# Will there be a construction? Predicting road constructions based on
heterogeneous spatiotemporal data

Amin Karimi Monsefi  The Ohio State University2015 Neil AveColumbusOhio43210
<EMAIL_ADDRESS>,  Sobhan Moosavi  Lyft Inc.185 Berry StreetSan
FranciscoCalifornia94107<EMAIL_ADDRESS>and  Rajiv Ramnath  The Ohio State
University2015 Neil AveColumbusOhio43210<EMAIL_ADDRESS>
(2022)

###### Abstract.

Road construction projects maintain transportation infrastructures. These
projects range from the short-term (e.g., resurfacing or fixing potholes) to
the long-term (e.g., adding a shoulder or building a bridge). Deciding what
the next construction project is and when it is to be scheduled is
traditionally done through inspection by humans using special equipment. This
approach is costly and difficult to scale. An alternative is the use of
computational approaches that integrate and analyze multiple types of past and
present spatiotemporal data to predict location and time of future road
constructions. This paper reports on such an approach, one that uses a deep-
neural-network-based model to predict future constructions. Our model applies
both convolutional and recurrent components on a heterogeneous dataset
consisting of construction, weather, map and road-network data. We also report
on how we addressed the lack of adequate publicly available data - by building
a large scale dataset named “US-Constructions”, that includes 6.2 million
cases of road constructions augmented by a variety of spatiotemporal
attributes and road-network features, collected in the contiguous United
States (US) between 2016 and 2021. Using extensive experiments on several
major cities in the US, we show the applicability of our work in accurately
predicting future constructions - an average f1-score of $0.85$ and accuracy
$82.2\%$ \- that outperform baselines. Additionally, we show how our training
pipeline addresses spatial sparsity of data.

US-Constructions, Road Constructions Prediction, Dataset

††journalyear: 2022††copyright: rightsretained††conference: The 30th
International Conference on Advances in Geographic Information Systems;
November 1–4, 2022; Seattle, WA, USA††booktitle: The 30th International
Conference on Advances in Geographic Information Systems (SIGSPATIAL ’22),
November 1–4, 2022, Seattle, WA, USA††doi: 10.1145/3557915.3560943††isbn:
978-1-4503-9529-8/22/11

##  1\. Introduction

Road constructions are essential to transportation infrastructures. Recently
released data by the United States Census Bureau showed that the annual value
of road constructions increased from 87.9 billion dollars in 2017 to 100.4
billion dollars in 2021 - an over 18% increase in just five years111Visit
https://www.census.gov/construction/c30/prpdf.html for detailed reports.. In
order to make the best use of the substantial amount of capital invested in
this sector, it is crucial to determine construction sites wisely. Deciding
what the next construction project is and when it is to be scheduled is
traditionally done through inspection by humans using special equipment - an
approach that is expensive and limited in coverage. We therefore explored an
alternative for this paper, namely, the use of computational solutions to
determine future constructions.

To our knowledge, determining when and where road constructions are needed via
computational solutions is a relatively new, less explored research area.
Existing studies have focused on image analysis, such as for detecting cracks
and potholes in road surface Guan et al. (2021); Bibi et al. (2021); Arya et
al. (2021); Wen et al. (2022) and detecting road closures via telematics and
vehicle probe data Kataoka et al. (2018). While promising, the lack of
extensive input data (e.g., fine-grained satellite or street imagery at scale)
has limited the applicability and extensibility of these approaches.
Additionally, the data used are mostly private, which limits both independent
validation and the ability to build on and extend prior work. In general,
current computational solutions have been limited due to the need for data to
build good models with real-world applicability.

The work presented here begins by addressing challenges with data. To this
end, this paper introduces a unique dataset of 6.2 million road constructions
in the United States between 2016 and 2021. Our dataset offers a variety of
contextual details for each construction, including location, time, a brief
human provided description, daylight and weather at the start of each
construction, and several map related features that contextualize the location
of a construction (e.g., if it is close to a highway junction, road-type,
etc.). We also carefully describe our process for building this dataset. Thus,
researchers may either directly use our dataset or mimic our approach and
build their own dataset.

Next, we explore an important and useful research problem, that of
“identifying future constructions from past constructions along with their
spatiotemporal context (e.g., traffic, weather, and map imagery) for certain
locations (represented by their geographical region hexagon – see Section 4)
during specific time frames (e.g., the next 15 days). Our goal is to develop a
cost-saving, coverage-enhancing approach complementary to current practices.
For example, we see our approach as being used to quickly identify potential
sites, which can then be evaluated by human inspections.

In our approach, we model heterogeneous spatiotemporal information using a
deep-neural-network that combines recurrent and convolutional components. The
convolutional component is used for extracting latent information from map
imagery. The recurrent component models time-series data (e.g., traffic and
weather) along with additional spatial information (e.g., features of the
road-network) about a location. The output of these components are then
concatenated and fed to a fully connected component which produces the final
output. Our goal with this model is to predict the possibility of a
construction event in the near term (specifically, the next 15 days). While
this formulation best suits short-term constructions (i.e., those that take a
few hours to a few days), we believe it is very useful effective, given that
the majority of constructions in our data can be considered as short-term (see
Section 3). Extensive experiments and results demonstrate improvements over
traditional and neural-network-based machine learning baselines. On average,
our proposed model outperformed the best baseline model by $3.2\%$ in accuracy
and by $2.8\%$ in F1-score, when tested over multiple major cities in the
United States. Our model also demonstrated robustness in dealing with spatial
sparsity in tests at the state level - a real-world scenario of training data
being only available for parts of a region. In summary, the main contributions
of this paper are:

  * •

Dataset: We introduce a new dataset of road constructions and closures for the
continental United States, with about $6.2$ million cases from the years
$2016$ to $2021$. To our knowledge, this is the first public dataset that
offers this type and scale of data.

