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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JIOT.2017.2665662, IEEE Internet ofThings Journal

Congestion Detection and Propagation in Urban Areas Using Histogram Models

Hesham El-Sayed and Gokulnath Thandavarayan

College of Information Technology, United Arab Emirates University, UAE

[email protected]

Abstract— Rapid growth of urbanization makes the

roadways exacerbate many problems like traffic congestion,

road accidents and passenger discomfort. Many actions

have been taken globally to solve or reduce this impact but

still the congestion problem seems to be persistent globally.

In this paper, we propose a new histogram-based model for

congestion detection. We subsequently extended our model

with the base platform concept and use Intelligent

Transportation System (ITS) technologies to provide a

novel rerouting strategy. The proposed model enables the

microscopic visualization of the traffic patterns for every

individual lane and predicts the congestion in priori and

takes actions proactively. The rerouting strategy used in our

approach provides a novel solution to the congestion

problem by taking precaution measures prior to the critical

point of congestion occurrence. The proposed algorithm is

compared with various existing algorithms and the

simulation results show that the proposed model addresses

the congestion problem effectively and provides better

solution compared to existing algorithms.

Keywords— ITS; Congestion Estimation; Histograms;

Congestion Propagation, Route Guidance.

I. INTRODUCTION

Traffic congestion affects major cities and engenders pollution

[1], high waiting time [2] and economic loss. According to the

recent survey presented by the traffic information company

Inrix[3], it is estimated that, by 2030 USA will be affected by

2.8 trillion dollars loss due to traffic congestion and in countries

like France, UK and Germany each individual citizens need to

spend 2,902 dollars annually due to traffic. Road Transport

Authority [4] from Dubai has claimed a loss of 7.6 billion

during the year 2006 to 2013 due to the traffic congestion in

roadways. The congestion will also create negative

consequences on passenger’s health. Sugiyama et al [5]

concluded that the prolonged travel time over 1hr per day

increases cardio-metabolic disease risk and increase major

health problems. For these reasons, managing the congestion in

roadways is meant to be an open problem for decades; many

techniques have been proposed to address the congestion

problem. Despite of all methods, recent technologies like ITS

provide promising results to handle the congestion problems

and solve other transportation related issues. ITS enables

various technologies to be integrated into a single cluster, for

example it uses Vehicular Ad-Hoc Network (VANET)

technology for wireless communications, integrate GPS

technology for positioning and uses various sensors and

cameras for detection schemes. Nevertheless, it has some

limitations in real time implementation, where it demands more

data exchange and processing power. As the structure of the

urban areas is heterogeneous in nature, implementing a generic

spatial-temporal model for congestion detection could increase

the complexity. In addition, most of the existing systems

demand On-board unit and Global Positioning System (GPS)

module to be integrated on every vehicle, which makes the

system costly. Current route guidance models, which address

congestion problem are based on considering the static

information and initiate the route guidance reactively after the

congestion hits the roadways. Unfortunately, this late detection

schemes make the route guidance ineffective and just shift the

congestion from one region to the other.

For the aforementioned reasons, there is a need for an intelligent

congestion detection scheme, which detects the congestion in

advance and trigger the route guidance dynamically. In this

paper, a microscopic visualization model is utilized which

adapts a dynamic congestion estimation strategy to estimate the

congestion for every lane. The congestion detection technique

is based on the lightweight histogram-based model [6], which

captures the higher order distribution stochastic function for

vehicles. The proposed model will characterize the traffic

behavior of urban areas using base-platform technology and

analyze the congestion propagation in roadways. Further, this

information will be used to detect the critical point of

congestion for every lane and the rerouting strategy will be

initiated before the congestion happens in the network. Due to

its simplicity and generality, the proposed model requires

minimum data transfer and can be easily implemented in urban

areas for congestion estimation and route guidance. A

microscopic simulation model is created with different

scenarios and compared with the existing algorithms. The

proposed model provides better solution than the existing

algorithms and keeps the trip time to be lower for vehicles.

Finally, to validate the robustness of the proposed model, an

optimized model called Shortest Path Algorithm (SPA) is

utilized for comparison. As the SPA model provides route

guidance based on all the information about the entire network,

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it demands a very complex system. It is practically less feasible

to be implemented in roadways due to its high cost and

complexity. Promisingly, the results of the proposed model

show that the proposed model works closely with the theoretical

model (SPA) and uses less processing and less complexity

when compared with the theoretical model (SPA). The rest of

the paper is structured as follows. In Section II we discuss the

existing related work and discuss its limitations. In Section III

a brief overview about the system is described. Section IV

presents the histogram data computing methodology. Section V

describes the network model. Section VI discusses the

methodology for congestion detection in urban areas. Section

VII provides analysis and prediction of the congestion

propagation. Section VIII describes the rerouting strategy.

