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Off-peak delivery (OPD) is the delivery of goods during the evening and overnight hours. This strategy has the potential to alleviate peak period congestion, improve efficiency of delivery firms, and reduce emissions. This paper investigates benefits and challenges of a pilot OPD program in the Region of Peel, with the goal of informing potential broader implementations of OPD. In contrast to other previously implemented OPD projects, this OPD pilot focuses on deliveries in suburban areas. Three firms, delivering to 14 pilot retail stores, participated in the OPD pilot in the Region of Peel from March to August 2019. The analysis shows that during the six-month pilot, the average speed of the trips that were made in off-peak hours, from 7:00 p.m. to 7:00 a.m. the next day, is 18.1% faster than those that happened in day-time hours. Furthermore, the total greenhouse gas emissions/km decreased by 10.6%, and emissions factors for air quality pollutants, including CO, NOx, PM10, and PM2.5 reduced by 10.8% to 15.0% in off-peak hours. Results for service times varied between firms, but on average increased by 15.2%, indicating activities in the off-peak hours at the retail stores that prevented overall improvements in service time compared to day-time deliveries. A post-pilot interview was done with logistics managers of the three firms, which provides rich insights about challenges, successes, and ways that the OPD program could be improved.
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Pilot Off-Peak Delivery Program in the Region of Peel
Kianoush Mousavi 1, * , Sabrina Khan 2, Sabbir Saiyed 2, Glareh Amirjamshidi 1and Matthew J. Roorda 1
Citation: Mousavi, K.; Khan, S.;
Saiyed, S.; Amirjamshidi, G.; Roorda,
M.J. Pilot Off-Peak Delivery Program
in the Region of Peel. Sustainability
2021,13, 246.
Received: 12 November 2020
Accepted: 17 December 2020
Published: 29 December 2020
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1Department of Civil & Mineral Engineering, University of Toronto, 35 St. George Street, Toronto,
ON M5S 1A4, Canada; (M.J.R.); (G.A.)
2Region of Peel, 10 Peel Centre Drive, Brampton, ON L6T 4B9, Canada; (S.K.); (S.S.)
Off-peak delivery (OPD) is the delivery of goods during the evening and overnight hours.
This strategy has the potential to alleviate peak period congestion, improve efficiency of delivery
firms, and reduce emissions. This paper investigates benefits and challenges of a pilot OPD program
in the Region of Peel, with the goal of informing potential broader implementations of OPD. In
contrast to other previously implemented OPD projects, this OPD pilot focuses on deliveries in
suburban areas. Three firms, delivering to 14 pilot retail stores, participated in the OPD pilot in the
Region of Peel from March to August 2019. The analysis shows that during the six-month pilot, the
average speed of the trips that were made in off-peak hours, from 7:00 p.m. to 7:00 a.m. the next day,
is 18.1% faster than those that happened in day-time hours. Furthermore, the total greenhouse gas
emissions/km decreased by 10.6%, and emissions factors for air quality pollutants, including CO,
NOx, PM10, and PM2.5 reduced by 10.8% to 15.0% in off-peak hours. Results for service times varied
between firms, but on average increased by 15.2%, indicating activities in the off-peak hours at the
retail stores that prevented overall improvements in service time compared to day-time deliveries.
A post-pilot
interview was done with logistics managers of the three firms, which provides rich
insights about challenges, successes, and ways that the OPD program could be improved.
off-peak delivery; congestion; suburban areas; sustainable development; sustainable trans-
1. Introduction
Off-peak delivery (OPD) is the delivery of goods during the evening and overnight
hours. This strategy has the potential to alleviate congestion during peak periods and
increase the utilization of excess transportation infrastructure capacity during off-peak
periods. It is expected to offer greater efficiency to delivery firms by increasing travel speeds
and reducing delivery service times. Greater travel speeds, with fewer accelerations and
decelerations have potential to reduce greenhouse gas emissions (GHG) and air pollutants.
However, concerns exist about community impacts, especially noise at night. This paper
investigates the benefits and challenges of an OPD pilot in the Region of Peel. The OPD
pilot was initiated with the intention of informing the design and potential implementation
of large-scale permanent OPD programs across the Province of Ontario.
OPD programs have been implemented in numerous cities such as New York City,
Paris, and Barcelona due to the observed benefits in the urban areas [
]. These urban OPD
programs have documented benefits and challenges for society and business as outlined
in Section 2. In contrast, this pilot is, to our best knowledge, the first OPD program that
focuses on suburban areas. Three firms with a total of 14 pilot retail stores participated in
the OPD pilot in the Region of Peel from March to August 2019. Pilot retail stores were
chosen where OPD was feasible and beneficial for participating firms, with fewer expected
community conflicts. During the OPD pilot program, local municipalities in the Region of
Peel provided a noise-by-law exemption to allow the firms to deliver to the pilot stores in
the off-peak hours (7:00 p.m. to 7:00 a.m. the next day).
Sustainability 2021,13, 246.
Sustainability 2021,13, 246 2 of 15
This paper analyzes truck trips and stop records from the three participating firms to
investigate benefits and challenges of the pilot program in terms of average travel speed,
service time, emissions, and noise complaints. Furthermore, this paper describes post-pilot
interviews held with logistics managers of the three firms. The results from these interviews
provide rich insights about successes, challenges, and ways that the OPD programs could
be improved.
Overall, this paper aims to provide lessons from this pilot to inform potential broader
implementations of OPD. The desired outcomes are:
To assess the benefits and challenges of the OPD pilot program in the Region of Peel;
To provide evidence and insights for firms that may consider OPD to improve the
efficiency of transporting goods;
To provide evidence and insights for municipalities or other government organiza-
tions that are considering implementation of a large-scale permanent OPD programs.
The paper is organized as follows; first, a background review is provided, followed
by a description of the OPD pilot in the Region of Peel. The data cleaning and processing
steps are then described, followed by data analysis results, lessons learned from post-pilot
interviews, and conclusions.
2. Background Review
A review of international case studies in OPD was conducted, prior to the pilot, in
order to improve our understanding of: (a) the key benefits and challenges to expect
in a pilot program, and (b) the appropriate strategy for evaluation and benchmarking
of results. A tabulation of these case studies is provided in the Appendix A. The case
studies include applications in Sao Paulo, New York City, Denmark, Colombia, Stockholm,
Barcelona, Orlando, Toronto, and London, UK. Table A1 in the Appendix Acategorizes
the case studies in terms of the geographical location, time frame (year and duration), the
scale of operations (number of shippers, carriers and receivers involved), performance
measures used, strategies for recruitment, unique technologies employed and current status.
