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This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Conference Paper

NREL/CP-5400-72890

January 2019

A Hybrid Tour-Based Model for Energy 

Analysis of Multi-Modal Intra-City 

Freight: A Case Study of Autonomous 

Electric Vehicles

Preprint

Yi Hou,

1 Amy M. Moore,2 Adam Duran,1 Kevin Walkowicz,1

and David Smith2

1 National Renewable Energy Laboratory

2 Oak Ridge National Laboratory

Presented at Transportation Research Board (TRB) 98th Annual Meeting

Washington, D.C.

January 13–17, 2019
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

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15013 Denver West Parkway

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Conference Paper

NREL/CP-5400-72890

January 2019

A Hybrid Tour-Based Model for Energy 

Analysis of Multi-Modal Intra-City 

Freight: A Case Study of Autonomous 

Electric Vehicles

Preprint

Yi Hou,

1 Amy M. Moore,2 Adam Duran,1 Kevin Walkowicz,1

and David Smith2

1 National Renewable Energy Laboratory

2 Oak Ridge National Laboratory

Suggested Citation

Hou, Yi, Amy M. Moore, Adam Duran, Kevin Walkowicz, and David Smith. 2019. A Hybrid 

Tour-Based Model for Energy Analysis of Multi-Modal Intra-City Freight: A Case Study of 

Autonomous Electric Vehicles: Preprint. Golden, CO: National Renewable Energy 

Laboratory. NREL/CP-5400-72890. https://www.nrel.gov/docs/fy19osti/72890.pdf.
NOTICE

This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding 

provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Vehicle

Technologies Office. The views expressed herein do not necessarily represent the views of the DOE or the U.S. 

Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges 

that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce 

the published form of this work, or allow others to do so, for U.S. Government purposes.

This report is available at no cost from the National Renewable 

Energy Laboratory (NREL) at www.nrel.gov/publications.

U.S. Department of Energy (DOE) reports produced after 1991 

and a growing number of pre-1991 documents are available 

free via www.OSTI.gov.

Cover Photos by Dennis Schroeder: (clockwise, left to right) NREL 51934, NREL 45897, NREL 42160, NREL 45891, NREL 48097, 

NREL 46526.

NREL prints on paper that contains recycled content.
1

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Abstract

With the emergence of cutting-edge transportation technologies, such as electric vehicles (EVs), 

connected and autonomous vehicles, and drones, the adoption of multimodal freight mobility has 

the potential to improve efficiency and save more energy. This paper proposes a hybrid tourbased model to evaluate the energy impact of multimodal intra-city freight movement for future 

scenarios. The model is built based on the traveling salesman problem model and clustering 

techniques. A case study using autonomous electric vehicles for package delivery is evaluated. 

The study was conducted using data analyzed from the Columbus, Ohio, metropolitan area. The 

initial results show that multimodal package delivery using autonomous EVs reduced total travel 

time by 45% and saved total energy use by 19%, but increased total vehicle miles traveled by 

15% when compared with the baseline scenario.

Keywords: energy analysis, multimodal, intra-city, freight, autonomous vehicles, electric 

vehicles

Introduction

Modeling freight movement data and route choice information is crucial to planning; improving 

current and future transportation infrastructure; locating businesses; improving efforts to 

streamline goods movement, especially urban goods; and reducing energy use for freight 

transportation. Also, with increasing demands and changing consumption habits, mobility being 

offered as a service, and recent advancements in electric vehicle (EVs) and connected and 

autonomous vehicle technologies, the landscape of freight planning is changing as businesses 

compete to meet the needs of a growing customer base, while transportation planners aim at 

meeting these needs by maintaining and updating the transportation infrastructure. There is a 

growing interest in examining freight movements, and although previous research has been done 

to look at freight route choice regarding individual trips, there is increasing interest in freight 

tours.

Consideration of new transportation technologies and the effect that these technologies have on 

the existing freight network and freight movements are often lacking in the literature. The use of 

electric and autonomous vehicles for freight movements should be considered, especially for 

local freight tours. How the increasing use of these vehicle types affects energy use and 

emissions locally and regionally, determining the location of charging stations, how this affects 

route choice decisions, and the effects that these vehicle types have on neighborhoods (possibly 

reducing air and noise pollution levels) should all be considered when developing local-level 

tour models. Autonomous vehicle use should also be considered, as advancements in 

autonomous vehicle technology will likely lead to more of these vehicle types being used for 

deliveries and long-haul freight shipments in the near future, which will have an effect on 

efficiency and travel times, as autonomous trucks will not have the rest periods that are required 

for long-haul truck trips.