Section IX evaluates the performance of the proposed scheme.

Finally, the conclusion is averred in Section X.

II. RELATED WORK

Projects like Nav-N-Go [7], iGo-Navigation [8], Google-Auto

[9] are introduced for vehicle navigation. In these projects,

vehicles are equipped with various sensors, GPS modules and

synchronized with the smart phones for navigation purposes.

The system provides many services to use like route

information, traffic update and parking facilities. However,

these projects heavily rely on the centralized server and expect

that the vehicles need to be connected with Internet. These

systems process the information based on the static maps and

rely on user’s information to determine the congestion. This

makes the system less reliable to detect the real-time congestion

in roadways. As the urban roadways have heterogeneous

behavior, detecting the congestion and providing a reliable

precautious measure should be based on the higher order

distribution function with dynamic update. Further, the system

shouldn’t demand any On-Board devices on the vehicles, to

keep the cost of vehicle less.

In recent years, many researchers have been working on the

congestion detection problem using advanced technologies that

exploit the traffic behavior of urban areas. In this section, we

sum up some of the related work based on the congestion

detection problem. Leontiadis [10] have proposed a system

NavSys that enable vehicles to exchange the traffic traces using

ad hoc technology. The degree of congestion was reported by

accessing the street section delay and the shortest path was

estimated by the weighted graph. From entry and exit of road

segments, the system collects GPS information of individual

vehicles and stored it in the database. A gossip based routing

protocol is used to disseminate the information to the network.

Although the system collects data to find the shortest path, it is

dubitable to the interference problem and processing heavy load

of traffic information in a stipulated time. Likewise, Wang [11]

et al proposes an interactive visual analysis system to analyze

traffic jams. It also uses GPS trajectories from taxis to build the

flow speed for the road network, and uses map matching

techniques for structured visualization of propagation graphs.

However, the model uses static data for computing relative low-

speed detection that is non-pragmatic to urban areas. Gustavo

[12] et al proposed a new definition according to which a road

is in a congested state only when the likelihood of finding it in

the same congested state is high in the near future. Based on this

new definition, it can classify the congested lane from the non-

congested lane for the given time interval. Further, it estimates

the short-term traffic congestion forecasting for any given road.

However, the model assumes that every vehicle mounting an

advanced PND integrated with a GPS receiver and a full-duplex

communication device, which is not practically possible to

impede on every vehicle in urban areas. The second problem is

that this mechanism lacks the ability to predict how long a state

of congestion will last during the trip time. Fernando [13] et al

put forward an event-driven architecture (EDA) as a mechanism

to detect different levels of traffic jams and also consider other

environmental data that come from external data sources, such

as weather conditions. The problem in that paper is that it does

not take into account other traffic metrics that do not rely on the

penetration rate of the system and the EDA to use segments

instead of a cluster bases digital map to divide the road during

the CEP processing. Several studies exhibit that they use GPS

devices as the important factor to collect traffic traces, but in

real time scenarios it would make the system open to more

errors and complex calculations. As noticed in the existing

systems, there can be a myriad of problems. In places like urban

area, there can be irregularities in GPS signals [14] due to

periods of high geometric activity. Accordingly, by having the

amplitude fading and phase scintillations, it is difficult to

predict congestion in places like intersections and roundabouts.

Furthermore, the existing schemes require special equipment,

like OBU and GPS, to be installed in vehicles.

For the aforementioned reasons, there is no doubt that there

should be a cogent methodology to avoid the previous

problems. In this paper, a novel congestion detection and

propagation scheme is presented based on the ITS technology.

For conducting traffic analysis, we use a light-weight

histogram-based model with sliding window properties. The

proposed scheme effectively detects the congestion level at

every lane and predicts the congestion propagation patterns for

enabling the rerouting strategy.

The proposed methodology is cost effective and doesn’t require

any additional equipment to be installed in vehicles. By using

the light-weight histogram models, the data exchange rate is

greatly minimized and thus reducing the overhead in the

network.



III. OVERVIEW

In this paper, we propose a light weight and efficient congestion

detection and congestion propagation scheme for urban areas.

As shown in Figure 1, the system adapts a microscopic

monitoring of vehicles in urban areas and detect the congestion

for individual lanes. A base platform concept is utilized in

individual lanes to monitor the traffic patterns. The degree of

congestion rate for every lane is computed periodically by light

weight histogram models and is used by the congestion

propagation scheme to detect the lanes that will be congested in

the near future. This information is disseminated to all vehicles

travelling in the network using simple infrastructure-to-vehicle

(I2V) and vehicle-to-vehicle (V2V) communication

mechanisms. Once the congestion rate in any lane(s) increases

above certain threshold value, the adaptive rerouting strategy

will be initiated to avoid the congested lanes.