  * •

Insights: We glean a variety of insights by analyzing the US-Constructions
dataset. We detail these insights with a view to inspiring other researchers
to use our data for other applications, especially those aimed at enhancing
transportation infrastructures and their safety.

  * •

Model: We present a deep neural network model to predict short-term
constructions. Our model is capable of using heterogeneous data, and resulted
in superior prediction outcomes when compared to several state-of-the-art
traditional and deep-learning models.

##  2\. Related Work

We examined several previous studies on road construction issues. These
studies examined a variety of topics ranging from detection of road issues
(such as cracks) Han et al. (2021), analysis of the maintenance of roads Issa
and Abu-Eisheh (2017); Sunitha et al. (2013); Li and Liu (2019); Shtayat et
al. (2020); Krishna and Tadi (2022), prediction and management of the costs of
road construction Moretti et al. (2016); Chong and Hopkins (2016);
Petroutsatou et al. (2012); Onyango et al. (2018); Nahvi et al. (2021), and
lifecycle analysis of roads Hoxha et al. (2021); Gulotta et al. (2019); Jiang
and Wu (2019); Hasan et al. (2019); Trunzo et al. (2019). We highlight some of
this work next.

Tong et al. Tong et al. (2018) employed deep convolutional neural network
(DCNN) models for finding the length of cracks in asphalt pavement from gray-
scale images. This work used a dataset of $8,000$ images, $7,500$ images with
cracks and $500$ images without. They classified images to 8 different classes
according to the length of the crack in centimeters, achieving an accuracy of
$94.36\%$ in this classification. In another study, Ye et al. (Ye et al.,
2019) employed a convolutional neural network (CNN) model to identify potholes
in asphalt pavements. They used a dataset of 400 images that were collected
from different pavements under different lighting conditions. These images
were cropped into $96,000$ smaller images of size $256\times 256$ pixels.
Authors reported $98.95\%$ precision to detect potholes, and their stability
study suggested robustness in various real-world settings.

Automatic detection of road-closures is the topic of another group of studies
Pietrobon et al. (2019); Cheng et al. (2017). Cheng et al. Cheng et al. (2017)
presented a high-efficiency road closure detection framework based on multi-
feature fusion. Their framework had two parts, an offline road closure feature
modeling part and an online identification part. For the offline modeling,
they first partitioned the road-network into grids, and then extracted their
road closure features of these grids and the roads within them from historical
data. The online component screened out closed grid candidates based on the
plunge in traffic flow. They also identified sections with road closures based
on variations in turning behavior by drivers on these roads. Their framework
was evaluated on three real-world datasets - from Chengdu, Shanghai, and
Beijing.

With respect to the life-cycle analysis (LCA) of roads Gulotta et al. Gulotta
et al. (2019) applied a life-cycle approach to evaluate the energy and
environmental impacts of a typical urban road in Italy. They evaluated the
energy and environmental impact of various bituminous combinations. For each
analyzed scenario, the contribution of each life-cycle phase to the total
effects and to the energy and environmental hot-spots were identified as
opportunities for improvement. Jiang et al. Jiang and Wu (2019) reviewed and
analyzed 94 papers that adopted LCA methods to assess the environmental
effects over the life cycle of roads. Their study resulted in multiple
outcomes including identifying limitations and challenges of using LCA in the
environmental assessment of roads, as well as in identifying future research
directions.

Our paper borrows concepts from the papers described above while tackling the
different problem of predicting the possibility of a future construction event
at a location. To our knowledge, this is the first study that seeks to solve a
problem of this type using a purely computational approach. This work has
real-world application, in that it can be used to find areas in need of
maintenance. It is also cost-effective because it is based on analyzing
already recorded and available information such as past road constructions,
road-network features (e.g., road class data, average speed, and map
annotations), weather data, and coarse-grained map images. As we show in this
paper, the input data we employ is easy to collect and available to the
public. This is in contrast to those studies so far, which utilized private or
extensive datasets.

##  3\. The US-Constructions Dataset

In this section, we describe the countrywide traffic construction dataset,
which we have named US-Constructions, and our process for building it. The ten
major steps in this process are shown in Figure 1 and described in this
section. The resulting dataset contains 6.2 million road constructions that
took place in the continental United States between January 2016 and December
2021, and is publicly available at
https://www.kaggle.com/datasets/sobhanmoosavi/us-road-construction-and-
closures.

Figure 1. Process of Building US-Constructions Dataset

###  3.1. Collecting Raw Construction Data

We collected streaming traffic data from two real-time data providers -
“MapQuest Traffic” (MapQuest Traffic API, 2021) and “Microsoft Bing Maps”
(Bing Map Traffic API, 2021). These providers broadcast real-time road
construction and closure data collected by a variety of entities (e.g., law
enforcement agencies, traffic cameras, and traffic sensors). We integrated the
raw data to remove duplicates using a set of heuristic spatiotemporal filters.
0.63 million cases were pulled from MapQuest, and 5.54 million cases from
BingMaps, with less than $1\%$ overlap between the two sources.

###  3.2. Augmentation with Reverse Geocoding

This is the process of translating raw location data (i.e., GPS coordinates)
to addresses that included elements such as street number, street name, city,
state, and zip-code. We employed the Nominatim tool (Nominatim Tool, 2021) to
perform this reverse geocoding.

