Muntaser A. Salman, Estimating Intersection Traffic ...Muntaser A. Salman, College of Computer Sciences and Information Technology, University of Anbar, Iraq [email protected]

REVISTA AUS 26-2 / Muntaser A. Salman et al.,/ DOI:10.4206/aus.2019.n26.2.25/ www.ausrevista.com/ [email protected]

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ABSTRACT/ Vehicular networks have become an attractive traffic conditions surveillance method. Ratio of number of vehicles equipped with

communication capability to the total number of vehicles with time, known as penetration rate, is the major factor that influence this

method. Traffic congestion can be estimated based on either static or dynamic measurements. For static measurements, such as count of

vehicles or average speed of vehicles can be utilized through vehicle to vehicle (V2V) communication. V2V communication based traffic

congestion estimation can be represented as an optimization problem (i.e. finding minimum PR with good estimation accuracy). In dynamic

measurements, such as period of sensing or vehicles' presence can be utilized through vehicle to infrastructure (V2I) communication. In the

same time an estimation problem can be represented as a real time model designing (i.e. finding time-dependent model cover the lack of

knowledge of vehicles information). In this context, traffic congestion estimations that utilize vehicle to everything (V2X) communication

require both static and dynamic measurements to be taken into consideration. To fulfill these measurements requirements, a new approach

has been proposed in this paper based on delay time estimation model under different PR. Average delay time is the major indicator for such

measurements requirements. Delay time can be measured and estimated based on three categorized: acceleration, deceleration and stopped

delay. To estimate these categories fuzzy system using average speed of vehicle or group of vehicles (and its derivative) is proposed in this

paper. The proposed approach is simulated using COLOMBO framework with suitable modifications and investigated under different PR.

Simulation results reveal that the proposed approach can estimate traffic delay for the whole intersection as well as for each incoming and

outgoing edge. These results, encourage us to propose a simple procedure for traffic congestion estimation and evaluation, that is suitable

for traffic light controller in future work even under low PR.

Keywords: Traffic congestion; estimation; V2X communication; intersection; fuzzy system; acceleration, deceleration and stopped delay time.

RESUMEN/ Las redes vehiculares se han convertido en un método atractivo de vigilancia de las condiciones del tráfico. La relación entre el

número de vehículos equipados con capacidad de comunicación y el número total de vehículos con tiempo, conocida como tasa de penetración,

es el factor principal que influye en este método. La congestión del tráfico puede estimarse en base a mediciones estáticas o dinámicas. Para

mediciones estáticas, como el recuento de vehículos o la velocidad promedio de los vehículos se pueden utilizar a través de la comunicación

de vehículo a vehículo (V2V). La estimación de congestión de tráfico basada en la comunicación V2V puede representarse como un problema

de optimización (es decir, encontrar un PR mínimo con buena precisión de estimación). En mediciones dinámicas, como el período de

detección o la presencia de vehículos, se puede utilizar a través de la comunicación de vehículo a infraestructura (V2I). Al mismo tiempo, un

problema de estimación puede representarse como un diseño de modelo en tiempo real (es decir, encontrar un modelo dependiente del

tiempo que cubra la falta de conocimiento de la información de los vehículos). En este contexto, las estimaciones de congestión de tráfico

que utilizan la comunicación del vehículo a todo (V2X) requieren que se tengan en cuenta las mediciones estáticas y dinámicas. Para cumplir

con estos requisitos de medición, se ha propuesto un nuevo enfoque en este documento basado en el modelo de estimación del tiempo de

retraso bajo diferentes PR. El tiempo de retraso promedio es el indicador principal para tales requisitos de medición. El tiempo de retraso

puede medirse y estimarse en función de tres categorías: aceleración, desaceleración y retraso detenido. En este documento se propone

estimar el sistema difuso de estas categorías utilizando la velocidad promedio del vehículo o grupo de vehículos (y su derivada). El enfoque

propuesto se simula utilizando el marco COLOMBO con modificaciones adecuadas y se investiga bajo diferentes PR. Los resultados de la

simulación revelan que el enfoque propuesto puede estimar el retraso del tráfico para toda la intersección, así como para cada borde entrante

Muntaser A. Salman, College of Computer Sciences and Information Technology, University of Anbar, Iraq [email protected] Suat Ozdemir, Computer Engineering Department

Gazi University, Turkey [email protected] Fatih Vehbi Celebi Computer Engineering Department Ankara Yildirim Beyazit University, Turkey [email protected]

Estimating Intersection Traffic Congestion through

V2X Communication

Estimación de la congestión del tráfico de intersección a

través de la comunicación V2X

Recepción/ 27 junio 2019

Aceptación/ 25 agosto 2019

http://www.ausrevista.com/


mailto:[email protected]




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y saliente. Estos resultados nos animan a proponer un procedimiento simple para la estimación y evaluación de la congestión del tráfico, que

sea adecuado para el controlador del semáforo en el trabajo futuro, incluso bajo PR bajo.

