Modelling the Crossing Behavior of Pedestrian at ... the Crossing Behavior of Pedestrian at...

Modelling the Crossing Behavior of Pedestrian at

Uncontrolled Intersection in Case of Mixed

Traffic Using Adaptive Neuro Fuzzy Inference

System

Harsh J. Amin Department of Civil Engineering, IIT Guwahati, Guwahati-781039, India

Email: [email protected]

Rutvij N. Desai and Priyesh S. Patel Department of Civil Engineering, L.D College of Engineering, Ahmedabad-380015, India

Department of Computer Engineering, Nirma University, Ahmedabad-382481, India

Email: {rd271989, priyesh.jas26}@gmail.com

Abstract—Critical gap is the most important parameter

associated with gap acceptance study especially in case of

delay and capacity estimation. Many studies are reported

on gap acceptance behavior of pedestrian but most of them

confined to developed country where traffic is homogeneous

and traffic rules are strictly followed. Uncontrolled

intersections in case of developed countries control the

traffic and pedestrian movements based on priorities but in

India, no one follows the stop and yield signs, even the rules

of zebra crossings. It creates more conflicts and increases

delay to both. This paper systematically analyzes the

behavior of pedestrian during the gap acceptation at two

four legged TWSC intersection located at Ahmedabad in

state of Gujarat, India. Data on gap acceptance behavior is

obtained by video recording technique and analyzed the

various parameters relate to crossing behavior of pedestrian.

The critical gaps for pedestrians estimated by raff method

and results shows that values of critical gap estimated are as

low as 3.20 sec. which is smaller than reported in other

studies. An Adaptive Neuro Fuzzy Inference System

(ANFIS) has been established to estimate the possibilities of

accepting a given gap or lag considering the combinations of

different parameters which affects the crossing behaviors of

pedestrians. Nine different combinations are considered.

However, the age of the pedestrian is found to be most

important variable in crossing behavior compared to other.

Index Terms—pedestrian, gap acceptance, critical gap,

anfis, mixed traffic, TWSC intersection

I. INTRODUCTION

Urbanization and traffic growth rapidly increasing in

Indian cities as increasing in economic growth of India. It

has resulted in increase in delay to vehicular traffic and

occurrences of fatalities.

Manuscript received February 20, 2014; revised December 11,

2014.

Now these days, many passengers are diverted to

public transports and increase in the use of public

transportation trips. Public transport trips are normally

connected to walk trips either destination or origin or

both and sometimes at mode transfer points. Therefore

passengers may need cross the road either at intersections

or at mid-block sections for many reasons. Now these

days, many variety of vehicles are available in India like

car, scooter, bike, auto rickshaw as fast moving vehicles

and cycle, pedal rickshaw etc. as slow moving vehicles.

All these vehicles share the same roadway width without

any physical separation and resulted in heterogeneity

condition in India. The effects of heterogeneity and

reckless walking behavior of pedestrian creates sever

conflicts and fatalities. It has resulted in reduction of

pedestrian’s safety. As per data from National crime

records bureau, 10,125 pedestrian deaths were reported

for the year 2007 in India. The precise number of

pedestrians injured and killed is difficult to get and could

be approximately 40,000 deaths annually in India. More

than half of injured and killed pedestrians were young

men in 16 to 45 age group [1]. In spite of these fatalities

and accidents, limited studies are reported on gap

acceptance behavior of pedestrians in Indian condition.

This paper attempts to model the gap acceptance

behavior of pedestrian at TWSC intersections in order to

analyze and evaluate the performance of TWSC

intersections.