These case studies have shown that a successful off-peak delivery program can result in
productivity improvements in freight operation, decreases in truck traffic, and decreases in
environmental externalities. However, these benefits are dependent on cooperation and
communication between shippers, receivers and carriers. This section reviews the benefits
and challenges associated with delivering during off-peak hours.
2.1. Benefits and Challenges
Benefits of off-peak delivery include societal benefits to residents, and operational
benefits to businesses. Both are important goals for urban residents, since productive
goods delivery improves the health of the regional economy, which leads to jobs and less
expensive consumer products. A primary goal of off-peak deliveries is to reduce congestion
during the day-time hours when traffic congestion is the greatest. Removing truck traffic
during congested periods frees up roadway capacity during the day and potentially reduces
travel times for commuters, including those using transit modes that operate in mixed
traffic. Reduction in truck travel during the day-time also reduces air quality pollutants
emitted at that time. Diesel engines in trucks disproportionally produce emissions of
NOx and particulate matter (PM) which have harmful health effects [
]. Cyclists and
pedestrians, who are more likely to be at the roadside during the day-time hours, could
therefore experience positive health effects from a shift in truck travel (and the resulting
emissions) to the evening or earlier morning. Reductions in truck idling and slower travel
speeds in congestion are also expected to reduce the total emissions for the same number
of deliveries. Reduced interactions between trucks and vulnerable road users such as
pedestrians and cyclists, who more often travel during the day, also have the potential to
benefit from an improvement in safety [3].
For businesses, the benefits of off-peak delivery can include faster travel time while
traveling to and from customers and reduced unloading time during the delivery. Lower
Sustainability 2021,13, 246 3 of 15
traffic levels are generally experienced during the overnight hours, leading to shorter travel
time, higher travel speeds, less idle time, and fewer emissions. Holguin-Veras et al. [
organized a pilot off-peak delivery project that moved the delivery schedules of 35 food
delivery firms to off-peak hours. The trucks were monitored using GPS technology to track
location and speed. A speed increase between the depot and first customer was observed,
from 11.8 mph to 20.2 mph, while a smaller increase was found while traveling between
subsequent customers. It was estimated that the delivery time during the pilot project was,
on average, half of what would usually be experienced during the morning hours, when
the majority of the deliveries would have taken place.
Making deliveries in urban areas can also be difficult due to the lack of proper truck
parking. It is estimated that upwards of 96,000 additional vehicle kilometers are traveled
every year on an average city block due to the search for parking [
]. For commercial
vehicles, the additional parking search time is likely to have a significant impact on total
tour time. In addition, the delivery of many types of goods requires parking near the
delivery location. As a direct result of this, many commercial vehicles are forced to park
illegally closer to their delivery location, resulting in parking tickets. Parking is easier to
find in the off-peak period [6].
Several studies have shown the potential of off-peak delivery in reducing truck emis-
sions. Yannis et al. [
] used a traffic simulation model to show improvements in overall
traffic emissions by restricting truck movement during peak hours. Campbell [
] generated
analytical models which showed that emissions reductions were possible, but only under
conditions where the average speed increased. Holguin-Veras et al. [
] collected GPS data
of delivery trucks from three cities in the Americas to analyze the effect of off-peak time
versus regular-time deliveries on emission estimation. They showed that off-peak delivery
can reduce the emission by 13% to 67% compared to regular-hour delivery.
For the case of Los Angeles, where there were night-time restrictions on truck move-
ments in some areas, the increase in average speed is negated by the extra distance needed
to travel to avoid the restricted areas [
]. The Barcelona night-time delivery project showed
the potential for reducing the number of trucks required to make deliveries. The project
showed that seven smaller trucks that would normally make the daily deliveries could
be replaced by two larger trucks that would normally not be able to maneuver through
downtown peak hour traffic [10].
The implementation of an off-peak delivery program requires addressing a number
of challenges, including receiver participation in the off-peak delivery program, noise
restrictions when delivering near residential areas, and security issues associated with
making deliveries at night.
Considerable research [
] suggests that a critical barrier to consider is receiver
willingness or ability to accept off-peak delivery. Selection of participants in an off-peak
delivery program should consider the ability of their receivers to accept off-peak deliveries.
Noise regulations and by-laws may restrict what activities may be done at night.
Night-time noise can generate opposition to off-peak delivery projects from members of
the community [
]. Noise can come from moving products within the vehicle, loading
and unloading the ramp, backup beepers as well as closing doors [
]. Wang et al. [
] and
Holguin-Veras et al. [
] suggest some possible solutions to reduce noise, including electric
trucks, isolated and insulated refrigeration units, low noise lifts, “quiet” truck beds or
liners and driver training. In Barcelona, delivery trucks were refurbished to include many
of the low noise technologies. Noise caused by the delivery was shown to differ very little
from ambient background noise [
]. In Copenhagen, Denmark, noise has been considered
the biggest challenge in the implementation of OPD due to strict noise regulation during
off-peak hours [
]. A pilot off-peak delivery project organized by Ontario’s Ministry of
Transportation [
] monitored the noise levels of deliveries in downtown Toronto. The
conclusions of the pilot project were that the “background hum” of the urban environment
was able to mask the sounds of the off-peak delivery, and that the noise produced in the
Sustainability 2021,13, 246 4 of 15
residential areas was sufficiently low as not to bother the residents. No complaints were
received during the pilot project from residents.
2.2. Off-Peak Delivery Methods
Assisted delivery is the most common delivery method for day-time deliveries. It
involves having a person present in the receiving store to accept deliveries. Assisted
delivery poses a barrier to OPD if the receiver does not otherwise maintain staff in the
off-peak hours.
Different methods for unassisted delivery exist, depending on the type of product
being delivered and the business setup. Delivery lockers or staging areas do not require
direct access to the store [
]. Both methods require a space separate from the interior of the
business premises where the delivery can be placed. The downside is that some products,
like perishable goods, frozen items, or high-value goods require extra infrastructure that
can be expensive. Virtual cages use a series of small sensors to monitor a small area of floor
space in the store [
]. Drivers are allowed access to this small area inside the store where
they can leave the goods. The sensors are able to detect if the driver leaves the virtual
cage and enters into restricted areas of the store. A driver may be given access to the store,
either through the use of a key or electronic code [
]. This is less expensive than delivery
lockers because no additional space or infrastructure is needed. However, trust between
carrier/driver and receiver is required and security measures would have to be altered
when drivers changed jobs.
The OPD pilot in this project seeks to avoid the challenges of unassisted delivery by
recruiting firms that operate a distribution center and the retail stores. This way, both
shippers and receivers are the same firms, so the coordination between these stakeholders
is not as challenging for the implementation of the pilot program.