Palabras clave: congestión del tráfico; Estimacion; Comunicación V2X; intersección; sistema difuso aceleración, desaceleración y tiempo de

retraso detenido.

1. Introduction

An intersection is the key node to urban

scenario where space is shared by more than

one edge at a time. Traffic signals used for

such intersection to solve its sharing in time

and space. Estimating traffic congestion of

such intersection, with time and space sharing,

is a difficult task. There are a number of

measures that have been used in this context,

delay is the most important one. This is

because it is directly related to the time loss

that a vehicles experience while crossing an

intersection. Another reason may be it is an

essential indicator of level of service (LOS) in

a signalized intersection.

However average delay time is a static

measure that is not easily determined due to

the dynamic behavior of vehicles at the

intersection. Most of the traditional

technologies (i.e. inductive loop, camera,

radar … etc.) used mathematical models to

describe arrival and departure of vehicles in

dynamic manners. These models have been

proved to work under specific traffic condition

with an assumption to simplify the complexity

of model’s behavior. On the other hand,

recently emerged technologies (more

specifically vehicular communication networks

based) proposed with higher potential to

monitor dynamic behavior of vehicles. Main

factor that influence this technology is PR.

From dynamic point of view, with high PR the

stability of clustering algorithm that is required

for dynamic behavior consideration is

increased, but wireless channel traffic load will

increase as well (i.e. decrease the accuracy of

communications). This is done through either

the continuous exchange of information

between vehicles with V2V communications or

between vehicles and infrastructure nodes with

V2I communications or cooperative of them

with V2X communications. On the other hand,

i.e. from static point of view, with high PR

average delay time for vehicle or group of

vehicles is increased, but the problem of

finding suitable dynamic model for measuring

delay time for vehicle or group of vehicle will

increase as well. This is because there are no

direct measurements for delay time. Though

estimation based on static measurements as

well as dynamic measurements are required.

Both of these points of view should be

considered. In this context, research is

required to estimate traffic congestion through

V2X communication while addressing static

and dynamic measurements under different

PR. This paper follow characteristics of both

traffic congestion and V2X communication with

different PR. More specifically, the dynamic

delay time under different PR is estimated

using fuzzy model. This is based on static

measurements using V2X communication

protocol with vehicles speed averaging.

Vehicles speed is effected with low PR. Thus,

more investigation is required in compared

with the existing literature for traffic

congestion estimation.

This is done here with many steps. First,

average speed for vehicle or group of vehicles

detected under low PR with V2X

communication protocols given in COLOMBO

framework is analyzed. Since delay time is the

summation of acceleration, deceleration and

stopped delay time. Thus, fuzzy delay

estimation system is proposed with three

outputs that represent delay categories. This is

done per edge per moment for each incoming

and outgoing edge based on its vehicles

average speed and their derivatives. In order

to have dynamic behavior for such estimation,

each incoming and outgoing edge averaged

delay time is accumulated with suitable

adaptation procedure. This procedure should

take into consideration the low PR issue. The

traffic congestion for each incoming and

outgoing edge, as well as for the intersection

as a whole, can be estimated with online

procedure based on their estimated average

delay time. In this context, a novel indicator

called accumulated delay percent (ADP) is

proposed with suitable adaptation procedure.

Where ADP is defined as the ratio of average

delay time estimated to vehicles sensed time

under communication coverage area.

The remainder of this article is organized as

follows. Section 2 presents related works on

traffic congestion estimation methods. Section

3 describes the proposed approach in details.

Section 4 analyzes the primary simulated

results collected so far. Evaluation of the

proposed approach is presented in Section 5.



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Discussions on conclusive remarks are

provided at the end of the paper.

2. Related Works

Recently European Framework Programme EU

FP has funded many projects in the field of

traffic surveillance method under vehicular

communication networks.

For instance, in V2V communication context,

the EU FP7 iTETRIS (2007-2010) project [1]

has developed six traffic efficiency intelligent

transportation system ITS applications. One of

them, the CoTEC distributed cooperative traffic

surveillance system [2] propose to rely on

cooperative V2V communications to guess the

density and speed of the neighboring vehicles.

They use fuzzy logic based on estimated traffic

in different LOS to map the density/speed to

the occurrence of a traffic jam without

deploying additional infrastructure nodes. The

traffic surveillance solutions shown to be

capable of detecting traffic congestion very

efficiently and at very little overhead (as it is

based on cooperative awareness messages,

CAMs). However, this solution has been

evaluated only on highway scenario based on

LOS defined by [3].

