Generating large-scale real-world vehicle routing dataset with novel spatial data extraction tool

Abstract

This study delves into the critical need for generating real-world compatible data to support the application of deep reinforcement learning (DRL) in vehicle routing. Despite the advancements in DRL algorithms, their practical implementation in vehicle routing is hindered by the scarcity of appropriate real-world datasets. Existing methodologies often rely on simplistic distance metrics, failing to accurately capture the complexities inherent in real-world routing scenarios. To address this challenge, we present a novel approach for generating real-world compatible data tailored explicitly for DRL-based vehicle routing models. Our methodology centers on the development of a spatial data extraction and curation tool adept at extracting geocoded locations from diverse urban environments, encompassing both planned and unplanned areas. Leveraging advanced techniques, the tool refines location data, accounting for unique characteristics of urban environments. Furthermore, it integrates specialized distance metrics and location demands to construct vehicle routing graphs that represent real-world conditions. Through comprehensive experimentation on varied real-world testbeds, our approach showcases its efficacy in producing datasets closely aligned with the requirements of DRL-based vehicle routing models. It’s worth mentioning that this dataset is structured as a graph containing location, distance, and demand information, with each graph stored independently to facilitate efficient access and manipulation. The findings underscore the adaptability and reliability of our methodology in tackling the intricacies of real-world routing challenges. This research marks a significant stride towards enabling the practical application of DRL techniques in addressing real-world vehicle routing problems.

Full text

RESEARCH ARTICLE
Generating large-scale real-world vehicle
routing dataset with novel spatial data
extraction tool
Hina AliID
1,2
*, Khalid Saleem
1
1Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan, 2Department of
Computer Sciences, National University of Modern Languages Islamabad, Islamabad, Pakistan
*hali@numl.edu.pk
Abstract
This study delves into the critical need for generating real-world compatible data to support
the application of deep reinforcement learning (DRL) in vehicle routing. Despite the
advancements in DRL algorithms, their practical implementation in vehicle routing is hin-
dered by the scarcity of appropriate real-world datasets. Existing methodologies often rely
on simplistic distance metrics, failing to accurately capture the complexities inherent in real-
world routing scenarios. To address this challenge, we present a novel approach for gener-
ating real-world compatible data tailored explicitly for DRL-based vehicle routing models.
Our methodology centers on the development of a spatial data extraction and curation tool
adept at extracting geocoded locations from diverse urban environments, encompassing
both planned and unplanned areas. Leveraging advanced techniques, the tool refines loca-
tion data, accounting for unique characteristics of urban environments. Furthermore, it inte-
grates specialized distance metrics and location demands to construct vehicle routing
graphs that represent real-world conditions. Through comprehensive experimentation on
varied real-world testbeds, our approach showcases its efficacy in producing datasets
closely aligned with the requirements of DRL-based vehicle routing models. It’s worth men-
tioning that this dataset is structured as a graph containing location, distance, and demand
information, with each graph stored independently to facilitate efficient access and manipu-
lation. The findings underscore the adaptability and reliability of our methodology in tackling
the intricacies of real-world routing challenges. This research marks a significant stride
towards enabling the practical application of DRL techniques in addressing real-world vehi-
cle routing problems.
Introduction
Vehicle route optimization is pivotal in modern logistics, aiming to enhance operational effi-
ciency and cost-effectiveness. Efficient routing ensures timely delivery and reduces resource
consumption, aligning with sustainability goals. Vehicle Routing Problem (VRP) being
incepted by George Dantzig and John Ramsar in 1959 [1] and grounded in the realms of
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OPEN ACCESS
Citation: Ali H, Saleem K (2024) Generating large-
scale real-world vehicle routing dataset with novel
spatial data extraction tool. PLoS ONE 19(6):
e0304422. https://doi.org/10.1371/journal.
pone.0304422
Editor: Peng Guo, Southwest Jiaotong University,
CHINA
Received: January 20, 2024
Accepted: May 11, 2024
Published: June 21, 2024
Copyright: ©2024 Ali, Saleem. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper.
Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist.

