# The Potential of Ridesharing Adoption and its Effects on CO2 Emissions and
Customer Experience

Maximilian Kaufmann  Deep Dynamics Group, Frankfurt School of Finance &
Management, Frankfurt, Germany
Centre for Human and Machine Intelligence, Frankfurt School of Finance &
Management, Frankfurt, Germany  Jan Nagler  Deep Dynamics Group, Frankfurt
School of Finance & Management, Frankfurt, Germany
Centre for Human and Machine Intelligence, Frankfurt School of Finance &
Management, Frankfurt, Germany

###### Abstract

Taxi services are an integral part of urban transport and are a major
contributor to air pollution and traffic congestion, which adversely affect
human life and health. Sharing taxi rides is one way to reduce the unfavorable
effects of cab services on cities. However, this comes at the expense of
passenger discomfort, quantified in terms of longer travel times. Taxi
ridesharing is a sophisticated mode of urban transport that combines
individual trip requests with similar spatiotemporal characteristics into a
shared ride. We propose a one-to-one sharing strategy that pairs trips with
similar starting and ending points. We examine the method using an open
dataset with trip information on over 165 million taxi rides. We show that the
cumulative journey time can be reduced by 48 percent while maintaining a
relatively low level of passenger inconvenience, with a total average delay
compared to an individual mobility case of 6 minutes and 42 seconds. This
advantage is accompanied by decreases in emissions of 20.129 tons on an
ordinary day and a potential fare reduction of 49 percent, which could point
to a widespread passenger acceptance of shared taxi services. Overall, a
matching rate of 13 percent is reached while a 27 percent matching rate is
attained for high-demand areas. Compared to many-to-many sharing dynamic
routing methodologies, our scheme is easier to implement and operate, making
fewer assumptions about data availability and customer acceptance.

##  1 Introduction

One of the biggest problems facing cities worldwide is vehicular traffic
congestion and the resulting air pollution. It has a high price in terms of
money and life [26].

The widespread use of private cars to meet daily mobility needs is one of the
most significant issues with managing the planet’s finite resources. In
addition to being utterly inefficient, it involves massive unneeded material
transport, excessive energy waste, urban traffic jams, air pollution, and the
exploitation of human resources for driving cars rather than engaging in paid
employment or leisure activities [15].

Meanwhile, the World Health Organization estimates that outdoor air pollution
is one of the significant risks for non-communicable diseases, which is
primarily brought on by vehicular traffic and is responsible for over three
million deaths per year worldwide. About ninety percent of people living in
urban areas are exposed to levels of fine particulate matter that exceed WHO
Air Quality Guidelines. As New York City constitutes the basis of the cab
trips referred to in this research, we need to mention that air pollution has
significantly decreased in many high-income countries, including the US,
because of efforts to reduce smog-forming emissions as well as particulate
matter [32].

Another major problem that almost every populous city must contend with is
congested roads, from which, in addition to higher pollution levels, economic
losses result [30]. According to a study from the Partnership of New York
City, the annual cost of traffic congestion in the New York metropolitan area
for 2018 alone is estimated to be 20 Billion USD [25]. To curb this issue to
some extent, The City Council of New York City is considering introducing a
tolling program that would price every trip into southern Manhattan at up to
23 USD [17].

However, with the recent advancements in communication technologies, new
business models and approaches could be installed that have the potential to
address the problems of road congestion and air pollution in urban areas. For
instance, using real-time data creates new opportunities to exploit spare
capacity and enables unprecedented monitoring of the urban mobility
infrastructure. These developments open up the potential to create new, more
innovative transportation systems based on the sharing of cars and effectively
provide services that could replace old public transportation infrastructure
with on-demand taxi sharing. The advantage of the widespread use of
smartphones and their ability to run real-time applications is another driver
for implementing ridesharing services [27]. UberPool or Lyft Shared Rides
provides examples for successfully implementing ridesharing services, which
may raise hopes for a more efficient future.

On the other hand, ridesharing can also come with disadvantages. The overall
service and waiting times may increase, and passengers may be concerned about
the reliability of the shared ride. Moreover, privacy and security issues may
be related to traveling with strangers in a shared vehicle. Lastly, the
potential positive environmental effects must be treated with caution, as the
reduced cost of taxi rides could also lead to increased demand or other
rebound effects ([20], [29]).

Here, we analyze the potential of taxi ridesharing in New York City using an
approach that could be installed in real-life cases. We also address the
effects of such an approach on the environment and ride quality.

The structure of our paper unfolds as follows. Chapter 2 outlines the data
structure and substance. The central part is presented in Chapter 3, where the
one-to-one ridesharing approach is portrayed, and the empirical findings are
described. Chapter 4 discusses the main findings and how a ridesharing
provider may benefit.

#### Ridesharing

When using ridesharing, two or more trip requests with similar origins and
destinations are combined to transport multiple passengers simultaneously in
one vehicle ([1]). By combining different trips into one shared ride,
ridesharing increases the average number of people per vehicle, decreases the
number of vehicles needed to meet the exact demand, and reduces traffic and
other adverse effects of urban mobility on the environment [21].

Taxi ridesharing, also known as shared taxis or collective taxis, is an
innovative mode of public transport with flexible scheduling and routing that
matches, in real-time, at least two different trip requests with similar
spatiotemporal characteristics to a shared taxi [3].

Earlier studies on what we today call ridesharing have focused on small-scale
ride-pooling approaches, which are not based on analyzing taxi trip records
but rather on surveys. One example is an analysis of [4] on one-day data of
commuting trips reported as part of a census survey made in Dublin. Caulfield
found that 4 % of respondents carpooled to work. They estimated that this
ridesharing saved 12,674 tons of CO2 emissions per year.

#### Dynamic Ridesharing

The developments in information and communication technologies have not just
benefitted businesses that could implement a better ridesharing approach using
real-time data. It also allowed the analysis of large-scale data using a
dynamic approach. Dynamic ridesharing allows ridesharing to occur at short
notice and between strangers who do not know each other’s itineraries. The
greater flexibility of dynamic ridesharing provides additional opportunities
to maximize the benefits of sharing and improve the system’s efficiency [19].

