Early Detection of Driving Maneuvers for Proactive
Congestion Prevention
Debasree Das1 , Shameek Bhattacharjee2 , Sandip Chakraborty1 , Bivas Mitra1 , Sajal K. Das3
1

Indian Institute of Technology Kharagpur, Kharagpur 721302, INDIA
2
Western Michigan University, MI 49008, USA
3
Missouri University of Science and Technology, MO 65409, USA
Email: debasreedas1994@gmail.com, shameek.bhattacharjee@wmich.edu, sandipc@cse.iitkgp.ac.in,bivas@cse.iitkgp.ac.in, sdas@mst.edu

Abstract—Road traffic congestion affects not only the commute
delay but also a city’s overall social, economic, and environmental
growth. Existing approaches for road congestion mitigation
primarily adopt a reactive approach by detecting congestion after
it occurs and recommending alternate routes to the vehicles,
which fails to prevent congestion cascading. In contrast, we propose a pervasive platform called ProCon that proactively infers
the driving micro-behaviors that can contribute to congestion
formation and assist the drivers in avoiding such maneuvers
in real time during the navigation. Thorough evaluations overmultiple real-life and simulated datasets indicate that ProCon can
reduce congestion for more than 60% of the scenarios on average
while significantly reducing the travel time of the vehicles.
Index Terms—Road congestion, Proactive mitigation, Driving
behavior, Pervasive recommendation

RerouteR2: Alternate
R1: Fastest
GMap Recommended
R1: Fastest

Traffic Flow at 16.00 Hours

R2: GMap Recommended

Traffic Flow at 16.30 Hours:
Google Maps Rerouted Vehicles
More
RerouteStill Rerouting

R2: Still GMap Recommended
R2: Still GMap Recommended
R1: Congested
R1: Congested

Still Fastest

I. I NTRODUCTION
Road congestion impacts navigation as well as the overallsocio-economic-environmental growth of a region. According
to the congestion reports published by TomTom1 , the top3 traffic-congested megacities (Lima in Peru, Bengaluru andMumbai in India) spent an additional 24 minutes on average intraveling 10 km in 2022. Existing approaches of road navigation primarily use a reactive approach, i.e., predict congestion
based on the vehicle density and associated events, like theaverage speed of the cars, nature of the road, demographic factors, etc. Although such approaches may work intermittently
and help for long-term policy planning with reduced recurring
traffic congestion, they do not explicitly consider congestion
prevention as an objective during pervasive navigation.
One weakness of reactive congestion measures is that they
overlook the ripple effects of individual driving actions that
impact the traffic even at a city scale. While traffic congestion can be recurring and local (like congestion near a
shopping/event complex daily during busy hours), it triggers acascading traffic pattern that influences several non-recurringcongestion hotspots [1] across the city depending on theperceptual bias [2] of the driver (habitual perception based onstandard driving practices) towards the regional driving microcultures. For example, a driver might look for an alternate
exit while observing a lane block ahead. However, suppose
multiple drivers follow the same alternate exit based on
1 https://www.tomtom.com/traffic-index/ranking/?population=
MEGA(Accessed: May 9, 2024)

Traffic Flow at 17.30 Hours:
Google Maps Rerouted Vehicles, Route 2
Becomes More Congested

Traffic Flow at 17.00 Hours:
Google Maps Rerouted Vehicles, Route 2
Becomes Congested

Fig. 1: Congestion cascading due to map-navigated rerouting(Data from Chicago, USA on September 6, 2023)
their local perception. In that case, the alternate route might
get congested, and the impact of congestion thus gradually
propagates to other parts of the city (see Fig. 1). Coupledwith such perceptual bias, several anomalous driving behaviors
originating from specific driving micro-cultures, such as sideslipping to avoid snows or potholes on the road, frequent
overtaking on the lanes, etc., influence the traffic cascading
pattern that leads to eventual road congestion [3]. Considering
the situation discussed above, this paper tackles the following
research problem. Considering the runtime driving microbehavior, can a navigation app recommend maneuvers thatproactively prevent traffic congestion cascading while notsignificantly impacting travel time or distance?
Challenges. Although existing works [4]–[7] have developedmodels to correlate historical data and run-time information topredict future traffic density, there are challenges in proactively
adjusting real-time navigation to prevent congestion.
1 Traffic density alone can not predict congestion wellahead before it starts occurring. Traffic density enables us
to understand congestion only after it starts to form and does
not allow preventive measures. In contrast, tracking changes
in the driving micro-behavior along with context information

enables an a priori understanding of the traffic cascading that
may lead to eventual congestion.