combinatorial optimization and integer programming, aims to address questions such as,
‘How can we determine the most efficient routes for a fleet of vehicles to deliver goods to a
specified group of customers?’ [2]. However, the modern landscape of supply chains, marked
by fluctuating demand and diverse vehicle capacities, presents significant challenges to the tra-
ditional routing methods commonly employed in the Operations Research (OR) community.
Additionally, in OR, regardless of whether the VRP instance consists of 20 or 1000 nodes, only
a few instances are typically required in experimentation. This renders the conventional OR
dataset unsuitable for DRL based setup. The Vehicle Routing Problem (VRP) encompasses a
fleet of vehicles, various locations to visit, and customer demands. These locations are catego-
rized into depots and customers, with depots serving as the starting points for vehicle routes.
Each location, excluding the depot, signifies a demand that must be met. Key components of a
VRP dataset include the number of vehicles (capacitated or incapacitated), customer locations,
customer demands, and distances between locations. Dataset generation typically follows one
of three approaches for constructing the transportation network: (i) generating synthetic data-
based transportation networks, as seen in studies such as [3–5]; (ii) simulating transportation
networks, as evidenced by research like [6–8]; or (iii) constructing transportation networks
using real-world data, as demonstrated by works such as [9] for test datasets, along with [10,
15] The most prevalent approach for crafting a transportation network involves the use of syn-
thetic data. In this scenario, data is dynamically generated within a unit square with coordi-
nates ranging from [0, 1] [0, 1]. Euclidean distance is utilized to measure cost of path between
randomly chosen coordinates within this square. In a simulation setting, a simulator is
employed to emulate real-world traffic conditions. The simulator generates data instances
based on the guidelines established through the synthetic data generation process. It continu-
ously monitors vehicle movement to meet demand by considering the vehicle’s dynamic posi-
tion and locations.
In research, real-world datasets commonly incorporate geospatial coordinates and path
costs, often based on Euclidean distances or those provided by mapping service APIs. How-
ever, these datasets present several limitations [6,15]. Firstly, due to their limited size, these
datasets are primarily suited for OR studies or as test/validation sets for deep learning models
(limitation 1). Secondly, all delivery points are treated equally without consideration for factors
such as order types, frequency, or the nature of locations (e.g., residential versus commercial),
significantly impacting the optimization of last-mile delivery routes (limitation 2). Finally, the
provided locations may not align with actual street addresses; rather, they are often randomly
generated points sourced from traffic datasets, possibly representing non-existent locales, (lim-
itation 3).
Currently, there is a notable absence of real-world Capacitated VRP(CVRP) datasets to
address these limitations. To bridge this gap, it is crucial to develop a robust tool for extracting
and curating real-world data, particularly tailored for data-intensive VRP frameworks. Our
proposed solution is the Spatial Data Extraction and Curation Tool (SDECT), which harnesses
mapping service APIs to extract VRP datasets from any global region. SDECT effectively tack-
les the limitations inherent in current VRP datasets for data-intensive applications by present-
ing data in a graph format. Moreover, the generated dataset is deemed suitable for a wide array
of studies, accommodating both graph-based and non-graph-based analyses.
To facilitate a deeper understanding of the dataset, we present a glimpse into its structure
through two illustrative figures. In Fig 1, a concise representation of a graph instance is pro-
vided, offering insight into the dataset’s composition. The graph comprises five nodes, each
serving a unique purpose. The central node, denoted as the depot, is distinguished by a blue
square, serving as the hub for vehicle dispatch. Surrounding it are delivery/customer locations,
identified by red markers labeled in the format [alphabet:number]. Here, alphabets A, B, C,
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and E denote the respective delivery/customer locations, with accompanying numerical values
representing the associated demands to be fulfilled by the fleet of vehicles stationed at the
depot. In Fig 2, we present the distance matrix of a graph instance consisting of five nodes.
The first column details the latitude and longitude coordinates of all locations, while the subse-
quent columns specify the distances between these locations in their respective cells. The initial
Fig 1. Illustration of a graph instance with 5 location nodes, depicting edge weights as distances. Depot marked in blue, delivery locations marked in
red.
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Fig 2. Visualization of a distance matrix for a 5-node graph. The first row corresponds to depots, while the
subsequent rows represent delivery locations.