In a dynamic ridesharing system, matching the appropriate drivers to form the
shared ride is critical. Therefore, many researchers are focused on developing
algorithms for perfect ride-matching. These calculations get more complex the
more dimensions someone considers [19]. For this reason, one can see that the
recent works [26] differ from the more recent ones [1] regarding the number of
ride requests pooled into one vehicle.

Santi and coworkers [26] introduced the concept of shareability networks and
proposed a mathematical model to quantify the benefits of ridesharing. This
work is seen as a pioneer in dynamic ridesharing as its results and
methodology refer to numerous newer research pieces ([1], [19], [3]). [26]
analyzed taxi trip data in New York City and concluded that ridesharing could
reduce cumulative trip length by more than 40 %. This model restricted the
sharing to a maximum of two ride requests per driver, ignoring the potential
benefits of a more flexible system. Moreover, it assumed that all riders’
tolerance level for journey delays was the same for drivers and passengers.

#### Ridesharing with Autonomous Vehicles

With the rapid development of autonomous vehicles, the interest in the
potential of ridesharing using such automobiles has received significant
attention ([1],[10]). Unlike taxis, whose drivers need to change shifts and
take breaks, autonomous vehicles can be available around the clock. Moreover,
there is no need to consider the taxi drivers’ preferences and waiting times.
Therefore, shared autonomous taxis can offer additional benefits compared to
traditional ridesharing. The high-capacity ridesharing model of autonomous
vehicles by [1] can satisfy up to 99 % of requests, with an average total
delay of 2.5 minutes, by reducing the active operating taxis by 25 %.

#### Main Idea of Static Ridesharing

Although the implementation of a flexible and dynamic method has the potential
to optimally address the problem of sharing taxi rides, these systems reveal
inconveniences in real life scenarios because customers might be reluctant to
accept picking up and dropping off multiple passengers, almost all of them
strangers, during a shared ride [3].

As mentioned, the many-to-many approach relies on complex matching algorithms,
such as optimal routing, combining, and rerouting. Therefore, it could impose
operational challenges for taxi operators [3].

An excellent example of a recent study focusing on ridesharing using a non-
dynamic approach was created by [3]. As an example of large-class spatial
sharing problems, this work proposes a framework that enables the analysis of
fundamental trade-offs between the advantages and drawbacks of taxi
ridesharing systems at the city level. Dividing taxi trips into clusters based
on their pickup and drop-off destinations and grouping after a spatiotemporal
constraint of matched trips, the research indicated that 48% of taxi trips
could be shared. The underlying assumption made implies that every passenger
can walk to an assigned pickup destination if the distance is at most 500
meters. The drop-off of all passengers would happen in the same manner, and
the targeted location must be reached on foot too. [3] argues that this model
makes taxi ridesharing easier to implement. It reduces customers’ perceived
inconvenience by having all passengers meet at the pickup destination and
letting them decide whether to share the ride with the other party [3].

For the reasons named above, the benefits of a static approach may outweigh
those of a dynamic approach. Especially implementing a static model that
considers a multi-ridesharing scenario may be easy to realize for a shared
service provider. Therefore, we employ the ridesharing potential using a one-
to-one model, in contrast to [3], by proposing a system that does not cluster
the demands but uses a grid system of hexagons to define the pickup area.
Moreover, our approach assumes smaller walking distances at the pickup
locations and no walking distance at the drop-off location, assuming that a
city walking difference of a maximum of 500 meters is unrealistic and drop-
offs should be made at every target destination. Overall, this should limit
the total delays. A time penalty for every drop-off is installed to account
for a reasonable drop-off delay.

#### Economic Incentives and Psychological Barriers

Despite the numerous benefits of ridesharing, some challenges need to be
addressed in the analysis to get a realistic understanding of the whole. One
of the main challenges is understanding the factors that drive individuals to
adopt ridesharing and how to effectively encourage more people to use shared
rides instead of traditional modes of individual mobility. The current
understanding of these conditions needs to be improved, making it challenging
to implement effective strategies for promoting the widespread adoption of
ridesharing [29].

Recent research [29] suggests that people considering ridesharing services
weigh four primary incentives when deciding whether to request a single or
shared ride: financial discounts, anticipated detours, the unknown length of
the journey, and the inconvenience of riding in a car with strangers.

[29] predicts a sharp transition to high-sharing adoption for any given user
preferences, implying that even a moderate increase in financial incentives or
a minor improvement in service quality may disproportionately increase
ridesharing adoption of user groups currently in the low-sharing regime under
a variety of conditions.

In our analysis, these significant incentives are well considered. In
particular, we study the relative potential of the fare reduction in a shared
scenario and compare airport fares with total fares showed the possible
financial incentives based on the pickup and drop-off locations.

To determine the resulting delays of this approach, the three primary
variables assessing the trip delay are the size of the pickup and drop-off
hexagon, the time interval or departure interval, and the drop-off rank. For
every drop-off, a ridesharing participant can expect a delay of 20 seconds.

The inconvenience of riding with strangers can also be contained using this
methodology. Every trip passenger, like in public transportation, can first
assess with whom they would potentially share the trip and decide whether they
want to do it. Compared to a dynamic approach, every ridesharing participant
in the vehicle is not forced to share with whoever and whenever they wish [3].

Finally, deciding on the proper drop-off resolution size, only journeys that
concluded at a limited distance range were matched. As a result, we only
consider trips that did not make lengthy detours to other places, and due to
the small size of the drop-off destination, there was no need to add a
distance constraint to reach comprehensive results.

##  2 Data Profiling

The dataset is accessible through NYC OpenData and contains pickup and drop-
off geo-locations in the form of longitude and latitude and the respective
trip dates and times, accurate to the second. Other variables include the
passenger count, payment type, fare amount, extra costs, tax, airport fee,
tolls, tips, and total amount in US-Dollars [5]. The baseline of all initially
considered variables is listed in Table 1.

