A Simple Queuing System to Model the Traffic Flow at the Toll-Gate: Preliminary Results – Wahju Sediono Dwi Handoko
ISSN 1858-1633 2005 ICTS 153
a
b Fig. 6: Traffic densities for various processing time. The processing
time in b is about 2.5 times longer than a in average
Fig. 7: Change of traffic densities blue curve by opening two additional gates
4. DISCUSSION
The model of simple queuing system is created in accordance with an existing toll-gate in Tomang area,
West Jakarta. In a series of computer simulations we have examined several traffic conditions that can
happen at the toll-gate. These examinations are carried out by varying parameter values relevant to the real-
existing condition on-site. In this case a simple 2,4,2 queuing system is used to model the real-existing
situation. Figure 5 shows the difference of the normalized
traffic densities on two different arrivals rates of the vehicle at the toll-gate. The traffic density is defined
as the ratio between the number of vehicles and the capacity of the queue in a certain interval time. It can
be shown that a higher arrival’s rate fig. 5a will result in a higher traffic density.
Another important aspect of a queuing system is the length of the processing time. This processing time
includes the elapsed time since a vehicle enters a gate until it leaves the gate again. It is shown in fig. 6 that a
longer processing time can be indicated by the higher traffic densities before gate red curve.
In fig. 7 we can clearly recognize the increasing traffic density in the gate blue curve after the half
time of the measurement interval. This simulation result shows that the change of the operating toll-gate
gives an influence on the traffic density in the gate.
From a series of the above simulations we can see that the traffic density is an important index can be
used to characterize the traffic conditions at a toll- gate. The traffic density itself is determined by the
combination of at least three factors: the arrival’s rate r
, the processing time t at the gate and the number g of the simultaneous operating gates.
5. CONCLUSION
From the previous sections it can be shown that a simple queuing system can be effectively used to
model the traffic flow at the toll-gate. This model of a simple queuing system is already included in a
software tool, and can be used to make performance tests on the operation of the existing toll-gates. The
results of such performance tests can be very helpful in designing or planning new toll-gates.
REFERENCE
[1] D. Handoko, Desain Simulator Kendaraan, Proc. KOMMIT
, 2002, B-16 – B20 [2] W. Sediono and D. Handoko, Pemodelan dan
Simulasi Antrian Kendaraan di Gerbang Tol, Proc. Semiloka Teknologi Simulasi dan
Komputasi serta Aplikasi 2004 , 2004, 11-14
[3] B. Walke,
Datenfernverarbeitung II: Verkehrs- theoretische Modell von Echtzeitsystemen und
Rech-nernetzen , Aachen; RWTH, 1991
[4] J. N. Daigle, Queuing Theory for Telecommu- nications
, Reading; Addison-Wesley, 1992 [5] E. Lieberman and A. K. Rathi, Traffic Simu-
lation, Traffic Flow Theory, Oak Ridge National Laboratory, 1997
Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 154
MULTIMODAL-ELIZA PERCEIVES AND RESPONDS TO EMOTION
S. Fitrianie and L.J.M. Rothkrantz
Man-Machine-Interaction Group, Delft University of Technology E-mail: {s.fitrianie, l.j.m.rothkrantz}ewi.tudelft.nl
ABSTRACT
A growing number of research programs aimed at development human-computer dialogues to be more
like human-human dialogues. We develop a question answering system that can perceive and respond to
user emotions. Based on the famous Weizembaum’s Eliza, the system can communicate with human users
using typed natural language. It is able to reply with text prompts and appropriate facial expressions. An
experiment has been conducted to determine how many and what kind emotional expressions produced
by humans during conversation. Keywords :Weizembaum’s Eliza, human-computer
dialogue, emotion
1. INTRODUCTION
Emotions play an important role in communication. They are communication and control
systems within the brain that mobilize resources to accomplish the goals specified by our motives.
Humans convey their emotion thoughts through verbal and nonverbal behaviors synchronously.
Composing linguistic contents is probably the only method that can simultaneously convey speaker’s
belief, intentions, meta-cognitive information about mental state along with the speaker’s emotional state.
We are used to convey our thought through our conscious or unconscious choice of words. Some
words possess emotive meaning together with their descriptive meaning. The descriptive meaning of this
type of words along with a sentence structure plays a cognitive role in forming beliefs and understanding.
The instantaneous emotional state is directly linked with the displayed expression 0. Emotion expressions
have three major functions: 1 they contribute to the activation and regulation of emotion experiences; 2
they communicate internal states and intentions to others; and 3 they activate emotion in others, a
process that can help account for empathy and altruistic behaviour. The human face in particular
serves not only communicative functions, but they are also the primary channel to express emotion. Each
facial expression provides very different
information. Seeing faces, interpreting their expression, understanding the linguistics contents of
speech are all part of our development and growth. Many researchers showed that the capability of
communicating with humans using both verbal and nonverbal behaviors will make the interaction more
intimate and human-like 000. Using facial displays as means to communicate have been found to provide
natural and compelling computer interfaces 000. The challenge is that facial expressions do not occur
randomly, but rather are synchronized to one’s own speech or to the speech of other 00.
As a proof of concept, we developed a demonstrator of a multimodal question answering
system based on the famous Eliza program 0. The system simulates human-human conversation using
typed natural language. It is capable to reason about emotions in the natural language. This system will
show a facial expression for each user input as its stimulus response. Subsequently, it will give a natural
language reply together with an appropriate facial expression to convey emotional content. Our
developed system has a list of facial expressions that corresponds to possible emotions.
2. NATURAL LANGUAGE PROCESSING