II. A S MALL -S CALE S TUDY WITH R EAL DATASETSExisting studies have shown that recurring congestion is predictable based on the demographic and spatio-temporal geopatterns of the traffic movements [12]; however, it influences
several non-recurring congestion-cascades across the city depending on the run-time traffic behavior and the local driving
micro-cultures [13]. To understand whether such congestion
cascading can be prevented by intelligently controlling thedriving behavior during run-time, we conduct a small-scale
pilot study to answer the following research questions. a
Does a particular vehicle maneuver always trigger the congestion cascading or do different maneuvers trigger congestion
cascading at different times and places? b Does rerouting
with “Lane Change” maneuver trigger congestion cascadingin neighborhood roads? c Do all the vehicles exhibit a similar
behavior during congestion cascading?
We conduct the study using TomTom and Google Maps
data, and two publicly available driving datasets, namely
Berkeley Deep Drive (BDD) [9] and inD [8]. The BDD is
the largest driving dataset collected by 10k drivers voluntarily
in the USA and Israel, covering 18 cities. The multimodal data
covers IMU, GPS and video recording as the driver view. The
German dataset inD covers 4 road junctions with IMU and
GPS data from each vehicle along with the drone-recorded
videos. We utilize two cities from the two datasets, Aachen,
Germany & Boston, USA, for the following study.

2 Traffic congestion does not linearly depend on the driving
micro-behavior. Anomalous maneuvers by a few vehicles do
not always lead to traffic congestion. Instead, there is acomplex interplay among the maneuvers taken by different
vehicles coupled with the road condition, lane type, and latent
factors to determine whether the scenario may lead to traffic
congestion soon. Further, using such information in real-time
to predict congestion before it actually happens is a challenge.
3 Proactive congestion mitigation demands highly dynamic
route planning. Unlike reactive approaches where the vehicles
are rerouted after observing traffic congestion on one path,navigation planning to proactively mitigate the congestion
cascading needs frequent runtime analysis of the contextual
information that depends on several highly dynamic latent
factors (e.g., weather, road, lighting conditions, etc.).
Our contributions. To address the above challenges, thispaper proposes ProCon, a multimodal domain adaptive modelthat pervasively learns the contextual impact of different driving maneuvers on the possibility of local congestion formationand proactively alerts the driver to avoid the maneuvers having
a high probability of forming congestion. ProCon runs locallyon a smartphone as a standalone wrapper on top of thenavigation app and uses the IMU and GPS data coupledwith the associated map information. In cotrast to the existing
works, our contributions in this paper are as follows.

A. Impact of Vehicle Maneuvers on Congestion Formation
We start by analyzing how different vehicle maneuvers
influence congestion formation over two cities with different
driving micro-cultures – Aachen, Germany, and Boston, USA.
We focus on two different maneuvers – “Lane Change” (LC)and “Abrupt Stop” (AS), extracted using existing studies [14],under two different scenarios – low vehicle density (LD,< 80 vehicles/km) and high vehicle density (HD, > 200
vehicles/km). Notably, Aachen observes more frequent lanechanges, while Boston observes frequent abrupt stops andacceleration/deceleration on the road. Fig. 2(a) indicates that
Aachen observes a higher probability of congestion duringhigh and low vehicle densities in the presence of frequentlane changes. On the contrary, we observe that abrupt stops
influence congestion in Boston with a higher probability forboth low as well as high vehicle densities. Such observations
indicate that different vehicle maneuvers influence congestion
differently, which depends on the driving micro-cultures of a
locality (like lane changes are more common in Aachen, while
abrupt stops are common in Boston) and their spatio-temporal
impacts on the overall traffic movements.

1 Characterize driving behavior leading to congestion. We
conduct a small-scale study using digital map data (e.g.,
Google Maps, TomTom) and naturalistic driving trajectory
dataset inD [8] and the largest driving dataset Berkeley Deep
Drive (BDD) [9] to understand the aspects of driving behavior
and spatio-temporal road properties that lead to varying levelsof congestion (e.g., low, moderate, high).
2 Self-Explainable on-device model for proactive congestion
prediction. The crux of ProCon leverages (a) vehicle trajectoryand interactions with peer vehicles and (b) spatio-temporalroad properties to detect its correlation with driving behavior.
Following this, we design a model that understands the temporal dependency among different types of driving behavior withdynamic road properties. The model-identified explanations
responsible for congestion help us predict congestion aheadof time and accordingly recommend driving micro-behaviors
that help in macro-scale traffic management (like dynamic
rerouting to prevent congestion cascading).
3 Evaluation with real and simulated datasets. We use twonaturalistic driving datasets [8], [10] along with runtime simulation through Anylogic 8.8.4 simulator [11] to evaluate ProCon comprising more than 10 various road intersections with
70 driving hours. Thorough experiments show that ProCon can
prevent 50–70% of the cascading congestion scenarios when
the congestion possibility is predicted 30 minutes ahead oftime. Further, it results in a 10–40% reduction in the travel
time with around 0–6% increase in the travel distance.