Before starting the gap acceptance study, the term of

gap should be clarified. Ashworth and Green [2]

measured gap from front of following vehicle to rear of

the first vehicle. Polus [3] defined it as the time interval

between two successive vehicles in road stream. Along

with term of gap comes another term lag. Lag is defined

as the time interval between the arrivals of pedestrian at a

stop line and the arrivals of the first vehicle at upstream

side of the conflicts zone [3]. Critical gap is most

Journal of Traffic and Logistics Engineering Vol. 2, No. 4, December 2014

263©2014 Engineering and Technology Publishingdoi: 10.12720/jtle.2.4.263-270

important parameter in gap acceptance behavior. Critical

gap is the minimum gap time that is acceptable to a

pedestrian, intending to cross a conflicting stream. For a

consistent pedestrian its value lies between largest

rejected gap and accepted gap. It is very difficult to

measure the critical gap in field. Its value differs from

pedestrian to pedestrian, time to time, intersection to

intersection, type of movement and traffic situations. All

these factors make an estimation of critical gap a

complex and tedious process.

Various literatures defined the term of critical gap in

different manners. Greenshield defined critical gap is the

gap range that has equal number of rejection and

acceptance. Raff and Hart [4] defined it as size of gaps

whose number of rejected gap longer than it is equal to

number of accepted gap shorter than it. The Highway

Capacity Manual (HCM 2000) [5] defines the term of

critical gap as “the time in seconds below which a

pedestrian will not attempt to begin crossing the street”.

It is assumed that if the given gap is greater than the

critical gap, the pedestrian will cross, but if the given gap

is smaller than the critical gap, pedestrian will not cross.

The term adequate gap is used in the Manual on Uniform

Traffic Control Devices (MUTCD) and is assumed to be

the same as the critical gap in the HCM (2000) [5], [6].

Estimation of critical gap under heterogeneous traffic

condition is more complex than that under homogeneous

traffic conditions. Gap acceptance process becomes

complex due to non-adherence to the rules of priorities,

improper parking facilities, weak lane discipline,

improper lane marking, and variation among the static

and dynamic characteristics of vehicles types. In India

more than 60% of pedestrians creates forcefully gaps in

traffic stream. It affect the natural behavior of traffic

stream and cause delay and decline the capacity of

network with increase in accidents and fatalities.

Model of gap acceptance behavior of pedestrian,

considering more realistic scenario and behavior, have

attempted in this paper by using different combinations

of pedestrian and traffic parameters. The evaluation of set

of explanatory attributes in decision process is affected

by imprecision and uncertainty. Fuzzy logic plays an

important role in the human ability to make decision in

an environment of uncertainty and imprecision. For the

present study, we use neuro-fuzzy approach to establish

the model of gap acceptance of pedestrian at TWSC

intersection. MATLAB is used to develop an Adaptive

Neuro-Fuzzy Inference System (ANFIS), which provides

an optimization scheme to find the parameters in the

fuzzy system that best fit the data. A network-type

structure is similar to a neural network [7]. The present

study attempt an estimation of critical gap by Raff

method and develop a model to estimate possibilities of

accepting a given gap or lag based on different traffic and

pedestrian attributes using ANFIS.

This paper is organized in 8 sections, including this

section. Section 2 discusses the background and the

literature review on the gap acceptance studies while the

data collection and extraction is discussed in Section 3.

Estimation of critical gap and the detailed structure of

ANFIS given in Section 4 & 5 respectively. In section 6

development of the model is discussed and in Section 7

discussed the validation of model. Section 8 concludes

the paper by presenting important conclusions, and

pointing the directions for further research.

II. BACKGROUND LITERATURE REVIEW

Large number of studies has been reported on gap

acceptance behavior of driver since last three decades but

only limited studies are reported on behavior of

pedestrian. Several procedures have been developed in

literature to estimate the critical gap as closely as

possible. Normally, accepted or rejected gaps, age,

gender and speed of pedestrian as well as speed of the

conflicts vehicles are used as the basis for estimation of

critical gap.

