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THE CHARACTERISTICS AND TRAFFIC IMPACTS OF RESIDENTIAL DELIVERY
ACTIVITY:
A NEW YORK CITY CASE STUDY
Quanquan Chen*
Graduate Research Assistant, Department of Civil Engineering
The City College of New York, Steinman Hall T-196
160 Convent Avenue, New York, NY 10031
Email: [email protected], Phone: (212) 650-8091
Alison Conway, Ph.D.
Assistant Professor, Department of Civil Engineering
The City College of New York, Steinman Hall T-195
160 Convent Avenue, New York, NY 10031
Email: [email protected], Phone: (212) 650-5372
Jialei Cheng, Ph.D, PE
Parsons Corporation
100 Broadway #18, New York, NY 10005
Email : [email protected]
*Corresponding Author
Word Count: Words 4025 + 5 Tables + 5 Figures = 6525 Words
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ABSTRACT
Recent growth in ecommerce has quickly impacted the distribution of parcel
deliveries in urban areas, with residential deliveries accounting for a rapidly growing
share of freight movement. Through field observation and development of a
simulation model, this study aims to investigate the characteristics of parcel delivery
activity in a heavily residential areas of Manhattan, New York City, and to investigate
the traffic impact of double parking parcel delivery vehicles. Field observations
revealed a number of unique characteristics of parcel deliveries and of individual
carriers. Despite model limitations, analysis of simulation results also revealed some
interesting relationships that may be of interest to decision-makers in targeting
parking policies for parcel delivery activity.
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INTRODUCTION
Due to the rapid emergence of ecommerce and omni-channel shopping models, a
large share of the household goods that a decade ago would have been delivered to
retail stores for purchase by a consumer are now delivered directly from national or
regional warehouses to consumers’ homes. In 2014, UPS, one the three primary
logistics providers serving the U.S. residential market, predicted that residential
deliveries would reach 50 percent of its business within five years[1]. In addition to
having broad implications for the organization of retail logistics [2], this shift has
changed the volume and spatial and temporal distribution of freight activity in many
locations, especially in residential areas not necessarily designed with the expectation
of frequent deliveries [3]. In many of these areas, zoning requirements and curb
regulations have not been updated to account for residential buildings as a freight trip
generator [4].
A number of recent efforts have employed innovative modeling approaches to study
commercial vehicle parking and impacts. Jaller, Holguin-Veras, and Hodge employed
a zip-code level employment based trip generation model to evaluate the available
capacity for commercial vehicle parking in Manhattan, New York. Nourinehad et al.
[5] developed an integrated parking behavior-simulation model to evaluate the
potential impact of parking policies on urban freight in Toronto. Chiabaut developed a
modeling framework to investigate the impacts of delivery activity on traffic flow.
He concluded that delivery trucks’ double-parking behavior has a major impact on
traffic conditions near maximum capacity, and that dedicated parking policies may be
an effective way to improve both the efficiency of the transportation network and the
logistics system [6]. Chow and Amer presented an analytical equilibrium model that
evaluated the effects of different parking policies in urban centers with respect to
network congestion, cruising, double-parking, and the travel behavior of commercial
and passenger vehicles [7]. Gao and Ozbay employed both queuing and simulation
models to examine the impacts or both passenger and commercial vehicle double
parking locations. They determined that events occurring at the end of the block have
a 5.15% higher impact on travel time than midblock events [8].
However, none of these studies has isolated parcel delivery activity from other
types of commercial vehicle parking. As summarized in Visser, Nemoto, and
Browne[9] and Chen and Conway[4], parcel deliveries, many of which are associated
with E-Commerce, are very different from traditional commercial movements. Parcel
deliveries generally consist of smaller lot sizes dispersed by smaller vehicles across a
broader area with a different temporal distribution. Through case study field
observation and development of a basic simulation model, this study aims to
investigate the specific characteristics of and impacts from parcel delivery activity.
METHODOLOGY
This study employed field observation and a microsimulation modeling approach
to characterize the behavior of commercial vehicles conducting parcel delivery
activities in the case study area and to quantify the expected traffic impacts from
parcel delivery double parking activity in a heavily residential area in Manhattan,
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New York City.