B. Congestion Propagation due to Frequent Rerouting
To observe how frequent rerouting coupled with the Lane
Change maneuver influences congestion cascading, we consider the scenario shown in Fig. 1. As we have seen earlier,the congestion cascades from R1 to R2 and then persists inR2 even when it gets mitigated from R1 (after 17:00 hours).
To explore this further, we observe that while R1 is a straight2

0

LC LC AS AS
HD HD HD HD

LC
LDLC AS ASLD LD LD

17
0
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11
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80

2

50

1

Slo

10

w:
0

20

almost simultaneously (at 880 seconds), possibly replicating
the behavior of car 1. However, the truck moved slowly and did
not show any abrupt behavior. At the same time, the bicycle
tried to avoid traffic by frequent lane changes followed by an
abrupt stop with sharp deceleration. We observe a gradually
increased congestion on that road between 600 seconds to900 seconds, primarily due to the abrupt maneuvers taken bysome vehicles, which got further contaminated to the following
vehicles. Therefore, such diversity in vehicle behavior may
trigger abrupt maneuvers, worsening the congestion scenario.
These observations indicate that it is crucial to proactively
inform or alert the drivers not to take abrupt maneuvers thatcan lead to traffic congestion.

Vehicle Density (#vehicles/km)

R1: Vehicle Density
R2: Vehicle Density

Fa
st:

30

R1: Avg. Speed
R2: Avg. Speed

340

Traffic Speed (Slow to Fast)

Abrupt Stop:Boston
Abrupt Stop:Aachen

16
:0
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Probability of Congestion (in %)Lane Change:Boston
Lane Change:Aachen

Two Road Junctions in Different Cities

Time (in HH:MM)

(a)

(b)

Truck
Car 1

Car 2
Car 3

BicycleCongestion Start

Truck
Car 1

Abrupt Stop
Lane Change

Car 2
Car 3

BicycleCongestion Start

Abrupt Stop
Lane Change

12

1.0

10

0.5

Speed (in m/s)

Accelerati n Al ng Y-axis (in m/s2)

Fig. 2: (a) Probability of congestion under different maneuvers,(b) Congestion propagation due to frequent rerouting.

0.0

(0.5
(1.0

8
6

III. P ROBLEM D EFINITION AND S YSTEM OVERVIEW

4
2

(1.5
0

200

400

600

Time (in s)

800

1000

(a) Acceleration pattern

0

200

400

600

Time (in s)

800

Consider a set of vehicles A traveling on a road segmente having a set of spatio-temporal road properties w ∈ W. The
vehicles perform a set of maneuvers Mt′ at time window t′ .
Further, we consider that maneuvers of one vehicle a ∈ A may
impact maneuvers of its peer vehicle a′ ∈ A, denoted as impact
propagation (Ia,a′ ). The objective of this paper is to measure
the influence of Ia,a′ for each pair of peer vehicles {a, a′ } ∈ A
on congestion (Ct ) formation at a later time window t (t′ ≤ t).
We consider four types of congestion scenarios during timewindow t, Ct ∈ [0, 3] signifying {no, low, moderate, high}congestion, respectively. We aim to solve the congestion
prediction Ct as a function F ∶ R → (Mt′ , w(e), Ia,a′ ), where
R represents the derived context in the form of a sequenceof maneuvers {m1 , m2 , ⋯, mn } ∣ mi ∈ M and road properties{w1 , w2 , ⋯, wn } ∣ wi ∈ W.

1000(b) Speed patternFig. 3: Impact of congestion on driving behavior.
drive, R2 has multiple lanes. Fig. 2(b) depicts the two routes’
congestion information and the vehicle density on a temporalscale with their traffic speed as reported by the Google Maps
API. The figure indicates that the vehicle density increased
non-linearly in R2. As R2 already has more provision forchanging lanes, vehicles start to take alternate lanes to avoid
traffic. However, the lane change behavior further influenced
the average speed of the cars on R2. The figure indicates that
although R2 is a multi-lane highway and the vehicle density onthat route was close to R1 at around 17:45 hours, it observed
more congestion and lower vehicle speed than R1. The figure
indicates that to mitigate congestion in the current route(R1), diverting traffic to other routes (R2) uncontrollably may
propagate congestion more rapidly to the neighborhood roads.
This also establishes a concrete base that further performingsome maneuvers (say, lane change) to avoid traffic can worsen
the congestion over time.

Maneuvers+
Road Properties

Predict
Congestion Output: Generate Driver Alert

Input: Road Network, IMU,
GPS from each Vehicle

Annotate
Ground-truth

Feature Engineering & Data Annotation(a)New
Model Construction

RetrainPattern?

Validation

Identify Maneuvers Leading to Congestion
and Recommending Driving Guidelines(b)Fig. 4: ProCon system architecture.

