26th ITS World Congress, Singapore, 21-25 October 2019

AP-CP1850 #

Simulation Analysis of TrafficSens Adaptive Mode and Fixed Time Traffic

Signal Strategies

Fatin Ayuni Bt Aminzal1*; Nabilah Binti Mohd Isa2; Siti Atiqah Binti Azman3
1. Sena Traffic Systems Sdn Bhd, Malaysia

*Email: fatin@senatraffic.com.my

Abstract

Traffic signal control is a core element for efficient traffic control since the widening of road becomes
a limitation for local authority to obtain fund. An efficient traffic control can maximize the capacity of
the road with less expensive method. Today, numerous traffic signal strategies have evolved over the
time, from fixed time to adaptive control that respond and react to traffic conditions in real time. This
paper presents an evaluation and comparison of the impact of fixed time and TrafficSens adaptive
traffic signal control on the chosen urban road network in Kuala Lumpur. To evaluate the performance
of both modes, a safer and more cost-effective method of microscopic simulation is conducted. Results
obtained from both operating modes are analyzed in detail and compared in the aspects of the
following performance measures: delay, average and maximum queue length, travel time and number
of stops.

Keywords: Adaptive traffic control, TrafficSens, micro simulation

Introduction

The initial purpose of traffic light installed at the junction is for the safety of the road user to cross the
junction especially at high volume junction. However, as the number of vehicles is constantly
increasing, traffic light had eventually become one of major factor contribute to the traffic congestion
mainly at signalized junction. It caused delay for the driver because they need to queue and wait for
the green light to pass through the junction. Previously, most of the traffic control system is using the
conventional operation mode known as Fixed Time. It works according to the program that does not
have the flexibility of modification on real-time basis (1). Due to the fixed of green time, traffic light
had created queue at junction at the same time increased waiting time for the drivers. To make traffic
light controlling more efficient, an adaptive signal control system known as TrafficSens was
introduced. This system is specially designed to optimize performance of the junction by increases
road capacity and traffic flow which believed can reduce traffic congestions.
Simulation Analysis of TrafficSens Adaptive Mode and Fixed Time Traffic Signal Strategies

The aim of this paper is to introduce and describe the evaluation process on the effectiveness of
TrafficSens adaptive signal control system on a chosen urban traffic network in the city of Kuala
Lumpur. For this evaluation process, a simulation model was developed using VISSIM microscopic
simulator.

Traffic Signal Strategies for Evaluation

There are two traffic signal timing strategies as described below are used in this evaluation analysis:

Fixed Time
Fixed Time is predetermined timing plan where cycle length, phase plan and green time is fixed. It is
ideally operated at isolated junction where traffic volumes and patterns are consistent on a daily or
day-of-week basis (2). This traffic control does not need the presence of the loop detector. Thus, it
required significant traffic input such as daily volume and traffic pattern at junction. The advantage of
having this signal control is it require less cost for equipment and maintenance (2). It is advisable to
have multiple plan to cope with different traffic especially during peak time. However, the risk of
using this operation mode is it may result in poor operation when the traffic flows is fluctuated. Hence,
the adaptive and responsive traffic signal control system is needed to ensure that excessive delays are
not experienced at any time and which would optimize operations over the full time period.

TrafficSens Adaptive Mode

TrafficSens is an adaptive traffic control system implemented in Malaysia mainly in Kuala Lumpur
City Centre. It was developed with aims to have high efficiency of green time management for any
signalize junction in order to minimize wasting green and delay time, increase capacity of junction as
well as to avoid ghost junction phenomenon. This system is fully distributed without peer to peer
communications capability among controller. The data acquisition is mainly from detector information
and the decision making is made only after every two cycles is completed. The new minimum and
maximum green time for each approach will be proposed by using the information gather from
detector data and parameters input which had been studied by the engineer during pre-installation.

Compared to Fixed Time, this adaptive mode running with only one reference plan without timetable
and multiple plan basis. The input for the optimization of traffic timing relies on detector as well as
few selected parameters unique for each junction. This controller engine has an ability to cope with
different kind of traffic pattern for both peak and off-peak period. It runs with automated phase split
adjustment with the proposed timing responsive to the level of traffic flows approaching the junction.
Like any other traffic control system, this TrafficSens system allows intervention from system
operator. Timing plan, real-time timing and detector’s health can be seen by system operator. They can
do traffic monitoring at the same time have the capability to change timing plan, controller operation
mode and use other additional function such as extend the green time, reset the loop detector and send
command like turn the controller into flashing mode.