###  3.3. Augmentation with Weather Data

Weather information is a useful context for traffic constructions (in
particular short-term cases). We employed Weather Underground API (Weather
Underground, 2020) to obtain the weather during each construction. Raw weather
data was collected from $2,072$ airport weather stations from around the
United States. This raw data comes in the form of observation records, where
each record consists of several attributes such as temperature, humidity,
wind-speed, pressure, precipitation (in millimeters), and condition222Possible
values are clear, snow, rain, fog, hail, and thunderstorm.. We collected
several records per day from each station. Note that each record is generated
when any significant change occurs in any of the measured weather attributes.

Each construction event $c$ was then augmented with weather data as follows.
First the closest weather station $s$ was identified. Then, of the weather
observation records reported from $s$, we looked for the weather observation
record $w$ whose reported time was closest to the start time of $c$. $c$ was
then augmented with $w$.

###  3.4. Augmentation with POI Annotation

Points-of-interest (POI) are locations annotated on a map as amenities,
traffic signals, crossings, etc. These annotations are associated with the
nodes on a road network. A node can be associated with many POI tags; in this
work, we used the 12 tags described in Table 1. We obtained these tags from
the OpenStreetMap (OSM) (OpenStreetMap contributors, 2021) system, and for the
continental United States. The applicable POI annotations for a traffic
construction $c$ are those that are located within a threshold distance $\tau$
from $c$. We adopted the same threshold as in (Moosavi et al., 2019b).

Table 1. Definition of Point-Of-Interest (POI) annotations based on OpenStreetMap (OSM). Type | Description
---|---
Amenity |  | Refers to particular places such as restaurant,
---
library, college, bar, etc.
Bump | Refers to speed bump or hump to reduce the speed.
Crossing |  | Refers to any crossing across roads for
---
pedestrians, cyclists, etc.
Junction | Refers to any highway ramp, exit, or entrance.
No-exit |  | Indicates there is no possibility to travel further
---
by any transport mode along a formal path or route.
Railway | Indicates the presence of railways.
Roundabout | Refers to a circular road junction.
Station | Refers to public transportation stations (bus, metro, etc.).
Stop | Refers to stop signs
Traffic Calming | Refers to any means for slowing down traffic speed.
Traffic Signal | Refers to traffic signals on intersections
Turning Loop |  | Indicates a widened area of a highway with
---
a non-traversable island for turning around.

###  3.5. Augmentation with Period of Day

Given the start time of a construction record, we used “TimeAndDate” API (Time
And Date website, 2021) to label it as day or night. We assign this label
based on four different daylight systems, namely Sunrise/Sunset, Civil
Twilight, Nautical Twilight, and Astronomical Twilight.

###  3.6. Augmentation with Road Class

Road class (e.g., primary, secondary, and motorway) is an important feature of
the location of a construction. We used OSM to obtain this information and
adopted its road classification system333See
https://wiki.openstreetmap.org/wiki/Key:highway for a list of OSM-based road
types. to annotate constructions. We describe our annotation process below.

For a given construction $c$, the goal is to find the most relevant road class
based on its start and end locations444If the end location is not available,
then we only use the start location. The OSM map data contains nodes and ways.
A node is a single point defined by latitude, longitude, and a node id. A way
represents a route in a road-network by an ordered list of nodes. We map
location of a construction to an OSM node by using the “Nearest Service” OSRM
APIs555See http://project-osrm.org/docs/v5.5.1/api/#nearest-service to find
the $S$ nearest nodes to the start and end location of a construction (we
empirically set $S=10$). After finding the $S$ nearest nodes, we prune outlier
nodes using an aggressive distance threshold $D=50\textit{meters}$. This
results in a set $\mathcal{N}$ of nodes nearest to the location of a
construction. Next, for each node $n\in\mathcal{N}$, we find a set $W_{n}$ of
the OSM map ”ways” that contain $n$, and build an aggregated set
$\mathcal{W}=\bigcup_{n\in\mathcal{N}}W_{n}$. For each way $w\in\mathcal{W}$,
we then use the OSM service “Full“ to obtain road-class information666See
https://wiki.openstreetmap.org/wiki/API_v0.6 and check out GET
/api/0.6/[way—relation]/#id/full. Finally, we look for the most frequent road
class that was returned for the ways in $\mathcal{W}$, and annotate the
construction $c$ with that.

###  3.7. Augmentation with Average Road Speed

Next, we obtain the average speed for each construction event from its start
and end locations. For the about $10\%$ of constructions that do not have an
end location, we do not infer an average speed. Instead, we use the OSM “Route
Service” 777see http://project-osrm.org/docs/v5.5.1/api/#route-service to
estimate a free-flow speed for the roads with those constructions.

###  3.8. Augmentation with Closure Type

Some of the constructions could result in road closures. We introduce a rule-
based process to annotate each construction with a closure type, if there is
one. To begin with, we define three cases: road-closure, lane-closure, and no-
closure. Our process of closure-based-annotation uses the human-provided
description of each construction event, and employs several regular-expression
patterns to infer the closure type. Examples of human-provided descriptions
are listed in Table 2.