C. Change in Driving Behavior During Congestion

A. System Overview

Here, we examine a non-congestion-to-congestion scenario
using the data from Frankenberg, Germany, (on August 20,
2018, weekday) during the early evening hours. To check
whether all types of vehicles behave similarly in terms of taken
maneuvers, we pick 5 vehicles (3 cars, 1 truck, and 1 bicycle),which were peer vehicles in one directional traffic, and theirtrajectories are plotted in Fig. 3(a,b) using acceleration along
Y-axis and speed values. We consider two maneuvers as usedearlier – abrupt stop (black circle) and lane change (redtriangle). Initially, the road had no congestion, and all thevehicles were moving with almost constant speeds (close
to zero acceleration) as depicted in Fig. 3(b). However, ataround 400 seconds, we observe some mild congestion on
the road, and the vehicles start maneuvering abruptly. From
Fig. 3(a), we observe that while car 2 decelerated smoothly,car 1 stopped abruptly (at 650 seconds) and further changed
the lane (at 870 seconds). Notably, car 3 also changed the lane

We propose a system architecture comprising two modules:
Feature Engineering and Data Annotation (see Fig. 4(a)) and
Identify Maneuvers Leading Congestion (see Fig. 4(b)).
1) Feature Engineering: ProCon relies on each vehicle’s
timestamped IMU and GPS data to infer per-vehicle maneuvers and map-extracted data to infer the road properties. We
use existing literature [15] to extract 4 different maneuvers:“Stop”, “Turn”, “Lane Change”, and “Jerkiness”, from eachvehicle using IMU/GPS data. We further extract vehicle dynamics, such as “Relative Distance” and “Relative Speed”
between two consecutive vehicles using their position (GPS)information, which helps us to encode impact propagation
Ia,a′ among the nearby vehicles during model development.
To extract road features w, we design a map-extracting
framework to infer 9 spatio-temporal features using Google
Maps, TomTom, and OpenStreet Maps API, utilizing GPS3softmaxDense2: 4

tanh

Dense1: 16

tanh

LSTM2: 50

Trainable

Dropout: 20%

tanh

LSTM1: 50

Input

Dropout: 20%

Fused

FrozenDropout: 10%

Combined
Transitional
Probabilities& Labeldata. The features such as “Road Segment Distance”, “Traffic
Density”, “Traffic Speed”, “Speed Limit”, “Presence of Multimodal Transportation”, “Weather Type”, “Type of Road”,“#Point of Interest (Police Station, Hospital, Bank, ATM,
Supermarket, Post Office, Institute)”, “#Connected Neighborhood Road Segment”, are extracted in a recurring mannerfor consecutive time-stamps (t) where t is a 20-second time
window. Note that, whenever a vehicle performs a maneuver,its probability is calculated based on previously performed
maneuver. Basically, we utilize the Markov property to compute the maneuver chain N denoting transitions of maneuvers
performed by a set of vehicles. Next, we utilize these roadproperties to comprise a [1 × 9]-dimensional feature vector
w to fuse with the computed maneuver chain N . Some of
the road properties (like “#Point of Interest” & “#Connected
Neighborhood Road Segments”) are encoded according to theiractual values, whereas the rest are encoded as a discrete value∈ [0, 3] where 0 represents normal situations and a higher value
represents poor situations.
2) Annotating Ground-Truth: We use the video data captured using a camera-equipped drone to annotate the groundtruth congestion level and vehicle maneuvers. Notably, thisannotated data is required only to develop the model andnot required during the run-time. We annotate the congestion
level as a human-annotated label, Ctg , with the help of 3
volunteers by showing the video recordings. Each recording
is annotated for 60 seconds window, and a label ∈ [0, 3]
is assigned denoting {“No”, “Low”, “Moderate”, “High”}
congestion, respectively. As the congestion level observed overthe video may vary with human perception, we fetch the traffic
speed for the corresponding trips as {“Fast”, “Moderate”,“Slow”, “Stop and Go”} mapping to [0, 3] from HERE Maps2
to cross-verify the sanity of the human-annotated ground-truth.
Now, to distinguish the events that occurred ahead of different congestion levels, we instruct the volunteers to observe
closely and figure out the maneuvers (say, over-speeding,turns, etc.) along with road demography and note them at
every 60 seconds time window. We do not restrict them byproviding any a priori information about maneuvers or roadproperties set to avoid bias and rely only on visual observation.
These ground-truth observations are kept in Lg , maintaining
their sequencing order for model construction and validation.
3) Identifying Maneuvers Leading to Congestion: We divide each trip into continuous non-overlapping time windowsof size δ where a sequence of driving behavior with its congestion label helps to construct a model with input features as atime-series data, as shown in Fig. 4(b). Following this, ProCon
proceeds to analyze the influence of certain maneuvers thathave a higher probability of congestion formation (higher Ctp
levels) using a self-explanatory method, called the historicalmodel. During run-time execution, ProCon intelligently fuse
information independently obtained from each vehicle (say,ego vehicle) along with the live available information on astandard map-based navigation service to predict the possibil-

Output:CongestionType[0,1,2,3]

LIME

GenerateSequence

Test Data

Road
Properties

RetrainDetect Change inTraffic Density

Fused
Input

Test Data
Generate
Congestion
Label

Ct p

Lp

Present?

No

Yes

M1

M0

M3

...