In V2I communications context, the EU FP6

SAFESPOT (2006-2010) project [4] has

demonstrated and tested a large number of

applications via vehicle and/or infrastructure

based. One of infrastructure based

applications, intelligent cooperative

intersection safety ICIS [5] propose to rely on

V2I communications to identify a safety

situation as fast as possible to prevent traffic

conflict and collision to be happened. Although

effective dynamic model of the vehicle’s

surrounding environment can be achieved, this

clearly has limits with low PR.

In V2X communications context, the EU FP7

COLOMBO (2012-2015) project [6] has

developed new protocols under urban

scenario. One of them, [7] propose to

determine traffic surveillance information

about local traffic conditions near traffic lights.

To update traffic surveillance information at

the RSU, a novel protocol is introduced. The

main idea of which is to group vehicles in traffic

light proximity in order to reduce the

communications needed. The traffic

surveillance solution is shown to be capable of

detecting average speed efficiently even under

low PR. However, this solution has been

simulated and evaluated for an abstract of

traffic conditions without neither traffic

congestion nor dynamic model estimation.

Most related solutions in the literature, based

on the powerful enabler of cooperative

vehicular communications, assume the

complete penetration rate over the targeted

vehicle population, thus making them not

applicable nowadays. Few research deals with

this issue. It is started with iTETRIS project for

traffic congestion estimation with highway

scenario and developed by COLOMBO project

for an intersection scenario. Recently,

Bellavista et al. [8] proposes an innovative

solution for cooperative traffic surveillance

based on vehicular communications capable of

working with low penetration rates and

collecting a large set of monitoring data about

vehicle mobility in targeted areas of interest.

They presents insights and lessons learnt from

the design and implementation work of the

proposed solution. Moreover, they reports

extensive performance evaluation results

collected on realistic simulation scenarios

based on the usage of iTETRIS platform with

real traces of vehicular traffic of the city of

Bologna. Their proposal solution capable to

estimate the real vehicular traffic till 10% of

penetration rate. This is done by direct effect

with measurements based on number of

vehicles counting. In our proposal, to

overcome the low PR issue, average traffic

delay is estimated independently on number of

vehicles (i.e. based just on their existing).

From the over mentioned projects, it is clear

that for vehicular communication networks

under low PR a dynamic delay model is

required to estimate traffic congestion at

signalized intersections. A lot of delay models

have been proposed but the oldest and the

most popular one is Webster’s delay model [9].

Although many researchers modified this

model (e.g. [10-13]), but all of these modified

models were developed for specific traffic

conditions. Modelling the relationship between

control delay and stopped delay of vehicles

under mix traffic conditions is done in [14].

Also researchers of [15-16] developed a model

to estimate average overall delay to mixed

traffic conditions. But acceleration and

deceleration delay component was not

considered in the modelling. Study done in

[17] used artificial neural networks with fuzzy

logic to estimate traffic delay. Another study in

[18] used data fusion model to handle more

types of data but demands less data transfer

for online traffic condition evaluation.

Most of the above-mentioned models analyze

delay time separately from its categories.




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Moreover, total delay alone does not reflect

traffic congestion dynamic at an important

time characteristics. Therefore, it is hard for

such analyses to draw deeper conclusions on

the true causes and patterns of intersection

delay. With the development of vehicular

communication networks reviewed, now it is

time to focus on the above issue with a novel

approach. In which, both static and dynamic

measurements can be included.

Promising solution to do that in an intersection

is the intersection-based geographic routing

protocol. Few surveys have been done in this

field, e.g. [19] and [20]. One of these surveys

done in [20], address some issues (one of

them traffic light at intersections) and suggest

important guidelines for network designers. In

this paper, a fuzzy system to estimate the

categories of signalized intersection delay

utilizing V2X communication is proposed.

Then, the proposed approach investigated

under different PR with suitable adaptation and

accumulation procedure to reflect traffic

congestion dynamic. Finally, simple criteria for

traffic congestion evaluation is extracted based

on LOS criteria. To the best of our knowledge,

this is the first attempt for traffic congestion

estimation and evaluation utilizing V2X

communication under different PR for an

intersection. The following subsection describe

our approach in details.

3. Proposed Approach

Our approach use V2X communications to

estimate the traffic congestion of a signalized

intersection by RSU. This is done by estimating

average delay time of vehicles as a direct

indicator while number of cars as a constraint.

First, vehicles’ average acceleration,

deceleration and stopped time is estimated

based on average speed (and its derivative) for

each edge of an intersection using fuzzy

system. This will be done per second to provide

the static measurements (i.e. an average) of

the estimated model. Since average delay time

is basically the sum of average stopped time,

acceleration and deceleration losses; then it

can be determined using the summation of

them respectively. The dynamic

measurements of our model can be provided

by accumulating average delay time through

suitable adaptation procedure. This adaptation

should be not sensitive to low PR. Thus,

existing of vehicle or group of vehicles (i.e.

group leader) will be considered as a constraint

for adaptation procedure instead of their

count. Therefore, the accumulated average

delay per edge, called group time delays

(GTD), can be calculated. A new concept called

average delay percent (ADP) is also proposed

to evaluate our estimation for each incoming

and outgoing edge. ADP is defined as the ratio

of GTD for each incoming and outgoing edge

to the total vehicle sensed time. To evaluate

our model estimation for all the incoming and

outgoing edges (as well as for the intersection

as a whole), GTD per all incoming and outgoing

edges are averaged separately per direction.