IV. HISTOGRAM DATA COMPUTING

Congestion estimation is the act of estimating the continuous

density of a particular lane from a discrete sample sets drawn

from the corresponding lane individually. In urban areas, the

traffic pattern [15] created in every lane is unique and dynamic

in nature. To analyze every characteristics of lane individually,

a large amount of database should be used to track the

congestion. In order to solve this problem, we propose a simple

congestion estimation technique by using histograms, which is

usually presented in one dimension. Though seemingly simple,

the effective use of histograms can be surprisingly subtle. We

present a simple nonparametric modeling approach using

Knuth’s rule [16], which is based on optimization of a Bayesian

fitness function across fixed-width bins. The congestion

estimation is done by choosing the bins sufficiently large

enough due to random sampling fluctuations. Initially the

density pattern across the network data is collected by sensors

over the time called observation period (O). Further, the

observation period is fragmented into time slots of sampling

period (T) where the density is calculated. Therefore, the

number of sampling windows N within an observation period is

equal to O/T. Therefore, the number of sampling windows N

within an observation period is equal to O/T.

Let 𝑉(𝑡) be a discrete random variable representing the number

of vehicles entering the street during the (𝑡𝑡ℎ) sampling period.

Hence, the total number of vehicles entering the street during a

sampling window 𝑉(𝑡)|𝑡 ∈ {1,2, … 𝑁} can be described as a

discrete random process with a state space, denoted as(𝐼),

which is a set of integers between 0 and the maximum number

(𝐾) of vehicles held in the street. Note that, V(𝑡) can be

obtained by on-side road traffic sensors. As the statistical 𝐼

∶= {0,1, … . 𝐾} variable can vary over a big range of values [0,

K]. We divide this range into Nc limited number of classes and

compute the grouped probability distribution (gpd) over each

class. For example, Table 1, shows the statistics of number of

vehicles on road that has been analyzed using a sampling period

of T = 15 seconds by a wireless sensor network over a total

observation period of five minutes, where N = 20 sampling

interval. The range of the number of vehicles during the

observation period varies from (<15), (15-19), (20-25), (>25)

which is divided into Nc = 4 classes. The bins used in the

histograms are classified using the process developed by

Skycomp (Major highway performance rating and inventory-

State of Maryland – spring 2008) [18]. The level-of-service for

signalized intersection is grouped as Light, Moderate, Heavy

and Congested. Based on this grouping, the (<15) bin denotes

Light, (15-19) bin denotes Moderate, (20-25) bin denotes

Heavy and (>25) bin denotes Congested. The histogram defined

in this paper is a form of a bar graph representation of a grouped

probability distribution (GPD) [19], which is shown in Figure

2, representing the number of vehicles in an observation

window basis against their corresponding probabilities (or

frequencies). This is important, since 𝐶 = (𝐶̅, 𝑃) can be easily

obtained by vehicles moving on the road, and this statistical

Class

number

I

Class

Interval

{𝑪𝒊−, 𝑪𝒊

+}

Midpoint

𝑪𝒊

Probability

𝑷(𝑪𝒊)

0 {0, 15} 7 0.2

1 {15,19} 17 0.4

2 {20,25} 23 0.2

3 {>25} 30 0.2

Figure 1. Overview of the proposed system

Table 1. Example of class distribution during a single observation

period



information is to be shared by all other vehicles through an ad

hoc based infrastructure-to-vehicle network.

Here, we note that the process of vehicles input into the queuing

system is stationary at a uniform rate, and the number of

vehicles 𝑉(𝑡)|𝑡 ∈ {1,2, … , 𝑁}arriving in an observation period

is independent and has a distribution modeled by a

histogram 𝐶 = (𝐶̅, 𝑃). Therefore, 𝑉(𝑡)|𝑡 ∈ {1,2, … , 𝑁} is a

stochastic process used to characterize the variable number of

vehicles input into the queue under investigation [6].

Our research indent was to estimate the congestion propagation

and use it for the rerouting strategy. In urban areas the

characteristics of each lane could vary with each other. For

example, the lane length would be different and the maximum

speed limit would not be the same. It is difficult to have a

generalized method to identify a congested lane in particular.