Low
Congestion

Online Dictionary D
Recommend
Guidelines

Fig. 5: Working principle of ProCon.
ity of congestion if the ego vehicle takes a specific maneuver,by harvesting the historical model. We also incorporate model
retraining after carefully validating the unforeseen data for
adapting the model for changing traffic scenarios.
IV. M ODEL D EVELOPMENT
ProCon leverages extracted features, viz., maneuver chain
N and road features w ∈ W. However, as these features
are in time-series format with variable lengths, capturing
their temporal dependencies in the feature space is essential.
Thus, we need a sequential model like a recurrent neuralnetwork (RNN) to capture the sequence of maneuvers leading
to different congestion levels. We use an LSTM model withretraining to overcome performance drops on non-IID data.
Additionally, we make the model self-explanatory to overcome
the non-interpretability of black-box models.
A. Training PhaseAs the maneuvers involved in N can either be “safe”
or “risky” (denoted as anomaly x), depending on how well
it is performed, it can contribute differently to congestion.
Additionally, confidence in computing such an anomaly of the
maneuver will help us to build a robust model. As maneuvers
are the core component in constructing our model, leveraging
their computation quality (i.e., confidence) is necessary to
avoid false-positive derivation. Thus, we represent anomaly
type as a discrete variable x ∈ {−1, 1} of maneuver m andconfidence of anomaly as a continuous variable y ∈ {0 − 1}.
Next, we adapt Dempster-Shafer (D-S) [16] theory to associate
both the confidence y and probability of performing m-th
maneuver to compute subjective probability. Following this,the subjective probability is multiplied with x to associatelevel of anomaly in the maneuver chain N . Then, we interleave
derived maneuver chain N with its corresponding road feature
vector w denoting road properties at each time-stamp t. This
interleaving process transforms a single scalar value from one
time-stamp into a l1 × (l2 + 1)-dimensional vector; the first
dimension l1 represents the length of the Markov chain while
the l2 dimension represents the associated road properties, andthe extra +1 dimension added represents the time-stamp. We
make l1 as variable length as within a time window numberof transitions between maneuvers can not be of fixed length.
1) Architecture considerations: Consequently, the resulting
input to our LSTM model for each sequence is a matrix ofsize < (l1 × (l2 + 1)), (1 × 4) >, where 1 × 4 is the one-hot

2 https://www.here.com/ (Accessed: May 9, 2024)4

vector of the ground-truth congestion level Ctg as shown in
Fig. 5. Our model comprises one LSTM layer of 50 units as
the input layer, followed by two hidden layers as one LSTM
(50 units) and Dense layers (width as 16), as shown in Fig. 5.
Between the input and the two hidden layers, three dropout
layers of 20% (first two) and 10% are added, respectively, toprevent over-fitting, as it will exclude a few random inputs
before feeding it to the next layer. The output from the last
dropout layer is finally fed to a dense layer of the same
dimension, i.e., a width of 4 as of the number of output labels[0−3] with the softmax activation function. Softmax generates
a label-wise probability vector as a multinomial one, and thelabel with the highest probability is chosen as the predictedlabel Ctp . We use “tanh” activation function for the other layers,
as it works well with continuous input range values.
2) Loss function and optimization: As an optimizer, we
use Adam as it converges rapidly and faster to compute forlarge datasets. For minimizing the loss between Ctg & Ctp , we
use Categorical Cross Entropy (CCE) as it works best withmultinomial representation of labels.
3) Handling class imbalance: Our training data has a classimbalance known to drop the model performance. Typicallabel balancing techniques like SMOTE oversample training
instances blindly without concisely selecting neighborhood
examples, which can also introduce noise, hence unsuitablefor our approach. As a solution, we use the “Hard Negative
Mining” technique to boost the performance of our model.
We refer to the training instances with low frequencies for
specific labels and more entropy in the feature space as “Hardto Classify” examples. We carefully choose them during modeltraining by inspecting the loss function as well as their distribution in training data and increase their weight by b% according to the distance between the ground-truth and the predictedcongestion labels as b = ∣Ctg − Ctp ∣ × 10∣b ∈ [10% − 30%]. Hence,
the model weights are readjusted accordingly to weighted
training samples to learn the best parameters.
Now, at the end of the training, the model can predict Ctp ;we are interested in identifying the sequences of maneuvers
and road properties responsible for such prediction. Hence, we
extend the model by making it interpretable as follows.
4) Generating interpretable sequences Lp : ProCon ascertains the influence of specific maneuvers from the Markov
chain and road properties on the predictions from the LSTM
model. For this, we employ LIME (Local Interpretable Modelagnostic Explanations) method [17], which is a modelagnostic technique to faithfully explain any classifier’s predictions by approximating them locally with an interpretablemodel. For each test sequence, LIME highlights the features(comprising the maneuvers and road properties) that playeda pivotal role in the model’s prediction. Thus, the significant
transitions are identified, and following this, the corresponding
maneuvers and road properties are decoded at given timestamps, forming a sequence Lp . These sequences are stored in
an online dictionary D (see Fig. 5) where congestion type Ctp
is the key and a set of sequences comprising maneuvers androad properties are populated as a value against the key. Thisway, major possible sequences responsible for a typical typeof congestion will be captured, which can be further used inruntime for proactive prediction. For example, if the predictedcongestion type is 2 denoting “Moderate” congestion, and aset of sequences are derived as {“Sudden U-turn → Abrupt
Deceleration by Peer Vehicles given Evening Time, Presenceof Trucks”, “Abrupt Stop → Lane Change → Relative Distance
Variation given Morning Time, Presence of Pedestrians, High
Traffic Density”,⋯}, it represents the possible causes behindthe “Moderate Congestion” key. We use D during the runtime.
B. Opportunistic (re)Training
As traffic congestion has regional influence, the feature
distribution might vary for an unseen region. Therefore, newsequences of maneuvers and road properties can always arrive
at runtime. To make our model generalized enough to workand adapt itself at runtime, we incorporate transfer learning
architecture [18]. Hence, we freeze all the layers except the last
three to save on computational resources. The frozen layers
will be used as a bootstrapped model in a new region, whereas,
the last three will adjust their weight parameters to learn the
differences in data distribution so that the loss function CCE
incurs minimal loss.
While running the model from an edge device deployedon a vehicle, we continuously sense the GPS data to infer asignificant change (increase) ϵ in the traffic density in ∆t timeinterval. At that point, the collected IMU and GPS data are
fed into our system to infer the congestion level Ctp , and its
corresponding sequence of maneuvers and road properties are
generated subsequently. This ensures our system can predict
congestion proactively while saving computational resources.
To identify new data points that the model needs to learn, we
need both the ground-truth congestion labels Ctg and observed
data along with identification of erroneous prediction from themodel. We leverage the fact that most sequences are derived
during the training phase. Hence, for the sample test data, if
we get a new sequence and the predicted Ctp also does notexist in D, we consider the sample as a retraining candidate.
However, we cannot use human annotators to collect Ctg during the runtime as it can be biased or even malefic. Therefore,
we use a decision-tree model trained on several road properties{traffic density, vehicle speed, time of the day, speed limit} toannotate Ctg at runtime. This validation property is legit as itlies on popular digital maps inference techniques3 . Following
this, the model gets retrained and accordingly adapts itself.
C. Runtime Inference
Finally, given the road properties, our method periodically
predicts the congestion level Ctp at time tnow + t, where tnow
is the current time. Suppose the predicted congestion level ishigher than the current one (as returned by the Map API). In
that case, ProCon checks D to figure out the chains of maneuvers Lp , which can be responsible for an increasing congestion
level. ProCon recommends that drivers avoid such maneuvers
3 https://googleblog.blogspot.com/2009/08/bright-side-of-sitting-in-traffic.
html (Accessed: May 9, 2024)5

