with
above 0.8 considered ‘good’ and above 0.9 considered
‘excellent’ predictive performance [13].
TABLE III. REGRESSION MODEL PERFORMANCE - 10-FOLD CROSS
VALIDATION ON 10,000 ROW RD DATASET
MODEL
CORRELATION
COEFFICIENT
MEAN ABSOLUTE
ERROR (SECS)
SVM REGRESSION
0.355
145.8866
LINEAR REGRESSION
0.3282
160.683
K-NEAREST NEIGHBORS
0.2872
195.1336
REPTREE
0.5546
154.7369
DECISION TABLE
0.5464
160.5489
REPTREE - BAGGING
0.5747
148.8249
ADDITIVE REGRESSION -
DECISIONTABLE
0.5871
156.4668
MULTISCHEME: ADDITIVE
REGRESSION -
DECISIONTABLE + REPTREE
- BAGGING
0.5782
148.1987
MULTISCHEME:
DECISIONTABLE + REPTREE
0.5412
155.9432
STACKING - META:
DECISIONTABLE, STACKED:
REPTREE, DECISIONTABLE
0.5591
157.2248
RANDOMSUBSPACE -
REPTREE
0.5836
158.1587
RANDOMSUBSPACE -
DECISIONTABLE
0.5695
166.9425
This indicates that the machine learning technique, even for
a basic set of available attributes that would be available in any
anticipated AMoD system is able to predict with high accuracy
at the coarse level of high vs low wait time until next ride
request for that vicinity (ZIP code), either at the point of
preceding passenger pickup, or at the time of completing the
passenger drop off. It could be anticipated that emerging AMoD
systems will additionally capture far more per-ride/per-request
data attributes that have the potential to improve upon
predictive model performance.
The best performing regression model in terms of
correlation coefficient is Decision Table-based Additive
Regression, achieving a correlation coefficient of 0.5871. Such
a result might be considered on the lower boundary of a ‘strong
correlation’ [14]. It achieves an MAE of 156.5 seconds. Other
similarly performing regression models include Random
Subspace - REPTree and Bagged REPTree with correlation
coefficients of 0.5836 and 0.5747 respectively. With MAEs in
the range of 150 seconds (approximately two and a half
minutes), what may be a relatively small amount of time
compared with that required typically to relocate an
autonomous vehicle to another ZIP code, this suggests the time-
to-wait prediction may have value in the decision as to whether
to relocate or not, or to where. It should be noted that the
regression model with the lowest MAE was SVM regression
achieving an MAE of 145.89 seconds.
Here we have concerned ourselves with evaluating the
predictive performance of exploratory or demonstrator models,
but various techniques can also draw upon these predictive
models so as to integrate the uncertain knowledge provided by
these or combine the uncertain predictions of multiple models
[15][16].
The results suggest that models, even based upon a limited
number of per-trip data attributes, can provide good predictive
performance.
A. Generalizability of Models
Such trained models for MoD or AMoD systems are
inherently city/region or geography specific, and also service
specific. That is, for a given AMoD system, the predictive
model would be trained from recent service usage data for each
particular city/region. That is, the model is specific to a given
city’s demand patterns. Such models would typically be trained
off-line on historical data, and then be used in real-time
inference to assist pickup and mission decision-making. It is
possible to update the training of the models fairly continuously
as new AMoD service data becomes available for that area.
Low-latency, fog computing-based architectures may provide
the distributed architecture for real-time inference
computation[17]. There is the potential in future work to extend
the training datasets with additional contextual attributes such
as fine-grained neighborhood-specific population numbers or
in-area workforce or pedestrian numbers or the potential for
ridesharing services to draw in desired attributes through such
customizable data collection approaches as crowdsensing [18].
The models are AMoD service specific in that they are
dependent upon the customer demand patterns of the service
users. Such demand patterns may have similarities or
differences between services, depending on whether the
customer bases differ in their location distribution and typical
usage/travel patterns.
A possible immediate future step is to consider such models
based on class of vehicle service-specific datasets. That is, the
demand patterns for different service levels will be distinct.
VI. CONCLUSION
In this paper we have described an exploratory study
demonstrating and evaluating the efficacy of machine learning-
based predictive models in predicting wait time until the next
ride request in a given vicinity for Autonomous Mobility-on-
Demand systems. Given the lack of available large-scale
historical AMoD trip datasets, we have demonstrated and
evaluated this approach on a real-world, ridesharing service
dataset, provided from such a service in Austin, TX. The
predictive performance demonstrated is good both in terms of
the classification and regression models developed, suggesting
the value and promise of extending such an approach to future,
more attribute-rich AMoD ride datasets.
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