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Simulation Analysis of TrafficSens Adaptive Mode and Fixed Time Traffic Signal Strategies

Research Methodology

To evaluate the performance of Fixed Time and TrafficSens Adaptive traffic signal strategies, a safer
and more cost-effective method of microscopic simulation is conducted. Gai (2005) has suggested that
VISSIM is the most advanced and widely used microscopic traffic simulation software (3). The study
attempts to use simulation tool to determine the impact of different traffic signal strategies by
comparing several performance measures: delay, average and maximum queue length, travel time and
number of stops (4). Based on the above objectives, the content of this study will include interfacing
TrafficSens controller software and VISSIM software, network model building and model calibration
and validation.

Connecting TrafficSens and VISSIM

The adaptive traffic signal control system TrafficSens and microscopic simulator VISSIM were
connected using the interface called TrafficSens COM Interface. Two different interfaces were
developed to connect controller software and VISSIM model; Signal State Interface (SSI) and Vehicle
Detector Interface (VDI). The first interface is developed to transmitted message from controller
software to VISSIM simulator. The controller software will inform VISSIM the current phasing or
colour running in real time. The VISSIM model signal group, the controller software and the interface
were synchronized by using 16 NEMA phasing including pedestrian.

Figure 1 shows the 16 phases or movement used in TrafficSens system. The movement number, for
example movement #2 (west to east), should be used when defining the VISSIM’s Signal Groups and
Signal Heads.

Figure 1 - Movement in TrafficSens System

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Simulation Analysis of TrafficSens Adaptive Mode and Fixed Time Traffic Signal Strategies

The one-way communication between controller software and COM Interface is handle by a TCP/IP
socket client server. This Signal State Interface (SSI) will act as the socket server and will receive
every signal state data coming from the controller software. All external controller will connect to the
server as socket client. The following diagram illustrated the message flow for the Signal State
Interface (SSI).

Figure 2 – Message Flow for Signal State Interface

The second interface send VISSIM’s vehicle detector signal detection to the TrafficSens controller in
real time. Vehicle Detector Interface (VDI) was developed using VB.NET programming language.
Same as the first interface, the communication between the COM interface and the controller software
is handle by a TCP/IP socket client server. The interface will open one socket server for each external
controller that need to connect to the interface. Every vehicle detection for each signal controller in the
VISSIM’s model will be send to the respective external controller software via the socket
communication. Figure 3 shows the TrafficSens COM interface screen.

Figure 3 – Interface Screen

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Simulation Analysis of TrafficSens Adaptive Mode and Fixed Time Traffic Signal Strategies

Building VISSIM Model

The chosen road network consists of two (2) signalised 4-arm junction located at Jalan Jalil Perkasa 1,
Kuala Lumpur which identified as Junction 986T and Junction 953T. Inductive loop detector was
installed at each lane to collect traffic flow data at Jalan Jalil Perkasa 1. However, for the estimation of
several approached especially left turn lane acquired to do manual count survey to gather traffic flow
data (5). A sample data from TrafficSens system control centre for Junction 986T is shown in Table 1.

Table 1 – Sample of Traffic Flow Data for Junction 986T

The performance evaluation for this study is using the morning peak data. The loop detector data from
8.00am to 9.00 am showed the highest traffic volume for the 24-hour traffic volume counting. Table 2
below shows the total traffic flows during morning peak hours.

Table 2 - Total Traffic Flows for Morning Peak

Junction Total Flow (vehicle/hour)
986T 3,679
953T 3,505

Network geometry for both junctions is presented in VISSIM graphical user interface (GUI) as
displayed in Figure 4. In this study, vehicle composition input also included to ensure VISSIM model
was illustrated the actual traffic condition. This is followed by specifying the various routes of
vehicles travel of the network model. Signal timing of fixed time mode at real site also will be
recorded as part of evaluating purposes.

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Simulation Analysis of TrafficSens Adaptive Mode and Fixed Time Traffic Signal Strategies

Figure 4 - VISSIM Model Road Network

Model Calibration and Validation

Calibration is the process in which various parameters of the simulation model are adjusted till the
model accurately represents field conditions (6). VISSIM has several parameters that can be change
during calibration until it finds the least mean absolute percentage error value between the actual and
simulated scenario. In this study, the model is run with Wiedemann 99 car following behaviour model.
Some parameters driver behaviour characteristics suggested to be modified in during calibration is car
following, lane change and lateral distance (7). Figure 5 depicted the indicative calibration result for
the model.