Moore [8] found the relation between walking speed

and gap acceptance. He noticed that the pedestrians who

accepted longer gaps decreased their crossing speeds and

concluded that those who accepted long gaps like 7 s

walked at a speed of 1.20 m/s while those accepting gaps

smaller than 3 s walked at 1.57 m/s. Cohen et al. [9]

noticed that no one accepting a gaps smaller than 1.5 s

and everyone accepting a gaps longer than 10.5 s while

they also noticed 92% of pedestrians crossed a 7.0 m

wide road when the available gap was 7 s. DiPietro and

King [10] concluded that if the waiting time on the curb

is increases, accepted gaps becomes longer. Palamarthy

et al. [11] noticed that pedestrians preferred an overall

gap than separate gaps in individual traffic streams. Das

et al. [12] collected the data with using video recording

of a crossing in India, and found the gaps rejected by

older pedestrians were accepted by young pedestrians,

but it was not found any difference between male and

female pedestrians in gap acceptance. Brewer et al. [13]

have collected data of pedestrians crossing from 42 study

sites of seven different country and they evaluated the

gap acceptance behavior of crossing pedestrians based on

a two-part analysis: behavioral analysis and statistical

analysis. Zhao [14] analyzed gap acceptance behavior of

pedestrian at two way stopped controlled intersection and

established micro simulation model using VISSIM and

CORSIM and concluded that compared to CORSIM

model, the VISSIM model provides more detailed

information, such as the individual pedestrian

characteristics and behavior, the length of crosswalks,

and even the marking length on the crosswalks. Grebert [15] observed pedestrian waiting time at the

curb before crossing a road plays a key role in decision

and it was found that very long waiting time lost patience

of the pedestrians and resulted in illegal crossing. Wang

et al. [16] modeled the gap acceptance behavior of

pedestrian when they jaywalk outside crossing facilities,

based on non-intrusive observations from real pedestrian

vehicle interaction scenarios, using binary logit technique.

Yannis et al [17] analyzed the gap acceptance behavior

of pedestrian for mid-block street in urban areas and

concluded that pedestrians’ decision to cross the street

depends on the traffic gap, the waiting time, type of the


264©2014 Engineering and Technology Publishing

incoming vehicle and the presence of illegally parked

vehicles. Ottomanelli et al. [18] made model based on

decision of pedestrians during gap acceptance at

midblock section using Adaptive Neuro Fuzzy Inference

System and concluded that ANFIS model allow to

reproduce human approximate reasoning, providing

reliable and useful results within soft computing

environment with low data requirements. Kadali and

Vedagiri [19] modeled the behavior of pedestrian during

road crossing at the uncontrolled midblock location in

India under mixed traffic condition using multiple linear

regression (MLR) technique and concluded that increase

in the pedestrian waiting time at curb or at median they

may loses their patience and lead to increase in rolling

gap behavior and accepted gap size will increase when

they accept the lag. They also concluded that speed of the

conflicts vehicle plays major important role in gap

acceptance behavior. Chandra et al. [20] analyzed gap

acceptance data collected at 17 locations in five cities of

India and demonstrated the effect of carriageway width

(number of lanes), directional movements, traffic volume

and pedestrians’ characteristics on the gaps acceptance

study.

A critical review of available literature indicates that

the gap acceptance is function of the accepted and

rejected gaps, age, waiting time, gender and speed of

pedestrian, speed and type of conflicts vehicles. Many of

above literature conducted their study in developed

country but only few of them conducted for mixed traffic

condition as same as in India. The present study attempt

an estimation of critical gap by Raff method and develop

a model to estimate possibilities of accepting a given gap

or lag based on realistic attributes of traffic and

pedestrian using ANFIS.

III. DATA COLLECTION AND EXTRACTION

Data for this study were collected at two right angle

two way stopped controlled intersections in semi-urban

areas of Ahmedabad in state of Gujarat. Gujarat is

located in western part of India. Both intersections were

almost similar in geometry with four lanes on major

street approach and two lane on minor street approach.

Geometry of the intersections are shown in Fig. 1. All

sites were located in plain terrain and adequate sight

distances were available for each movement. It was not

possible to conduct the videography of both roads

therefore we conducted the videography only for major

road as pedestrian movement on major road was more

than that of minor road. The total approach traffic on

major stream was in the range of 800 vehicles per hour

(vph) to 2500 vph and on the minor stream were in the

range of 350 vph to 1100 vph.