(a) Training Data: inD Dataset Based On
City Scapes

Town 1the hyperparameters of ProCon. Our model takes 0.5 seconds
with 10% CPU, 1 GB of GPU, and 12% primary memory
consumption to run on each trip with retraining if required,thus making the system suitable to deploy at run-time.
For the simulation setup to test ProCon at runtime, we use
Anylogic 8.8.4 simulator running over 20.0.2 JDK environment to simulate 3 urban traffic scenarios based on trafficintersections, comprising “City Street”, “Highway”, “Parking
Lot”, and a variety of traffic participants (see Fig. 6(c)). Traffic
congestion due to cascading majorly originates from trafficintersections, in terms of longer waiting time, increased traffic
density, etc., and propagates through connecting roads, eventually paralyzing the neighborhood road network [1]. As trafficintersections are a significant source of traffic phenomena suchas congestion, we focus on emulating different intersection
structures to achieve diversity and increased complexity. The
urban cities with varying demography are emulated as follows- (i) A simple highway junction with 2 forward and 2 backward
lanes with one-way traffic and traffic participants are cars,
trucks, and vans, respectively (Town 1), (ii) a 5-way city roadintersection with 2 lanes on each road and one-way traffic withtraffic participants are pedestrians, cars, buses, respectively(Town 2) & (iii) a complex city with two 4-way road junctions
with utmost 6 lanes and 2-way traffic, 2 flyovers and a varietyof traffic participants as buses, lorries, trucks, cars, vans (Town
3). As these types of road intersections are mostly observed
throughout different nations, the broad characteristics during
proactive congestion predictions will be captured.

(b) Testing Data: exiD Dataset
Based On Highways

Town 2

Town 3(c) Simulated Testbench: Based on Highway

Fig. 6: Experimental setup with different datasets.
until the next prediction window. We analyze the impact of t
during the evaluation. Notably, although avoiding maneuvers is
recommended at a micro-level during the runtime, they capture
a wide range of fine-grained recommendations and help tomanage macro-scale traffic. For example, if turning towards
an exit may reroute the vehicle, resulting in a high congestion
probability at that route, ProCon recommends not to take theturn at that exit, thus even balancing traffic across different
routes at a macro-scale.
V. E XPERIMENTAL E VALUATIONIn this section, we evaluate how well ProCon can proactively identify the variation in maneuvers and road properties
to alert drivers about possible congestion formation. Notably,
evaluating ProCon from real-time deployments is not feasible;
hence, we use a simulator “AnyLogic” [11] to emulate driving
behavior with a naturalistic driving environment along with
two other real-world datasets as follows (see Figs. 6(a), (b)).