Initially, for validation, we developed a simple scenario using

the SUMO [20] simulator using only one intersection to justify

our model. Further, the congestion created in every single lane

is measured using different volumes, and different speed

scenarios. In this model, we use a base platform technique,

which computes the Congestion Probability Index (CPI) value

to determine whether the lane is congested or not. In every lane,

we select a particular portion called base platform as shown in

Figure 3, and use it to estimate the congestion for that particular

lane.

To begin with, each lane length is set as one kilometer and two

induction loop sensors are implanted in every lane to analyze

the traffic behavior. We considered that the traffic created near

the intersection is due to the traffic signal and it will disperse in

the green signal period. The first sensor fixed after 300 meters

from the intersection, is due to the factor that we want to neglect

the traffic created by traffic lights as we avoid the vehicles

waiting for green signal and consider it as the normal

phenomena. The occurrences of vehicles waiting in the traffic

light during the red signal period are quite normal until it

exceeds the limit of 300 meters. If the vehicle queue length

exceeds after the 300 meters from the intersection, we may

predict that the lane is likely to be get congested in the near

future. Only estimating the congestion index of that particular

lane could validate this prediction. Since congestions are

always created near to the intersection and propagate backwards

over the period of time, it is well founded that the base platform

could be placed near to the intersection. The base platform

should identify the congestion prior to the critical point and we

fix the base platform distance as 400 meters. The second sensor

is placed in the distance of 700 meters from the intersection and

validated that it is too early to detect the congestion if we fix the

sensor before 700 meters and too late to fix the sensor after 700

meters. The sensors will count the number of vehicles in the

base-platform to identify whether the lane is congested or not.

We run the simulation for 30 minutes and build histograms with

regular intervals. Further, we divide the base-platform into two

regions called 𝑀𝐷𝑇 and 𝑁𝐷𝑇, which are used for re-routing

strategy. Initially the congestion starts building over the region

𝑀𝐷𝑇 followed by 𝑁𝐷𝑇. Where, vehicles become full halt in the

region 𝑀𝐷𝑇 , while vehicles kept moving in the region 𝑁𝐷𝑇.

Further, the number of counted cars in the base-platform is

classified into four different bins, based on the level of service

provided for the congestion detection. The bins used to build

the histograms have four ratings as shown in Table 2

(Skycomp2008) [18].

For every 15 seconds, the sensors count the vehicles presented

in the base platform and increment the frequency of the

corresponding bin. For example, if the vehicle count is 17, the

frequency of the moderate-traffic bin (BIN-II) is increased by

1. This process is repeated for the stipulated time of five

minutes and the histograms are drawn. A new histogram is

drawn every five minutes; however, the first five minutes are

considered as warm-up time, thus five different histograms are

created for every 30-minute experiment. Figure 4 (a-e) shows

the individual histograms for source volume 700, and Figure 4-

f shows the combined histograms for all experiments, which

provides a better visualization of the traffic distribution during

the experiment. Different source volumes vary from 500 – 1000

burst rate are simulated to analyze the traffic behavior. The

cumulative graph for all different source volumes is presented

in Figure 5. As we can see from Figure 5 (a-b), in source

BIN-I

Light

BIN-II

Moderate

BIN-III

Heavy

BIN-IV

Congested

<15 15-19 20-25 >25

Figure 2. Statistical histogram for class distribution shown in

Table 1

Table 2. Classification of congestion levels based on vehicle density



volume 500 and 600, the traffic is nominal and most of the

traffic is occurring between the light and moderate bins. When

the source volume increases, traffic rate increases and

congestion starts to build up.

For example, when we increase the source volume to 900 and

1000, most traffic occurs in the heavy and congested bins. Thus,

the volume injection rate is directly proportional to congestion

propagation.

V. CONGESTION DETECTION

In this section, we derive a congestion detection scheme, which

estimates whether a lane is congested or not. Congestion in

roadways is problematic, where vehicles hinder with each other

and enter into a full halt or a stop and go fashion. Once the

roadway capacity is full, every new vehicle imposes further

delay in the network. It affects the roadway passengers and

tends to increase the total travel time, fuel wastage, and

pollution problems.

In the previous section, histograms for different source volumes

are computed for a single lane, and the congestion rate for

different source volumes can be identified by analyzing the

histograms. Nevertheless, this scheme needs to be generalized

for the different lane characteristics. As the histograms provide

an overall traffic pattern for a single lane, it cannot be directly

used for congestion avoidance. To avoid congestion and enable

re-routing strategy, it is important to detect any congested lane

well in advance before the congestion accumulates in that

particular lane. Therefore, we compute a benchmark congestion

probability index (CPI) to detect whether a lane is congested or

not. This benchmark CPI value can be applied to all type of

lanes with different lane length, different speed and different

volume rate.