Field Observation
Field observation was conducted along Lexington Avenue, a three-lane southbound
commercial corridor and designated local truck route on Manhattan’s Upper East
Side. The four-block study segment was bounded by 78th Street to the south and
82nd Street to the north (Figure 1). Three streets cross the corridor segment - 79th
Street, which carries one travel lane in each direction, and 80th and 81st Streets, which
each have one travel lane in a single direction. Curb regulations on Lexington
Avenue vary throughout the day; during the morning rush hour, no parking is
permitted on the west side of Lexington Avenue, as the curbside is reserved for bus
operations. From 7 AM to 10 AM, all parking on the east side of the street is
designated for commercial loading; after 10 AM, both sides become one-hour meters.
Parking on all three cross streets is unregulated except for twice weekly street
cleaning. The area includes a mix of commercial and residential land uses and
building types.
Figure 1 Study Area Tax Lot Map
The study area is dominated by buildings serving residential and mixed land uses,
although some large commercial buildings are also located in the study area [10].
Residences on the cross-streets are primarily traditional brownstones containing
multiple apartments. The majority of mixed use lots include mid- to high-rise
residential buildings with ground level retail. Much of the “commercial” activity in
the area includes religious institutions and community services
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Table 1 Tax Lot Summary
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Tax Lot Type
Commercial
Residential
Mixed
Total Buildings
14
100
80
Units per Building
1.3
7.9
24.4
Average Building Height (# Floors)
2.3
5.1
7.9
145,270
1,440,050
3,132,400
% Retail
19.7
0.0
5.3
% Office
23.7
0.0
1.4
% Garage
0.0
0.0
0.3
% Storage
0.0
0.0
0.0
% Other
56.6
0.0
0.2
% Residential
0.0
100.0
92.8
Total Building Space (ft2)
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Two data collection efforts were performed for this study. On Wednesday, October
28th, 2015, road network data – including traffic volumes and composition, turning
movements, signal timings, and parking regulations - were collected between 9 AM
and 11 AM and between 4 PM and 6PM. On April 19th and 20th, 2016 between
8:30AM-5PM, parking observations were conducted on Lexington Avenue and 79th
Street to record in detail the parking characteristics of local parcel delivery vehicles.
Parking Simulation Model
A discrete, stochastic, and time-step based microscopic model was developed
using VISSIM 7.0 to investigate the impact of parcel delivery vehicle double parking
on corridor traffic delay. A one-hour model (10 AM to 11 AM) was calibrated using
field observation data, then validated using afternoon data (4 PM to 5 PM). Network
configuration data, including links and connectors, were obtained from Google Earth
[11]. Traffic volumes, turning movements, and traffic compositions were observed
from video footage recorded at three intersections: Lexington Ave and 81st Street,
Lexington Avenue and 80th Street, as well as Lexington Avenue and 79th Street.
Curb space parking regulations reflected posted signage. Bus volumes were
determined using the MTA Route M101, M102, M 103, and M79 bus schedules[12].
Model default pedestrian crossing inputs were assumed to account for interactions
between pedestrian and turning vehicles. Signal controller settings were directly
observed during the October 28th field observation. Truck parking duration
distributions were obtained from the field observations described above The share
of trucks attempting to park was adjusted to approximate arrival rates from observed
data.
Model calibration and validation were conducted following criteria recommended
by the Federal Highway Administration (FHWA)[13]. The calibration targets
evaluated were saturation flow rate, traffic volume, and traffic speed. The calibrated
saturation flow rate was estimated to be 1172 vehicles/hour/lane, very close to the
1176 vehicles/hour/lane estimated in the field using the 2010 Highway Capacity
Manual procedure [14]. The GEH statistics were better than the minimum criteria
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value of 5.0 [13] and less that 15% difference between model and observed approach
volumes was obtained for all link flows. Vehicle speeds were calibrated using GPS
data collected on the same corridor for a previous study [15]. The calibration
followed a procedure implemented by Li et al. that used vehicle trajectories from
video observations for speed calibration [16]. Speed calibration considered observed
and model data only on the primary corridor, Lexington Avenue between 82nd Street
and 78th Street, again using the target provided by FHWA [13]. Speed calibration
results are shown in Table 2. Following all calibration, the model was validated with
one-hour (4 PM-5PM) afternoon traffic to demonstrate reasonable prediction capacity.
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Table 2 Simulated Average Speed
Average Speed (mph)
Random Seed
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VISSIM
Field
% Diff.