B. Baseline Implementation(1) We use a rule-based supervised Random Forest (RF) modelwith 50 decision trees, which branches out to an unlimited
depth for training. Although RF model predictions can be
explained using its feature importance map, we use LIME
only to generate the sequences responsible for congestion.
Using this baseline, we can compare both congestion levels
and sequences of maneuvers.
(2) We implement DTW [19] that identifies anomalous trajectory of vehicles by adapting the model for congestion
scenarios using inD [8] using hierarchical clustering. It captures individual vehicles’ spatial & temporal dynamics andlabels trajectories anomalous or normal. We compare how
well ProCon can infer maneuvers responsible for congestionin contrast to the anomalous maneuvers inferred by DTW.
(3) We adapt an existing work SPAT [7] by incorporating
W from current and all the neighborhood road segments
to understand the spatial correlation between road segmentson congestion formation and propagation from neighborhood
roads. We leverage our model only with variable length input
dimension, as W from each road is fed into a time-series
format with the corresponding road’s congestion level.

A. Experimental Setup
We train ProCon on a computing system with Intel(R)Core(TM) i7 − 1255U processor with 1.70 GHz speed, 16 GB
primary memory, and 500 GB disk space. We use Python 3.8.3
with Tensorflow 2.13.0v running in the background to implement our model architecture. We use 1500 training samples
from the inD dataset running over 200 epochs consuming 30%
CPU, 3 GB of GPU, and 13.64% primary memory, runningover ≈ 400 seconds. For the retraining architecture, we use
exiD [10], which is a highway dataset extracted from German
Autobahns, comprising 16 hours of driving data with 3 typesof traffic participants as “Car”, “Truck” & “Van”, respectively.
As proposed in §III-A3, the maneuver’s distribution from theannotated sequences Lg , responsible for congestion, differs
significantly for inD and exiD datasets, respectively. For top-3
maneuvers “Stop”, “Lane Change”, and “Relative Speed”, thedistribution for inD and exiD datasets vary as {(32%, 16.4%),(12.3%, 17.2%), (8.2%, 22.8%)}, respectively. Certain differences in maneuvers for the two datasets, such as stop pattern,
relative distance in city-street vs highway or others, further
highlight the importance of the retraining architecture. We
consider δ = 60 seconds, ϵ = 20%, and ∆t = 10 minutes as

C. System Improvement
We start by inspecting the improvement in terms of congestion reduction if the vehicles are alerted to avoid maneuvers

6

Town 2

70

40

10
30

25

20

15

E. Performance of Congestion Analysis

Town 3

10

5

10
0
−10
−20
−30
−40
−50

Rise/Drop Impact (in %)System Improvement (in %)Town 1
100

Town 1
Town 2
Town 3Distance

Time (in Minutes)(a) Congestion reduction.

ProCon’s congestion analysis is evaluated from two aspects- (a) congestion prediction, (b) congestion reasoning inference.
Congestion Prediction: We achieve a micro-F1 score [20]of 86% for 5-fold cross-validation and 83% for the test set
for congestion prediction for inD dataset with a 70:15:15
train:validation:test split, as shown in Fig. 8(a). However,while training the data with inD and testing over exiD dataset
and simulation, we achieve 80% and 75% micro-F1 score,respectively. Further, we observe that ProCon performs atleast 25% better than the two other baselines for congestion
prediction, over all the three datasets. RF+LIME could not
adapt itself with the shift in data distribution while treatingeach data sample as an independent instance. In contrary, SPAT
uses a rich set of road and traffic properties; so, it could predictthe congestion instances that are due to narrow roads or highvolume of traffics. However, it failed to predict the congestion
cascading due to the influence of the driving behavior.
Congestion Reasoning Inference: For measuring the accuracy as how correctly the interpretable sequences are generated, we use Jaccard Similarity [21] measure between
the
g
∩Lp
.
generated Lp and annotated Lg sequences as J = L
Lg ∪LpFig. 8(b) shows the results for all the three datasets and also
compare them against two baselines (§V-B). We obtain J as
80%, 75% & 71% for inD & exiD datasets and simulation, respectively. But, for the two baselines, especially for RF+LIME,
J is worse due to temporal independence. In contrast, DTW
performs quite well for the inD dataset (71%) as it considers
the temporal ordering of vehicle trajectory information but
fails for other datasets due to non-adapting model architecture. Also, the high values of J for all the datasets denotea decent overlapping between human-annotated and modelgenerated inference for predicting congestion, thus making
ProCon robust enough.

Time(b) Impact on time & distance.