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row is dedicated to depicting the latitude and longitude of the depot, along with its distances
from delivery locations. Following rows continue this pattern, showcasing the coordinates and
distances from each node to every other node.
Notably, each location is interconnected, forming a network of links or edges. The numeri-
cal values adjacent to these links signify the distance, measured in kilometers, between the con-
nected locations. Scalability of SDECT is worth mentioning, which seamlessly adapts to graphs
of varying sizes.
SDECT showcases exceptional versatility beyond its application in vehicle routing scenar-
ios. It extends its capabilities to tackle a spectrum of optimization challenges across various
domains. For example, it excels in strategically placing cell towers, optimizing routes for fiber
optic cables, coordinating drone-based internet delivery, and establishing emergency commu-
nication networks. Telecommunications companies can harness the power of SDECT to
enhance their operations significantly. Moreover, by optimizing routes for repair crews,
SDECT contributes to increased efficiency in maintenance operations, thereby benefiting the
telecommunications sector as a whole. This tool empowers researchers to validate their models
in real-world scenarios, thereby enhancing the practical applicability of their findings.
Below, we outline the key contributions of our work.
1. Our foremost contribution revolves around the creation of SDECT, a sophisticated tool,
designed to extract and organize real-world datasets using mapping service APIs. This com-
prehensive dataset not only includes locations of depots and customer’s delivery points, but
also provides an adjacency matrix detailing distances between these locations. To further
enhance its utility, we have categorized these locations into eight primary types, encompass-
ing offices, banks, commercial hubs, residential neighborhoods, hospitals, places of wor-
ship, educational institutions, and miscellaneous areas. Moreover, each delivery point is
mapped with its unique set of demands, ensuring a granular understanding of the delivery
landscape and address limitations 1 and 2.
2. To make dataset of locations precise, we utlized shape files from mapping service providers.
Only shapes files of building and landcover are selected to ensure that extracted locations
corresponds to actual street addresses to address limitation 3
3. Finally, it can be used in both planned and unplanned areas to create location graphs of
required sizes. Consequently, it emerges as a crucial instrument for generating dataset that
facilitate in optimizing transportation logistics in any real-world setting.
The organization of this paper is as follows. In beginning we offer a concise overview of the
existing literature in the field, highlighting relevant methodologies. Its is followed by method-
ology, that delineates the tool’s mechanisms for extracting, organizing, and refining spatial
data, elucidating its pivotal role in the domain. The result and discussion part of study outlines
the experimentation and the data utilized along with the approach taken to achieve results.
Finally, the concluding section captures the main findings resulting from the experiments car-
ried out and subsequent discussions.
Current state of work
The VRP has been a subject of interest in OR for many years. Since the VRP is a specialized
version of the Traveling Salesman Problem(TSP), the basic dataset characteristics and genera-
tion method are comparable. The ongoing research effort is reflected in the OR dataset reposi-
tory CVRPLIB [11], which includes datasets spanning from 1987 to the present. Two of these
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benchmark datasets are listed in the OR section of Table 1. The reason for including OR data-
sets is that they represent the fundamental criteria for creating additional VRP datasets.
The second section of Table 1 contains VRP datasets used in deep learning setups and men-
tioned as benchmarks in [4,12]. the initial attempt to create a VRP dataset for deep learning
occurred in 2018 [12].
A review of the articles listed in Table 1 reveals that most datasets are synthetically gener-
ated, eliminating the need for a data extraction tool. Geo spatial dataset based research studies
are categorized into two groups: 1) OR and 2) deep learning. Datasets utilized for solving rout-
ing problem in OR are often constrained in size, rendering them inadequate for deep learning
applications. However, they can serve as valuable resources for testing in deep learning, facili-
tating comparative analyses. In one such OR based real-world VRP study, researchers gener-
ated a transportation network for transit services using 25,843 trip requests. The dataset
includes the location coordinates of pickup and drop-off locations, along with pickup times.