Video recording technique was adopted for data

collection. Data were collected on first week of January

2014 during as this month do not present extreme

weather conditions in western India. Video recording was

done during morning peak time (9.00-12.00 A.M) on

weekdays. Six video cameras had used to find out

various parameters like vehicle arrival rate, accepted and

rejected gap/lag time, age of pedestrian, speed and type

of conflict vehicle and speed of the pedestrian. Four

video cameras were placed on roof of the building nearby

intersection in such a way that possible to catch various

traffic attributes like speed of the conflicts vehicle, speed

of the pedestrian, type of conflicts vehicle and gap/lag

time. Two video cameras were placed at road level with

using tripod of around 4 feet long to obtain age of

pedestrians. Lags/gaps were measured with an accuracy

of 0.01th of second. Age of the pedestrian is grouped in

three category: young (below 30 years), middle (30 to 50

years) and old (more than 50 years).

The data extraction resulted in observations for 254

pedestrians, and the total lags/gaps (both accepted and

rejected) are 433. The total data set extracted of 254

pedestrians includes 191 male and 63 female; 72 young

pedestrian, 134 mid-age, and 48 old pedestrians.

(a) (b)

Figure. 1. Schematic diagram of intersection 1 (a) and intersection 2 (b)

IV. MEASUREMENT OF CRITICAL GAP From the set of collected data, it is observed that

minimum accepted lag is 1.27 second and minimum



accepted gap is 1.08 second. From the observation of the

data, it is concluded that young pedestrian accepted

smaller gaps than old age pedestrian. Aggressiveness

behavior of the young pedestrian is clearly reflected in

collected data. While old age pedestrians are accepting

longer gaps compared to other two category. Almost no

gap is rejected by young pedestrian longer than 6.5 sec.

but around 12 % of old age pedestrian reject a gap of 6.5

sec. Furthermore, it was observed from collected data that

if speed of the conflict vehicle is high, pedestrian

searching for higher gap.

(a)

(b)

Figure 2 (a) and (b): Critical gap estimated by raff method for both intersections

The values of the critical gap is calculated using Raff

method proposed by Raff in 1950 [4]. Raff method is

based on macroscopic model and it is the earliest method

for estimating the critical gap which is used in many

countries because of its simplicity. According to Raff

method, sum of cumulative number of accepted gaps 𝐹𝑎

and rejected gaps 𝐹𝑟 is equal to 1 then a gap of length t is

equal to critical gap 𝑡𝑐. It means the number of rejected

gaps larger than critical gap is equal to the number of

accepted gaps smaller than critical gap. Raff method

gives the median value of the critical gap [21]. The

separate analysis for critical gap, combining gap and lag

data, for both intersections is given in Table II. These

values are quite smaller than the critical gaps reported in

other studies, given in Table I. The present study

develops a model to estimate possibilities of accepting a

given gap or lag using different combinations of collected

parameters. Data for this study were collected at two right

angle two way stopped controlled intersections in semi-

urban areas of Ahmedabad in state of Gujarat.

TABLE I. CRITICAL GAP OF PEDESTRIAN IN DIFFERENT COUNTRIES

Year Author’s name Country Critical gap in

second

1953 Moore United Kingdom 3.00 to 7.00

1955 Cohen et al. United Kingdom 1.50 to 7.00

1970 DiPietro and King United States of

America 3.00 to 10.00

2003 Zhao and Wu China 5.79

2005 Das et al. India 8.00

2006 Brewer et al. United States of

America 5.30 to 9.40

Source: Chandra et al. [20]

TABLE II. CRITICAL GAP FOR BOTH INTERSECTIONS

Critical gap in second Intersection 1 Intersection 2

Overall 3.20 3.45

Young pedestrian 3.05 3.30

Old pedestrian 3.40 3.65

V. ARCHITECTURE OF ANFIS

An Adaptive Neuro Fuzzy Inference System uses two

fuzzy logic and neural network approaches. When these

two systems are linked, they may qualitatively and

quantitatively obtain an appropriate result that will

include either calculative abilities of neural network or

fuzzy intellect. This makes it possible to combine the

advantages of neural network and fuzzy logic. A network

obtained this way could use excellent training algorithms

that neural networks have at their disposal to obtain the

parameters that would not have been possible in fuzzy

logic. ANFIS structure is organized of two part same as

Fuzzy system; First is introductory or antecedent part and

second is concluding parts which are connected together

by a set of rules. We may recognize five different layers

in the structure of ANFIS which makes it as a multi-layer

network. A kind of this network, which is demonstrated

in five different layers, is shown in Fig. 1. This system

contains two inputs (x and y) and one output (Z) which is

associated with the following rules:

Rule 1 If (x is A1) and (y is B1) then Z1=p1x+q1y+r1

Rule 2 If (x is A2) and (y is B2) then Z2=p2x+q2y+r2

where A and B are the input, Z is the output and p, q and

r is a set of consequent parameters of rule. If we consider

the output of each layer in the ANFIS network as (ith

node

output in jth

layer) then we may explain the various layers

functions of this network as follows.

Figure 3. Structure of ANFIS (Source: maharani et al. [22])



Layer 1 is the fuzzification layer where every node i in

this layer is an adaptive node with a node function,

),(,1 xO Aii for i = 1, 2

),(,1 yO Bii for i

where Ai is the membership grade of x in Ai is fuzzy

set and Bi is the membership of y in Bi fuzzy set. In

this model, fuzzification nodes have a bell activation

function. A bell activation function, which has a regular

bell shape, is specified as,

b

i

i

A

a

cxx

2

1

1)(

(2)

where (ai, bi, ci) is the parameter set. The bell-shaped

function varies with these parameters, thus exhibiting

various forms of membership function for fuzzy set A.

Parameters in this layer are called premise parameters.

Layer 2 is the rule layer. Each node in this layer

corresponds to a single Sugeno-type fuzzy rule whose

output is the product of all the incoming signals:

)()(,2 yxO BiAiii , for i = 1, 2. (3)

Each node output represents the firing strength of a

rule.

Layer 3 is the normalized layer. Every node in this

layer is a fixed node labeled N. The ith

node calculates the

ratio of the ith

rule's firing strength to the sum of all rules'

firing strengths:

,21

,3

i

iiO for i = 1, 2. (4)

Outputs of this layer are called normalized firing

strengths. Layer 4 is the defuzzification layer. Every node

i in this layer is an adaptive node with a node function:

)(,4 iiiiiii ryqxpfO (5)

where ANFIS is a normalized firing strength from layer 3

and (pi, qi, ri) is the parameter set of this node. These

parameters are called consequent parameters. Layer 5 is

represented by a single summation node. Which

computes the overall ANFIS output as the summation of

all defuzzification nodes:

i

i

i

ii

i

iii

f

fO

,5 (6)

Briefly, second layer executes the fuzzy AND of the

antecedent part of the fuzzy rules, the third layer

normalizes the membership functions (MFs), the fourth

layer executes the consequent part of the fuzzy rules, and

finally the last layer computes the output of fuzzy system

by summing up the outputs of layer fourth [7], [23], [22].

VI. DEVELOPMENT OF ANFIS

An Adaptive Neuro-Fuzzy Interface System (ANFIS)

has been developed using Fuzzy Logic toolbox in

MATLAB. Five input variables i.e. size of lag/gap(sec.),

age of the pedestrian, speed and type of conflict vehicle,

speed of pedestrian and one output variable i.e. gap/lag

acceptance or rejection are considered. Size of lag/gap

(sec) is divided by a set of five linguistic fuzzy values viz;

very low, low, medium, high, and very high. In similar

manner speed of the conflict vehicle and pedestrian

divided by five and three linguistic fuzzy values

respectively. Membership function of gap/lag size and

speed of conflict vehicle are shown in Fig. 2. It was not

possible to get actual age of the pedestrian hence it is

taken as crisp variable, 1- Young, 2 - Middle, 3 – Old

pedestrian. In similar manner, we considered type of

conflict vehicle as crisp variable (1- Car, 2- 2 Wheeler, 3-

Bus, and 4- Auto Rickshaw).