3. Overview of the OPD Pilot in the Region of Peel
Region of Peel, located to the west of the City of Toronto, is a major freight hub in
North America with a population of 1.4 million. Approximately 1.8 billion dollars’ worth
of goods moves every day through the region and goods movement contributes $49 billion
CAD of GDP to the region’s economy [
]. The Region of Peel represents around 25% of all
truck activities in Ontario. Therefore, implementing the OPD program, as a truck demand
management strategy, can result in a significant improvement in goods movement in the
Three firms participated in the off-peak delivery program over a six-month period
from 25 February to 31 August 2019. These firms include the Liquor Control Board of
Ontario (LCBO), Loblaw Incorporated, and Walmart Canada. The three firms shifted
deliveries to a total of 14 participating retail stores. The retail stores were selected, in
collaboration with each firm and the Region of Peel, on the basis of their proximity to
residential areas and the expected operational benefits. The selected retail stores are shown
in Figure 1. For the three firms, two distribution centers are located in the Region of Peel
(these two are shown in Figure 1), one is located in London, Ontario, and one in Cambridge,
Ontario (not shown in Figure 1).
The local municipalities in Peel, which include the Cities of Brampton, Mississauga
and the Town of Caledon, provided a blanket exemption from the noise by-laws to allow
deliveries to be made in the evening for the duration of the pilot program.
Sustainability 2021,13, 246 5 of 15
Figure 1. Off-peak delivery (OPD) retail locations in the Region of Peel.
4. Data
We received truck tracking and other databases from each of the three participating
firms for the duration of the six-month pilot period. Data were received from each firm start-
ing from end of February or early March 2019 (depending on the firm) to
31 August 2019
Data received from each participating firm included:
Truck trip information (either provided as tracking information or trip summaries);
Retail outlet (receiver) addresses;
Truck fleet attributes (including age and truck type).
The following tasks were performed:
Develop a procedure to clean the data that were received, and impute missing infor-
Process the data to extract estimates of performance measures including travel speed,
delivery service time, and vehicle emissions for each trip that has accessed the 14
participating retail centers;
Summarize the differences between day-time (7:00 a.m. to 7:00 p.m.) and off-peak
(7:00 p.m. to 7:00 a.m.) performance for each of these performance measures.
This section summarizes the methodology used to clean and process the data.
4.1. Data Cleaning
Data received from each firm include information about each stop within each tour
(where a tour is defined as a sequence of consecutive trips starting at a depot, moving
to other locations and back to the depot). The cleaned dataset includes only those tours
that originated from the firms’ depot(s) and that accessed one of the pilot retail stores
participating in the off-peak delivery program. In addition to the travel times calculated
from the GPS-based data sets, we also extracted truck trip information (e.g., distances
and travel time from Google Maps, since none of the datasets from the participating firms
provided us with truck travel distance). Data management, data cleaning, and processing
Sustainability 2021,13, 246 6 of 15
were conducted using Pandas package in Python. The following steps were taken as part
of data cleaning:
Sort truck trips and stop records and delete any duplicates
Identify trips and stops for each truck tour
Link stops to retail store addresses, to allow for spatial analysis in Google maps
Identify and impute missing data (i.e., trip arrival and departure times) using travel
time estimates from Google maps
Estimate the route, travel distance and speed using Google Maps
4.2. Data Processing
The following attributes are calculated for each stop or trip, based on GPS-based truck
tracking information received from each participating firm:
Stop GPS service time: the difference between departure and arrival times for each
stop, which represents amount of time spent for loading or unloading goods at the
Trip GPS travel time: the difference between the arrival time of the end stop and
departure time of start stop.
Google maps was used to supplement the GPS data where concern arose due to GPS
inaccuracies, as follows:
Trip Google Map (Gmap) travel time: travel time between two stops estimated using
Google Map API;
Trip Google Map (Gmap) distance: the trip distance between two stops estimated
using Google Map API;
Stop Adjusted Service Time: This is an adjusted stop GPS service time, where the GPS
service time is adjusted by a factor that is equal to the division of trucks
tour Google
Map service time by truck tour GPS service time;
Emissions: Five emission measurements, including GHG, CO, NOx, PM10, and PM2.5
are calculated from the MOtor Vehicle Emission Simulator (MOVES) [
] based on
trip speed, distance, vehicle type, and vehicle age.
5. Results
Performance measures include average service time, average speed, emission factors,
and noise. These performance measures are summarized separately for trips that are
performed in the day-time period (7:00 a.m. to 7:00 p.m.) and in the off-peak period
(7:00 p.m. to 7:00 a.m.).
5.1. Service Time
Average service time is assessed at each pilot retail store as the first element of perfor-
mance measurement. For two firms, the stop GPS service time is selected; however, for
the third firm, the accuracy of GPS service time is questionable. The company reported
that since the GPS tracker ping is recorded every 15 min, the accuracy of recorded entry
and exit times to stores is +/
15 min. We also observed negative and zero service times,
which were removed as outliers. Therefore, the stop adjusted service time is chosen as the
indicator of service time for the third firm.
Table 1provides the summary of average service times for the 14 pilot retail stores
in the day-time (7:00 a.m. to 7:00 p.m.) and off-peak hours (7:00 p.m. to 7:00 a.m.), after
data cleaning, from March to August 2019. Table 1shows average service time in off-peak
hours compared to the day-time. The average service time is equal to 72 min based on
3714 observations during day-time hours. A significant variance was found in service
times. For some firms, the service times increased in the off-peak hours, while for others
the service time decreased. Discussions with logistics staff indicated that there were a
variety of logistical reasons for delays in the off-peak period. Insights into the reasons are
provided in the Lessons Learned section. Table 1also provides a result of a t-test for the
Sustainability 2021,13, 246 7 of 15
difference between off-peak and day-time service times. This analysis shows a statistically
significant higher service times in off-peak time compared to day-time stops with over 99%
confidence level.
Table 1. Average stop time for day-time and off-peak hours for the pilot stores.
Day-Time (7:00 a.m. to 7:00 p.m.) Off-Peak (7:00 p.m. to 7:00 a.m.) t-test: Off-Peak vs.
Day-Time (t-stat,
Number of Truck Stops at
the Pilot Stores
Average Stop Time
Number of Truck Stops
at the Pilot Stores
Average Stop Time
March to
August 2019 3714 72 1599 83 (8.76, 0.00)
Table 2shows the percentage of off-peak stops for the pilot retail stores. Overall, the
proportion of stops made at participating retail stores during off-peak hours during the
pilot was 1.30%.
Table 2. Percentage of off-peak stops for pilot retail stores.
Month Percentage of Stops in the Off-Peak Hours (7:00 p.m. to 7:00 a.m.)