Finally, the whole intersection average delay

time is determined by summing accumulative

average delay time for all directions

respectively. There is no need to stress here

that all the above steps are done

instantaneously (i.e. second by second) under

RSU communication coverage area without

any additional communication required.

3.1. Average delay estimation

Average delay at signalized intersections are

associated with the time loss to vehicles and/or

drivers/pedestrians because of the operation

of the signals, the geometric of the intersection

and traffic conditions present at the

intersection. In the literature review models,

delay can be essentially computed with

knowledge of arrival rates and departure rates

of vehicles. However, this knowledge required

exact number of arrival and departure vehicles

per time which is difficult to obtain with

vehicular communication networks even under

full PR. Instead of that, an approach with fuzzy

system proposed to estimate average delay

dynamically. Our aim is to characterize

average delay as a time-dependent model (i.e.

dynamic) when a vehicle or group of vehicles

becomes under RSU communication coverage

area. With vehicles grouping protocol used in

COLOMBO project, it is assumed that average

speed of vehicles is less affected by PR. With

this assumption, average delay can be

estimated based on average speed. Average

delay is basically the sum of an average

acceleration, deceleration and stopped time as

shown in the following equation: 𝐷𝑒𝑙𝑎𝑦(𝑘) = 𝐴𝑐𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛(𝑘)

+ 𝐷𝑒𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛(𝑘)+ 𝑆𝑡𝑜𝑝𝑝𝑒𝑑(𝑘)

(1)

Where, 𝐴𝑐𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛(𝑘) is the time determined

with low speed and increase acceleration of

vehicle or group of vehicles entered to the

edge under the RSU communication coverage area per moment. While, 𝐷𝑒𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛(𝑘) is the

time determined with high speed and decrease

acceleration of vehicle or group of vehicles



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entered to the edge under the RSU

communication coverage area per moment.

Finally, 𝑆𝑡𝑜𝑝𝑝𝑒𝑑(𝑘) is the time determined with

zero speed and/or no acceleration of vehicle or

group of vehicles entered to the edge under

the RSU communication coverage area per

moment. As it is clear, the uncertainty in the

definitions of acceleration, deceleration and

stopped delay (i.e. low and high) can be

managed with simple fuzzy system. To this

point, the average delay will be estimated per

moment (e.g. per second) for each edge of the

incoming and outgoing edges of an

intersection without counting the number of

vehicles. The following subsections describe

the above steps in details.

3.1.1. Fuzzy system

As described previously, fuzzy system is

proposed to estimate the average acceleration,

deceleration and stopped delay of vehicle or

group of vehicles for each incoming and

outgoing edge of an intersection. This is done

based on the average speed of vehicles, as well

as its derivative, which can be easily obtained

from the protocol presented in [7] and used by

COLOMBO framework. Since COLOMBO

framework is discrete, we make use of a

discrete derivative (as used by COLOMBO

framework but without historical knowledge),

which is computed as the difference between

the average speed at two subsequent time

instants divided by the interval between them.

In our approach (in order to compare our

results with COLOMBO’s), time steps with fix

length of one second have been used and the

division is always by one. The input/output

variables, as in any fuzzy system, are first

classified into different categories or fuzzy

sets. Possible inputs in our case are average

speed and their derivative. Where average

speed fuzzy sets are L for low, M for medium,

and H for high (see Figure 1.(a)); and for the

average speed derivative, the defined fuzzy

sets are N for negative, Z for zero, and P for

positive (see Figure 1.(b)). The required

outputs, corresponding to average

acceleration, deceleration and stopped delay

time respectively, have also been defined for

one second time span, with L for low, M for

medium and H for high (see Figure 1.(c)).

Figure 1. Fuzzy delay estimation system (a)

Average speed (m/s/veh) (b) Average speed

derivative (m/s2/veh) (c) Average

acceleration, deceleration and stopped delay

(s/veh).

The degree of membership of the input and

output values to each fuzzy set are determined

using membership functions. In our approach,

membership functions implemented based on

simple rating system, are illustrated in Figure

1. (where d, a and max is the average

deceleration, average acceleration and

maximum allowed speed respectively for

vehicles used in the simulated scenario).

Fuzzy rules that relate the inputs (average

speed and its derivative) with the outputs

(average acceleration, deceleration and

stopped delay per second) are established

based on logical input/output relations (see

Table 1.).