Researchers utilized OSM for road network data and travel time calculations, employing the
shortest travel time to construct the road network. [13] In the context of deep learning, real-
world datasets are mentioned in [14]. They have generated routes from traffic data, which
includes road network data but lacks information about street data, location types, and other
relevant details. same datset is used in another approach thus having Same limitations [15] In
another example, a simulated dataset for the electric vehicle routing problem was created in
the form of a transportation network with 30 customers and five vehicles, where all parameters
except vehicle dimensions were simulated [2]. This information about real-world dataset
might exist within specific companies that have already adopted these tools to assist in plan-
ning tours. However, obtaining generalize data for scientific use, especially in vehicle routing
domain is challenging, As companies are hesitant to fully disclose their operations [16]. There-
fore most of research in this domain is carried out on simulated data [17]. Unavailability of
real world data is evident in field of automated guided vehicle, wherein utilization of spatial
data is adopted in testing phase [18]. The research domain encounters ongoing challenges in
Table 1. Table list of benchmark data-sets of operation research and deep learning.
Size of Problem (N) Type of Dataset Limitations Reference
Operation Research(OR) (CVRPLIB)
N = 100 Synthetic Solomon Instance • Very Small in size
• Can be used as test
instances in DL only
• No real-time distance and traffic information
[19]
N = 200, 400, 600, 800, 1000 Synthetic Instance • Same as above [20]
N = 25, 500, 100 Synthetic Instance • Same as above [21]
N = 25, 500, 100 Real-World • Same as above [22]
Deep Learning (DL)
N = 10, 20, 50, 100 Synthetic Instances • Euclidean distance
• No real-time distance and traffic information
[12]
N = 10, 20, 50, 100 OR, Synthetic and Real-time data • Average traffic speed taken
• Location Chosen randomly
• No information about categorization of location basis of types
[14]
N = 10 to 100 Synthetic and Real-time data Same as [14] [15]
N = 10, 20, 50, 100 Synthetic and Real-time data • Same as [12] [4]
N = 20, 50, 100 Synthetic collected through Simulator • Same as [4,12] [23]
N = 20, 50, 100 Synthetic Data-set • Same as [4,12] [24]
N = 20, 50, 100 Synthetic Data-set • Same as [4,12] [25]
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overcoming constraints related to obtaining genuine operational data. This underscores the
importance of employing innovative approaches to tackle these obstacles and enhance com-
prehension in the realm of vehicle routing problems and their applications. SDECT has been
developed to specifically address the challenge of generating real-world graph datasets tofill
the void in dataset generation and curation.
Methods
Our research is dedicated to extracting vector data in the form of location graphs paired with
distance matrices. Initially, we obtain location data with the support of mapping service pro-
viders, gathering latitude and longitude coordinates. From these coordinates, we construct
graphs by specifying the number of location nodes and generating graphs of particular sizes.
The working of SDECT for dataset extraction and curation is depicted in Fig 3.
Using mapping service direction APIs, we generate adjacency matrices representing the dis-
tances between locations in the graph, as showcased in Figs 1and 2. Before converting the data
into location graphs, it undergoes rigorous curation. Initially, we filter out only valid addresses
by leveraging shape files of buildings and land cover from OpenStreetMap APIs. This step
Fig 3. SDECT. A flowchart illustrating the progression of steps within SDECT.
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eliminates unwanted addresses that may be randomly generated within a range of minimum
and maximum coordinates, such as those associated with roads, water bodies, and public
parks.
Next, the coordinates undergo reverse geocoding using multiple mapping service provider
APIs like Google, OSM, and GraphHopper to obtain corresponding addresses. Coordinates
that do not yield addresses are discarded. Subsequently, the addresses are subjected to filtration
through an address parser to categorize them into eight distinct categories. This categorization
process involves filtering addresses based on keywords such as road names, shops, schools, etc.
If an address contains a specific keyword, it is allocated to the corresponding category. Addi-
tionally, redundant addresses referring to the same location are also discarded to enhance the
dataset quality.
SDECT, our proposed method, finds applications in various settings, including urban (both
planned and unplanned) and rural areas. In the case of planned urban areas, we divide the city
into a grid of square cells, each measuring 2 km *2 km. These cells, known as sectors, are iden-
tified by a combination of a letter and a number (e.g., F-10 and I-10). We select the latitude
and longitude coordinates of the northwestern and southeastern corners of these sectors. To
illustrate coordinate extraction from unplanned areas, we choose a city characterized by non-
uniform territorial division. Despite having administrative divisions, its geographical distribu-
tion lacks uniformity, featuring varying sector sizes and irregular shapes. To standardize data
extraction, we partition these sectors into smaller rectangles or squares. Then, we extract the
latitude and longitude coordinates of the northwestern and southeastern corners of each sub-
division for utilization in SDECT.