###  2.1 Data Availability

In order to assess the ridesharing potential using the one-to-one approach, a
publicly available dataset on yellow taxi trips from 2014, published by the
NYC Taxi and Limousine Commission (TLC), is evaluated. The TLC provides data
from 2009 to 2021, including several hundred million journeys conducted by
yellow taxis, green taxis, and ride-hailing services. In recent years, the
data supplier TLC has discontinued entering the precise geo-coordinates of
pickup and drop-off points. Consequently, instead of working with a more
recent dataset, the research must rely on outdated data from 2014, 2015, or
2016 [5].

The dataset of 2014 consists of more than 165 million trips and a file size of
25 gigabytes [5]. The dataset of 2014 was preferred over the others because
ride-hailing services like Uber and Lyft had yet to be extensively adopted to
influence taxi ride demand at that time. Therefore, the 2014 trip data is more
typical of the city’s total demand than the 2015 and 2016 data [23].

Variable | Description
---|---
Pickup Location | Longitude and Latitude
Pickup Datetime | Accurate to the Second
Drop-off Location | Longitude and Latitude
Drop-off Datetime | Accurate to the Second
Trip Distance in KM | Trip Distance in Miles * 1.609
Passenger Count | Passengers excl. Taxi driver
Fare Amount in USD | Total Fare Amount - Tip Amount
Avg. Speed in Km/h | (Trip Distance / Trip Duration) * 3600
Trip Duration in sec. | Drop-off Datetime - Pickup Datetime
Great Circle Distance | Pickup vs. Drop-off Coordinates
Table 1: Variables

###  2.2 Data Cleaning

####  2.2.1 Geo Locations

Severe outliers in the pickup and drop-off geo-locations were filtered first
using the grid filter (see Figure 1). Two percent of observations were omitted
in this step.

As a next step, the data must be cleaned from outliers close to New York City
but located in impossible locations that could not be addressed by the grid
filter (see Figure 1). A reason for these distortions could be uncontrolled
biases resulting from urban canyons that may have slightly distorted GPS
locations during data collection [26]. Using a publicly available library
called nycgeo, it is possible to filter for geographical and administrative
borders in New York City [14]. However, the New York City filter resulted in a
shallow loss of observations (see Figure 1).

The operational area of the yellow taxis is in Manhattan. Taxis outside of
Manhattan but in New York City are called green Taxis and are not considered
in this research. La Guardia and JFK airports are the only two dense pickup or
drop-off destinations outside Manhattan operated with yellow taxis [23].
Visual inspection shows that the density of pickups and drop-offs outside
Manhattan, apart from the airports, is decreasing dramatically. The last step
separates Manhattan, La Guardia, and JFK Airport journeys from the rest. This
step leads to a total data loss of 15 percent.

Figure 1: Geometric Locations and Outlier Detection

####  2.2.2 Trip Distance

The great circle distance, which is the direct distance taking into account
the spherical shape of the Earth, was used to compare all travel distances.
The calculated distance was sometimes less than the actual distance traveled,
which is geometrically impossible [8]. Those observations were therefore
excluded from the dataset. Additional cleaning procedures were carried out to
eliminate irrational entries.

Trips less than 500 meters or more than 60 kilometers were eliminated too.
Manual investigation revealed that those trips frequently had inflated times
or distances. Additionally, all journeys with similar origins and destinations
were abandoned by excluding all trip records with great circle distances below
100 meters [26]. Trips with extraordinary lengths, for instance, those that
span less than a minute or longer than two hours, and trips with speeds below
5 km/h and above 88 km/h, which resembles the maximum New York State speed
limitation of 55 mph were eliminated too ([3],[24]).

####  2.2.3 Passenger Count

Regarding the TLC [22], the maximum number of passengers allowed in a yellow
cab taxi is four or five, excluding the driver, depending on whether the trip
is operated with a four or five-person taxicab. However, additional passengers
below the age of seven are allowed when held on an adult’s lap. Hence, all
trips with a passenger count above six will be excluded [6].

####  2.2.4 Trip Fares

The research article [33] is based on the 2009 yellow taxi trip data, showing
that the distribution of fares closely resembles the distribution of trip
length. This demonstrates that the primary determinant of the fare amount is
trip distance. Nevertheless, [33] also suggests separating pickups and drop-
offs outside and inside the downtown area and considering rush hours that will
affect the trip duration and, therefore, the fare amount.

When analyzing Figure 2, we assess that airport pickups and drop-offs usually
come with the highest trip fare. The reason is that the two airports, JFK and
La Guardia, are the only two locations outside the Manhattan borough. We can
also assess a clear correlation between the fare amount and the trip distance.
The histogram allows a visual assessment of outliers. It is based on the total
ride fare per kilometer. All values below $ 1.5 and above $ 8.5 per kilometer
were excluded from future analysis.

Figure 2: Trip Fare vs. Trip Distance

###  2.3 Data Exploration

Figure 3 confirms the naive expectation that fare amount, trip duration and
trip distance are highly correlated. Yet, the trip fare exhibits a higher
correlation with the distance than the duration. This also aligns with an
analysis by Zhang and coworkers [33]. We further assume that the trip fare is
even better approximated using the great circle distance instead of the trip
duration. However, we must consider statistical measures to claim this
statement with confidence. We also notice the positive correlation for the
trips to and from the airport regarding trip length and travel costs.

Figure 3: Correlation Matrix

The highest demand for taxi rides, represented by the number of pickups, is
visualized in Figure 4. The darker a hexagon is colored, the more pickups or
drop-offs there have been in the area. There is a precise centering of areas
with the highest demand, as the top ten areas are all located in southern
Manhattan. The two locations with the highest taxi demand are both at Madison
Square Garden, with 340 thousand registered pickups in March. That area
accounts for 2.75 % of all recorded taxi trips of that month. The visualized
ten hexagonal areas comprise 1.25 million pickups and represent 10 % of all
registered pickups in Manhattan. The most popular exit points (see Figure 4 \-
right) display a similar picture. 330 thousand drop-offs are recorded at
Madison Square Garden, and the top 10 drop-off destinations sum up to 1.2
million taxi trips representing 9.6 % of all rides. Considering these figures
raises hopes that a static approach can yield good results.