March to August 2019 30.1%
5.2. Average Speed
Average speed is calculated as an indicator of travel time. Average speed is a preferred
indicator over travel time because truck routes selected from day-to-day vary as a result of
dynamic routing of the participating firms, which would pose difficulties in the comparison
of travel times. The average speed is based on the trips from participating firms’ depots
and retail stores to the pilot retail stores. These trips are extracted after cleaning of the data
provided by participating firms. Trip speeds are calculated using Google Map queries due
to the imprecise GPS tracking data of the third firm and unreasonably low GPS speed from
the first firm. We use Google Map speeds (determined for the observed trip departure time)
for all three firms so that the speed performance measurement is consistent across the three
Table 3presents a summary of the average speed for day-time and off-peak hours. The
average speed is weighted based on the trip length. Table 3shows a higher average speed in
off-peak hours compared to day-time hours. From March to August 2019, the average speed
of the trips in the off-peak hours is 18.1% faster than those that happened in day-time hours.
The last column shows the result of a weighted t-test on significance of the difference
between off-peak and day-time average speeds. The analysis confirms a statistically
significant difference between the two average speeds with over 99% confidence.
Table 3. Average speed for day-time and off-peak deliveries.
Day-Time (7:00 a.m. to 7:00 p.m.) Off-Peak (7:00 p.m. to 7:00 a.m.)
Percentage Increase
in Average Speed
in Off-Peak Hours
t-test: Off-Peak vs.
Day-Time (t-stat,
Number of
Average Speed
(Weighted by Trip km)
Number of
Average Speed
(Weighted by Trip km)
March to
August 2019 3405 46.0 951 54.3 18.1% (12.09, 0.00)
5.3. Emissions
Five emission estimates, including GHG, CO, NOx, PM10, and PM2.5, were deter-
mined from MOVES based on trip speed, distance, vehicle type, and vehicle age. The
average age of the truck fleet is considered for all trips instead of vehicle age specific to each
trip due to a lack of data on specific vehicles used for each trip. The average age of vehicles,
provided by each firm, is one, two, and eight years for the first, second, and third firms,
Sustainability 2021,13, 246 8 of 15
respectively. Table 4provides average emission factors (weighted by trip kilometers) for
the five emissions for day-time and off-peak. Improvements in GHG emissions averaged
at 10.6% for trips made in the off-peak. Reductions in air pollutants, including CO, NOx,
PM10, and PM2.5, ranged from 10.8% to 15.0%. The result of a t-test confirms a statistically
significant difference between GHG and air pollutants in off-peak compared to day-time
hours with a confidence level over 99%.
Table 4. Emission factors for GHG (CO2-eq) and air quality pollutants in day-time and off-peak.
Day-Time Emission Factors (grams/km) Off-Peak Emission Factors (grams/km)
(7:00 a.m. to 7:00 p.m.) (7:00 p.m. to 7:00 a.m.)
March to August 2019 1252 0.250 0.842 0.020 0.018 1119 0.223 0.747 0.017 0.016
% lower than day-time 10.6% 10.8% 11.3% 15.0% 11.1%
t-test: Off-peak vs. Day-time
(t-stat, p-value) (3.14, 0.00) (2.96, 0.00) (3.37, 0.00) (4.21 0.00) (3.09, 0.00)
5.4. Noise
Residents were informed of the off-peak delivery pilot via the Region of Peel website
and social media at the outset of the pilot. The project team diligently tracked any noise
complaints submitted to (a) the Region of Peel, (b) the Municipalities of Mississauga and
Brampton, and (c) the three participating firms. No noise complaints were submitted over
the course of the off-peak delivery pilot.
6. Lessons Learned
Post-pilot interviews were conducted with the logistics managers from LCBO, Loblaw
Incorporated and Walmart Canada that were most familiar with the OPD pilot. The
purpose of the interviews was to learn about challenges, successes, and ways that the pilot
program could be improved. The post-pilot interview includes the following 12 questions:
What are the most important challenges/barriers to making more deliveries in the
Were there any additional logistics costs associated with delivering off-peak? (e.g.,
staffing distribution center after hours, overtime pay).
3. Did your drivers prefer to work in the evening/early morning?
What challenges/feedback were expressed to you from the retail stores? Did the
retail store managers have any additional expenses as a result of the pilot, e.g., extra
evening staff)?
5. What advantages were there from a logistics perspective?
6. Did you learn of any security issues?
7. Did you learn of any safety issues (e.g., backing up at night, visibility)?
Was there less or more congestion at the drop-off sites (e.g., conflicts with other
Do you see the potential to further expand on the off-peak delivery program (if
the by-law exemption was made permanent)? Would this benefit your company’s
What proportion of your total operations do you think could be moved off-peak?
What changes would you recommend for the program (e.g., expand hours past
11:00 p.m.?)
Did you ever receive any noise complaints?
6.1. Overall Experience
First, the companies were asked about the challenges/barriers to OPD. Few challenges
were expressed, rather, all three firms identified that the OPD pilot was beneficial, and
in one case, complaints were received from the retail stores about returning to the orig-
inal schedule when the pilot ended. Since the companies involved in the pilot program
Sustainability 2021,13, 246 9 of 15
were large, major changes in routing procedures were not needed to accommodate the
adjustment to the off-peak for the limited number of participating retail stores.
Part of the success of the program was related to the careful selection of retail stores.
First, retail stores were selected in locations that were not remote, therefore routing to
multiple retail stores was facilitated, which was considered by one firm to be more cost
effective than engaging in OPD for a single remote retail store. Second, retail stores were
successfully chosen to avoid complaints from neighboring residents. While all firms were
concerned with the potential for noise complaints, none were received. Third, some advan-
tages were attributed to OPD in terms of staffing, store preparation and shelf stocking. One
firm pointed out that deliveries could be made at a time that facilitated store preparation
prior to opening time. Finally, it was noted by one firm that the truck/trailer assets could
be better utilized, because the same vehicle could make a trip during the day and then
another trip in the evening.
6.2. Logistics Costs
No additional logistics costs were identified by the participating firms as a result of
the OPD pilot. The project team was concerned in particular about any additional staffing
costs for the retail stores or distribution centers for increased off-peak activity. None of
the three participating firms identified any additional staffing costs. One firm pointed out
that the distribution centers ran overnight shifts even before the pilot started so there were
no incremental changes. Another pointed out that some of the cost savings associated
with faster travel speeds were mostly experienced by the carrier, who was under a fixed
contract, though in the longer-term this could lead to lower costs to the stores. The only
additional costs that were anticipated would be with the retail stores, which could incur
additional staffing costs for an overnight crew; however, the pilot retail stores did not incur
these costs because their current staffing systems could accommodate the OPD. Expansion
of the program to include retail stores without after-hours staffing could lead to additional
costs. One firm pointed out that the existing staff could be used more productively due to
the off-peak timing of the shipments.