Working of spatial data extraction and curation tool
To ensure that the data extracted from unplanned city layout remain consistent with planned
city layout, we further divided these layouts in basic 2 *2 km square kilometer for data extrac-
tion. We have chosen this size because (i) riders typically travel 2-3 kilometers, according to
the industry standard for ride-hailing services; and (ii) every unit in the selected planned city is
2 kilometers in size. But it is worth noting that size is a customized parameter in this study and
it can be altered to meet specific requirement, such as a smaller or larger region of interest.
SDECT has two modules: (i) a location extraction module, and (ii) a route matrix genera-
tion module.
Location extractions. The essential prerequisites for this module are given below detailed
in Tables 2and 3:
• Determining the country for generating location-based data.
• Basic knowledge of the geographical distribution and division of the selected country.
• Access to a map service provider and direction API key.
Table 2. Experimental parameters for planned area fix-size.
Experiment Area Limit Range of coordinates(min to max)
Case-1 G-10 1500 33.667126, 73.013042 33.689489, 73.018123
Case-2 G-11 1500 33.657809, 72.995419 33.680881, 73.001942
Case-3 F-10 1500 33.681637, 73.002202 33.703118, 73.008090
. . . . . . . . . . . .. . .. . .. . .. . .. . .. . .. . .. . .
Case-n F-11 1500 33.672170, 72.984135 33.694554, 72.991740
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After confirming the prerequisites, the process begins with selecting the region of interest,
which could encompass planned or unplanned areas within a city. For planned regions, we
identify the fundamental division components. Sebsequently, the system require the number
of locations coordinates to extract, offering users a wide range of flexibility—from as few as 2
locations to over 10,000,000 locations and beyond. To ensure an even distribution of these
locations across the entire city, an equal number of coordinates must be extracted from each
basic division. This is achieved using Eq 1, where the total number of locations (n) is divided
by the number of basic geographical divisions (m).
Number of Locations ¼n
mð1Þ
With the desired number of locations per basic geographical unit established, the location
extraction module is activated, utilizing map service provider APIs to extract minimum and
maximum coordinates defining specified boundaries. In our case, each “sector” is further
divided into 2km ×2km partitions. If the selected region’s area is less than 1 km
2
, it is merged
with the nearest region.
This process iterates across all geographical divisions until a predetermined number of loca-
tions are extracted from each. Locations lacking an address are promptly discarded to main-
tain data integrity. Moreover, coordinates repeatedly referring to general addresses, such as
city names, are excluded to ensure relevance. The extracted location coordinates undergo
address parsing through a dedicated module, leveraging Google Maps and OpenStreetMap
APIs for reverse geocoding.
The addresses are then categorized into eight distinct categories, including residential, com-
mercial, official, school, banks, hospitals, and public places. Public places are intentionally
omitted from the selected locations as they do not serve as valid delivery points. The categori-
zation process involves keyword searches within the filtered address list to assign locations to
their respective categories. In cases where coordinates remain uncategorized, an alternative
geocoding service is employed to obtain complete addresses. These addresses undergo further
screening and manual correction efforts if necessary, prioritizing data completeness and
accuracy.
Tables 2and 3display samples of regions and their respective parameters used for extract-
ing coordinates from both planned and unplanned regions. These tables provide specific
examples for clarity. The algorithm 1illustrates the functioning of the location extraction
module.
Computational complexity. Proposition 1 There are three procedures in Algorithm 1. (i)
The computational complexity of the procedure “GenerateCoordinates()” of the proposed Algo-
rithm 1 is O(n), (ii) GenerateAddress() is O(n), and (iii) procedure ValidAddress() has constant
time complexity.
Algorithm 1 An algorithm For Extraction and Filtration of Geo locations
Table 3. Experimental parameters for unplanned area.