Figure 4: Busiest Pickup (left) and Drop-off (right) Locations

In order to assess hours with the highest travel demands, the month of March
is visualized in groups (see Figure 5). Colored blue is the peak demand
period; The morning-noon peak, from 08:00 to 15:00, and the evening peak, from
17:00 to 22:00, show the most rides per hour. This is important as we can
later assess how the ridesharing potential reacts if solely implemented within
these peak demand periods.

Figure 5: Peak Hours

####  2.3.1 Unused Capacity

Inspection of Figure 6 suggests that despite the highest taxi trip fares due
to the longest average taxi journeys to and from the two airports (see Figure
2), the vehicle occupancy rates are low compared to the average occupancy in
New York’s Taxis. The only rides outside Manhattan we consider in this
analysis due to their high request rate are JFK and LaGuardia airports. Only
about 30% of taxi trips to and from the airports have a load factor of more
than one person, compared to the average total load factor of 65%. Since the
financial incentive should be highest for trips to and from the airports due
to the higher travel costs, we will also examine this in the following.

Figure 6: Occupancy - Total Trips vs. Airport Trips

##  3 Ridesharing Potential

Here, using a simple static approach, we analyze the ridesharing potential for
densely populated cities. Our approach is based on clustering pickup and drop-
off points [3], while also adopting the methodology of [18] where the urban
area is divided into hexagonal partitions.

With our analysis, we can evaluate the ridesharing potential without
restricting ourselves in terms of car capacity [3], whereas in a dynamic
routing system proposed by ([1],[26]), with an increasing number of potential
rides that could be shared, the routing system gets mere complex as an
increasing amount of nodes and therefore routes need to be taken into account.

The quality of service parameter is chosen as an absolute time rather than a
relative increase of the travel time because it is consistent with similar
realizations in the literature and is driven by the fact that absolute delay
information is likely more helpful than the percent assessment of travel time
increase for potential customers of a shared taxi service [26].

###  3.1 Model

Figure 7: Methodology

As a starting point, we divide the urban space into hexagonal partitions as L
= {l1, l2, li, ln}. The duration of a day is discretized into time intervals
as T = {t1, t2, ti, tn} which represent the time window at the pickup
destination.

In order to create these hexagons latitude and longitude of pickup and drop-
off points are converted to an H3 index, which is a string representation of
the hexagon that the point falls in ([31],[7]). The data is then grouped by
the pickup H3 indices, drop-off H3 indices, and pickup time within a given
time window.

Whenever the number of trips within a group exceeds 1, there is a demand for
ridesharing. We assume all trips that fall into the defined group can be
shared using one vehicle. As the number of ride requests and the accompanying
passengers sometimes exceed the capacity of yellow taxis, we assume that at
the determined pickup points, there is always the option to switch to a larger
vehicle, such as a van.

Total carpooling potential can be determined by calculating the number of
trips that start and end in the same H3 cell in a ridesharing scenario versus
the number of trips in a non-ridesharing scenario. As a result, the
ridesharing potential could be described as a ratio between the total trips in
a ridesharing scenario and whole taxi trips.

However, similar to more intricate many-to-many Taxi Ride Sharing systems,
this approach is constrained by several restrictions and other assumptions
listed below.

####  3.1.1 Vehicle Constraints and Assumptions

A regular yellow taxicab has a passenger limitation of only five persons [22].
Most ridesharing situations can be fulfilled with this type of vehicle [3].

Here, we allow for multi-ridesharing because this enables us to examine the
full ridesharing potential. After all, a limitation can drastically reduce
this potential [26].

In a real-life situation, multi-ridesharing that exceeds the 5-passenger limit
can be performed using vans, minibusses, or large buses. Allowing multi-
ridesharing, on the other hand, has a negative influence on waiting times [3].
However, passengers’ inconveniences could be compensated with increased
savings due to the greater total distance saved.

####  3.1.2 Spatial Constraints and Assumptions

The starting point of every participant in a shared ride scenario is the
original pickup location of the ride request.

We assume that the new pickup location is always the center of a hexagon by
neglecting that the exact point could be in an inaccessible location, for
instance, in the middle of the street or a park.

The pickup distance is the great circle distance between the starting point
and the pickup location. We assume every participant will walk this distance.

The drop-off location will be the exact location as in a non-ridesharing
scenario.

The total trip distance in a shared scenario is the maximum trip distance of
all ride requests or individual mobility trips that are pooled into a shared
ride. This statement agrees with the assumption that the drop-offs do not
result in any detours that could lengthen or shorten the trip.

The individual trip distance will stay the same as in the original individual
mobility case, as all participants are getting dropped off at their desired
destination. However, as we relocate the pickup location to the hexagon center
in a ridesharing scenario, the individual trip riding distance will change,
depending on the starting point. If the starting point is closer to the drop-
off location than the pickup point (hexagon center), the individual trip
distance will increase and vice versa. By analyzing the great circle distance
between all starting points and drop-offs in an individual mobility scenario
and comparing it with the great circle distances in a shared scenario, we can
observe that the difference in trip distance averages zero (see Figure 8).
Based on this observation, we assume that the individual trip distance for
every ridesharing participant stays approximately the same as in an individual
mobility scenario.

Figure 8: Great Circle Distance

####  3.1.3 Temporal Constraints and Assumptions

The starting time of every participant in a shared ride scenario is the
original pickup time of the ride request.

The arrival time is the walking duration to the previously defined pickup
location added to the pickup starting time.

The walking duration is based on a study [2] determining walking speed and
attributes of pedestrians on lower Manhattan’s sidewalks was observed. The
median speed of all observed pedestrians was 4.67 kilometers per hour, which
is slower than researchers found in previous studies ([11],[16]) but could be
explained by the fact that the majority of the observations took place in the
middle of the day [2]. Given that roughly 50% of individuals in this study
walked slower than the median speed, some participants were likely not walking
to a designated location, such as a taxi pickup place, and were not slowed
down by distractions. Therefore, we use the 4.67 km/h to determine the walking
duration. As a foundation, we use the pickup distance to approximate the
walking distance and calculate the walking speed using the 4.67 km/h.