Table 1. Fuzzy rules relating inputs (average

speed and its derivative) with outputs

(average acceleration, deceleration and

stopped delay time).

Average

accelerat

ion,

decelerat

ion and

stopped

Average

speed

derivative

N Z P

1

0

L M H

0 max/2 max

Deg

ree

of

mem

ber

ship

1 N Z P

-d 0 a

Deg

ree

of

mem

ber

ship

1

0

L M H

0 1/2 1

Deg

ree

of

mem

ber

ship




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delay

time

Avera

ge

Speed

L L,

H,

L

L,

L,

H

H,

L,

L

M L,

M,

L

L,

L,

M

M,

L,

L

H L,

L,

L

L,

L,

L

L,

L,

L

Delay estimation is done per second for each

edge. The estimation of the average delay for

the whole intersection should be rather

insensitive to very short peaks, like a singular

platoon in one edge, but should react rapidly

to more persistent traffic changes where we

expect a burst in traffic from a single edge that

will last for specific period (e.g. fifteen to

twenty minutes). In order to do that, a novel

accumulation and adaptation procedure is

proposed here with the following details.

3.1.2 Adaptation and accumulation

procedure

As described previously, every RSU in an

intersection monitors the individual average

delay for each incoming and outgoing edge,

and estimates through fuzzy based system the

delay time per moment. Every edge has a

counter associated to it, called group times

delay (GTD). This GTD represent accumulated

average delay per edge if vehicle or group of

vehicles through group leader is sensed

according to the following equation. 𝐺𝑇𝐷𝑒(𝑘)

= {𝐺𝑇𝐷𝑒(𝑘 − 1) + 𝐷𝑒𝑙𝑎𝑦𝑒(𝑘) 𝑖𝑓 𝑐𝑎𝑟𝑒(𝑘) > 0 𝑎𝑛𝑑 𝑘 ≠ 0

0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (2a)

𝑤𝑖𝑡ℎ 𝐷𝑒𝑙𝑎𝑦𝑒(𝑘) =∑ 𝐹𝑖 (𝑣𝑒(𝑘),𝑑𝑣𝑒(𝑘)

𝑑𝑘)

3

𝑖=1 (2b)

Where 𝐺𝑇𝐷𝑒(𝑘) and 𝐺𝑇𝐷𝑒(𝑘 − 1) are the new and

the previous value of accumulative GTD for edge e at time-step (𝑘)and previous time-step (𝑘 − 1) respectively. 𝐷𝑒𝑙𝑎𝑦𝑒(𝑘) is the summation

of estimated average acceleration 𝐹1, deceleration 𝐹2 and stopped delay

𝐹3 respectively based on the fuzzy inference 𝐹

with edge average speed 𝑣𝑒(𝑘) and its

derivative 𝑑𝑣𝑒(𝑘)/𝑑𝑘 as an inputs. Finally,

𝑐𝑎𝑟𝑒(𝑘) is the number of vehicles in edge e at

time-step (𝑘).

To this point, for each incoming and outgoing

edge, GTD is estimated based on the averaged

speed and its derivative with respect to their

sensed vehicles. In order to determine the

average GTD for all incoming and all outgoing

edges separately, the average GTD for each

edges’ direction are averaged as given in the

following equations. 𝐴𝑣𝑔 𝐺𝑇𝐷𝑒(𝑘)

= {𝐴𝑣𝑔 𝐺𝑇𝐷𝑖𝑛(𝑘) 𝑓𝑜𝑟 𝑖𝑛𝑐𝑜𝑚𝑖𝑛𝑔 𝑒𝑑𝑔𝑒

𝐴𝑣𝑔 𝐺𝑇𝐷𝑜𝑢𝑡(𝑘) 𝑓𝑜𝑟 𝑜𝑢𝑡𝑔𝑜𝑖𝑛𝑔 𝑒𝑑𝑔𝑒

(3a)

=

{

1

𝑛∑ 𝐺𝑇𝐷𝑒(𝑘)

𝑛

𝑒=1𝑓𝑜𝑟 𝑛 𝑖𝑛𝑐𝑜𝑚𝑖𝑛𝑔 𝑒𝑑𝑔𝑒

1

𝑚∑ 𝐺𝑇𝐷𝑒(𝑘)

𝑚

𝑒=1𝑓𝑜𝑟 𝑚 𝑜𝑢𝑡𝑔𝑜𝑖𝑛𝑔 𝑒𝑑𝑔𝑒

(3b)

Where n and m is number of incoming and

outgoing edges with sensed vehicles

respectively.

How to use these estimated average delays as

a direct indicator for traffic congestion

estimation is going to be explained in the next

subsection.