Table 2. Samples of human-provided description for some construction events that resulted in closure. “Source” shows which data provider was used to collect the data. Description of Construction Event | Source
---|---
Closed due to roadwork | $Bing$
Closed for bridge demolition work | $Bing$
Right lane blocked. | $Bing$
Two lanes blocked | $Bing$
Roadway reduced to 1 lane | $Bing$
Lane blocked. One lane closed | $Bing$
Lane closed due to construction work | $MapQuest$
Shoulder closed on entry ramp due to construction work | $MapQuest$
Intermittent lane closures due to utility work | $MapQuest$
Three lanes closed due to construction work | $MapQuest$
Right lane closed due to construction work | $MapQuest$
One lane closed due to maintenance work | $MapQuest$

From manual probing of the human-provided descriptions for a large set of
10,000 randomly selected construction events we found 14 distinct patterns
that represent closures. Of these patterns, four represent a road closure and
ten of them a lane closure. Table 3 shows these patterns along with
corresponding data source, as well as closure-type.

Table 3. Regular expression patterns to annotate closure type for construction events Pattern | Source | Closure Type
---|---|---
close* * roadwork | $Bing$ | $Road$
close* * bridge | $Bing$ | $Road$
hard shoulder close* | $Bing$ | $Lane$
* lane* block* | $Bing$ | $Lane$
* reduced * lane* | $Bing$ | $Lane$
* lane* close* | $Bing$ | $Lane$
close* * roadwork | $MapQuest$ | $Road$
road close* * | $MapQuest$ | $Road$
* lane closure* | $MapQuest$ | $Lane$
hard shoulder block* | $MapQuest$ | $Lane$
* reduced * lane* | $MapQuest$ | $Lane$
* lane* block* | $MapQuest$ | $Lane$
* lane* close* | $MapQuest$ | $Lane$
* shoulder close* | $MapQuest$ | $Lane$

To evaluate the effectiveness of our patterns in finding closure types, we
randomly selected $1,000$ construction events and annotated them with the
derived patterns, which resulted in 43 road closures and 137 lane closures.
After manually checking all resulting annotations and also cases without any
annotations, we observed $precision=100\%$ and $recall=100\%$, which showed
that our set of regular expression patterns was comprehensive and accurate.

###  3.9. Final Dataset

The final dataset comprises 6.2 million construction records, collected
between January 2016 and December 2021. Each construction record is described
by 45 attributes as shown in Table 4.

Table 4. US-Constructions Dataset (details as of Dec 2021) Total Attributes | 45
---|---
Traffic Attributes (8) |  | id, severity, start_time, end_time,
---
start_point, end_point, distance, and description
Address Attributes (8) |  | number, street, side (left/right), city,
---
county, state, zip-code, country
Weather Attributes (10) |  | timestamp, temperature, wind_chill, humidity,
---
pressure, visibility, wind_direction, wind_speed,
precipitation, and condition (e.g., rain, snow, etc.)
POI Attributes (12) |  All cases in Table 1
Period-of-Day (4) |  | Sunrise/Sunset, Civil Twilight,
---
Nautical Twilight, and Astronomical Twilight
Other Attributes (3) |  | Road class (e.g., highway, primary, etc.)
---
Average speed (e.g., 55 mph), and Closure type
Total Constructions | 6.2 million
# MapQuest Constructions | 634k (10%)
# Bing Constructions | 5.54 million (90%)
# Reported by Both |  1%

We ran a meta-analysis on this dataset. Our findings from this analysis are
detailed below:

  * •

Top states and cities: Figure 2 shows top states and cities, as well as top
states when the number of constructions are normalized by the aggregate length
of road network (in miles) for each state (data is taken from Federal Highway
Administration site888see
https://www.fhwa.dot.gov/policyinformation/statistics/2020/hm60.cfm). Note how
the state distribution changes after the normalization.

(a) Top States based on frequency

(b) Top states based on construction rate per mile

(c) Top Cities based on frequency

Figure 2. Construction distribution over top states (a), top states normalized
by length of road network in miles (b), and top cities (c).

  * •

Monthly distribution: Figure 3(a) shows that we should expect to see more
constructions start as well as finish towards the end of a year. The last four
months of a year typically see more constructions finish than start, likely
because of a pressure to finish ongoing work before the calendar year ends.

(a) Monthly distribution

(b) Closure distribution

(c) OSM-based road type distribution

(d) POI distribution

(e) Duration distribution

(f) Daylight distribution

Figure 3. (a) Monthly distribution of constructions based on their start and
end time; (b) Distribution of constructions resulting in road closure; (c)
OSM-based road-type distribution; (d) OSM-based road annotation type (aka POI)
distribution; (e) Duration distribution based on start and end time; and (f)
Daylight distribution to show percentage of constructions that started in a
day based on four different daylight systems.

  * •

Closure distribution: According to Figure 3(b), about 15% of constructions
result in a lane closure, while about 5% resulted in a complete road closure.
Also we see constructions reported by MapQuest had more closures, although the
overall numbers were dominated by the lower percentages reported by BingMaps.

  * •

Road-class distribution: Figure 3(c) shows that high-speed roads such as
“motorway” and “primary” led in construction numbers; however “residential”
roads (i.e., a low-speed road type) came third. This was an interesting
observation, in that it showed that construction takes place on most types of
roads.

  * •

Map annotation (or POI) analysis: The distribution of constructions annotated
by POI in Figure 3(d), showed that a good portion of constructions are
reported near intersections. Locations near amenities and stations also make
up a big share of the work.

  * •

Duration and daylight analysis: As shown in Figure 3(e), over 60% of
constructions only last a few hours. If we consider any construction that
lasts 15 days or more to be a long-term construction, then a little over 10%
of constructions are long-term. Further, Figure 3(f) shows the majority of
constructions started in the “day” based on all four twilight systems.
However, constructions reported by MapQuest mostly started during the “night”.
In other words, a good mix of construction times were present in our dataset.