6.3. Driver Experience
One concern of the project team was about the driver/carrier experience. Two of
the firms engaged their outsourced carriers in the OPD program and one firm used their
own fleet of drivers. Those that outsourced their transportation identified that the carriers
would likely prefer to make deliveries during off-peak hours because they are paid by the
kilometer, or by the shipment, and therefore would financially benefit from faster travel
speeds. For own-account transportation, no specific preference was identified, rather it
was pointed out that individual drivers have mixed preferences of the time of day they
prefer to work.
6.4. Distribution Centre Experience
It was noted by one firm that some benefits were experienced at a distribution cen-
ter because it was possible to spread out the workload to avoid bottlenecks. Another
firm pointed out that, since the pilot program was relatively small, there may have been
efficiency gains that were not noticeable given the scale of operations of the firm.
6.5. Retail Store Experience
The experience at the retail stores was generally positive. One firm pointed out that
the store presentation could be better in the morning because more time was available
for retail staff to set up the store before opening. Other firms noted the importance of
preparation time. If an OPD schedule can be organized with enough advance notice, then
staffing and space in the backroom could be arranged to handle the deliveries. The only
additional concern that was raised was that for some retail outlets the parking lots were
busier in the evening with customers, which could be more difficult for the drivers to
Sustainability 2021,13, 246 10 of 15
navigate. Therefore, evening deliveries would be better suited for retail stores with loading
bays. The biggest advantage noted from the retail store perspective was the predictability
of shipments. One firm noted that the deliveries arrived within minutes of the expected
arrival time, when made in the off-peak hours. Predictable delivery times were found to be
helpful with staffing.
6.6. Safety, Security, and Noise
No concerns were raised about any safety or security issues associated with OPD. All
participating firms pointed out that drivers are trained to operate safely, and their loading
docks are well-lit. It was noted by one firm that security was not an issue because stores
were not located in isolated locations. All three participating firms confirmed that no noise
complaints were made to the companies as a result of the OPD pilot.
6.7. Congestion at the Retail Stores
Logistics managers were asked about congestion at the retail locations in the off-peak.
While it was noted that there are more customers in the evening hours, there was some
diversity amongst firms about how busy staff were in the evening. One firm noted that
there is less congestion in the delivery bays in the evening, because most other carriers
deliver during the day and pointed out that OPD allowed for a spreading of the receiving
workload over the course of the day and easier management of space in the backroom.
Another firm suggested that retail staff may be busier in the evening, and some conflicts
happened with other carriers at that time.
6.8. Expansion of the OPD Program
All participating firms enthusiastically supported the continuation and expansion of
the OPD program. Substantial expansion of the program to new retail stores was advocated
by all three firms. Some limitations on which retail stores could participate were related to
(a) remoteness of stores (easier to deliver off-peak to stores that are not isolated), and (b)
stores that already had staff assigned to work in the off-peak. All three firms felt that the
pilot was run effectively and proposed few changes for expansion of the program. One
suggestion was to include delivery arrival time predictability as an additional performance
7. Conclusions
This paper investigated the benefits and challenges of off-peak deliveries for a pilot in
the Region of Peel. In contrast to previous off-peak delivery projects, this is the first project
focusing on deliveries in suburban areas. Three firms participated: LCBO, Loblaws, and
Walmart, involving deliveries to 14 pilot retail stores. The analysis shows that during the six-
month pilot, from March to August 2019, 30.1% of deliveries to pilot retail stores were made
in off-peak hours (7:00 p.m. to 7:00 a.m.). The average speed of the trips that were made in
off-peak hours during the six-month pilot is 18.1% faster than those that happened in day-
time hours. Having higher speed in off-peak hours leads to lower emission factors. The total
greenhouse gas emissions/km decreased by 10.6%, and emissions factors for air quality
pollutants, including CO, NOx, PM10 and PM2.5 reduced by 10.8% to 15.0%. Results for
service times varied between firms, but on average increased by 15.2%, indicating activities
in the off-peak hours at the retail stores that prevented overall improvements in service
time compared to day-time deliveries. Interviews with logistics managers identified that
some delivery activities took longer for one company in the off-peak hours, in part because
of the presence of potentially busier staff at that time of day.
The pilot off-peak delivery program is considered to be a success. From a public
policy perspective, the movement of delivery vehicles to times of day when congestion is
lower makes better use of available roadway capacity and reduces congestion for other
road users during the peak travel time. Increases in commercial vehicle travel speed
leads to lower emission factors which benefits public health and helps reduce the regional
Sustainability 2021,13, 246 11 of 15
contributions to GHG emissions. If any additional noise occurred as a result of the OPD
pilot program, it was not great enough to result in any noise-related complaints. From a
business perspective, the improvements in travel speed reduce logistics cost and improve
fuel efficiency and therefore enhance the business competitiveness of participating firms.
All three participating firms enthusiastically supported the continuation and expan-
sion of the OPD program and expressed a strong willingness to continue participation. All
of the participating firms considered the program to be well-organized and well-run and
proposed few suggested changes, aside from the program’s expansion. Given the success
of the Region of Peel OPD pilot, efforts were made to develop a program to expand the
Region of Peel pilot to encompass other municipalities in the Greater Toronto and Hamilton
Area and to expand the number of firms involved in the program.
On 19 March 2020, in response to COVID-19, the Ontario Government announced the
Municipal Emergency Act 2020 to relax noise by-laws for goods delivery across the entire
Province of Ontario. This measure to facilitate supply chains in an emergency situation
was made possible, in part, based on the outcomes of the pilot presented in this paper. Our
next step is to evaluate the experience of OPD in this Ontario-wide emergency to allow
Author Contributions:
The authors confirm contribution to the paper as follows: study conception
and design: M.J.R., S.K., and S.S.; literature review: G.A., M.J.R., and K.M.; analysis and interpretation
of results: K.M., M.J.R.; draft manuscript preparation: K.M., M.J.R. All authors reviewed the results
and approved the final version of the manuscript.
This study was funded by the Region of Peel, The Atmospheric Fund, Metrolinx, and the
University of Toronto.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
Restrictions apply to the availability of these data. Data were obtained
from Walmart, Loblaws, and LCBO and the data are unavailable to the public.
The contribution of MRK Innovations and Partners in Project Green are ac-
knowledged. We thank Walmart, Loblaws, and LCBO for participating in the OPD pilot program.
We acknowledge the help from An Wang from the Transportation and Air Quality Research Group
for helping with the emissions analysis in this project.