Experiment Area Limit Range of coordinates(min to max)
Case-1 Gulzar-e-Hijri 1500 24.937169, 67.140774 24.976558, 67.121766
Case-2 Faisal Cantonment 1500 24.887355, 67.146080 24.926599, 67.162313
Case-3 Feroz Abad Town 1500 24.869617, 67.035313 25.104026, 67.026997
. . . . . .. . ... . . . . .. . .. . .. . .. . .. . .. . .. . ..
Case-n Jamshed Town 1500 24.833337, 67.048609 24.887367, 67.052685
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1: procedure GenerateCoordinates
2: Require: Area: Predefined, Limit min(GraphSize)
3: minLat min(latitude)
4: minLong min(latitude)
5: maxLat max(longitude)
6: maxLong max(longitude)
7: while N 6¼Limit do
8: Latt RandomDistribution (minLat,maxLat)
9: Long RandomDistribution (minLong,maxLong)
10: Coordinates (str(lat) + “,” + str(long))
11: file Coordinates
12: procedure GenerateAddress
13: Require: List of coordinates for which addresses are generated.
14: if List of Coordinates then
15: while Coordinate in List do
16: Location Coordinate
17: Address Geolocator.Reverse(Location)
18: else File Not Found
19: end if
20: procedure ValidAddress
21: Require: List Addresses.
22: if List of Addresses then
23: regex all addresses with only city name mentioned in it
24: if regex 6¼ address then
25: if find(’Keyword’) in address then
26: return False
27: end if
28: return True
29: end if
30: end if
Proof 1 The proof of proposition 1 is given below.
1. In the procedure GenerateCoordinates(), the computation of minimum and maximum lati-
tude and longitude coordinates takes constant time, and the process of generating coordinates
from these minimum and maximum latitude and longitude coordinates takes O(n) time.
Therefore, the overall complexity of this procedure is O(1) + O(n) = O(n).
2. While in procedure GenerateAddress(), calling reverse geocoding on the list of addresses takes
O(n) time. Therefore, the overall time complexity of this procedure is O(n).
3. Finally, all operations in the validAddress() procedure are constant time operations, as it fil-
ters addresses on the basis of specific keywords. Therefore, it has a time complexity of O(1).
Note that from the above proofs, it can be inferred that the time complexity of Algorithm 1
is O(n).
Route matrix generation. The proposed algorithm requires the following parameters:
• The size of the graph of delivery points is specified.
• A mapping service provider to extract the distance matrix of specified graph of delivery
points.
• A list of filtered location coordinates, where all coordinates must belong to actual street
addresses of an entity and be at least 1 km apart from each other.
The constraints associated with this module are:
• Only the coordinates of actual street addresses of entities are used
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• All other coordinates belonging to places like parks, roads, and public areas are excluded.
• Locations must be at least 1 km apart from each other.
Working. To efficiently load and retrieve coordinate data in memory, the list is divided
into equal-sized chunks. These chunks are then combined to form the complete list, which is
used to create the distance matrix.
After all addresses are saved in the required file format, the filtered list of coordinates is pro-
cessed again using algorithm 2 to create distance matrices of specified sizes between source
and destination locations. The distance matrix contains the distance in kilometers. algorithm 2
can be used to generate a distance matrix of any size, but for experimentation purposes, we
generated distance matrices of sizes 20, 50, 100, 150, 200, 250, 300, and 350 kilometers.
According to research conducted in the industrial setting of a last-mile delivery service pro-
vider, the frequency with which orders are repeated is calculated, and these frequencies are
used to determine the repetition percentage of orders in the final list. Multiple lists are created
i.e. one list for each area, and all location coordinates are saved in these lists.
Proposition 2 The computational complexity of algorithm 2 is O(n
2
).
Proof 2 The procedure CreateDistanceMatrix() generates graphs of n x n combination of
source and destination locations with a complexity of O(n
2
). The size of graph is specified. The
next phase of the procedure requires O(n
2
)API calls to calculate the distance between sources
and destinations. Therefore, the overall complexity of this procedure is O(n
2
).