The time window or interval is the time difference between the trip
announcement and departure time. Within this time window, we assume a vehicle
with unlimited capacity is waiting at the previously defined pickup location.
It applies that the number of daily intervals is 1440 minutes divided by the
interval.

The departure time is constant and dependent on the size of the time window,
but for every sharing scenario, the departure time must be greater or the same
as the arrival time.

The drop-off rank is installed to approximate the individual delay for
dropping off passengers as we assume the taxi drops everyone off at an
individual location. This rank is based on the individual trip length in a
shared taxi. The passenger with the shortest trip length is ranked first, and
the passenger with the most extended trip length is ranked as the number of
pooled taxi trips in the vehicle. By ranking every trip request in a shared
scenario, we can penalize those with longer trip distances, as they are
getting dropped off based on individual rank.

To specify the drop-off delay, we assume that every drop-off delays the total
trip duration by 20 seconds. This delay is added to the remaining passengers
in the vehicle and depends on the drop-off rank.

The individual trip duration will stay the same as in the original individual
mobility case for the reasons described in the individual trip distance
section.

  * •

Drop-off delay = 20 sec. * rank – 20sec

  * •

Total delay = walking duration + departure time – arrival time + drop-off
delay

  * •

Drop-off time = Departure time + individual trip duration + drop-off delay

Using the great circle distance between the starting point and drop-off
location, we approximated the walking duration of the whole trip. Knowing the
total trip walking duration, we exclude every individual mobility scenario
that reaches the drop-off point earlier when walking compared to participating
in ridesharing.

Another constraint that must be installed is a fixed drop-off delay for every
passenger in case the number of ride requests in one shared vehicle is too
large. Too large means that the accumulated drop-off delay of all passengers
would exceed the delay they would suffer if they get dropped off all at once
and walk to the drop-off location.

###  3.2 What-If Analysis and Robustness

Previous studies show a positive correlation between ridesharing opportunities
and both variables; time interval and cluster size [3].

In general, a what-if analysis allows for the data-intensive modeling of
complicated systems and analyses the results under alternative hypotheses
[13]. In short, the analysis helps to assess the robustness of parameter
choices.

The analysis should help us to study how resolution size and time interval
will influence ridesharing opportunities. The following what-if study
evaluates the ridesharing potential with changing parameters resolution and
interval to quantify their impact on the outcomes for the proposed one-to-one
ridesharing model (see Table 2). The analysis considered taxi trips between
the 19th and 26th of March.

We consider time intervals from 1 to 25 and a pickup resolution of either 9 or
10. To add some flexibility while minimizing inconvenience for passengers, we
set the drop-off resolution to size 9, as the drop-offs are driven in the car.
Therefore, a resolution of 10 would likely be too small and drastically
influence the sharing potential. Whereas considering a resolution size of 8 is
irrational as the side length of that hexagon is 530 meters and too extensive
considering the presented drop-off methodology.

####  3.2.1 Hexagon Resolution

The two proposed resolution sizes are resolution 9 (see Figure 9 \- left),
with an average side length or maximum walking distance to the pickup
destination of 200 meters, and resolution 10 (see Figure 9 \- right), with an
average side length or maximum walking distance of 75 meters [31].

Figure 9: Resolution 9 (left), Resolution 10 (right)

Comparing the results from figure 10 with the given time intervals indicates
that the resolution size 9 performs better in the ridesharing scenario by
still restraining the total delay. Especially with higher time intervals,
there is observable that the lower resolution is more beneficial in terms of
delay and sharing potential. For example, we compare the average delays at 135
seconds and reckon that the ridesharing potential at resolution 9 is 10% but
only 4% with a resolution of 10 (see Table 2). This supposition becomes even
more evident when looking at Figure 10. The bigger hexagon size accounts for
more sharing potential per every second of total delay. The only periods where
resolution 10 outperforms resolution 9 are below 3 minutes of total delay.

Figure 10: Sharing Potential vs. Total Delay | Drop-off Resolution
---|---
| 9
| Pickup Resolution | Pickup Resolution
| 9 | 10
T | Sharing Potential | Total Delay | Sharing Potential | Total Delay
1 | 1.65 % | 137 | 0.57 % | 72
2 | 3.18 % | 168 | 1.08 % | 102
3 | 4.57 % | 198 | 1.56 % | 133
4 | 5.86 % | 228 | 1.98 % | 163
5 | 7.08 % | 258 | 2.39 % | 193
6 | 8.21 % | 288 | 2.77 % | 223
7 | 9.07 % | 321 | 3.04 % | 257
8 | 9.98 % | 351 | 3.37 % | 287
9 | 10.92 % | 383 | 3.70 % | 319
10 | 12.63 % | 404 | 4.12 % | 341
11 | 13.07 % | 440 | 4.24 % | 377
12 | 14.47 % | 460 | 4.68 % | 398
13 | 14.66 % | 501 | 4.72 % | 440
14 | 15.47 % | 520 | 5.31 % | 472
15 | 16.88 % | 539 | 5.41 % | 478
16 | 16.37 % | 585 | 5.36 % | 525
17 | 15.55 % | 612 | 5.42 % | 553
18 | 16.02 % | 627 | 5.59 % | 569
19 | 16.83 % | 641 | 5.90 % | 581
20 | 17.92 % | 659 | 6.31 % | 599
Table 2: Resolution Size vs. Delay

####  3.2.2 Trip Intervals

In order to evaluate a reasonable time window for subsetting the departure
time of all taxis at the hexagon center with resolution 9, Figure 11 assesses
the growth of the ridesharing potential dependent on the interval size. A
first glance reveals that with higher time intervals, the ridesharing
potential flattens and moves towards zero growth. Throughout the 25-time
intervals, the total delay of shared rides stays almost linear.

The percent difference in ridesharing potential follows a steady decrease
until interval 10 but then varies in growth from year to year. Following this
investigation, we can assume that setting the time interval to 10 and having
the pickup resolution at 9 is reasonable. This setting will result in the best
sharing potential compared to the total delay (see Figure 11 \- right).