The lack of an efficient energy analysis tool for freight movement considering adoption of 

emerging transportation technologies and multimodal shifts motivated this study. Although there 

are several sources of tour-based freight models that a Metropolitan Planning Organization or 

other planning entity can refer to when developing a location-specific methodology, these 
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sources are still missing pertinent information regarding changing manufacturing practices, 

evolving vehicle technologies, and improved delivery systems and technology. Consideration 

should be given to these issues to better model the existing and quickly changing economic 

landscape, and to better understand energy use and potential energy-saving strategies for freight 

transport. The objective of this paper is to fill the gap by developing a hybrid tour-based model 

for energy analysis of multimodal intra-city freight using the “traveling salesman problem”

(TSP) model and clustering techniques. A future scenario where autonomous electric vehicles 

(AEVs) are used for multimodal package delivery is evaluated using the developed model.

The remainder of the paper is organized as follows: the next section provides a brief review of 

literature on tour-based freight models; the third section presents the detailed description of 

methodology for model development; the fourth section presents the data collection process and 

experimental results of a case study, as well as a sensitivity analysis. The conclusions are 

presented in the final section.

Literature Review

Tour-based freight modeling, although only recently becoming part of the transportation and 

freight modeling literature, is crucial to transportation planning and engineering. Whereas tripbased freight models adhered to the traditional four-step travel demand model and were 

commonly used in modeling passenger vehicle movements, the importance of modeling tours 

has been realized as a means by which the underlying route decision process is modeled. 

Doustmohammadi et al. (1) suggests that tour-based models are more suitable for considering 

intermediate stops and the effect that these stops have on vehicle miles traveled (VMT), which is 

an important consideration regarding energy use. Trip-based models fail to include complexities 

and details, especially prevalent trip-chaining behavior. Although the traditional four-step travel 

demand model has typically been used to model passenger movement, it fails to capture 

information regarding the interdependency of multiple trips within tours, and thus is not 

necessarily suitable for modeling freight tours within an area (2). 

Global positioning system data (GPS) provide a way to collect very detailed data on freight 

movements. Although obtaining proprietary shipment data from a parcel delivery service is rare, 

the American Transportation Research Institute and travel data measurement companies are 

often the sources of GPS data used in tour-based freight models. Kuppam et al. (3) used 

American Transportation Research Institute GPS travel logs of truck tours from companies 

collected by the Maricopa Association of Governments and truck GPS data obtained from 

StreetLight as part of the development of the Mega-Regional Multimodal Agent-Based 

Behavioral Freight Model. Based on the results from this study, the authors concluded with the 

recommendation of using GPS data for developing truck tour-based models. Greaves and 

Figliozzi (4) also found that GPS data can be used to complement survey data collected from 

truck drivers. The methodology presented in this study is in-line with previous methods found 

throughout the tour-based freight modeling literature.
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Methods

Hybrid Tour-Based Model

In an effort to quantify the energy impact via adopting autonomous electric vehicles (AEVs) in 

multimodal package delivery on an intracity scale, a hybrid tour-based model was developed 

using the classic operations research model, the traveling salesman problem (TSP), combined 

with machine learning techniques, such as k-means clustering. Single modal package delivery 

using a conventional diesel-fueled delivery truck was selected as the baseline for comparison as 

it is most reflective of current intracity package delivery methods. FIGURE 1(a) shows an 

example route based on the traditional package delivery method. Blue dots indicate delivery 

destinations, and the blue lines represent the vehicle path of travel. Under the baseline scenario, 

all of the packages in a particular area of the city are delivered by only one conventional truck. 

To examine the opportunity for AEVs in multimodal shifts and associated potential fuel/energy 

savings, a multimodal scenario with subdepots, where a mode shift would take place, was 

developed around conventional vehicle delivery. Instead of delivering all packages in a particular 

area using only one truck, under this scenario the packages are dropped off at a number of 

distribution centers within the area by conventional trucks and then delivered to final 

destinations using smaller AEVs responsible for smaller subsections of the area. FIGURE 1(b) 

shows an example of a multimodal AEV scenario where a conventional diesel-fueled delivery 

truck drops off packages at four subdepots, which are indicated by the blue markers. The blue

route represents the conventional truck trajectory, and the red, purple, orange, and green routes 

represent the trajectories of four individual AEVs. The dots represent the same delivery 

destinations as in the baseline scenario; however, in the case of the multimodal AEV scenario,

these final deliveries are made by AEVs.