##  4\. Research Question

We define our research question in this section. Suppose we are given a set
$\mathrm{C}$ of construction events as follows:

###### Definition 4.0 (Construction event).

We define a construction event $c$ as $\langle
lat,lng,start\\_time,end\\_time,desc,temp,humid,w\\_cond,\\\
severity,road\\_info,road\\_type\rangle$. Here $lat$ and $lng$ are GPS
coordinates, $start\\_time$ is the time of occurrence, $end\\_time$ is the
completion time, $desc$ is a human-provided description, $temp$ is the
temperature in Fahrenheit, $humid$ is the percentage humidity, $w\\_cond$ is
the weather condition (e.g., rain, snow, and clear) with a $severity$ value an
integer between 1 and 4 (note that all weather features are for the start of
the construction), $road\\_info$ are additional details about the road (e.g.,
distance and average speed), and $road\\_type$ is the type of the road (e.g.,
motorway, primary, and secondary).

In addition to the above set of construction events, we have a database of
geographical map images $\mathrm{M}$ consisting of hexagonal tiles (in any
possible view, e.g., satellite, transit, and terrain) with sufficient
resolution. Additionally, we have a dataset of points-of-interest $\mathrm{P}$
(e.g., amenities, traffic lights, and stop signs) for a specific zone (or
region) of the map. Given these datasets, we define our research question
below.

Given:

  * –

A set of spatial regions $\mathrm{R}=\\{r_{1},r_{2},\dots,r_{n}\\}$, where
$r\in\mathrm{R}$ is a hexagonal zone, according to the definition provided by
Uber h3999see https://github.com/uber/h3 library Sahr et al. (2003). Here we
choose a resolution level $7$ which results in zones with edge size $1.2km$
and area $5.16km^{2}$.

  * –

A set of fixed-length time intervals
$\mathrm{T}=\\{t_{1},t_{2},\dots,t_{m}\\}$, where we set $|t|=15$ days, for
$t\in\mathrm{T}$.

  * –

A database of construction events $\mathrm{C}_{r}=\\{c_{1},c_{2},\dots\\}$ for
$r\in\mathrm{R}$.

  * –

A database of map image data $\mathrm{M}_{r}=\\{m_{1},m_{2},\dots\\}$ for
$r\in\mathrm{R}$.

  * –

A database of points-of-interest $\mathrm{P}_{r}=\\{p_{1},p_{2},\dots\\}$ for
$r\in\mathrm{R}$.

Create:

  * –

A representation $F_{rt}$ for a region $r\in\mathrm{R}$ during a time interval
$t\in\mathrm{T}$, using $C_{r}$, $M_{r}$, and $P_{r}$.

  * –

A binary label $L_{rt}$ for $F_{rt}$, where 1 indicates at least one traffic
construction was reported during $t$ in region $r$; 0 otherwise.

Find:

  * –

A model $\mathcal{M}$ to predict $L_{rt}$ using $\langle
F_{rt_{i-10}},F_{rt_{i-9}},\dots,F_{rt_{i-1}}\rangle$, which means predicting
the label of current time interval using observations from the last 10 time
intervals.

Objective:

  * –

Minimize the prediction error.

Note that we chose the size of regions and time intervals in order to address
the sparsity of input data, while still building a viable model that could
provide real-time insights. Interested researchers are encouraged to use
different settings to further explore the data and the task.

##  5\. Model

This section describes our construction prediction model. We start with a
description of the input to the model, that is the feature vector
representation of construction events and map image data. Then we describe the
model.

###  5.1. Feature Vector Representation

We create a feature vector representation for each hexagonal geographical
region $r$ of resolution $7$ during a time interval $t=15\textit{ days}$ by
aggregating all the events and averaging over them. To be precise, a
construction event includes the following features:

  * •

Weather (14): A vector representing two weather attributes temperature and
humidity; and $12$ indicators to represent special weather events light_rain,
moderate_rain, heavy_rain, light_snow, moderate_snow, heavy_snow, severe_cold,
severe_storm, severe_fog, moderate_fog, hail, and precipitation_other. Weather
data is obtained from Moosavi et al. (2019a).

  * •

POI (15): A vector of size $15$ to represent frequency of POIs (or map
annotations) within $r$. In addition to the cases described in Table 1, we
also consider entrance, give_way, and turning_circle. We obtain POI data from
OpenStreetMap contributors (2021).

  * •

Road type (25): A one-hot vector of size $25$ to show the type of the road in
region $r$ with construction. Road-type information is also extracted from
(OpenStreetMap contributors, 2021).

  * •

Road information (5): On a road segment with a reported construction, this
category offers five attributes, namely road segment distance, average speed,
approximate travel time, an indicate to show whether the traffic on road
segment was impacted during the construction, and severity of the
construction. The latter is an integer value between 1 and 4, where 1
indicates the least impact on traffic (i.e., short delay as a result of the
event) and 4 indicates a significant impact on traffic (i.e., long delay).

To build the aggregated view for all the construction events that occurred
during $t$ within region $r$, we simply average over the $59$ attributes
described above. Note that except for POIs, other features are different for
the different constructions that we are aggregating over the region. For
instance, two constructions that started at different times could have
different weather attributes; or they could be associated with different road
types. Thus, out of the 59 features, only 15 are constant over time.