(a)

(b)

Figure 4 (a) and (b): Membership functions of input using in model 2

Nine different combination of attributes are used to

construct nine different models. A network-type structure

similar to that of a neural network, which maps inputs

through input membership functions and associated

parameters with it, and then through output membership

functions and associated parameters to outputs, can be

used to interpret the input or output map. Three main

steps are required to develop ANFIS model in MATLAB.

First one is build the Sugeno type FIS, second is train the

FIS with using given input – output data set and last one

is validation of the model with using independent data [7].

Major important part of the generating model is

application of rules. Fuzzy rules are a collection of

linguistic statements that describe how the FIS should

make a decision regarding classifying an input or

controlling an output. Different rules cannot share the

same output membership function, namely the number of

output membership functions must be equal to the

number of rules. Number of rules applied in each model

is given in Table II.

Shape of the membership function is may be

Trapezium and Triangle or any other based on judgment

from collected data. It depends on parameter associated

with that function and it can be change through learning



= 1, 2 (1)

process. The computation of these parameters (or their

adjustment) is facilitated by a gradient vector, which

provides a measure of how well the fuzzy inference

system is modeling the input/output data for a given set

of parameters. Once the gradient vector is obtained, any

of several optimization routines could be applied in order

to adjust the parameters so as to reduce some error

measure. An ANFIS uses either back propagation or a

combination of least squares estimation and back

propagation for parameter estimation. Associated

parameter can be changed and adjusted automatic with

using training data. This training process is used out to

reduce and minimize the error between the actual

observed output (Field data) and estimated output by

model. Around 75 % extracted data is used to calibrate

the models. Error Tolerance which is related to size of

error is used to create a training stopping criterion. In this

case, behavior of error is unknown hence, it was set to 0.

Notice how the checking error decreases up to a certain

point in the training and then it increases. This increase

represents the point of model over fitting. ANFIS chooses

the model parameters associated with the least checking

error. Least error is found on 40th

epoch hence it is set

with 40.

VII. VALIDATION OF MODEL

In general, this type of modeling works well if the

training data presented to ANFIS for training (estimating)

membership function parameters is fully representative of

the features of the data that the trained FIS is intended to

model [7]. This is not always the case, however. In some

cases, data is collected using noisy measurements, and

the training data cannot be representative of all the

features of the data that will be presented to the model.

This is where model validation comes into play.

Validation data set is used to control the potential for the

model over fitting the data. When testing data is

presented to ANFIS as well as training data, the FIS

model is selected to have parameters associated with the

minimum checking data model error. Testing error of

nine different models is given in Table. Model 3 has least

error difference between testing error and training error

while model 1 has least training and testing error. In case

of output one assumption is considered that the values

greater or equal to 0.5 indicates the acceptance of gap and

smaller than 0.5 indicates the rejection of gap. Around

75 % extracted data is used to calibrate the models and 25%

data is used to validate the models.

(a) (b)

Figure. 5. Output of model 2 before training process (a) and after training process (b)