(a) (b)

FIGURE 1 (a) Single modal package delivery (b) Multimodal AEV scenario of package delivery

Starting with initial information of delivery destinations, a hybrid tour-based model was 

developed and applied to the data to generate optimized routes for the multimodal AEV

scenarios. The baseline in this project perfectly fits the TSP paradigm (5, 6, 7). TSP asks the 

classic question: “Given a list of cities and distance between them, what is the shortest route for a 
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traveling salesman to visit each city exactly once and return to the origin city?” Let cciiii denote the 

cost of traveling from destination ii to destination jj, xxiiii denotes whether the route between 

destination ii to destination jj is in the route, VV denotes the set of destinations, and SS denotes any 

subset of VV. The route optimization problem in this project can be mathematically formulated as 

follows:

min� � cciiiixxiiii

nn

≠ii, =1

nn

ii=1

ss. tt. xxiiii ∈ {0,1} ii,jj = 1, ... , nn

� xxiiii

nn

ii=1, ii≠

= 1 jj = 1, ... , nn

� xx

nn

=1, ≠ii

= 1 ii = 1, ... , nn

� xx

nn

ii, ∈SS, ii≠

≤ |SS| − 1 ∀SS ⊂ VV, SS ≠ ∅

The first constraint indicates that there are only two states between any two nodes in the 

network. They are either connected or not. The second and third constraints make sure that there 

is only one path to enter and leave each node. The fourth constraint prevents the model from 

resulting in multiple separate independent loops.

In this study, the cost of traveling can be seen as energy consumption. The solution of the TSP 

can be solved by integer programming and directly applied to the baseline scenario. For the 

multimodal AEV scenario, a hybrid tour-based model was designed, as illustrated in FIGURE 2. 

The delivery destinations are first clustered to a few clusters by the k-means clustering algorithm 

(8, 9, 10), of which the essential idea is to assign locations close to each other to the same group.

The clustering is to identify how many subdepots should be selected for the scenario, as well as

which locations on the map would make the potential subdepots for transfer. Once the k-means 

cluster algorithm is applied and subdepots are identified, the conventional truck and AEV routes 

are then optimized separately using the TSP framework.

FIGURE 2 Hybrid tour-based model for multimodal AEV scenario
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Case Study

In order to provide estimated delivery scenarios for the modeling framework to function, a

sample case study was created to simulate typical package delivery services in Columbus, Ohio.

The framework and the logic of the case study are presented in FIGURE 3. Sample delivery 

truck trajectory data from the NREL Fleet DNA database (11) is used to estimate package 

delivery destinations. Then, an energy consumption matrix describing energy consumption for 

traveling between any two delivery destinations by the same truck is estimated for each truck 

based on travel distances between delivery destinations and the energy consumption rate. For the 

baseline scenario, TSP is directly applied to the energy consumption matrix to obtain the 

optimum routes and energy estimates. For the multimodal AEV scenario, the hybrid tour-based

model looks at both delivery destinations and the energy consumption matrix and outputs the 

optimum routes and total energy consumption. Energy impacts of adopting AEVs in multimodal

shifts are quantified by comparing with baseline results.

FIGURE 3 Case study framework and logic

Estimation of Routes and Destinations

A total of 18 parcel delivery vehicles were simulated along routes and for destinations created 

using sampled data from the National Renewable Energy Laboratory’s Fleet DNA database. 

Data sampled from the Fleet DNA database included vehicle location, vehicle speed, engine 

coolant temperature and engine speed at a one Hertz sampling rate. Data from the engine coolant 

temperature signal was used to identify “key-on” ignition conditions, which was then used to 

estimate package delivery destinations along the routes.

To accurately quantify the energy benefits of shifting to multimodal AEV package delivery 

within cities, delivery destinations needed to be identified for use as inputs into the hybrid

model. Since the number of total delivery destinations does not vary too much, randomly 
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selected weekday delivery data for all trucks were used for analysis to quantify the energy 

impact. Using the coolant temperature signal, a method to create package delivery “destinations”

was created and, based on this method, a total of 1,996 destinations were identified for the 18

delivery trucks in an average day’s delivery, as shown in FIGURE 4. The marker indicates a 

distribution center (origin) from which all the trucks depart. All the colored dots represent 

estimated delivery destinations of each individual truck that were used for the modeling and 

analysis. The number of destinations for each truck ranges from 37 to 163, with an average of 

111.