###  5.2. Map Image Representation

The road network represented in the map tiles is also a relevant context
within a spatial encoding of the context for constructions. Constructions
could be less prevalent on a road located in a remote area (with a sparse road
network), and more prevalent on a road in an urban area (with a dense road
network). Figure 4 shows an example of the type of map images we use, again
collected from OpenStreetMap (OpenStreetMap contributors, 2021). We associate
each zone with one image. To do so, we first obtain the center of our hexagon
in terms of GPS coordinates, and then collect a map tile with the same center
from OSM with a zoom level of $14$. According to OSM101010see
https://wiki.openstreetmap.org/wiki/Zoom_levels, a map tile of zoom_level =
$14$ is a square image of size $256*256$ pixels. Each pixel of this image
covers about $9.547\textit{ meters}$, that means the side size of the image is
about $2.44\textit{km}$ and its area is about $5.95\textit{km}^{2}$. Thus, one
single image can almost cover an entire zone, since each side of a zone is
about $1.2\textit{km}$ and its area is about $5.16\textit{km}^{2}$.

Figure 4. Example of a map image extracted from OSM to (roughly) represent a
hexagon zone (zoom_level = 14)

.

###  5.3. The Deep Road Construction Prediction (DRCP) Model

Our Deep Road Construction Prediction (DRCP) model is shown in Figure 5. Since
our input data contains both images and time-series data we use a mix of
convolutional (CNN) Wu (2017), recurrent (RNN) Lei (2021) and fully connected
components.

  * •

CNN component: The use of this component is to encode map image data to
extract latent spatial features. The size of an input image is $256\times
256\times 3$. The $CNN$ component built from these images is shown in Figure
5, and it comprises four sub-components, three convolutional blocks (with 4,
32, and 8 channels, respectively) and one decoder component. The decoder
component contains six decoder blocks, which are convolutional layers with $8$
channels in the first and $16$ channels in the last three blocks. All decoder
blocks include batch normalization Santurkar et al. (2018) to deal with
internal covariate shift, max pooling Graham (2014) for downsampling, and ReLU
Agarap (2018) as an activation function. The other sub-components (i.e., three
convolutional blocks) do not leverage max-pooling, but batch normalization is
used in two of them. The output of the CNN component is then converted to a
vector of size $128$ by using a flatten layer. Note that the activation
function used in the last sub-component is sigmoid to properly concatenate the
outputs of CNN and RNN components. The kernel size in all convolutional layers
is $3\times 3$, and stride size is $1$. It is worth noting that this design is
mostly driven empirically through exploring different architectures and
settings.

  * •

RNN component: To encode sequential data, we use two layers of Long Short Term
Memory $(LSTM)$ Zhao et al. (2017) with $59$ and $45$ neurons, respectively.
Both layers of $LSTM$ use sigmoid as activation function. The choice of $59$
neurons in the first LSTM layer is to utilize sequential input of size
$10\times 59$ that represents aggregated construction event data over the past
$10$ time intervals. The output of this component is then converted to a
vector of size $40$ by using a dense layer. We employed sigmoid activation
function for the dense layer too.

  * •

Fully connected component: This component includes three layers of size $64$,
$16$, and $1$. The input to this layer is simply a concatenation of the
outputs of the other two components, which is a vector of size $168$. The
final output of the fully connected component shows whether there will be a
construction in the next $15$ days (i.e., next time interval) or not.

We employed an extensive grid-search-based hyper-parameter tuning process to
find the best settings (e.g., layer size and network size) for each component.

Figure 5. Deep Road Construction Prediction (DRCP) Model

##  6\. Experiment and Results

In this section we first describe experiment setup, then describe data,
followed by baseline models description, and conclude it by presenting results
and discussions111111All codes and sample data are available at
https://github.com/7Amin/DRCP.

###  6.1. Experiment Setup

All implementations121212see our code on https://github.com/7Amin/DRCP are in
Python using Tensorflow Abadi et al. (2016), Keras Chollet et al. (2015), and
scikit-learn Pedregosa et al. (2011) libraries; and experiments were run on
machines at Ohio Supercomputer Center Center (1987). For training DRCP we used
the Adam Kingma and Ba (2014) optimizer. The maximum number of epochs was set
to be $1000$, with an early stopping policy where training stops if the loss
value on the validation set does not decrease after $30$ consecutive epochs.
The initial learning rate was set to $10^{-4}$, and if no improvements could
be observed for $5$ consecutive epochs, then the learning rate is reduced by a
factor of $0.9$ (i.e., $\textit{new learning rate }=\textit{learning
rate}\times 0.9$). This reduction could potentially continue until the
learning rate reaches $10^{-6}$. The loss function we used was Binary Cross
Entropy Ketkar (2017), since our problem is a binary classification problem
and this loss function is proven to work quite well for this class of
problems.

###  6.2. Data Description

We trained and validated our DRCP model, on eight states (New York,
Pennsylvania, Georgia, Texas, Colorado, Florida, Washington and Michigan).
Further, we selected nine cities (Columbus (OH), New York City (NY),
Pittsburgh (PA), Atlanta (GA), Houston (TX), Denver (CO), Miami (FL), Seattle
(WA), and Detroit (MI). The choice of these states and cities was primarily to
achieve diversity in traffic and weather conditions, population, population
density, and urban characteristics (road-network, prevalence of urban versus
highway roads, etc.). We split our data set into three parts. The first part
was our training set with records drawn from the date range of February 2016
to the end of December 2019. The second part was our validation set, whose
dates ranged from January 2020 to the end of May 2020. The last part of our
data ranged from June 2020 to the end of December 2020 and was used as our as
test set.