Conflicts of Interest: The authors declare no conflict of interest.
Sustainability 2021,13, 246 12 of 15
Appendix A
Table A1. Summary of Off-Peak Delivery Projects.
Location, Year [Reference]. # of Carriers/Shippers # of Receivers Duration 1Performance Measures Strategy Technologies Used Status of the Study
Sao Paulo, (FIFA), 2014 [21]. 1 carrier (DHL)
1 shipper 2 retail outlets 2 weeks
-Speed (travel time)
-Productivity (Unloading time)
-Safety/Security risks (qualitative)
-Noise complaints
-Staffed OPD
-No cash incentive Not mentioned Project continued to
second phase (next row)
Sao Paulo, 2014–2015 [22]. Carrier: SETCESP
11 firms with 45 retail
12 weeks October
2014 -
April 2015
-Safety (incidents from Police data)
-Noise (complaints and
-Staffed and unstaffed
-No cash incentive
-Shadowing (measuring
-Armed escort in 2 cases
-Truck GPS
-OPD a City policy
-Entire city implementation
planned for 2016
-16 large firms, 9 new
New York City, 2009 [4]. 20 trucks (8 vendors) 35 receivers 3 stages (each 1
-Speed/service time
-Survey satisfaction
-Staffed (50%) and
unstaffed (50%)
-Cash incentives
($2000/receiver and
$300/truck to carriers)
-GPS enable smartphones
-Network models to assess
to network wide impacts
-Follow-up survey
-Continued to second
phase (next row)
-50% (unstaffed) remained
with OPD
New York City, 2013 [14] 400 receivers Unknown
-Speed/service time
-Survey satisfaction
-Low noise
-Noise monitoring
-175–200 companies have
shifted to OPD
Denmark (Copenhagen),
2012–2013 [16]. 7 carriers Unknown Unknown
-Fuel consumption (data was
provided by the companies)
-No cash incentives
-Most companies were
happy to have participated.
-2 decided to continue OPD
Colombia, 2015 [23]. 17 8 weeks
-Scheduled one on one
-Air quality measurements
Use of GPS data loggers
-Truck GPS
-Air emissions sensors
-Noise monitoring
-5 firms (mostly
supermarkets) are
continuing with OPD.
Stockholm, 2014 [24].
1 shipper
1 carrier with 2 trucks
(1 hybrid, 1 biogas)
~30 restaurants and
hotels in downtown
2 years (including
-Driving efficiency
-Delivery reliability
-Energy efficiency
-Service efficiency
-Noise complaints
-Route efficiency
-Post surveys
-Trucks equipped with
noise monitor
-GPS data
-Fuel level measurement
-OPD was extended for
this carrier.
-Noise, cost benefit analysis
is on-going
Barcelona, 2003 [25].
One supermarket
-2 large trucks for
OPD replacing 7 vans
2 supermarket
11 p.m.–12 a.m. and
5–6 a.m.
For 4 months
-Noise measurements
-Noise complaints
-Staffed delivery—No
financial incentives
-Use of larger trucks
-Noise monitor (Police)
-By 2010, the supermarket
chain has expanded OPD
to over 407 store locations
across Spain.
-Other supermarkets have
considered OPD
Sustainability 2021,13, 246 13 of 15
Table A1. Cont.
Orlando, FL, USA [25]. 1 shipper: Orlando
1 receiver: Central
Florida hospital 9 months
-Air quality
-Walkability on campus and in the
Ontario, 2014
(Downtown Toronto) [26]. 5 carriers Over 30 receivers 4 weeks
-Travel time
-Participant experience
-No incentives
The downtown pilot was a
successful test in advance
of the Panam/Parapanam
Games (next row)
Ontario, 2015
(Panam/Parapanam Games) [26]. Unknown 100 businesses 6 weeks -Noise -No incentives
London Olympics, 2012 [27]. -Noise disturbance -TFL Code of practice
1This duration does not include the preparation time (survey, outreach, etc.). It only reflects the duration of the pilot.
Sustainability 2021,13, 246 14 of 15
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... There has been growing interest in OPHD in the Greater Toronto and Hamilton Area (GTHA), which is a major generator of freight traffic in Ontario thanks to its large consumer base, location of major transportation hubs, and manufacturing activities (1). A 2019 pilot program conducted in the Region of Peel, a suburban region in the GTHA showed promising results of OPHD (2). Near the beginning of the COVID-19 pandemic the Government of Ontario limited municipal authority across the province to regulate noise related to goods delivery. ...
... Emissions of greenhouse gases-CO, NOx, PM10, and PM2.5-also reduced markedly, ranging from 10% to 15% during the pilot. The participating firms supported an expansion of the OPHD program in the region (2). ...
... Only the scenario with 30% shift is demonstrated here but the same trend is true for other scenarios as evident from Figure 6. The 30% shift is a reasonable participation rate based on the Peel Region pilot study (2). Off-peak deliveries made to Toronto are associated with the highest savings, followed by Peel Region. ...
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Off-peak-hour delivery (OPHD) has the potential to reduce congestion in busy urban areas and at the same time improve the efficiency of logistics providers, shippers, and receivers. There has been growing interest in OPHD in the Greater Toronto and Hamilton Area (GTHA), an important freight hub in Canada. The Government of Ontario is considering permanently relaxing noise by-laws to promote OPHD throughout the province. The purpose of this study is to provide quantitative assessments of the impacts of region-wide adoption of OPHD for the GTHA. A recently developed commercial vehicle (CV) model for the GTHA is presented in the paper. Various OPHD scenarios have been tested with the CV model. The impacts of induced passenger demand have also been demonstrated. Modeling outcomes indicate that OPHD could result in 5,530 vehicle-hours saved in a day after induced demand is accounted for. Light truck carriers would benefit the most by shifting to off-peak hours and prioritizing Toronto and Peel Region customers would yield the highest travel time savings during the off-peak hours.
... Their results show emission per mile reductions for CO 2 , NO x and PM 10 ranging from 12% for OPD between 7 and 10 PM, to 65% for deliveries close to free flow speeds between 10PM and 6AM. Similarly, an OPD pilot program conducted in Ontario, Canada indicated emission reductions ranging from 10.6% to 15% for GHG, CO, NO x , PM 10 , and PM 2.5 for deliveries in the off-peak hours (7PM to 7AM) (Mousavi et al., 2021). These studies, however, rely on route level vehicle specific comparisons. ...
... In 2019, a pilot OPD program consisting of three major retail firms with 14 pilot stores in the Region of Peel, a regional municipality in the GTHA, indicated that 30.1% of deliveries to the pilot stores were made in off-peak hours (Mousavi et al., 2021). In response to the COVID-19 pandemic, the Ontario Government announced on March 19th, 2020 the Municipal Emergency Act, which permitted OPDs across Ontario. ...