Figure 5 - Actual Count vs Simulated Count After Calibration

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Simulation Analysis of TrafficSens Adaptive Mode and Fixed Time Traffic Signal Strategies

This study adopts the Geoffrey E.Heaver (GEH) statistic to compare field traffic volumes with those
obtained from simulation data. As a general guideline for model validation, GEH values less than 5
indicate good fit (7). Table 3 shows GEH value of microscopic model is less than 5 in which indicates
a well calibrated model and represents the field traffic condition with remarkable accuracy.

Table 3 - GEH Values of Different Approach of the Junction

Junction Segment Actual Count Simulated Difference % GEH
(Vehicles/900s) Count Count Difference
(Vehicles/900s) (veh/hr)
986T From Jalan Jalil Perkasa 1 282 257 25 9 1.52
To Jalan Jalil Perkasa 1 208 223 15 7 1.02
From Jalan Jalil Perkasa 7 97 95 2 2 0.2
To Jalan Jalil Perkasa 7 135 126 9 7 0.79
953T From Junction 986T 355 315 40 11 2.19
To Junction 986T 250 247 3 1 0.19
From KESAS Highway 44 47 3 7 0.44
To KESAS Highway 54 56 2 4 0.27

Result and Findings

The result for Measure of Effectiveness (MoE) generated by VISSIM will identified which are the
scenario of road network will be well performed. In this study, Scenario 1 is identified as fixed time
signal strategies whereas Scenario 2 is identified as TrafficSens adaptive signal strategies. The result is
divided into individual junction overall performance and travel time and delay at mainline road
section.

Individual Junction Overall Performance

Measure of Effectiveness (MoE)’s of the junction performance consists of average and maximum
queue length and number of stops. Table 4 presents the result of both scenarios.

Table 4 - Result of Scenario

Measure of Effectiveness Scenario 1 (Fixed Time) Scenario 2 (TrafficSens Adaptive)
(MoE) 986T 953T 986T 953T
Average Queue Length (m) 120.90 51.89 92.88 48.69
Maximum Queue Length (m) 320.17 219.01 219.23 218.52
Number of Stops 1.54 2.34 1.53 2.30

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Simulation Analysis of TrafficSens Adaptive Mode and Fixed Time Traffic Signal Strategies

Based on data tabulated in Table 4, both junction has shown a reduction of queue length after utilising
TrafficSens adaptive mode. It is recorded the maximum queue length at Junction 986T is 320m where
vehicles are nearly at standstill from Bukit Jalil approach while using fixed time. The queue has
reduced to 219m after change mode to TrafficSens adaptive. However, there is slightly impact on the
number of stops parameters as the highest number of stops is happened at both junction while using
TrafficSens adaptive mode.

Travel Time and Delay at Mainline Road Section

Travel time is a simple and robust network performance measure which is well understood by the
public, while delay means the time loss for vehicles from a starting point to an end point compared
with free-moving traffic. Table 5 shown a result of travel time and delay study

Table 5 - Result of Travel Time and Delay Measurement

MoE Scenario 1 (Fixed Time) Scenario 2 (TrafficSens Adaptive)
986T 953T 953T 986T 986T 953T 953T 986T
(L=672m) (L=672m) (L=672m) (L=672m)
Travel time (s) 85.72 310.01 138.67 246.28
Delay (s) 46.28 259.90 59.06 162.98
Stops 1.37 3.1 1.9 3.57

As shown in Table 5, traffic towards north (986T to 953T) experience an increasing travel time and
delay after using TrafficSens adaptive mode compared to fixed time. This is due to signal setting in
fixed time is always prioritising an approach with high traffic volume. Meanwhile, traffic towards
south 953T to 986T) indicates a reduction of travel time and delay resulted by operating with the
adaptive system. A small increase can be noticed in the number of stops using TrafficSens adaptive for
both direction. This result shows that TrafficSens adaptive adopt timing balance between all approach
instead of giving priority of certain approach.

Conclusion

This paper studies the operational performance of two scenarios involving a different traffic signal
strategies e.g. fixed timed and TrafficSens adaptive mode by using microscopic simulation software.
An urban traffic network of two signalised junction has been developed in VISSIM using real traffic
data from detector installed at site. After analysing the indicative parameters included vehicle
queueing, number of stops, travel time and average delay, Scenario 2 (TrafficSens adaptive) has
shown a significant improvement despite of conventional fixed time plan. Even though delay time
may increase at other direction, TrafficSens adaptive promoted a timing balance for all approach to
increase the efficiency of road capacity.

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Simulation Analysis of TrafficSens Adaptive Mode and Fixed Time Traffic Signal Strategies

References

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