FIGURE 4 Estimated delivery destinations for 18 delivery trucks in Columbus, Ohio

Once the delivery destinations were estimated from the truck trajectory data, a distance matrix 

that describes the travel distance between any two delivery destinations of the same truck was

obtained for each truck by querying the shortest route using the MapQuest direction application 

programming interface. Multiplying the travel distance by the average fuel consumption rate

derived from Fleet DNA delivery truck data, a fuel consumption matrix was estimated for each 

truck. In this study, the average fuel consumption rate of a conventional diesel fueled delivery 

truck was estimated to be 11.54 gallons per 100 miles.

Results

Other than total energy consumption, the performance metrics, including total delivery time and 

total vehicle miles traveled (VMT), were also evaluated. The performance metrics are defined as 

follows:
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• Total energy consumption: the total energy consumed, in gallons of gasoline, for 

delivering all packages to their destinations in a one-day analysis.

• Total delivery time: the total time for all vehicles to finish delivering all packages to their 

destinations and travel back to departure origins in a one-day analysis. It includes both

vehicle travel time and vehicle stopping time for package delivery. Estimated from the 

trajectory data, the average vehicle stopping time for a package delivery is 130.9 seconds.

The unit is vehicles per hour.

• Total VMT: the total miles traveled by all vehicles during the process of delivering all 

packages to their destinations. 

The TSP in both the baseline and multimodal AEV scenarios was solved by a solver in the R 

software package called “TSP” (12, 13). For the baseline scenario, TSP was directly applied to 

each delivery truck separately. For the multimodal AEV scenario, a number of assumptions need 

to be made before applying the hybrid model. They are as follows:

1. Each conventional delivery truck is responsible for the same packages that need to be 

delivered to the same destinations as the baseline scenario. Instead of delivering all the 

packages to the destinations, each truck drops off the packages at several subdepots. 

AEVs deliver the packages from subdepots to the final destinations. 

2. For the ii

th conventional delivery truck, to determine the number of subdepots, NNii, the 

average delivery capacity of AEV, CCii, needs to be assumed. The number of subdepots is 

calculated as:

NNii = cccc cc �

MMii

CCii

�,

where MMii is the number of packages the ii

th conventional delivery truck carries. In this 

analysis, the average delivery capacity of the AEVs is assumed to be 50 

packages/vehicle. 

3. For the ii

th conventional delivery truck, after clustering destinations to NNii destination 

clusters, the locations of subdepots are randomly selected within the cluster areas.

4. The AEVs are assumed to have the same vehicle size as a conventional delivery trucks 

and to consume one-third of the energy of a conventional delivery truck.

Subsets of the optimized routes for both the baseline and multimodal AEV scenarios are

displayed in FIGURE 5. The colored lines and dots in FIGURE 5(a) represent routes and 

delivery destinations for each conventional delivery truck. In FIGURE 5(b), the light green lines 

represent AEV routes, while other colored lines represent conventional delivery truck routes. The 

small light green dots represent delivery destinations, while the large colored dots represent

subdepots of each conventional delivery truck.
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(a)

(b)

FIGURE 5 Subset of optimized routes (a) Baseline scenario, (b) Multimodal AEV scenario
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The total energy consumption, along with the other two performance metrics, of both scenarios 

are compared in TABLE 1. In the “Savings” column, “-” indicates the reduction of performance 

metrics after adopting AEVs in the multimodal shift when compared with the baseline, and “+”

indicates an increase. The benefits are shown in green, while the negative impacts are shown in

red. Although the total VMT increased by 14.9% after adopting the AEVs, there were 19.4%

savings on total energy consumption, as well as 44.9% of savings on total delivery time. This 

means that the energy savings are solely attributable to the improvement of EV powertrain 

technology.

TABLE 1 Performance Metrics Comparison

Baseline Autonomous EV Savings

Total Energy Consumption (gal) 85.3 68.8 -19.4%

Total Delivery Time (veh×hour) 4.5 2.5 -44.9%

Total VMT (mile) 739.2 849.3 +14.9%

Sensitivity Analysis

A sensitivity analysis was performed to understand how AEV delivery capacity and subdepot

location choice affect the energy impact. The AEV capacity was set to be from 20 to 150 

packages per vehicle with an increment of 10 packages per vehicle. For each AEV capacity 

value, the hybrid tour-based model ran for 20 different random seeds with subdepot locations 

being randomly chosen within the cluster area. The changes in performance metrics are shown in 