3.2. Traffic congestion estimation and

evaluation

Although average delay considered as a major

indicator for estimating and evaluating the

traffic congestion but total estimation and

evaluation time interval should be taken into

consideration as well. Predefined threshold

value may be used and corresponds to the LOS

to be evaluated for each incoming and

outgoing edge or for the intersection as a

whole. For example, left hand side of Table 2.

summarizes the LOS criteria for signalized

intersections, as described in [12].

Table 2. LOS versus BAYAN criteria.

LOS criteria

[12]

BAYAN criteria

LOS

Control

delay

(sec/veh)

𝑨𝑫𝑷𝒆(𝒌)% Traffic

congestion

A ≤ 10 ≤ 10 No congestion

B 10 − 20 10 − 20 Little congestion

C 20 − 35 20 − 35 Low congestion

D 35 − 55 35 − 55 Medium congestion

E 55 − 80 55 − 80 High congestion

F > 80 > 80 Sever congestion

Signalized intersection LOS is stated in terms

of average control delay (in sec/veh) during a

specified time period, typically for a 15- minute

analysis period. It may be calculated per

intersection, per edge, or per lane group. In

order to determine the time period for vehicles

sensed under RSU communication coverage

area per edge, the following equation is used. 𝑇𝑖𝑚𝑒𝑒(𝑘)

= {𝑇𝑖𝑚𝑒𝑒(𝑘) + 1

0 𝑖𝑓 𝑐𝑎𝑟𝑒(𝑘) > 0 𝑎𝑛𝑑 𝑘 ≠ 0

𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

(4)



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From which, ADP per edge can be determined

using the following equation.

𝐴𝐷𝑃𝑒(𝑘)% =𝐺𝑇𝐷𝑒(𝑘)

𝑇𝑖𝑚𝑒𝑒(𝑘) (5)

Where 𝐴𝐷𝑃𝑒(𝑘)% is the ADP per edge e

measured in % at time-step (𝑘). 𝑇𝑖𝑚𝑒𝑒(𝑘) is the

accumulated total time for edge e where

vehicles have been sensed till time-step (𝑘). The proposed approach for estimating the

traffic congestion for each edge of an intersection, based on 𝐴𝐷𝑃𝑒(𝑘)%, is extracted

from LOS criteria of [12] as displayed in the

left side of Table 2. With these threshold value

an evaluation procedure, called BAYAN (Basic

traffic Acceptance via Yes and No evaluation),

can be derived for specific 15-minutes (i.e. 900

sec) period as follows. 𝐴𝑒(𝑘)

= {𝑌𝑒𝑠 𝑖𝑓 𝐴𝐷𝑃𝑒(𝑘)% ≤ 𝑡ℎ 𝑎𝑛𝑑 𝑇𝑖𝑚𝑒𝑒(𝑘) ≥ 900 𝑁𝑜 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

(6)

Where 𝐴𝑒(𝑘) is the acceptance logic Boolean

value (i.e. either Yes or No) and 𝑡ℎ is one of

the acceptable average delay percent

threshold values shown in Table 2. For example, 𝐴𝑒(𝑘) ≤ 35% (i.e. for low, little and no

traffic congestion) can be considered as an

accepted traffic congestion and not accepted

otherwise.

The same proposal can be used for traffic

congestion estimation of the intersection as a

whole. Since control delay for the whole

intersection is basically the sum of average

delays in the incoming and outgoing edges (as

well as in the intersection overlap area); then

it can be determined using the summation of

them respectively, as given by the following

equation. 𝑎𝑣𝑔𝐺𝑇𝐷(𝑘) = 𝑎𝑣𝑔𝐺𝑇𝐷𝑖𝑛(𝑘)

+ 𝑎𝑣𝑔𝐺𝑇𝐷𝑜𝑢𝑡(𝑘) (7)

In the same time, since RSU is assumed to be

located in the center of an intersection area, delay in the intersection overlap area is

assumed already included by 𝑎𝑣𝑔𝐺𝑇𝐷𝑖𝑛(𝑘) and

𝑎𝑣𝑔𝐺𝑇𝐷𝑜𝑢𝑡(𝑘) respectively.

BAYAN procedure allows online evaluation of

the individual local traffic congestion estimated

for each incoming and outgoing edge as well

as for the intersection as a whole.

4. Results

Although our approach could be investigated in

different traffic scenarios (highways, urban,

rural … etc.), an intersection from urban

scenario is selected to be simulated.

Traffic simulator SUMO used to produce

realistic vehicular movement traces. Network

simulator version-3 (NS3) used to implement

vehicular communication networks under

different PR. Finally, fuzzylite libraries for fuzzy

logic control [21] used to implement the

proposed fuzzy system. Then, COLOMBO

framework is selected to connect the above

simulators with the proposed approach

algorithm. An intersection with four (incoming

and outgoing) edges from COLOMBO

framework (called RILSA intersection [22], as

shown in Figure 2.) has been used. With this

intersection, a traffic conditions configured to

be realistically reflect urban intersection traffic

with varying intensity and duration in different

edges. Neither infrastructure nor traffic light

settings (i.e. traffic signal controller with

predefined time intervals) are assumed to

influence the quality of the performed

approach.