To derive the benchmark CPI value, we use the test-bed shown

in Figure 3 and analyze the histograms obtained from different

source volumes. Table 3 shows the discrete set of samples

collected from the base platform on a window time basis with

different source volume scenarios. The congestion severity can

be estimated by monitoring the different bins. It is apparent that

congestion happens in roadways when the vehicles are more

than the available capacity. In order to find the severe level of

congestion in a lane, the congested-traffic (BIN-IV) alone is

taken into consideration. Choosing the right value for

𝐶𝑃𝐼 benchmark is very important to predict the congestion. For

instance, if the value 𝐶𝑃𝐼 is selected low, the system will detect

the congestion early and initiate the re-routing strategy in

advance and make the system inefficient. If 𝐶𝑃𝐼 is having

higher value, the lanes are jam-packed and the re-routing

strategy would never be triggered on time. Because congestion

always happens when traffic increases in the congested traffic

bin (BIN-IV), the Congestion Probability Index is computed

from analyzing the congested-traffic (BIN-IV) only.

The congestion rate 𝐶𝑅 for every lane is computed by the

formula 𝐶𝑅 = ∑ (𝑎0 + 𝑎1+. . 𝑎𝑛)100⁄𝑛

𝑖=0 . Where 𝑎 represents

the frequency of occurrences in the congested-traffic bin (BIN-

IV) on a window basis derived from the histograms. For

example, in Figure 5-a, the congestion index for the source

volume 500, with lane maximum speed of 60 Km/hr., is

computed by 𝐶𝑅 =3+3+2+3+3

100 and the value is 0.14. Similarly,

in Figure 5-f, the 𝐶𝑅 value is computed as 𝐶𝑅 =7+7+6+6+7

100 =

0.33. The congestion rate 𝐶𝑅 for different source volume with

different lane speed is computed by monitoring the congested-

traffic (BIN-IV) as shown in the Table 3.

Figure 3. Base platform on a single lane

Figure 4. Individual histograms and the cumulative graph for source

volume 700



We observe the congestion rate 𝐶𝑅 varies based on source

volume and the label speed. For example, when the source

volume is 500, we compute different 𝐶𝑅 values 0.14, 0.13 and

0.13 for different lane speed. Furthermore, we observe that

when the source volume is 500-800, we do not encounter any

congestion during the experiments. When the source volume is

900, we encounter congestion when the lane speed is less than

60 km/hr, while we observe moderate flow when the lane speed

is greater than 80 km/hr. Moreover, when the source volume is

1000, we encounter congestion when the lane speed is less than

80 Km/hr.

In real time, the lanes in urban areas have different

characteristics, some lanes have high speed limit as above 100

Km/hr. and some lanes have less speed limit as 40 Km/hr. For

congestion detection, a benchmark CPI value should be derived

to detect the congestion of a lane irrespective of its lane

characteristics. To derive a benchmark CPI value it is necessary

to correlate the relationship between speed and the vehicle flow

rate in the lane. We analyzed the different scenarios, where we

encountered congestion and realized that in all experiments the

CR value was greater than or equal to 0.3.

We use the benchmark CPI value to be 0.3 as a median to

monitor the congestion. The 𝐶𝑅 value computed from Table 3

with different source volume is implemented in the heat

diagram shown in Figure 6. The diagram apparently shows that

the congestion starts to build up from source volume 900 and

become more congested when there is an increase in the flow

rate. It also shows that the CPI index of 0.3 is a good prediction

point for detecting congestion regardless of the value of the

source volume and lane speed. This analysis could be further

Figure 5. Histograms for different source volumes

Table 3. Congestion Rate (CR) for different source volumes



used for identifying the congestion lane in advance and helps to

initiate the precautious measure.

The curve shown in Figure 7 illustrates the traffic behavior of

each lane and is used to validate the benchmark CPI. Initially,

vehicles will move freely in a lane with its lane maximum speed

and the flow rate will be represented as vector 𝑣0. When the

flow rate 𝑣0 lies between the congestion rates 𝐼 and 𝐷, the

traffic is moderate and the lanes are well utilized. Further, when

the flow rate increases from 𝐷 to 𝐽, the congestion in the lane

starts to build up. When the flow rate exceeds 𝐽 and none of the

precautious measures are implemented in the network, the

vehicles will keep entering the lane until the lane becomes full

halt at point 𝑄. To prevent the vehicles from entering the

congested lane any further, the congestion rate 𝐷 will be

indicated as the CPI benchmark and the re-routing strategy will

be initiated accordingly.