FIGURE 6. The solid red line is the mean value, while the green dashes are the maximum and 

minimum bounds. FIGURE 6 demonstrates that adopting AEVs in multimodal package delivery,

on average, saved 13.6% to 24.3% of energy usage, and 8.0% to 57.3% of delivery time, whereas 

it increased 8.6% to 21.9% of VMT. The higher AEV delivery capacity resulted in more energy 

savings and fewer VMT, but also less delivery time savings. With the increase in AEV delivery 

capacity, package delivery required fewer subdepots, which resulted in fewer miles traveled for 

trucks to drop off packages at subdepots, as well as fewer miles traveled for AEVs to travel 

between subdepots and destinations at the beginning and end of delivery. Therefore, less energy 

was consumed. At the same time, more delivery time was needed with fewer AEVs delivering 

simultaneously. In addition, FIGURE 6 indicates that subdepot location choice had a more 

significant effect on the energy consumption and VMT as AEV delivery capacity increased. The 

higher delivery capacity enabled AEVs to deliver packages to more destinations in a larger 

cluster area, leading to a larger variation in subdepot location choice and route optimization 

results.
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(a) (b)

(c)

FIGURE 6 Performance metrics of changes with different AEV delivery capacity assumptions (a) 

Total energy savings, (b) Total VMT increase, (c) Total delivery time savings

Conclusions

Using a TSP framework combined with k-means clustering, a hybrid tour-based model was 

developed for multimodal intracity package delivery via adopting AEVs. The hybrid model was 

then applied to a case study in Columbus, Ohio, where conventional delivery truck trajectory 

data were analyzed. Based on a number of reasonable assumptions, the analysis results revealed 

that adopting AEVs in multimodal intracity package delivery was able to save 19.4% of energy 

usage and 44.9% of delivery time whereas it increased total VMT by 14.9%. In addition, a 

sensitivity analysis was performed to quantify the effect of AEV delivery capacity and subdepot

location choice on the performance metrics. It was found that higher AEV capacity resulted in 

more energy savings and less VMT, but also less delivery time savings. AEV capacity ranging

from 20 to 150 packages per vehicle on average resulted in 13.6% to 24.3% of energy savings

and 8.0% to 57.3% of delivery time savings whereas VMT increased by 8.6% to 21.9%. 

Subdepot location choice had a more significant effect on energy usage and VMT as AEV 

capacity went up.
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In this study, the energy savings estimation was conservative since AEVs were assumed to have 

the same capacity as conventional delivery trucks. But in real-world applications, the AEV 

vehicle size could be smaller since it carries only a subset of packages than conventional delivery

trucks. In future studies, detailed vehicle size and package weight information will be included. 

Instead of assuming average AEV delivery capacity, more realistic metrics, such as AEV 

maximum delivery capacity and charging distance, will be considered. A more comprehensive 

model that simultaneously optimizes both routes and subdepot location choice will be developed.

The methodology presented in this paper is location-agnostic. It can be applied to evaluate the 

energy impacts of adopting AEVs in multimodal shifts in any city, given the package delivery 

destinations. The location-agnostic methodology to evaluate and quantify the energy impacts of 

other multimodal intra-city package delivery scenarios, such as drone delivery, Uber-style

delivery, and the use of centralized package lockers will be also developed in the future.

Author Contribution Statement

The authors confirm contribution to the paper as follows. Study conception and design: Adam 

Duran, Yi Hou, and Amy Moore; Data collection: Adam Duran; Analysis and interpretation of 

results: Yi Hou and Amy Moore; Draft manuscript preparation: Yi Hou, Amy Moore, Adam 

Duran, Kevin Walkowicz, and David Smith. All authors reviewed the results and approved the 

final version of the manuscript.

Acknowledgment

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for 

Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DEAC36-08GO28308. Funding provided by the DOE Vehicle Technologies Office (VTO) under 

the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility 

Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. 

The authors acknowledge David Smith of ORNL for leading the Multimodal Freight Pillar of the 

SMART Mobility Laboratory Consortium. The authors would particularly like to thank David 

Anderson with DOE’s Office of Energy Efficiency and Renewable Energy (EERE) for helping 

to establish the project concept and advance its implementation, and for providing ongoing 

guidance and support. The views expressed in the article do not necessarily represent the views 

of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by 

accepting the article for publication, acknowledges that the U.S. Government retains a 

nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form 

of this work, or allow others to do so, for U.S. Government purposes.
12

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