Figure 11: Interval Size vs. Sharing Potential & Delay

###  3.3 Two-Scenario-Analysis

As observable in table 2, the trip sharing potential with a resolution of 9 at
the pickup and drop-off destination at a given 10-minute time window will
result in 12,1% sharing potential following the presented approach. It is now
essential to determine not just the overall average but also analyze how the
sharing potential behaves on an ordinary day and extraordinary day [3], as
well as figure out how far our findings could be generalized to times with
lower and higher taxi demands [26].

As can be observed in Figure 12, there is a clear connection between the
number of trips that happened within a day and the potential to participate in
ridesharing. The sharp decline in potentially shared rides on the 9th, 16th,
and 23rd of March can be traced back to the below-average total trips on these
respective days. Another exciting aspect observable in Figure 12 is that even
though the ridesharing potential is lowest on Sundays, it is only the second
lowest weekday regarding taxi demand. The three weekdays with the lowest taxi
demand in March were all Sundays. Comparing the color spectrum and the size of
the bars shows a clear positive correlation between taxi demand and
ridesharing potential. The same picture can be seen in other studies
([1],[3],[26]).

Figure 12: Taxi Demand and Ridesharing Potentialy

By comparing all daily taxi demands and ridesharing potentials in March, we
choose an ordinary day based on the average daily demand of 383 thousand trips
and the ridesharing potential of 12.9%. Wednesday, March 20, is likely to
represent an ordinary day with an average ridesharing potential of 13% and a
total taxi demand of 388 thousand, whereas Sunday, March 9, represents a
minimum sharing potential of 10.9 % in the observed month and fourth lowest
day in terms of taxi demand representing the extraordinary day [3].

####  3.3.1 Trip Savings and Environmental Benefits

As mentioned earlier, the trip distance of a shared taxi trip equals the
maximum individual trip distance of the pooled passengers in the ridesharing
scenario. The analysis reveals that, on average, this approach will save 48.6
% of the cumulative trip distance in a shared scenario. Examining multi-
ridesharing approaches with more than five pooled ride requests, the average
cumulative trip distance saved is even 82 % on both ordinary and extraordinary
days. We can estimate the daily environmental emission savings by following
these findings and the total distance saved in a day (see table 3). The
emission calculation is based on the average emissions of vehicles in the US
in 2016, which is approximately 250 grams per kilometer [9].

Metrics | Ordinary | Extraordinary
---|---|---
Individual Trips | 338,099 | 314,066
Shared Trips | 50,582 | 38,538
Total Reduction of Trips | 27,865 | 21,205
%. in vehicle reduction | 8.24 % | 6.75 %
Total Distance saved | 80,517 km | 64,743 km
% Avg. trip distance saved | 48.56 % | 48.63 %
Saved CO2* | 20,129 tons | 16,186 tons
Table 3: Saved Distance and Emissions

Within this approach, we assume that there is no capacity limitation.
Therefore, some trips must be made with greater capacity vehicles with higher
emissions per kilometer. We neglect this fact in our saved CO2 calculation.
Nevertheless, knowing what vehicle capacities must be available for the shared
ride is crucial. Table 4 illustrates how many vehicles of what type must be
used to serve the ridesharing demand implementing this approach. For regular
cars, a passenger limit of 5 was assumed. We assumed a capacity limit of 10
passengers for vans, and minibusses should transport up to 30 passengers.

Metrics | Ordinary | Extraordinary
---|---|---
Car Total Trips | 17,625 | 13,073
% Passengers carried | 37.8 % | 53.7 %
Van Total Trips | 4,631 | 3.746
% Passengers carried | 55.9 % | 37.2 %
Minibus | 461 | 514
% Passengers carried | 6.3 % | 9.1 %
Table 4: Vehicle Types

####  3.3.2 Delay

As shown in Table 5, the level of taxi demand has a negligible effect on total
delays. This favors the presented approach since, unlike the research of [3],
demand has little effect on delay. The delay is relatively independent of the
demands as we suppose a static departure time at the pickup location.

Metrics | Ordinary | Extraordinary
---|---|---
Average Total Delay | 402 sec. | 404 sec.
Average Walking Duration | 96 sec. | 94 sec.
Average walking distance | 124 m. | 122 m.
Average Delay until Departure | 390 sec. | 392 sec.
Average Delay at Drop-off | 10 sec. | 12 sec.
Table 5: Delay

####  3.3.3 Possible Cost Savings

The price of a taxi trip is dependent on the length and duration of the trip.
This section focuses solely on the potential cost savings passengers could
have and neglects that the taxi driver also suffers a delay due to the waiting
time at the pickup destination and the additional delay of 20 seconds for
every drop-off in the multi-ridesharing situation. Similar to calculating the
total Trip distance, we use the maximum fare amount in a ridesharing situation
as a foundation and assume that the remaining fares of the ridesharing
passengers could be seen as the maximum potential cost savings.

Metrics | Ordinary | Extraordinary
---|---|---
Average cost-saving potential | 49 % | 49 %
Table 6: Cost-Saving Potential

###  3.4 Cost Savings within Airport Trips

As mentioned in the previous analysis, car occupancy is significantly lower
with trips to and from the airport. However, the financial incentives for such
trips should be higher as the trip fares are relatively costly [29]. Contrary
to our expectations, the ridesharing potential at the airports JFK and La
Guardia is 5 % below the monthly average of the total analyzed in New York
City, showing an 8 % sharing potential. However, sharing a taxi trip with
pickup and drop-off destination at the airports comes with a higher cost
benefit, as these trips are, on average, the most extended (see Figure 2).
Taxis charge an extra fee as they operate at a different rate doing pickups at
JFK or La Guardia Airport [23]. In relative figures, the average cost savings
in the shared ride scenario at the airport account for 50 %, similar to the
ordinary case demonstrated above. However, on average, a trip from or to the
airport in a shared or unshared vehicle costs $ 42, but a regular trip within
Manhattan costs $ 12.50. Hence, the incentive to split fare costs using
ridesharing services is higher for trips from and to the airport than ordinary
taxi trips in NYC.