This study evaluates the potential impacts of off-peak delivery (OPD) for greenhouse gases and air pollutant emissions, with an application to the Greater Toronto Area. Multiple scenarios are formulated. OPD improves network congestion and travel times but increases vehicle kilometers travelled (VKT). The increase in VKT is attributed to road freight vehicles modifying their routing during the off-peak to longer routes. VKT generation is also attributed to induced passenger travel demand generated in response to improvements in traffic conditions during the daytime. Induced VKT plays an important role in limiting the reductions in greenhouse gases and nitrogen oxide emissions attributable to OPD. Yet, OPD scenarios reduce the emissions of fine particulate matter for both passenger and freight vehicles by reducing congestion. While total daily emissions either decrease or change minimally, the spatio-temporal distribution of emissions indicates increases in night-time emissions, particularly in communities located around highways and major arterial roads.
... The small number of truck traffic in the morning can be associated with the recent trend of increased use of off-peak delivery to avoid peak hour congestion and reduce energy use and improve the speed and reliability of freight movement. There have been some off-peak delivery pilot projects in Canada (e.g., Region of Peel) to improve the efficiency of delivery firms by potentially reducing costs for the shippers and the receivers (Mousavi et al., 2020). In recent years, the Good Movement Strategies of Calgary and the city of Edmonton promoted the use of offpeak deliveries to reduce peak hours congestion (The City of Calgary, 2018). ...
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Large trucking vehicles have a comparatively more significant impact on safety, traffic congestion, pollution, and pavement wear than passenger vehicles. Appropriate planning and operation of truck movement are necessary to reduce these impacts. While heavy truck movement has traditionally been measured through surveys, these remain limited because they are costly and time-consuming. In this study, we propose the use of large streams of GPS data to estimate truck origin-destination flows. Large streams of GPS data have typically been difficult to use as they lack descriptors for key events during a trip unless the data is accompanied by travel diaries. We address this problem by developing a heuristic-based approach to identify the key events, such as truck stops, trips, and other trucking activities. Then, a Pearson correlation coefficient and an entropy measure are applied to compare trucks' mobility patterns and to determine whether changes in trucks travel patterns have occurred over one year. Finally, we use a multinomial logit structure to estimate destination choice models for five time periods. This research provides a strong case study of how GPS data can be used along with outputs of existing travel demand model (a model created with data collected using traditional techniques) to estimate origin-destination and destination choice models of truck movement in a provincial model setting.
... Procedure 3 focuses on operational solutions, such as off-peak delivery, off-night delivery, and unloading bays, to reduce environmental impacts [2]. The operational solutions improve freight mobility and reduce delivery time and emissions [87][88][89]. Unloading bays are the most common solution for SUFT [90], contributing to improved mobility [91]. Establishment data could support unloading bay analysis [91], while operational tests could show off-peak delivery or off-night delivery benefits. ...
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Urban freight transport (UFT) is simultaneously responsible for maintaining the urban lifestyle and the negative externalities impacting urban areas, necessitating strategies that promote sustainable urban freight transport (SUFT). In addition, the stakeholders and geographic factors involved in UFT impose specific concerns in the planning and operation stages of SUFT. Therefore, this paper proposes a model addressing sustainable last-mile delivery considering the relationship between the activity system, transportation system, and stakeholders involved in UFT. Based on the literature review, we identified UFT planning procedures to achieve SUFT. In a cyclical process, these procedures were considered on the proposed model, integrating freight transport planning with urban planning to develop SUFT and, consequently, sustainable cities.
The loading and unloading operations carried out by transport and logistics operators have a strong impact on city mobility if they are not performed correctly. If loading/unloading bays, i.e., delivery bays (DB), are not available for freight vehicle operations, operators may opt to double park or park on the sidewalk where there is no strong enforcement of these laws, with significant impact on congestion. This paper proposes a methodology for verifying and designing the number of delivery bays needed for freight vehicles for not interfere with cars or pedestrians. The methodology consists of two stages: in the first stage, an initial estimation is made using queueing theory. Subsequently, in the second stage, using such tentative scenario, in order to take into account the system stochasticity involving different entities, a discrete event simulation is performed to more realistically verify and upgrade (if necessary) the number of delivery bays to obtain the expected outcomes. The methodology was applied in the inner area of Santander (Spain). The study area was subdivided into 29 zones where the methodology was applied individually. The results indicated that none of these zones currently have an optimal number of delivery bays to satisfy demand. In some zones, there is an excess of delivery bays, although in most of them, there is a deficit which can cause significant impacts on traffic. The method proposed can be an effective tool to be used by city planners for improving freight operations in urban areas limiting the negative impacts produced in terms of internal and external costs.
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The fundamental tenet of this paper is that moving deliveries by trucks to off-peak hours requires the implementation of comprehensive policies targeting the critical components of the supply-chain process. This is so because by far the most decisions concerning time of travel of commercial trucks in urban areas are either made by the receivers or made jointly by the receiver and the carrier. In this context, without receivers able and willing to accept off-peak deliveries, the ability of trucking companies to move out of normal hours is severely hampered. This paper analyzes the receptiveness of an important group of receivers, the restaurant sector in Manhattan, New York City, to policies aimed at fostering off-peak deliveries. This study focused on restaurants for several reasons. First, in-depth interviews with private-sector executives indicated that the restaurant sector could be a good candidate for off-peak deliveries. Second, and more important, the relatively high number of restaurants and drinking places in Manhattan (exceeding 6,500 establishments) generates an estimated minimum of 20,000 truck trips per day. These numbers suggest there would be a significant payoff if a significant portion of these truck trips were switched to the off-peak hours. The analyses are based on a small attitudinal survey used to evaluate the effectiveness of four different policies involving financial incentives to the restaurants accepting off-peak deliveries. Despite the small sample size, the key findings from these analyses are consistent with the estimates from behavioral models based on a larger data set. These preliminary results show that financial incentives may be effective in fostering off-peak deliveries.
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This paper presents a comprehensive review of the literature on off-peak hour deliveries (OPHD). The review identifies different approaches and policy levers used in the past, such as the laissez-faire approach, a road pricing approach, an incentives approach, and a regulatory approach. The paper also identifies different delivery reception schemes discussed in the literature. The authors complement the theory with a synthesis of pilot tests and the analysis of a set of interviews with practitioners (from the public sector and other organisations) in charge of OPHD programmes. The results from this review show the potential benefits that these programmes could bring about, the challenges faced in the early stages – along with potential solutions – and the significant progress that has been made in this domain in the last decade. According to the review, the results from the pilot tests tend to be positive, suggesting the importance of these programmes to reach more efficient and sustainable transportation systems.