1
5.30
8.61
2
5.15
5.53
3
4.94
4
4.68
-4.10
5
4.69
-3.89
4.88
1.23
Model Implementation
To quantify the impact of parcel delivery vehicle double parking behavior on local
traffic conditions, a parking simulation model was developed. A base scenario was
developed to reflect traffic conditions during the overall parcel delivery peak hour
between noon and 1:00 PM. The base Scenario 1 assumed an hourly volume of 940
vehicles arriving per hour on Lexington Avenue; this volume was estimated by
adjusting the observed peak hour volume using 24-hour count data collected by the
New York State Department of Transportation in 2014 [17]. Approximating observed
conditions,10 percent of vehicles were assumed to be trucks, and all arriving trucks
were assumed to double park in one of four frequently observed locations (Figure 2).
For Scenario 2, a higher arrival rate of 1200 vehicles per hour was assumed. For
Scenario 3, a lower share of trucks - 8.53% - was assumed.
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Double Parking
Location
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Figure 2 Double Parking Locations
For each scenario, 135 simulation runs were performed. For each run, the average
travel time and vehicle delay were estimated, along with the total duration and
frequency of parking in each of the double parking locations. In VISSIM, vehicle
delay in seconds is calculated by subtracting the theoretical free flow travel time to
traverse the corridor from north to south from the actual travel time; this includes both
stop delay and delay due to deceleration [18].
OBSERVATION RESULTS
In total, 55 residential delivery vehicle parking events were collected in the study
area. All parcel deliveries during the study period were made by four major logistics
operators – the United States Postal Service (USPS), FedEx, UPS, or DHL – and a
large online grocery retailer, Fresh Direct. The following sections detail observed
residential delivery vehicle parking operations.
Arrivals
Figure 3 shows the temporal distribution of observed parcel deliveries for the three
major carriers. Unlike other types of commercial vehicles that have a clear morning
arrival peak [19], these parcel deliveries occurred throughout the work day.
Deliveries in the study area peaked in the early afternoon between noon and 2:00 PM.
Deliveries during other periods varied by carrier; USPS conducted frequent deliveries
in the late morning and between 2:00 and 3:00 PM, while FedEx deliveries were
spread throughout the morning and UPS deliveries were concentrated in the
afternoon.
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USPS
4
UPS
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FedEx
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Figure 3 Parcel Delivery Vehicle Arrivals
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Parking Location Choice
Parcel delivery vehicles parked on Lexington Ave were primarily distributing parcels
to addresses on 80th, 81st, and 82nd Streets, although some did also make deliveries to
businesses on Lexington Avenue. As they were delivering to cross streets, the drivers
preferred to park at the ends of the block to minimize their walking distance. Vehicles
delivering to 79th Street addresses parked on 79th Street. Fewer than 5% of all
FedEx, UPS, and USPS vehicles parked at a legal curb space; most double parked in
the travel lane. A few USPS vehicles parked at bus stops or in front of fire hydrants
for short durations; this unique behavior can likely be attributed to differences in
parking enforcement for each carrier. While FedEx and UPS pay reduced fines for
parking violations as participants in a NYC Department of Finance discounting
program, as vehicles conducting federal government business, USPS vehicles are
essentially immune from local parking enforcement [20]. Table 3 summarizes the
parking location choices observed along each corridor. Additionally, 37% of parcel
vehicles on 79th Street and 75% of parcel vehicles on Lexington Avenue parked at the
end of the block.
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Table 3 Parcel Only Parking Location Choice
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Location
Total Counts
Legal @ Curb
Double Parked
Bus/Hydrant /No Parking
79 St
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0
11
7 (All USPS)
Lexington Ave
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3
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Parking Duration
Parking durations (Figure 4) varied based on the number of parcels delivered and as a
function of curbside logistics. Different behaviors were observed for each logistics
provider. USPS generally made short stops both to deliver parcels and to make mail
pick-ups or drop-offs. UPS primarily operated with a single driver who made all
deliveries from the vehicle; when this driver had to make more than two or three
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deliveries of 15 or more parcels per delivery, the truck parked for very long durations.
On some occasions, FedEx followed the same model as UPS with a single driver
making all deliveries. Under this model, when a large number of packages needed to
be delivered, a long time was required for sorting. However, FedEx also operated a
second model for larger deliveries; a single driver would arrive and meet a team of
local delivery persons, who would assist with offloading, sorting, and make deliveries
using hand carts. For this type of delivery, parking duration was shorter. As only
one DHL vehicle was observed, no behavioral trends could be identified.