VI. NETWORK MODEL

In this section, we discuss the network model used in our

proposed system for congestion propagation and re-routing

strategy. In the previous sections, we used our base-platform

model with various source volumes to identify the benchmark

CPI value. Further, this CPI value is used to predict the

congestion propagation and initiate the re-routing strategy

whenever necessary. In real time, the urban roadway

commonly has several types of road junctions including traffic

signals, road junction with roundabout and road junction with

fly-over.

To study the effectiveness of the congestion-detection scheme,

a grid network model is utilized. We use I-sim simulator to

create the scenarios [25]. The scenarios were created using

various source volumes, varying link speed and varying link

length. A network model with 7 x 7 grid structure is created as

shown in Figure 8. In the grid structure, the nodes are

considered as intersections and the edges are considered as

lanes. Each intersection is implemented with traffic light to

create the real time vehicle movement. Every node has one or

more lanes intersected to it. The vehicles initiate the trip

randomly from the edges and take the shortest route to reach the

corresponding destination. All the scenarios have identical 49

nodes, in which 14 nodes are selected as origin were the

vehicles started their trip to defined 28 destinations.

VII. ESTIMATION OF CONGESTION PROPAGATION

The congestion that happens in urban areas is usually dynamic

in nature and mainly based on the lane level implication. The

volatility of drivers and high dynamic movement of traffic in

network, makes the estimation task complicated. If congestion

persists for a long time it propagates to its adjacent lanes and

further makes congestion worse. To avoid further congestion,

the lanes that will be congested in near future should be detected

in prior and the vehicles can be re-routed to avoid further

congestion. By re-routing the vehicles, the uncongested lanes

are utilized and the congested lanes are avoided. This greatly

minimizes the frequency of congestion occurrences and the

make the congested lane takes less recovery time to resumes its

normal operation. The first observation in the experiment

focuses on the prediction of congested lane among the urban

area network. By identifying the congested lanes among the

Figure 6. Congestion rates for different volume and speed

Figure 7. Graphical Representation for CPI

Figure 8. Grid network topology



network, we can understand the congestion propagation and

capture the fundamental phenomena for traffic occurrences. In

this paper, we develop a new methodology to study the

empirical analysis of the congestion formation and its

propagation between the contiguous lanes as shown in Figure

9. The model captures the sequence of congestion propagation

by monitoring the traffic at fine granularity based on the traffic

flow. The traffic network are clustered into regions with respect

to intersections, each lane is monitored individually to analyze

the characteristic behavior of traffic flow.

Initially, all lanes are connected by intersection that can be

mapped by the graph representation using the geometric

function GS= (V, E), where V represents the vertices (nodes)

and E represents the edges (lanes) between the nodes. As

discussed in the network section, every node in the graph is the

cumulative summation of the individual lane lying adjacent to

each other in the grid and forms an intersection. Every edge

represents the lane connected by at most two intersections.

Edges are connected directly in a spatiotemporal region to

define an intersection and each lane is fixed with a lane

maximum speed. The base-platform model is installed in every

lane to compute the congestion value 𝐶𝑅 individually.

Congestion Probability Index CPI is set as a benchmark for

every lane to detect congestion a priori. During the simulation,

the congestion value 𝐶𝑅 is computed dynamically for every

lane based on histogram model and compared with the CPI

value.

If the 𝐶𝑅 value exceeds the benchmark CPI index it is an

indication for congestion likely to occur in that particular lane.

This strategy will help to distinguish the congested lanes from

free flowing lanes. By this differentiation, we classify the lanes

in two categories: Group A and Group B. Initially, all the lanes

are marked as Uncongested Lane (UL) and stored it in Group

A. Every five minute, a new histogram is built for every lane

and the corresponding 𝐶𝑅 value is computed and compared

with the benchmark CPI value. If the lane 𝐶𝑅 value exceeds the

benchmark index, the lane is marked as congested and moved

to Group B. From Group B, a lane is selected and the adjacent

intersections Vi and Vj are identified. Moreover, every lane E

connected to the Nodes Vi and Vj is identified and its

corresponding 𝐶𝑅 value is compared with the benchmark CPI.

If the congestion is detected the lane is moved to Group B. This

process is repeated for every five minutes and congestion

propagation is recorded. Figure 10 shows the flowchart for the

detection of congested lanes and the prediction of congestion

propagation.

Initially, the single lane roads of 1 km length are created using

7 x 7 roads connected with the intersections as shown in Figure

8. To achieve the urban traffic behavior, the vehicles injection

frequency is randomly created with different patterns of

platoons, and each platoon will range from 2 to 8 vehicles. The

simulation runs for 30 minutes and the congestion propagation

is computed for individual lanes with five-minute time interval

and the contour diagram is plotted as shown in Figure 11. From

the graph, we can estimate the initial congestion happens in the

base of the grid network and propagates backwards during the

course of time.