Algorithm 2 An Algorithm For Creating Geo-Saptial Distance Graph
1: procedure CreateDistanceMatrix
2: Require: GraphSize:VRPsize
3: Records read(DirToRead)
4: Start = 0, End = GraphSize,N
Graph
= 1
5: while N
Graph
sizeof(Records)/ GraphSize do
6: locationGraph = location Coordinates of depot
7: customerLocation = Records[Start:End]
8: for loc in customerLocation do
9: locationGraph.append((loc[latitude], loc[longitude]))
10: file = open(Directory, w)
11: writer = writer(file)
12: Writer locationGraph
13: count = 0
14: for locSource in customerLocation do
15: for locDestination in customerLocation do
16: dist = geodesic(loc1, loc2)
17: locSource append(dist)
18: writer locSource
19: Records read(DirToRead)
20: start + = GraphSize
21: end += GraphSize
22: N
Graph
+= 1
Results and discussion
Experiments are carried out on workstation equipped with Nvidia GeForce RTX 3060. Two
types of datasets i.e. dataset I and dataset II are generated. Dataset I consists of list of coordi-
nates that are extracted and from OSM [26] and Google Map [27]. Location extraction module
is utilized for generation and processing of dataset I. Parameter used for extracting locations
from both planned and unplanned areas are listed in Tables 2and 3respectively. The results of
location extraction modules for planned and unplanned areas are listed in Tables 4and 5
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respectively. These tables also affirm generalizability of SDECT which give consistent results
across planned and unplanned urban region of a country. Additionally, Table 6 presents the
results of the route matrix generation module of the SDECT tool for an urban region in the
country.
Experimentation procedure for planned areas
For planned area, a city with grid iron layout and uniform geographical distribution is selected,
which is already divided into zones and sectors, and further into sub sectors. Each division in
selected city is of square of size 2km *2km.
To exhibit the Location Extraction Module’s efficiency, four scenarios representing distinct
sectors within the chosen city are provided as examples. Table 2 details the experimental
parameters, encompassing experiment numbers, area names, extracted coordinate quantities
per area, and their respective coordinate ranges. The outcomes for the planned area are pre-
sented in Table 4. Each column is bifurcated: one part displays the total location count, while
the other illustrates the time taken by the location extraction module to generate these figures.
In Table 4, the initial column showcases the generated coordinates, followed by address
Table 4. Results of experiments on planned area fix-size.
Experiments Coordinates Addresses Filtered Addresses
Number Time(sec) Number Time(sec) Number Time(sec)
Case-1 1500 0.041 452 420 418 0.06
Case-2 1500 0.039 1500 783 1493 0.029
Case-3 1500 0.042 1500 755 1421 0.018
Case-n 1500 0.038 1500 751 1464 0.026
https://doi.org/10.1371/journal.pone.0304422.t004
Table 5. Results of experiments on unplanned area.
Experiments Coordinates Addresses Filtered Addresses
Number Time(sec) Number Time(sec) Number Time(sec)
Case-1 1500 0.036 1500 723 1432 0.06
Case-2 1500 0.039 1500 749 1461 0.03
Case-3 1500 0.039 1500 801 1497 0.02
Case-n 1500 0.038 1500 739 1454 0.026
https://doi.org/10.1371/journal.pone.0304422.t005
Table 6. Results of experiments of graph and distance matrix generation.
No. Graph size Time(sec) Total Records File size (csv)
1 VRP-20 0.07 100000 9 KB
2 VRP-50 0.500 10000 49 KB
3 VRP-100 1.851 100000 187 KB
4 VRP-150 4.18 100000 415 KB
5 VRP-200 7.410 100000 732 KB
6 VRP-250 11.703 100000 1.11 MB
7 VRP-300 16.66 100000 1.59 MB
8 VRP-350 22.97 100000 2.16 MB
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generation and subsequent address filtration. Notably, Case-1 yields a lower address count
compared to other cases in Table 4 due to a significant portion of the area being occupied by a
park, resulting in fewer available delivery addresses.
These results are depicted in Figs 4and 5.Fig 4 represents the distribution of location
extraction across all three component of location extraction module. Fig 5 depicts time taken
by coordinates generation address generation and address filtration module. The execution
time for address generation is plotted on the secondary axis and is represented as the most
time-consuming activity. This is because address generation depends on reverse geocoding
calls to the mapping service provider API.