Sixty-eight percent (26/38) of FedEx and USPS trucks parked for less than 10
minutes, and 95% (36/38) parked for less than 25 minutes. Around 60% of UPS
trucks parked for more than 25 minutes, with one parking for 168 minutes.
Parking Duration (Mins)
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200
150
FedEx
100
UPS
50
USPS
0
0
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15
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25
Individual Events
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Figure 4 Parking Duration Distribution
To evaluate parking space consumed by these vehicles during the observation
period, total parking duration time was multiplied by estimated vehicle lengths for
two observed vehicle types (a 27.1 ft single unit truck and an 18.7 ft commercial van)
to calculate a time length consumption rate for each operator (Table 4). Although
fewer UPS trucks arrived to the study area compared to other carriers, their
time-length consumption was about three times that of the other two distributors due
to long parking durations.
Table 4 Time-Length Consumption by Company
Single Unit
Van (18.68 ft)
Time-Length Consumed
Count
Total Duration
Count
Total Duration
(Min*ft)
USPS
19
180
3
21
5270.28
UPS
10
529
1
8
14485.34
FeDex
16
182
0
0
4932.2
Company
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FreshDirect operated very differently from the major parcel companies. As
FreshDirect delivery times are determined at the discretion of the customer within a
two-hour window, the company’s deliveries occured throughout the day. By
coordinating arrivals and departures, FreshDirect occupied a single curb parking
space with multiple trucks for much of the day, with new trucks arriving
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approximately every two hours. Each truck would wait for the next to arrive before
moving from the space. Several uniformed delivery persons remained in the area to
make deliveries throughout the day via hand carts.
In summary, observation showed that large couriers dominate the market. Unlike
other types of commercial vehicles that have a clear morning arrival peak, parcel
deliveries occurred throughout the day, peaking in the early afternoon. The vast
majority of vehicles double parked, although different behavior was observed for
USPS, who is essentially unregulated, and FreshDirect, who had a coordinated
strategy to maintain a parking space. Parking durations varied considerably by type
and based on the delivery model employed.
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SIMULATION RESULTS
As noted above, 135 simulations were conducted for each traffic scenario using the
calibrated model. As can be seen from Figure 5, the mean delay experienced by
travelers on Lexington Avenue in the high volume Scenario 2 (2.32 min) is about 25%
higher than that experienced by travelers in the base Scenario 1 (1.87 min). When
the share of trucks was reduced in Scenario 3 compared to Scenario 1, the mean delay
decreased by about 4 percent to 1.78 min. In the congested scenario, a much lower
standard deviation is observed, likely due to the increased influence of
non-intersection delay on all travelers.
70
Scenario 1
Share of Observations (%)
60
Scenario 2
50
Sceanario 3
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0
0
0.5
1
1.5
2
2.5
3
3.5
Average Travel Delay (min)
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Figure 5 Average Travel Delay Distribution by Scenario
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To specifically evaluate the influence of double parking behavior on this average
travel delay, a basic linear regression model was estimated from simulation results for
each scenario to predict average vehicle delay as a function of the total duration and
average duration in each double parking location. A series of model restrictions were
tested to develop a final linear model for each scenario (Table 5). The relatively low
final R2 values for each model (.43 for Scenario 1, .53 for Scenario 2, and .46 for
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Scenario 3) indicate that there are likely unobserved explanatory variables;
nevertheless, the results provide some interesting findings.
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Table 5 Linear Regression Results by Scenario
Variable
Scenario 1
Scenario 2
Scenario 3
Coefficient
t Stat
Coefficient
t Stat
Coefficient
t Stat
Intercept
1.59
31.13**
2.23
101.72**
1.39
29.95**
Total Duration 1
-4.09E-03
-2.27*
-5.25E-03
-5.55**
---
---
Total Duration 3
5.10E-03
3.61**
---
---
8.28E-03
5.19**
Total Duration 4
8.82E-03
4.40**
6.86E-03
6.26**
9.80E-03
6.31**
Avg Duration 4
7.66E-03
2.17*
6.24E-03
3.48**
---
---
*95% Significant
** 99% Significant
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For all three scenarios, total parking duration in Location 4 appears to generate the
greatest incremental increase in average vehicle travel delay on Lexington Avenue.