###  3.5 Peak-Hour Scenario

Next, we study how far higher demands could lead to a better performance of
the one-to-one sharing approach, and if it is worth implementing a shared
service solely available within these high-demand hours. In the peak hour
scenario, the observations in low taxi demand hours are (see Figure 5)
filtered out to assess the sharing potential only considering peak hours of
the day. This examination shows that the total ridesharing potential is 13.1%,
given a 10-minute interval. Compared to the base case, this is a negligible
increase. Following this, we assume that the ridesharing potential is
increasing with growing demand but is less drastic than the static one-to-one
approach by [3]. We can also assess that the average waiting time in such
high-demand situations is 390 seconds, 5% lower than the base case that
includes the off-peak hours.

###  3.6 Locations with the Highest Sharing Potential

Exploring all drop-off and pickup locations from March 2014 provides us with a
clear picture of destinations with the highest ridesharing potential. The
majority of taxi trips are in central Manhattan. However, the best ridesharing
opportunities are in southern Manhattan (see Figure 13). Based on these
findings, a Taxi Ride Sharing service in New York City should primarily serve
Manhattan’s central and southern areas to reach the highest ridesharing
potential.

Figure 13: Pickup (left) and Drop-off (right) locations with the highest
demand

A closer look at the pickup hotspots (see Figure 13) reveals three significant
clusters showing the highest ridesharing opportunity: Madison Square Garden,
The Grand Central Station, and the intersection at 42nd Street and 8th Avenue.
The most prominent clusters for drop-off locations are Madison Square Park and
Javits Center for this specific month.

In a circumstance where the ridesharing approach is limited to the ten
highest-demand pickup areas (visualized in Figure 4), we achieve a matching
rate of about 27% (see table 7). Restricting the model to serve these ten
hotspots excludes 90% of New York City’s taxi demand. Consequently, this
simple approach has the potential to transform 2.5% of New York taxi rides
into a shared service on an ordinary day and around 2% on an extraordinary day
by solely focusing on 10 pickup areas.

Metrics | Ordinary | Extraordinary
---|---|---
Ride Sharing Potential | 26.7 % | 22.9 %
Representation of total rides | 10.5 % | 9.2 %
Table 7: Hotspot Matching Rate

##  4 Discussion

We have proposed an easy and much-needed sharing strategy that pairs trips
with similar starting and ending points. For a dataset of more than 165
million taxi rides, we have shown that the cumulative journey time can be
reduced by 48 percent, fares can be reduced by 49 percent, while emissions can
be decreased by 20.129 tons of CO2 – on an ordinary day. Compared to many-to-
many sharing dynamic routing schemes, our scheme is substantially easier to
implement and operate.

We have demonstrated that a ridesharing provider can adjust to the complex
distance and matching time constraints simultaneously and how to do so. While
the more patient a passenger is, the more significant the matching
opportunities that occur. Details on passenger behaviors may vary and are
often scarce, making it difficult to develop a robust framework. We have
revealed that reducing the matching time interval has a significant
detrimental effect on the matching rate. Therefore, one may argue that a
10-minute time frame may be optimal for a taxi ridesharing service in New York
City, whereas at other places someone else would never want to get delayed by
400 seconds to save up to 49% of the trip costs. Nevertheless, the time
limitation mitigates the effect of the distance constraint, and its
arrangement also determines a fair distribution of disadvantages of the
passengers in a shared trip. While the distance limitation increases, the
resolution size decreases, and distant passengers must walk to join a
transport [3].

Our analysis robustly demonstrates that a varying trip density does not render
our one-to-one approach inefficient. A lower trip density results in a lower
matching rate, but considering the resulting 13 % matching rate on an ordinary
day, a 10.9 % matching rate on a low trip demand day, and a 13.1 % matching
rate only considering high demand hours demonstrates the robustness of our
conclusions. In addition, our approach is more durable than a previously
suggested one-to-one model [3]. Their approach showed 53 % ridesharing
potential in high demand but only 21% in low demand times. Nonetheless, as the
number of ride requests increases, the trip density’s impact on the matching
rate diminishes. In fact, we achieve a matching rate of 27% on an ordinary day
by concentrating on the local pickup hotspots. Yet, information on spatial and
temporal characteristics of such high-demand trips and matching situations may
be exploited for implementing a public transportation service with multi-
ridesharing potential.

[26] demonstrated that a flexible, dynamic ridesharing system may achieve
close to maximum matching by using 25% of daily cab journeys in New York City,
or around 100,000 trips per day. Therefore, they concluded that ridesharing is
also effective in low-density cities.

In contrast, [28] concluded that the ridesharing potential in traditional
ridesharing depends heavily on the density of spatial and temporal
distribution of the stated trips [3].

According to related studies, there is a low likelihood of passengers wanting
to adopt ridesharing, even if they are financially incentivized. [28]
demonstrated that ridesharing initiatives may fail. Understanding this is
ultimately important and particularly applicable to our suggested one-to-one
ridesharing strategy, as passengers must wait for possible mates and have the
time to decide until departure if they want to share the ride, especially
considering that the passengers can see with whom they would share the ride.
Our analysis suggests that the proportion of matched rides in New York City
may reach up to 13%, providing evidence that the proposed strategy can be
successfully applied. As we have mapped out certain high-demand areas and
hotspots for pickups and drop-offs, one could implement a ridesharing service
in these areas that performs at a higher rate.

Taxi firms should consolidate shared journeys using hotspots, such as transit
hubs or notable landmarks, as starting and ending sites [12]. Models suggest
that picking meeting locations with care can considerably enhance the number
of matched rides in traditional ridesharing scenarios [28]. Our findings
indicate that multimodal transportation hubs such as Madison Square Garden
could be ideal for a one-to-one sharing service. Also, considering airports as
shared-ride pickup hotspots due to the high financial incentives, they may
efficiently help increase adoption even though the matching success is lower
than in Manhattan.