Fig. 7: Overall system performance.
based on congestion
level prediction. We compute the imCtp w−Ctp w/oprovement as C p w/o , where Ctp w & Ctp w/o are predictedtcongestion with and without the driving alerts, respectively,
and t is varied between [30 − 5] minutes indicating how early
we want to predict the congestion levels. For each town in thesimulation, we run the experiments for 60 − 90 minutes witha minimum of 10 trips and report how system improvement
varies with respect to t. As depicted in Fig. 7(a), Town 1
gets the highest improvement with a mean 67% if vehicles
are alerted 30 minutes ahead. For Town 2 & 3, we get 56% &45% mean improvement in avoiding congestion. However, theimprovement is not worthwhile as the alert is generated later,i.e., 15 minutes ahead, even in Town 1 with a simple vehicle
movement pattern. Vehicles get enough time to rectify their
maneuvers for the former cases ([30 − 20] minutes); however,a delayed prediction fails to avoid congestion cascading, thus
exhibiting less improvements in avoiding congestion.
D. Impact of ProCon on Vehicles’ Travel Time and Distance

Performance on Congestion Prediction

80

ProConRF+LIME

SPAT

Next, we inspect how ProCon
performs while excluding two
feature categories, i.e., maneuver
ProCon
ProCon-man
chain (N ) and weight of the road
ProCon-road(W), one at a time. We call thesetwo variations as ProCon−man
micro-F1 Score
Jaccard Similarity 
where we exclude N and ProFig. 9: Ablation study.
Con−road where we exclude W.
For both the variants, we reportmicro-F1 score and Jaccard similarity for the test set of theinD dataset in Fig. 9. Although ablating both the features one
at a time drops both congestion prediction and its reasoning
inference quite poorly, still the drop is severe for ProCon−man.
The analysis indicates that both the feature categories havetheir importance on the model performance.

Accuracy (in %)

80
70
60
50
40
30
20
10
0

Performance on Sequence Inference

80
40
20

20
0

F. Ablation Study

60

60
40

Jaccard Similarity  (in %)micro-F1 Score (in %)While vehicles adhere to the recommended maneuvers to
avoid congestion as prescribed by ProCon, it is vital toanalyze if they need to travel more distance or time to reachthe destination. Therefore, we measure such impact as riseor drop in the travel time and distance for each town’scollective set of vehicles. We compare two scenarios, i.e.,
when vehicles travel based on Google map recommendation
and when ProCon recommends suitable maneuvers to vehicles
upon sensing congestion and report such impact in Fig. 7(b)as the percentage of rise/drop in travel distance/time. We
observe vehicles have to travel a minimal distance (a mean of
3%) while they follow ProCon’s recommendation. However,
we observe that the travel time reduces significantly when
ProCon’s recommendations are used. Notably, the proactive
approach not only reduces congestion on the road but also
helps the vehicles to dynamically choose a route at runtime
that reduces their overall travel times.

inD

exiD

Simulation(a) Congestion prediction.

0

ProConinD

RF+LIME

exiD

DTW

Simulation

VI. R ELATED W ORK(b) Congestion reasoning.

Recent works on estimating traffic congestion analysis [4],[5] focus on crowd propagation patterns from diverse traffic

Fig. 8: ProCon performance in congestion analysis.

7network properties, e.g., spatial relation between roads. Multiple sources responsible for congestion are studied in [6],showing influences of different zone types, sudden accidents,
etc. Utilizing traffic volume, road connectivity, etc., [22] extrapolate upcoming traffic congestion by anticipating a sudden
rise in traffic density. Additionally, [23] has developed a model
to predict the congestion propagation time. However, all suchapproaches require a diverse set of information, sometimes ina centralized manner and modification of road architecture,
optimizing traffic rules/signs, and changing traffic policies.
In contrast, different modalities such as spatial correlation
among road segments [7], GPS probes and crowd-sourced
tweet information [24], vehicle density, road capacity, vehicle
speeds, etc. are fused for predicting causes of congestion.
However, this happens reactively in retrospect and works
well for recurring congestion scenarios. However, attributing
driving behaviors to non-recurrent congestion [25] is very
challenging because they tend to have random patterns [26]as shown by SOTA methods. Already existing works [27]show a strong relation between dynamic traffic behavior andcongestion. Therefore, understanding the pattern of individual
driving behaviors in the context of the surrounding road
network and its effect on neighboring vehicles will establish a
causal relationship between maneuvers and eventual congestion. Learning this relationship will allow us to prevent maneuvers leading to non-recurring congestion. ProCon develops
a system in this direction by mitigating the limitations of theexisting approaches leveraging such causal relationships.

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VII. C ONCLUSION
This study developed a multimodal domain adaptive model
called ProCon that proactively predicts the possibility ofcongestion using the contextual impact of different driving
maneuvers and surrounding road information. Subsequently,
drivers are alerted to avoid such maneuvers to prevent congestion formation during navigation. Alerting the drivers wellahead of time and adhering to our recommendations shows thecredibility and efficiency of ProCon by reducing congestion
for most traffic scenarios. Although dependency on driving
maneuvers and road properties are well-captured, the effectof some unanticipated events, e.g., sudden natural or manmade disasters (say, thunderstorms or mob violence), are notcosidered in the current design of the model. Analyzing suchinformation needs additional modalities, such as fusing social
network information (say, tweets); we look forward to themas potential future directions.
Acknowledgement: This work was done while D. Das wasvisiting the Missouri University of Science and Technology
during May-October 2023 as a Visiting Scholar.
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