A more realistic approach for the city of Al Ain is used for

analyzing the congestion propagation. The downtown of the

city is captured using the OpenStreetMap application and

simulated in SUMO for 60 minutes. More specifically, the

behavior of the peak hour is taken into consideration and the

vehicle platoons were created to serve the nature of the traffic

Figure 9. The stages for predicting congestion propagation

Figure 10. Flow chart for detecting congested neighbor lanes



behavior. The route is modeled with great detail to have the real

time envision of the congestion propagation that particularly

overlaid with the main streets of the city. The histograms were

built for every five minutes and the CPI is used to predict for

the congested lane. The results obtained were promising and

show the starting point of congestion where it actually initiated

as shown in Figure 12(a-f). The traffic of every lane can be

identified over the period of time by analyzing the scatter

diagram, this can be further used to find the capacity of every

lane and helpful for rerouting strategy in order to avoid the

congestion.

VIII. REROUTING STRATEGY

If a traffic jam exists in a lane, it can be accumulated gradually

and would spread through other lanes following latent periods.

If a lane is identified to become congested in near future, the

vehicles ingress to the lane are to be rerouted with some

intelligent measures. This could avoid the lane to be further

congested and takes minimum time to recover from the

congestion. In our model, initially the vehicles are travelling

from source to destination using Dijkstra’s shortest path, and

the lanes are given equal weightage. If all the routes are stable

without congestion, the re-routing strategy stays dormant. If

there is a commensurate rise in the 𝐶𝑅 value for a particular

lane, it is an alarm that the lane likely to get congested soon.

Thus a reroute strategy is initiated which uses the entropy based

backtracking heuristic algorithm [21] to find the efficient

alternative route to destination. To avoid further congestion, the

reroute strategy employs to monitor the entire network on the

window basis and update the 𝐶𝑅 value index value

periodically.

Initially all the vehicles travelling in the network will have

predefined routes based on the Dijkstra’s shortest path

algorithm. Information like the average travel time and the

number of intersections is known. Once the vehicles start their

trip randomly, the congestion starts to build up slowly. When

the system detects congestion in a lane based on the 𝐶𝑅 value,

the congestion propagation is estimated. From the congestion

propagation model, we identify the lanes that are likely become

congested in near future. The weightage of lanes is increased

for the congested lanes and the predicted lanes. The routing

information is revised for every vehicle and a new route is

estimated based on the back tracking algorithm. The proposed

algorithm checks the lane weightage and avoids lanes with high

weightage in the route computation. The new average travel

time is computed based on the new route and compared with the

existing route. If the new average travel time is less than the

existing travel time the vehicles are recommended to take the

alternate path.

Re-Routing Algorithm

1) For each vehicle, find the route from source to destination and estimate the

average travelling time using

Dijkstra’s shortest path algorithm.

2) Compute the congestion rate 𝐶𝑅 value for every lane.

3) If congestion detected in any lane, compute congestion propagation to

predict the future congested lanes.

4) Identify congested lanes and move

them to Group B. Increase the

weightage of the identified lanes.

5) Initiate the re-routing strategy. 6) Compute new routes for all vehicles,

which plan to use the congested lanes,

using heuristic backtracking algorithm

and estimate the average travelling

time.

7) Re-route the vehicles using the

updated routes, and repeat the process

from step#2 until no congestion is

triggered and the traffic is equally

distributed.

Figure 11. Grid scenario for congestion propagation

Figure 12. Congestion propagation in Al-Ain city center during six

10-minute observation intervals.



In order to verify the effectiveness of the rerouting strategy, we

employ the proposed model in the SUMO simulator and

compare it with an existing model in literature called Random

K Shortest Path (RkSP) [22]. Initially, the map for downtown

of Al Ain city was downloaded from OpenStreetMap as an osm

format, then it is converted to SUMO application format using

a command line application called Netconvert and Polyconvert.

A fixed volume of 1000 cars will be randomly generated and

the simulation run is fixed for 1000 seconds. The average

travelling time of every vehicle from source to destination is

computed and shown in Figure 13. As the proposed model uses

the heuristic backtracking algorithm for the rerouting, the travel

time taken by every vehicle is minimum, when compared with

the Random K Shortest Path (RkSP), as shown in Figure 13.

The results show that the proposed model establishes the

rerouting strategy effectively and thus reducing the trip time of

vehicles.

By this analysis, an alternate route with minimum travel time

will be selected for rerouting the vehicles. The next section will

perform the comparative study with the other existing models

with different scenarios.