## References

  * [1] Javier Alonso-Mora et al.  “On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment”  In _Proceedings of the National Academy of Sciences_ 114.3 National Acad Sciences, 2017, pp. 462–467
  * [2] M Amanda and NYC Director  “New York City Pedestrian Level of Service Study Phase I”  Citeseer, 2006
  * [3] Benjamin Barann, Daniel Beverungen and Oliver Müller  “An open-data approach for quantifying the potential of taxi ridesharing”  In _Decision Support Systems_ 99 Elsevier, 2017, pp. 86–95
  * [4] Brian Caulfield  “Estimating the environmental benefits of ride-sharing: A case study of Dublin”  In _Transportation Research Part D: Transport and Environment_ 14.7 Elsevier, 2009, pp. 527–531
  * [5] cityofnewyork  “Yellow Taxi Trip Data 2014” https://data.cityofnewyork.us/Transportation/2014-Yellow-Taxi-Trip-Data/gkne-dk5s, 2014
  * [6] Dylan Costello  “Big Data in the Big Apple”, 2018
  * [7] crazycapivara  “H3 Library” https://github.com/crazycapivara/h3-r
  * [8] Brian Donovan and Daniel B Work  “Empirically quantifying city-scale transportation system resilience to extreme events”  In _Transportation Research Part C: Emerging Technologies_ 79 Elsevier, 2017, pp. 333–346
  * [9] EPA  “Greenhouse Gas Emsissions Typical Passenger Vehicle” https://www.epa.gov/greenvehicles/tailpipe-greenhouse-gas-emissions-typical-passenger-vehicle
  * [10] Daniel J Fagnant and Kara M Kockelman  “Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas”  In _Transportation_ 45 Springer, 2018, pp. 143–158
  * [11] John Fruin  “Designing for pedestrians”  In _Public Transportation United States_ Prentice Hal Englewood Cliffs, 1992
  * [12] Masabumi Furuhata et al.  “Ridesharing: The state-of-the-art and future directions”  In _Transportation Research Part B: Methodological_ 57 Elsevier, 2013, pp. 28–46
  * [13] Matteo Golfarelli, Stefano Rizzi and Andrea Proli  “Designing what-if analysis: towards a methodology”  In _Proceedings of the 9th ACM International Workshop on Data Warehousing and OLAP_ , 2006, pp. 51–58
  * [14] Herman  “NYCGEO” https://github.com/mfherman/nycgeo
  * [15] Stephan Herminghaus  “Mean field theory of demand responsive ride pooling systems”  In _Transportation Research Part A: Policy and Practice_ 119 Elsevier, 2019, pp. 15–28
  * [16] Richard L Knoblauch, Martin T Pietrucha and Marsha Nitzburg  “Field studies of pedestrian walking speed and start-up time”  In _Transportation research record_ 1538.1 SAGE Publications Sage CA: Los Angeles, CA, 1996, pp. 27–38
  * [17] Ana Ley  “Why Drivers Could Soon Pay $23 to Reach Manhattan” https://www.nytimes.com/2022/08/18/nyregion/nyc-congestion-pricing-manhattan.html, 2022
  * [18] Kai Liu, Zhiju Chen, Toshiyuki Yamamoto and Liheng Tuo  “Exploring the impact of spatiotemporal granularity on the demand prediction of dynamic ride-hailing”  In _IEEE Transactions on Intelligent Transportation Systems_ IEEE, 2022
  * [19] Mustafa Lokhandwala and Hua Cai  “Dynamic ride sharing using traditional taxis and shared autonomous taxis: A case study of NYC”  In _Transportation Research Part C: Emerging Technologies_ 97 Elsevier, 2018, pp. 45–60
  * [20] Luis Antonio López, Tiago Domingos, Marı́a Ángeles Cadarso and Jorge Enrique Zafrilla  “The smarter, the cleaner? Collaborative footprint: A further look at taxi sharing”  In _Proceedings of the National Academy of Sciences_ 111.51 National Acad Sciences, 2014, pp. E5488–E5488
  * [21] Louis A Merlin  “Transportation sustainability follows from more people in fewer vehicles, not necessarily automation”  In _Journal of the American Planning Association_ 85.4 Taylor & Francis, 2019, pp. 501–510
  * [22] NYC-TLC  “Passenger Frequently Asked Questions” https://www.nyc.gov/site/tlc/passengers/passenger-frequently-asked-questions.page
  * [23] NYC-TLC  “TLC Factbook 2014” https://www.nyc.gov/assets/tlc/downloads/pdf/2014_tlc_factbook.pdf, 2014
  * [24] NY-Safety-Council  “Speed limits in new york” https://www.newyorksafetycouncil.com/articles/speed-limits-in-new-york/
  * [25] Partnership-for-NYC  “Congestion Pricing New York City” https://pfnyc.org/wp-content/uploads/2020/01/2018-01-Congestion-Pricing.pdf, 2018
  * [26] Paolo Santi et al.  “Quantifying the benefits of vehicle pooling with shareability networks”  In _Proceedings of the National Academy of Sciences_ 111.37 National Acad Sciences, 2014, pp. 13290–13294
  * [27] Susan Shaheen and Adam Cohen  “Shared ride services in North America: definitions, impacts, and the future of pooling”  In _Transport reviews_ 39.4 Taylor & Francis, 2019, pp. 427–442
  * [28] Mitja Stiglic, Niels Agatz, Martin Savelsbergh and Mirko Gradisar  “The benefits of meeting points in ride-sharing systems”  In _Transportation Research Part B: Methodological_ 82 Elsevier, 2015, pp. 36–53
  * [29] David-Maximilian Storch, Marc Timme and Malte Schröder  “Incentive-driven transition to high ride-sharing adoption”  In _Nature communications_ 12.1 Nature Publishing Group UK London, 2021, pp. 3003
  * [30] Matthias Sweet  “Does traffic congestion slow the economy?”  In _Journal of Planning Literature_ 26.4 Sage Publications Sage CA: Los Angeles, CA, 2011, pp. 391–404
  * [31] Uber-Technologies  “H3 Source” https://h3geo.org/
  * [32] WHO  “World Health Statistics 2016: Monitoring Health for the SDGs Sustainable Development Goals” https://www.who.int/docs/default-source/gho-documents/world-health-statistic-reports/world-heatlth-statistics-2016.pdf, 2016
  * [33] Jianting Zhang  “Smarter outlier detection and deeper understanding of large-scale taxi trip records: a case study of NYC”  In _Proceedings of the ACM SIGKDD International Workshop on Urban Computing_ , 2012, pp. 157–162