We prepared our data for use in the machine learning process as described in
Section 5. Each record of data includes aggregated feature vector
representation for a $15$ days time interval, as well as a map image to
represent the corresponding geographical zone. Note that our goal is to
predict a binary label for the next interval, using data from the past $10$
intervals. To mitigate label imbalance, we use class weights to better train
different models. We empirically found the weights $1.01$ and $16.01$ for the
classes $0$ and $1$, respectively. These weights were found from the use of
the validation data in the DRCP model.

###  6.3. Baseline Models

We choose models described below as baselines to compare our proposal against
them.

  * •

Logistic Regression (LR): LR is a well-known model for binary classification
tasks. To train the model we set penalty = l2, random state = 0, and used
“Limited-memory Broyden Fletcher Goldfarb Shanno (lbfgs)” as the solver.

  * •

Random Forest(RF): Given the nature of our input data, RF seems a natural
choice to be used for the task defined in this paper. To train this model, we
set number of estimators = 10.

  * •

Gradient Boosting Classifier (GB): GB is a strong tree-based boosting model
for a wide range of classification tasks, thus we choose it as a baseline in
this work. We set number of estimators = 10 to train the model.

  * •

Multi-layer Perceptron (MLP): MLP is another strong choice to compare our
proposal against for the construction prediction task. Here we use a hidden
layer with $100$ neurons, Relu as activation function, and Adam as optimizer
to train the model.

Since these baseline models cannot work with sequential data, we vectorize
inputs and feed them as a single vector to these models. The vectorization
process makes use of both construction events data as well as map image data
in a concatenated form.

###  6.4. Results and Discussions

In this section we define two experimentation scenarios and present results
for each. The first scenario considers the case where construction data is
potentially available for every zone in a region, thus training and inference
can be done based on input data from all zones. The second scenario, on the
other hand, assumes we only have data for some of zones in a region, but still
seek to make future predictions for all zones (i.e., sparsity).

####  6.4.1. Scenario I: training based on all zones

According to this scenario, the input data to train different models includes
all the zones that have any data to offer. Using the settings described in
previous sections, we trained all the models, and Tables 5 and 6 show the
results based on f1-score and accuracy metrics, and for the selected cities.
Our proposed model (i.e., DRCP) outperforms other models with significant
margin for 8 out of 9 cities based on both f1-score and accuracy metric. On
average, we observe $3.2\%$ improvement in accuracy and $2.8\%$ improvement in
F1-score when compared to the best baseline for each city. This demonstrates
the effectiveness of our proposal to make better use of spatiotemporal
information to make future predictions.

Table 5. Comparing different models for selected cities for Scenario I using “F1-score” City | LR | RF | GB | MLP | DRCP
---|---|---|---|---|---
Atlanta (GA) | $0.773$ | $0.751$ | $0.731$ | $0.725$ | 0.793
Columbus (OH) | $0.859$ | $0.87$ | $0.855$ | $0.891$ | 0.902
Denver (CO) | $0.756$ | $0.767$ | $0.753$ | $0.761$ | 0.794
Detroit (MI) | $0.774$ | $0.786$ | $0.743$ | $0.728$ | 0.795
Houston (TX) | $0.891$ | $0.895$ | $0.892$ | $0.884$ | 0.908
Miami (FL) | $0.856$ | $0.843$ | $0.845$ | 0.868 | $0.845$
New York City (NY) | $0.895$ | $0.898$ | $0.884$ | $0.906$ | 0.942
Pittsburgh (PA) | $0.798$ | $0.794$ | $0.782$ | $0.768$ | 0.832
Seattle (WA) | $0.852$ | $0.825$ | $0.803$ | $0.794$ | 0.853
Table 6. Comparing different models based on Scenario I using “Accuracy” and for the selected cities City | LR | RF | GB | MLP | DRCP
---|---|---|---|---|---
Atlanta (GA) | $74.85\%$ | $72.55\%$ | $70.32\%$ | $69.44\%$ | 77.9%
Columbus (OH) | $82.3\%$ | $83.19\%$ | $82.81\%$ | $86.11\%$ | 87.2%
Denver (CO) | $73.56\%$ | $74.53\%$ | $73.48\%$ | $73.68\%$ | 78.2%
Detroit (MI) | $74.46\%$ | $75.36\%$ | $70.9\%$ | $68.47\%$ | 78.2%
Houston (TX) | $86.68\%$ | $86.8\%$ | $86.9\%$ | $86.21\%$ | 88.1%
Miami (FL) | $82.86\%$ | $81.09\%$ | $82.17\%$ | 83.91% | $81.6\%$
New York City (NY) | $85.01\%$ | $83.95\%$ | $85.41\%$ | $86.33\%$ | 89.7%
Pittsburgh (PA) | $76.18\%$ | $74.82\%$ | $\%75.53$ | $71.9\%$ | 77.7%
Seattle (WA) | $81.39\%$ | $79.23\%$ | $77.52\%$ | $75.81\%$ | 81.43%

To visualize how our model performs, Figure 6 shows prediction results (a, b,
and c) using the DRCP model and expected outcomes (d, e, and f) for Houston
(TX) over three consecutive time frames. A zone represented by “green”
indicates there are no road constructions reported or predicted, while a zone
represented by “red” shows otherwise. It is interesting to see how the model
performs over different time frames with different distributions of
constructions. For instance, Figure 6-(e) shows more sparsity, while Figure
6-(f) is more dense; and in both cases our proposed model performs reasonably
to make future predictions (see Figure 6-(b) and Figure 6-(c), respectively).
This shows how the proper use of useful spatiotemporal information can result
in satisfactory model outcomes, which is a strong indicator of applicability
of this setup in the real-world. In Section 8 we provided further visualized
results of this kind for two other cities in the United States (see Figures 7
and 8).