TABLE III. RESULTS OF MODELS WITH DIFFERENT COMBINATION OF VARIABLES WITH TRAINING & TESTING ERROR

Model No Input for ANFIS Model Number of

Rules

Training

Error Testing Error

Difference between

errors

1 Lag or Gap size (sec) 5 0.31138 0.36842 0.0570

2 Lag/Gap (sec), Conflicting veh. Speed 25 0.32066 0.39843 0.0778

3 Lag/Gap (sec), Ped. Age 15 0.32457 0.37071 0.0461

4 Lag/Gap (sec), Ped. Speed 15 0.38157 0.43854 0.0570

5 Lag/Gap (sec), Conflicting Veh. Speed, Ped. Age 75 0.34589 0.43247 0.0866

6 Lag/Gap (sec), Ped. Speed, Ped. Age 45 0.40451 0.47743 0.0729

7 Lag/Gap (sec), Type of conflict Veh. Ped. Age 60 0.34541 0.40146 0.0561

8 Lag/Gap (sec), Type of Conflict Veh. 20 0.37541 0.46891 0.0935

9 Lag/Gap (sec), Type of Conflict Veh, Ped. Speed 60 0.41843 0.49327 0.0748

VIII. CONCLUSION

The main aim of this paper is development of gap

acceptance behavioral model for crossing pedestrian at



four legged two way stop controlled intersection using an

ANFIS. Video recording technique was adopted for data

collection & extraction. The various parameters that are

used in making of model including accepted and rejected

gap/lag time, age and speed of pedestrian, speed and type

of conflict vehicles. Nine different combinations of

attributes are used to construct nine different models. By

observing the testing and training errors value Model 1

has minimum errors. Also the difference between the

training and testing error for Model 1, Model 3, Model 4

and Model 7 is minimum as compared to others. But

Model 4 and Model 7 have more training and testing

error as compared to Model 1 and Model 3. Model 8 has

highest error difference while Model 9 has highest

training and testing error. The value of critical gap is 3.55

sec. which is obtained from Model 1. In other words 50%

decision is more than 3.55 sec. However the age of the

pedestrian is most effective parameter of pedestrian

crossing behavior while type of conflicting vehicle

producing least effect on decision process compared to

other variables.

Limited studies in the past have conducted on the gap

acceptance behavior of pedestrian at four legged TWSC

intersection under mixed traffic condition. Walking trips

of passengers day by day increasing as increasing public

transport modes (BRTS, Metro, Mono rail etc.). Thus

understanding the behavior of pedestrian is necessary in

constructing delay models. In this study we collected

data from only two intersections located at Ahmedabad.

We plan to collect data at more number of intersections

located at different region of India and develop the

models which include the waiting time, number of

rejection, travel time and more, to obtain the realistic

behavior.

REFERENCES

[1] Special Report. Pedestrian Safety. NIMHANS BISP Fact Sheet. [Online]. Available:

http://www.nimhans.kar.nic.in/epidemiology/epidem_who2.htm

[2] R. Ashworth and B. D. Green, "Gap acceptance at an uncontrolled intersection," Traffic Engineering and Control, vol. 7, no. 11, pp.

676-678, March 1966.

[3] A. Polus, "Gap acceptance characteristics at unsignalised urban intersection," Traffic Engineering and Control, vol. 24, no. 5, pp.

255-258, May 1983.

[4] M. S. Raff and J. W. Hart, "A volume warrent for urban stop signs," in Eno Foundation For Highway Traffic Control,

Saugatuck, Connecticut, 1950.

[5] HCM, Highway Capacity Manual, SR 209, Transportation Research Board, National Research Counsil, Washington D.C.,

1985, 1994, 2000.

[6] J. L. Gattis and S. T. Low, "Gap acceptance at typical stop controlled intersection," Journal of Transportation Engineering,

vol. 125, no. 3, pp. 201-207, July 1999.

[7] J. P. Sangole, G. R. Patil, and P. S. Patare, "Modelling gap acceptance behavior of two-wheelers at uncontrolled intersection

using neuro fuzzy," Procedia Social and Behavioral Science, vol. 20, pp. 927-941, Aug. 2011

[8] R. L. Moore, "Pedestrian choice and judgment," Journal of the

Operational Research Society, vol. 4, pp. 3-10, Mar. 1953. [9] J. Cohen, E. J. Dearnaley and C. E. M. Hansel, "The risk taken in

crossing a road," Journal of the Operational Research Society, vol.

6, no. 2, pp. 120-128, Sept. 1955. [10] C. M. DiPietro and L. E. King, "Pedestrian gap-acceptance," in

Highway Research Record, Washington, D.C, 1970, pp. 80-91.

[11] S. Palamarthy, H. Mahmassan, and R. Machemehl, "Models of

pedestrian crossing behavior at unsignalized intersection," Center

for Transportation Research, Austin, 1994.

[12] S. Das, C. F. Mansk, and M. D. Manuszak, "Walk or wait? An empirical analysis of street crossing decisions," Journal of

Applied Economics, vol. 20, pp. 529-548, Mar. 2005.