IX. EXPERIMENT RESULTS

In this section we compare the proposed algorithm with two

different existing algorithms IVC [23] and HBA [24]. Three

different scenarios with varying source volume, varying link

speed and varying link length are created to measure the

average travel time of the vehicles.

A. Source volume scenario

The experiment is carried out with various source rates that vary

from 500 to 2000 vehicle/hour. The maximum speed limit for

every lane is fixed at 80 km/hr. The vehicles are created

randomly using 14 origins which closely mimic with the real

time environment. The vehicle platoons are created at the

starting point of the lane; however, they may change during the

course of the trip time by various factors. All trip parameters of

every vehicle (destination, speed, type) are stored into the OD

matrix and given as input to the simulation. The simulation ran

for 60 minutes and the average travel time from source to

destination is recorded for every vehicle.

The proposed model uses Dijkstra’s algorithm to find the

shortest path from source to destination until the congestion

happens, then the rerouting strategy is initiated to avoid further

congestion. The performance of the proposed algorithm is

compared with the existing algorithms as shown Figure 14. The

recorded average travel time shows that the proposed algorithm

gives better results than the HBA and IVC algorithm.

B. Link Speed Scenario

In urban areas the lane speed is almost the same for all roadways

connecting with each other, however the speed limit will vary

from region to region. It makes the interesting factor to measure

the impact of speed limit in urban areas. In our scenario three

different speed limits were set to lanes in the grid network form

60, 80 and 100 km/hour. The source volume is fixed as 2000

vehicle/hour for all the three scenarios, the vehicles were

injected randomly from 14 origins and equally distributed to the

28 destinations. The simulation ran for 60 minutes and the

average travel time of vehicles is recorded. From the results

shown in Figure 15, it is clearly evident that the proposed

algorithm has better results for different lane speed when

compared with the existing ones.

Figure 13. Average travel time

Figure 14. Source volume scenario

Figure 15. Link speed scenario



C. Link Length Scenario

In this experiment, three different scenarios were created with

different lane lengths. The lane speed is kept at 80 km/hour in

all scenarios, the source volume demand rate is fixed at 2000

vehicle/hour, and the simulation is run for 60 minutes.

Figure 16 shows that the proposed algorithm makes the

congestion to be abated for every lane and makes the total

travelling time less when compared with two other existing

algorithms. The above scenarios clearly show that the proposed

algorithm performs better than the existing ones.

D. Comparing with Optimal Algorithm SPA

To check the robustness and efficiency of the proposed model,

we compare it with the optimal dynamic shortest path algorithm

(SPA). The SPA is a theoretical model that provides the optimal

solution for the congestion problem. Nevertheless, it assumes

the availability of all information about the road traces. This is

practically impossible using current technology as it demands

very powerful resources and high implementation cost. We

compare the performance of our proposed algorithm with that

of the SPA to show that the proposed model achieves close to

optimal performance with very less resources. Figures (17-19)

clearly show that our proposed model performs closely to the

optimal theoretical model (SPA), under different scenarios.

Since the proposed algorithm use histogram models to

characterize traffic and estimate congestion, the resource

utilization and time-delay are effortlessly reduced. Further, the

reroute strategy is established only if the lane exceeds the CPI

value. This makes the system more robust and free from getting

choked at some point. By its erudite, and smart precautious

measure to avoid congestion, the proposed model can be widely

implemented in urban areas.

Figure 17. Performance Comparison with SPA - Source Volume

Scenario

Figure 18. Performance Comparison with SPA - Link speed scenario

Figure 19. Performance Comparison with SPA - Link length scenario

Figure 16. Link length scenario



X. CONCLUSION

In this paper, a novel congestion detection schema and rerouting

algorithm are presented based on histogram modeling and the

base platform concept. The detection scheme can be

implemented effectively in real time with less resource

utilization. Using the proposed scheme, we predict the lanes

that could be congested in near future and estimate the

congestion levels. To make the system more efficient, a re-

routing strategy based on heuristic backtracking algorithm is

employed to reroute the vehicles away from potential

congestion. Furthermore, the re-routing scheme avoids further

congestion by utilizing the uncongested lanes during the

recovery time. This countermeasure activity enables the

proposed algorithm to guide vehicles to choose the best path

with minimum travel time. The simulation results show that the

proposed approach outperforms the existing approaches in

literature. This proactive approach along with the VANET

technology can carry out the congestion problem effectively

and keep the resource utilization at minimum.

ACKNOWLEDGEMENT

This research was supported by the Roadway, Transportation,

and Traffic Safety Research Center (RTTSRC) of the United

Arab Emirates University (grant number 31R058).

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