Figure 2. RiLSA Intersection with incoming and

outgoing direction.

For simulation purpose, V2X communications

are performed by ns-3 standard WiFi model

using IEEE 802.11p with ETSI ITS G5

standards. A fixed 170m transmission range

are assumed. This value is chosen to match the

same range used in COLOMBO framework. A 6

Mbps bandwidth rate with OFDM and finally to

compute signal loss default log-distance

propagation model is used.

In the simulation study, RSU periodically

receive messages from group leader (if exist)

within one second sampling resolution

indicating existing of vehicle or group of

vehicles and average speed per incoming and

outgoing edge respectively. All simulations

were performed in the same one hour time

span. Vehicle densities are changed during

time according to a wave trend that follows the

green and red timings controlled by the traffic

light. The main simulation parameters and

configurations used here are reported in Table

3.




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Figure 3. GTD estimated per incoming edge with (a)180o, (b)90o, (c)0o and (d)-90o direction

respectively.

Figure 4. Average GTD estimated for all the incoming edges of an intersection.

0

4

8

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16

20

24

0

200

400

600

800

0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400

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of

veh

icle

s (v

eh)

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D (

sec/

veh

)

Time (sec)

(a)GTD

Number of vehicles

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)

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Number of vehicles

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(c)GTD

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um

ber

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eh)

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)

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(d)GTD

Number of vehicles

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eh)

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age

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Figure 5. ADP estimated per incoming edge with (a)180o, (b)90o, (c)0o and (d)-90o direction

respectively.

Figure 6. Average ADP estimated for all the incoming edges of an intersection.

0

4

8

12

16

20

24

0

0.2

0.4

0.6

0.8

1

0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400

Num

ber

of

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icle

s (v

eh)

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P %

Time (sec)

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Number of vehicles

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eh)

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Number of vehicles

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um

ber

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s (v

eh)

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P %

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Number of vehicles

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hic

les

(veh

)

Aver

age

AD

P %

Time (sec)

Average ADP

Number of vehicles




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Figure 7. GTD estimated per outgoing edge with (a)180o, (b)90o, (c)0o and (d)-90o direction

respectively.

Figure 8. Average GTD estimated for all the outgoing edges of an intersection.

0

4

8

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16

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5

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20

25

0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400

Num

ber

of

veh

icle

s (v

eh)

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D (

sec/

veh

)

Time (sec)

(a)GTD

Number of vehicles

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s (v

eh)

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veh

)

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Number of vehicles

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veh

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veh

)

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Number of vehicles

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Figure 9. ADP estimated per outgoing edge with (a)180o, (b)90o, (c)0o and (d)-90o direction

respectively.

Figure 10. Average ADP estimated for all the outgoing edges of an intersection.

0

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16

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Number of vehicles

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eh)

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Number of vehicles

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1

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ber

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veh

icle

s (v

eh)

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P %

Time (sec)

(c)ADP

Number of vehicles

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8

12

16

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0.4

0.6

0.8

1

0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400N

um

ber

of

veh

icle

s (v

eh)

AD

P %

Time (sec)

(d)ADP

Number of vehicles

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8

12

16

20

0

0.1

0.2

0.3

0.4

0.5

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agr

AD

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Table 3. Simulation parameters.

Parameter Value

Wi-Fi mode 802.11p/ETSI ITS 5G

Transmission

mode

6 Mbps (OFDM)

Node radius 170 m

Propagation loss Logarithmic

Propagation

speed

Constant (3x108 m/s)

Penetration rate 100,50,20,10,5,2,1%

Simulation time 1 hour

In the first step, we simulate the selected

scenario with constant traffic light control

setting (i.e. fixed time) and real number of

vehicles (i.e. with no error position). This

produce benchmark traffic conditions against

which to compare those estimated through our

proposal with different PR. This step is depicted

in Figure 3. for GTD estimated versus real

number of vehicles for each incoming edges.

The average GTD estimated for all the

incoming edges of an intersection is shown in

Figure 4. Then, ADP is determined (as

described previously) for each incoming edge

(see Figure 5.) as well as for all the incoming

edges (see Figure 6.) respectively. The same

steps are done for outgoing edges as shown in

Figure 7. - Figure 10. respectively.

4. Evaluation

To evaluate the proposed fuzzy delay

estimation system, results shown previously

with real number of vehicles have been

compared with the proposed approach results

under different PR. In this phase, Figure 11(a).

depicts the estimated delay for each incoming

edge by our approach with different PR as well

as the average for all the incoming edges. The

same thing shown in Figure 11(b). for outgoing

edges. Figure 12. and Table 4. depicts the

estimated delay for the intersection as a whole

(as well as for all incoming and outgoing

edges) by our approach with different PR as

well as delay measured from SUMO output.