Experimentation procedure for unplanned areas
To depict unplanned areas, we opted for a city featuring various planned divisions that, geo-
graphically, vary in size. Some divisions are smaller, others larger, making it challenging to
directly obtain minimum and maximum coordinates. Hence, we segmented these sectors into
approximately rectangular or square shapes and extracted coordinates using a method akin to
that used for planned regions. Table 5 represents, the number of coordinates, addresses, and
filtered addresses generated along with time to generate them. It is clearly represented in table
Fig 4. Result on planed area. Comparison of number of locations generated by location extraction module for
planned Area.
https://doi.org/10.1371/journal.pone.0304422.g004
Fig 5. Execution time. Comparison of execution time of each module for planned area.
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that results of unplanned area location extraction module are similar to that of planned area
location extraction module. It strongly support the generalization aspect of SDECT.
Fig 6 illustrates the distribution of location extraction across all three components of the
location extraction module in unplanned areas, while Fig 7 showcases the time taken by the
coordinates generation, address generation, and address filtration modules. The similarity of
these results to those in planned areas further solidifies the applicability of SDECT in both
scenarios.
Experiments on graph generation module
The Table 6 displays the outcomes of experiments depicting graphs generated from delivery
point locations extracted through the location extraction module. This module further derives
a distance matrix for each graph, saved in distinct files. The data in Table 6 reveals the creation
of eight graph sizes to showcase SDECT’s functionality. Notably, the size parameter is custom-
izable to fit specific requirements. Each graph is labeled as VRP-n, denoting its extraction for
the vehicle routing problem, with ‘n’ indicating the total number of locations within the graph.
These graphs encompass a depot location, serving as the initial point, distinct from the remain-
ing delivery locations (n-1). The time taken for generation varies based on graph size: smaller
graphs, like VRP-20, take 0.07 seconds, whereas larger ones, such as VRP-350, require 22.97
Fig 6. Result on unplanned area. Comparison of number of locations generated by location extraction module.
https://doi.org/10.1371/journal.pone.0304422.g006
Fig 7. Execution time. Comparison of execution time of each module for unplanned area.
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seconds. This duration represents the generation time for a single graph. The ‘total record’ col-
umn indicates the generation of 100,000 graphs for each graph size. The choice of CSV file for-
mat aims to highlight the dataset’s maximum memory usage; using a lightweight file format
can significantly reduce memory consumption. These experiments cover eight diverse graph
sizes, implying potential variations in SDECT performance based on size and structure.
It is important to note that the experiments are conducted on eight different graph sizes. It
is possible that the algorithm performance may vary depending on graph sizes and structures.
Conclusion
In conclusion, our Spatial Data Extraction and Curation Tool (SDECT) offers a novel
approach for generating real-world compatible data for DRL-based vehicle routing models.
This tool addresses the critical need for high-quality datasets, enabling practical application of
advanced techniques in route optimization that extends beyond vehicle routing, benefiting
various industries reliant on real-world location data. For instance, telecommunications com-
panies can optimize cell tower placement, design efficient fiber optic cable routes, plan drone-
based internet delivery, and deploy emergency communication networks. Moreover, SDECT
enhances maintenance operations by optimizing routes for repair crews, increasing efficiency
throughout the telecommunications sector. However, it’s essential to acknowledge the chal-
lenges that SDECT faces during the dataset extraction phase. Acquiring accurate real-world
data can be costly, and striking the right balance between security and user privacy for location
information is crucial. Additionally, the dynamic nature of traffic and unforeseen events
necessitates constant adaptation during data extraction. These challenges present opportuni-
ties for future improvement of the proposed tool.
Author Contributions
Conceptualization: Hina Ali, Khalid Saleem.
Data curation: Hina Ali.
Investigation: Hina Ali.
Methodology: Hina Ali.
Project administration: Khalid Saleem.
Resources: Hina Ali, Khalid Saleem.
Software: Hina Ali.
Supervision: Khalid Saleem.
Validation: Hina Ali.
Visualization: Hina Ali.
Writing – original draft: Hina Ali.
Writing – review & editing: Khalid Saleem.
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