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The most significant negative environmental impacts of urban trucking result largely from travel in congested traffic. To illustrate the potential of innovative solutions to this problem, this paper presents new research on the emission reductions associated with off-hour freight deliveries (OHD). The paper uses fine-level GPS data of delivery operations during regular-hours (6 AM to 7 PM), and off-hours (7 PM to 6 AM), to quantify emissions in three major cities in the Americas. Using second-by-second emissions modeling, the paper compares emissions under both delivery schedules for: reactive organic gases, total organic gases, carbon monoxide, carbon dioxide, oxides of nitrogen, and particulate matter. The results show that the magnitude of the emission reductions depends on the extent of the change of delivery time. In the case of the “Full” OHD programs of New York City and São Paulo—where the deliveries were made during the late night and early morning periods (7 PM to 6 AM)—the emission reductions are in the range of 45–67%. In the case of the “Partial” OHD used in Bogotá (where OHD took place between 6 PM and 10 PM), the reductions were about 13%. The emission reductions per kilometer are used to estimate the total reductions for the cities studied, and for all metropolitan areas in the world with more than two million residents. The results indicate the considerable potential of OHD as an effective—business friendly—sustainability tool to improve the environmental performance of urban deliveries. The chief implication is that public policy should foster off-hour deliveries, and all forms of Freight Demand Management, where practicable.
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Events that mobilize large crowds, such as the 2014 FIFA World Cup, present challenging obstacles to operations attempting to deliver goods efficiently in major urban centers. This study presents a logistics business case that implemented a pilot project during the 2014 FIFA World Cup in Brazil, which shifted delivery times to off-hour deliveries (10:00 p.m. to 6:00 a.m.). The pilot project was implemented to identify the constraints and opportunities related to off-hour deliveries in São Paulo, Brazil. Additionally, the project was able to address the congestion and delays experienced during daytime deliveries. The study relied on a partnership forged among a logistics company, a sportswear company, and an academic center, and there was no government support for this case study or project.
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The paper identifies and discusses the fundamental tenets that should guide planning and implementation of City Logistics projects, and the chief lessons learned from the off-hours delivery project conducted in New York City.
Commercial vehicle movements have a large effect on traffic-related air pollution in metropolitan areas. In the Greater Toronto and Hamilton Area (GTHA), commercial vehicles include large and medium diesel trucks as well as light-duty gasoline-fuelled trucks. In this study, the emissions of various air pollutants associated with diesel commercial vehicles were estimated and their impacts on urban air quality, population exposure, and public health were quantified. Using data on diesel trucks in the GTHA and a chemical transport model at a spatial resolution of 1 km2, the contribution of commercial diesel movements to air quality was estimated. This contribution amounts to about 6-22% of the mean population exposure to nitrogen dioxide (NO2) and black carbon (BC), depending on the municipality, but is systematically lower than 3% for fine particulate matter (PM2.5) and ozone (O3). Using a comparative risk assessment approach, we estimated that the emissions of all diesel commercial vehicles within the GTHA are responsible for an annual total of at least 9810 Years of Life Lost (YLL), corresponding to $3.2 billion of annual social costs. We also assessed the impact of decreasing freeway-sourced diesel emissions along Highway 401, one of the busiest highways in North America. This is comparable with a removal of 250 to 1000 diesel trucks per day along that corridor, which could be replaced by alternative technologies. The mean NO2 and BC exposures of the population living within 500 m of the highway would decrease by 9% and 11%, respectively, with reductions as high as 22%. Such a measure would save 1310 YLL annually, equivalent to $428 million in social benefits.
Off-peak-hour delivery programs are a promising but challenging concept for promoting sustainable urban logistics. Stockholm, Sweden, initialized a nighttime freight deliveries program in 2014, aimed at a more efficient and environmentally friendly delivery system within the central area of the city. The policy of shifting freight deliveries from daytime to off-peak hours generates a wide range of effects that can be analyzed from several angles. This paper identifies the social costs and benefits, how these are distributed between stakeholders, and their effects on the everyday life and operations of all interested parties. According to information and data collected through in-depth interviews with private and public stakeholders, the social benefits mainly consist of increased efficiency and productivity for carriers and receivers, reduced transport costs, fuel cost savings, and reduced congestion and accidents when trucks are moved from peak to off-peak hours. Social costs may include increased noise levels and noise disturbances; additional staff requirements, equipment, and wage costs; and higher risks in handling goods deliveries at nighttime, especially in the case of unassisted deliveries. This paper concludes by discussing the lessons learned from the trial, challenges and opportunities that arose during implementation, and the implications for enhancement of off-peak-hour delivery in Stockholm and other cities.
The paper addresses road freight transport operations during the London Olympic and Paralympic Games in 2012. It presents work carried out prior to the Games to understand pre-Games patterns of freight deliveries in London (for both light and heavy goods vehicles) and the results of modelling work carried out to assess the likely impacts of the Games road restrictions on freight operations. The modelling results indicated that increases in total hours travelled carrying out collection and delivery work would range from 1.4% to 11.4% in the six sectors considered. The results suggested increases in hours travelled in excess of 3.5% in four of the six sectors modelled. The possible actions that could be taken by organizations to reduce these negative impacts were also modelled and the results indicated that such actions would help to mitigate the impact of the road restrictions imposed on operators during the Games. The actual impacts of the 2012 Games on transport both in general terms and specifically in terms of freight transport are also discussed, together with the success of the actions taken by Transport for London (TfL) to help the road freight industry. The potential freight transport legacy of the London 2012 Games in terms of achieving more sustainable urban freight transport is considered and the steps being taken by TfL to help ensure that such a legacy can be realized are discussed. Such steps include policy-makers continuing to collaborate closely with the freight industry through the ‘London Freight Forum’, and TfL's efforts to encourage and support companies revising their delivery and collection times to the off-peak; improving freight planning in the design and management of TfL-funded road schemes; electronic provision of traffic information by TfL to the freight industry, and the further development of freight journey planning tools.
The main objective of this paper is to provide an overview of the technologies available for use in unassisted off-hour deliveries (i.e., deliveries made outside of regular business hours without the presence of receiving staff). The focus is on technologies that monitor and provide access, on some level, to the establishment and those that lessen or eliminate the noise created by delivery trucks and equipment. This paper also touches on the costs and benefits to companies that use unassisted off-hour deliveries and, ultimately, the question of who should cover the costs of the technologies. This issue is tied to the discussion of policy implications and how the public sector can help increase the adoption of these delivery programs.