The result is unsurprising, as Location 4 obstructs the travel lane south of 79th Street,
the highest volume cross street on the corridor. This obstruction may inhibit left
turning movements from the major intersection. For the higher volume and higher
truck volume Scenarios 2 and 1, the duration of parking in this location is also
significant, with longer parking durations associated with higher delay. For
example, applying the model developed in Scenario 1, a single truck parked for 45
minutes in Location 4 would increase average traveler delay by 44.5 seconds;
however, 9 trucks parked for an average duration of 5 minutes each would generate
only 26.1 seconds of delay.
For the lower volume scenarios, the duration of double parking in Location 3
north of 79th Street also significantly increases delay; however, for the higher volume
scenario, this duration is no longer significant. This difference likely results from
the influence of two other double parking locations. First, under the heavier
congestion scenario, queue formation from double parking in Location 4 is more
likely to extend past Location 3, limiting the independent impacts of these events.
Similarly, as can be seen for the higher volume and higher truck volume Scenarios 1
and 2, an increase in parking duration in Location 1 actually reduces overall average
traveler delay; this is likely due to a model limitation. Queuing congestion generated
by double parking in Location1 likely extends beyond the model starting point;
vehicles passing obstructed Location 1 limit entry flows, instead generating delays
that are not captured by the extent of the model. This limitation makes comparison
of relative delay impacts across Scenarios challenging.
CONCLUSIONS AND FUTURE RESEARCH
Together, results from field observations and the simulation model provide insights on
how parcel delivery activities impact local streets. Unlike other types of commercial
deliveries, parcel delivery activity in this study area is distributed throughout peak and
off-peak periods, with peak delivery observed in the early afternoon. The duration
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of parcel deliveries is heavily dependent on the curbside delivery model employed.
A truck operated by a single driver who is solely responsible for a large volume of
deliveries will likely park for a long duration. If additional staff are available at the
curbside to assist with deliveries, this duration can be shortened considerably.
Parking regulations appear to have some influence on parking locations, although not
necessarily as intended. First, Lexington Avenue does have dedicated commercial
parking from 7:00 AM until 10:00 AM, but this parking is not available to parcel
operators working throughout the remainder of the day. The majority of FedEx and
UPS trucks double parked on Lexington Avenue served both Lexington Avenue and
the narrow side streets where unregulated parking is consumed by personal vehicles
with little turnover and where double parking would completely obstruct the street.
USPS vehicles were observed to not only double park, but also to frequently park in a
variety of illegal spaces such as in front of bus stops and fire hydrants. FreshDirect
had a system to reserve a curbside parking space by coordinating entries and exits
throughout the day.
While limited in predictive power, regression results reveal a number of variables
that may be of interest in analysis of parcel delivery vehicle double parking behavior.
Results for higher traffic volume scenarios in the most critical location indicate that
the duration of individual vehicles double parking – not just their aggregate sum –
may impact delay, with longer events generating more delay than multiple shorter
events of equal sum. This indicates that parking policies should be designed and
enforced to limit the duration of double parking events, rather than the frequency.
Results also indicate that the specific turning volumes obstructed may be important to
consider when determining where double parking should be permitted.
Much future research is needed to address the limitations of this modeling
approach and to obtain more rigorous results. First, the model design should also be
evaluated with additional consideration given to the likely interactions of specific
double parking events so that individual impacts can be better isolated. In terms of
field observation, parking and loading operations of parcel delivery vehicles were
only collected for a short duration in a limited study area. Additional data from
multiple locations is needed to improve the confidence of the parking duration
distribution employed in this study as well as to enable detailed statistical analysis of
carrier, area, and regulatory variables that influence these parking durations. A
comparative dataset of other types of commercial vehicle parking should also be
collected in the same areas.
Currently, on Lexington Avenue, there appears to be a mismatch between the
hours when dedicated space is available for commercial parking and when parcel
delivery vehicles operate. Future research is needed to evaluate the adequacy of
existing parking regulations to enable parcel delivery vehicles to efficiently conduct
deliveries without significant detrimental impacts on other types of commercial
activity or on the surrounding traffic network.
Modeling alternatives to evaluate
more variables of interest for policy design, such as expected impacts from changes in
available curbside capacity or sensitivity to time-or-duration based pricing, should
also be considered. An improved model could also be employed to quantify the
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broader cost impacts associated with travel delays generated by parcel delivery
vehicles under these various regulatory scenarios.
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