(a) Predicted / 6-1-2020 to 6-15-2020

(b) Predicted / 6-16-2020 to 6-30-2020

(c) Predicted / 7-1-2020 to 7-15-2020

(d) Actual / 6-1-2020 to 6-15-2020

(e) Actual / 6-16-2020 to 6-30-2020

(f) Actual / 7-1-2020 to 7-15-2020

Figure 6. Example of prediction results (a to c) by the “DRCP” model along
with actual labels (d to f) over three consecutive time frames for Houston in
Texas. Here we make predictions for individual zones that are represented by
hexagons. A “green” hexagon shows no construction is reported or predicted
during the corresponding time frame, and a “red” hexagon shows otherwise.

####  6.4.2. Scenario II: training based on limited zones

Unlike the first scenario, here the assumption is only a limited number of
zones have data to offer for training. To do so, we randomly select $60\%$ of
our zones for training, $20\%$ for validation, and $20\%$ for testing. To make
the task even more challenging, we do state-level training and testing (as
opposed to city-level as in scenario I), which results in more sparsity, and
perhaps closer to a real-world setup. Using a similar training process, we
individually trained the DRCP model for the selected states, and Table 7 shows
the results based on “F1-score” and “Accuracy” metrics. Based on the results,
we see our model performs quite reasonably when predicting on test data that
include held-out zones. This is an important observation since it shows that
even a partial dataset collected from a limited number of zones in a state is
enough to train a generalizable model like DRCP to make inferences about
future constructions for all zones in a state. Additionally, this is useful in
real-world, where we may never have access to high quality data for all zones
in a state or even in a city. Lastly, we note that similarities in input data
(e.g., map structure and weather condition) within a state (at least for the
states that we experimented with) could be another reason to justify the
promising outcomes of our model in this scenario.

Table 7. Prediction results using DRCP model based on Scenario II (i.e., studying spatial sparsity) for selected states State | F1-score | Accuracy
---|---|---
Colorado | $0.934$ | $84.4\%$
Florida | $0.942$ | $85.7\%$
Georgia | $0.942$ | $88.1\%$
Michigan | $0.941$ | $88.2\%$
New York | $0.925$ | $84.6\%$
Pennsylvania | $0.912$ | $85.0\%$
Texas | $0.938$ | $84.7\%$
Washington | $0.947$ | $88.9\%$

##  7\. Conclusion and Future Work

In this paper we tackle the problem of future constructions prediction using
heterogeneous spatiotemporal information such as past constructions, weather
data, and geographical map data. To our knowledge, this is a relatively new
problem space that has not been explored by the research community, maybe due
to lack of comprehensive historical data about past constructions. To address
this gap, this paper introduces a novel dataset of 6.2 million constructions
in the United States between 2016 and 2021, that offers a variety of details
around location, time, weather condition, map, and road-network. Additionally,
we formulate and solve the problem of predicting the possibility of future
constructions using such data. We present a deep-neural-network-based model to
efficiently utilize the heterogeneous input by combining convolutional and
recurrent components in a reasonable setting. Through extensive experiments
over multiple major cities and states in the United States, we show the
usefulness of our proposal in a real-world setting and in comparison to
several state-of-the-art baselines.

As directions for future research, we can extend our input data and leverage
information such as traffic load, past traffic accidents, finer-grained
weather data, demographic data for each zone, as well as other map imagery
views (e.g., satellite view). In addition to data, the model that we presented
in this paper can potentially be improved by jointly modeling spatial and
temporal data, instead of a separate utilization. Lastly, we can extend the
task that is defined in this work and tackle more advanced problems such as
“construction type” prediction; that is, if a certain construction could
result in a closure or not.

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##  8\. Appendix

###  8.1. Visualizing road construction predictions

In this section we visualize road construction prediction results for Columbus
(in Ohio) and Miami (in Florida) by Figures 7 and 8, respectively. Settings
and details are the same as what we earlier described in Section 6.4.1.
Similar to what we presented before, we can see how our proposed model
performs future construction prediction in two different cities. It is worth
noting again that some of the chosen time frames are sparse, given the number
of reported constructions during those. However, our proposed model is capable
of making solid predictions even when there is sparsity.

(a) Predicted / 6-1-2020 to 6-15-2020

(b) Predicted / 6-16-2020 to 6-30-2020

(c) Predicted / 7-1-2020 to 7-15-2020

(d) Actual / 6-1-2020 to 6-15-2020

(e) Actual / 6-16-2020 to 6-30-2020

(f) Actual / 7-1-2020 to 7-15-2020

Figure 7. Example of prediction results (a to c) by the “DRCP” model along
with actual labels (d to f) over three consecutive time frames for Columbus in
Ohio. Here we make predictions for individual zones that are represented by
hexagons. A “green” hexagon shows no construction is reported or predicted
during the corresponding time frame, and a “red” hexagon shows otherwise.

(a) Predicted / 6-1-2020 to 6-15-2020

(b) Predicted / 6-16-2020 to 6-30-2020

(c) Predicted / 7-1-2020 to 7-15-2020

(d) Actual / 6-1-2020 to 6-15-2020

(e) Actual / 6-16-2020 to 6-30-2020

(f) Actual / 7-1-2020 to 7-15-2020

Figure 8. Example of prediction results (a to c) by the “DRCP” model along
with actual labels (d to f) over three consecutive time frames for Miami in
Florida. Here we make predictions for individual zones that are represented by
hexagons. A “green” hexagon shows no construction is reported or predicted
during the corresponding time frame, and a “red” hexagon shows otherwise.