Figure 11. Delay estimated with different PR for (a) incoming and (b) outgoing directions

respectively.

Figure 12. Delay estimated with different PR for the intersection as a whole.

0

10

20

30

40

50

Real 100 50 20 10 5 2 1

Del

ay (

sec/

veh

)

PR%

180 90 0 -90 incoming

0

0.5

1

1.5

2

2.5

Real 100 50 20 10 5 2 1

Del

ay (

sec/

veh

)

PR%

180 90 0 -90 outgoing

0

20

40

Real 100 50 20 10 5 2 1 PR%

incoming outgoing all sumo

(a)

(b)



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To evaluate the overall performance of our

approach, a simple summarizing metric can be

introduced. Delay difference between the one

determined from SUMO output and the one performed by our approach, called delta (∆).

Its value is referred to a single direction of

interest computed using data from a whole

batch of simulation runs for one hour under

different PR (see Table 4.).

Table 4. Delta evaluation with different PR.

PR% 𝐃𝐞𝐥𝐚𝐲 ∆

Incoming Outgoing All SUMO

Real 24.9807 0.1959 25.1766 21.942 -3.2346

100 23.7271 0.2360 23.9632 21.942 -2.0212

50 24.1915 0.8187 25.0103 21.942 -3.0682

20 29.6518 0.4097 30.0615 21.942 -8.1195

10 27.2398 0.3763 27.6161 21.942 -5.6741

5 26.4994 0.1814 26.6808 21.942 -4.7388

2 22.3421 0.1115 22.4536 21.942 -0.5117

1 22.2213 0.0000 22.2213 21.942 -0.2793

4. Discussion

Results are depicted with real number of cars

in Figures 3 – 6. for incoming edges and in

Figure 7–10. for outgoing edges. To effectively

measure the performance of our proposed

approach, we compare the group time delay

GTD (and average delay percent ADP)

estimated by our approach (solid line) with

dynamic behavior of the number of vehicles

sampled via a real-mode procedure (dashed

line). In real-mode procedure, the real position

of every vehicle in a 170 meters range from

the RSU is detected with full accuracy and

precision. As it can be observed, the tendency

of the GTD simulation results follows the

dynamical change of real number of vehicles

with time, although a drift is clearly visible.

This is due to two major aspects: first, the

exact number of vehicles cannot be precisely

estimated through vehicular communication

networks even under real-mode procedure.

Second, our proposed dynamical delay model

does not include the vehicles count, which

change (increasing or decreasing) gradually

with time. Both aspects shall be more

investigated in future work. Nevertheless, we

can see that beside the drift, curves are

capable of providing an average delay given a

traffic flow speed.

One central result of high relevance is that the

proposed approach has the ability to estimate

the dynamic delay with different PR. This very

positive dynamic behavior of increasing and

decreasing of delay with the existing of actively

cooperating vehicles made the proposed

approach suitable even when one vehicle is

sensed. One obvious issue is the lack of data

for intervals where no equipped vehicle was

sensed. The probability to have no data for an

interval depends on the aggregation interval’s

duration and the PR. For this reason, low PRs

show data absence at times where no equipped

vehicle has been within the communication

range. Further investigations should be

performed to evaluate the performance of the

proposed approach under more realistic

scenarios with different interval periods.For evaluating results, Table 4. shows delta (∆) where higher values indicate poorer system

performance. It is clear that our approach has




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good performance under low PR (i.e. <5%)

while not for others. In fact, even under real

vehicle position have less performance than PR

with 100%. On the other hand, negative values

indicate overestimation in delay with our

approach. This result from accumulating

previous delay time if traffic light cycle fail to

pass waiting vehicles even if only one vehicle

stayed. Optimizing fuzzy system parameters

with suitable threshold for accumulative

procedure may help to solve this problem. This

require keeping accumulative errors as small

as possible and should be considered for future

work.

5. Conclusions

A novel time dependent approach for

estimating and evaluating traffic congestion of

an intersection is proposed and investigated

under different PR. The proposed approach is

simulated using COLOMBO framework through

V2X communication. The simulation results

reveal that the proposed approach can

estimate traffic delay with low PR (till 1%) over

time for the whole intersection as well as for

each incoming and outgoing edge. These

results are based on traffic delay estimation

with fixed time interval for evaluation. In fact,

using threshold values for traffic congestion

estimation is not preferred. This should be

considered in future work, under traffic light

control algorithm.

6. References

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Documents

Muntaser A. Salman, Estimating Intersection Traffic ...Muntaser A. Salman, College of Computer Sciences and Information Technology, University of Anbar, Iraq [email protected]