[13] M. A. Brewer, K. Fitzpatrick, J. A. Whitacre, and D. Lord, "Exploration of pedestrian gap-acceptance behaviour at selected

locations," Transportation Research Record, Washington, D.C.,

pp. 132-140, July 2006. [14] Y. Zhao, "Exploration of pedestrian gap acceptance at twsc

intersections using simulation," in Proc. ITE Western District

Annual Meeting, Mar. 2012. [15] J. Grebert, "Pedestrian safety consideration enhancement," SICA

Project Proposal Report, SIMBA Project, Jan. 2008.

[16] T. Wang, J. Wu, P. Zheng, and M. McDonald, "Study of pedestrians’ gap acceptance behavior when they jaywalk outside

crossing facilities," in Proc. 13th International IEEE Annual

Conference on Intelligent Transportation Systems, Madeira Island, Portugal, Sept. 2010.

[17] G. Yannis, E. Papadimitriou, and A. Theofilatos, "Pedestrian gap

acceptance for mid-block street crossing," in Proc. 12th WCTR, Lisbon, Portugal, July 2010.

[18] M. Ottomanelli, L. Caggiani, G. Iannucci, and D. Sassanelli, "An

adaptive neuro fuzzy inference system for simulation of pedestrian’s behaviour at unsignalized roadway crossings,"

Advance in Intelligent and Soft Computing, vol. 75, pp. 255-262,

2010. [19] V. P. Kadali B R, "Modelling pedestrian road crossing behaviour

under mixed traffic condition," European Transport\Trasporti

Europei, vol. 55, no. 3, Dec. 2013. [20] S. Chandra, R. Rastogi, and V. R. Das, "Descriptive and

parametric analysis of pedestrian gap acceptance in mixed traffic

conditions," KSCE Journal of Civil Engineering, vol. 18, no. 1, pp. 284-293, Jan. 2014.

[21] A. Gavulová, "Use of statistical techniques for critical gap

estimation.," in Proc. 12th International Conference Reliability and Statistics in Transportation and Communication,

Lomonosova, Oct. 2012. [22] V. S. Ghomsheh, M. A. Shoorehdeli, and M. Teshnehlab,

"Training anfis structure with modified pso algorithm," in Proc.

15th Mediterranean Conference on control & Automation, Athens

– Greece, July 2007.

[23] M. Mehrabi and S. M. Pesteei, "An adaptive neuro-fuzzy

inference system (anfis) modelling of oil retention in a carbon dioxide air-conditioning system," in Proc. International

Refrigeration and Air Conditioning Conference, Iran, July 2010.

Harsh J. Amin holds a degree of bachelor of Civil Engineering from Gujarat University

located at Ahmedabad, India in 2011. He is pursuing in his final year of M.Tech from

Indian Institute of Technology Guwahati,

India in Transportation System Engineering. His M.Tech thesis is related to gap

acceptance study at uncontrolled intersection

in case of mixed traffic condition and weak lane discipline. He is engaged in this study

since last one year. Mr. Amin has student memberships of Institute of

Transportation Engineering (ITE) and American society of Civil Engineering (ASCE).

Rutvij N. Desai holds a degree of bachelor of Civil Engineering and master of Engineering

in Transportation from Gujarat University

located at Ahmedabad, India in 2011 and 2013 respectively. He has conducted research

during his masters on pedestrian modelling.

Since few months, he is engaged with one of the leading Transportation Company of India

as an Asst. Engineer. He is student member

of Indian Road Congress (IRC).



Priyesh S. Patel is pursuing his Bachelor of

Technology in Computer Engineering from

Nirma University located at Ahmedabad,

India. His project is on Neural Network and optimization technique. He is engaged with

his project since last few months. He

developed this skills during his project time and he is awarded by some leading

companies of IT sectors for his works and

technical skills.



Modelling the Crossing Behavior of Pedestrian at ... the Crossing Behavior of Pedestrian at...

Documents

Transcript of Modelling the Crossing Behavior of Pedestrian at ... the Crossing Behavior of Pedestrian at...