Driver s Decision Model at an Onset of Amber Period at Signalised Intersections

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1 Probablty to stop st veh n platoon 2nd veh n platoon 3rd veh n platoon 4th veh n platoon Dstance from the stop lne (meter) Jurnal Teknolog Full paper Drver s Decson Model at an Onset of Amber Perod at Sgnalsed Intersectons Othman Che Puan a, *, Muttaka Na ya Ibrahm a, Nur Syahrza Muhammad Nor a, Era Trana a a Faculty of Cvl Engneerng, Unverst Teknolog Malaysa, UTM Skuda, Johor, Malaysa *Correspondng author: othmancp@utm.my Artcle hstory Receved XXXX Receved n revsed form XXXX Accepted XXXX Graphcal abstract Abstract Drvng s a complex task and, probably, the most dangerous actvty on roadways because t nvolves nstantaneous decson makng by drvers. A traffc sgnal controlled ntersecton s one of road facltes whch requre drvers to make an nstantaneous decson at the onset of amber perod. Ths paper descrbes the applcaton of a regresson approach to evaluate the factors that nfluence the decson made by a drver whether to proceed or to stop at the stop lne at the onset of amber perod at sgnalsed ntersectons. More than 2,700 drvers approachng the stop lne at the onset of amber perod at sx ntersectons nstalled wth a fxed tme traffc sgnal control system were observed. Two vdeo cameras were used to record the movements of vehcles approachng the ntersecton from a dstance of about 150 metres. The data was abstracted from the vdeo recordngs usng a computer event recorder program. The parameters consdered n the analyss nclude vehcles approachng speed, dstance from the stop lne at the onset of amber, the poston n the platoon as well as the types of vehcles drven. The result of the analyss shows that about 13.43% of the drvers tend to accelerate to clear the ntersecton at the onset of amber perod and about 26.32% of the drvers ended up wth runnng the red lght. A bnary logstc model to explan the possble decson made by a drver for a gven set of condtons was developed. The analyss shows that the probablty of drvers decson ether to stop or proceed at an onset of amber perod s nfluenced by hs/her dstance from the stop lne and hs/her poston n the platoon. Keywords: Drver s decson, amber perod, bnary logstc model, red lght runnng 2014 Penerbt UTM Press. All rghts reserved. 1.0 INTRODUCTION Intersectons are one of the areas contrbutng to hgh percentage of accdent occurrences. Malaysan road accdent statstcs n 2003 showed that more than 20% of total accdents were at ntersectons [1, 2]. Traffc sgnal control system s the most common method used to control and regulate traffc movements at ntersectons. However, such a system may expose drvers and other users to rsk of accdents because t nvolves decson makng by a drver whether to stop or cross the ntersecton at an onset of amber phase. Makng a wrong decson on ether to stop or proceed, wll lead to a red lght runnng volaton or a sudden stop at the ntersecton [3]. Ths could lead to rear-end collson or other forms of accdents. In Malaysa, red lght runnng practce by drvers was demonstrated as one of the reasons for the hgh number of accdents and fataltes at ntersectons [4] whch had been on the ncrease n the past several years [5] of whch human factor was reported responsble for; accountng for about 94% of the total accdents [6]. Kulanthayan et al. [7] reported that traffc lght runnng volatons are nfluenced by factors such as tme of the week (weekdays or weekend), type of vehcle, locaton and type of traffc lght (normal or countdown tmer). In general, a drver s exposed to the rsk of accdent f he takes too long tme to react wth the nstantaneous changes n traffc stuatons. On the other hand, he s also exposed to the same rsk of accdent f hs decson was wrongly made. Ths paper dscusses the result of a study carred out to evaluate the factors nfluencng drver s decson at the onset of amber sgnal at ntersecton and the form of a decson model developed n ths nvestgaton. 2.0 BACKGROUND Decson makng n response to the onset of an amber phase at sgnalsed ntersecton s one of the most crtcal stuatons of drvng. Drver s wrong decson could result n red lght runnng volaton or traffc conflct wth the leadng vehcle whose drver decded to stop at the onset of amber [3]. The drver s decson makng s usually affected by multtude of traffc, stuatonal, and behavoural factors. These nclude, among others, atttude, emotonal state, crossng ablty before red sgnal, consequence of stoppng or not, nteractve behavour wth other drvers, approach xx (201x) xx-xx eissn ISSN

2 Author et al. / Jurnal Teknolog (Scences & Engneerng) xx (201x) xx xx speed and dstance to stop-lne [8]. Drvers are expected to consder certan factors when approachng an ntersecton [9]; ncluded among the factors are: Montorng and adjustng speed; Mantanng lane wdth; Beng aware of other vehcles; Attendng to sgnals or sgns; Scannng for pedestrans, bcyclsts, people n the wheelchars and blnd or vsually-mpared people; Deceleratng for a stop; Searchng for path gudance; and Selectng proper lane. Ste Table 1 Lst of Intersectons Studed Locaton No. of Arms Posted Speed Lmt (km/h) Cycle Tme (sec) A Permatang/Seta Tropka B Persaran Utama/Persaran Utama C Ser Melaka D Jalan Teberau E Jalan Johor Jaya F Jalan Kula/Saleng Prevous studes [10, 11] dscussed the varous factors affectng drver s decson on whether to stop or proceed through the ntersecton upon seeng the onset of the amber. They reported that drvers are less lkely to stop under the followng stuatons: havng a short travel tme to the ntersecton; drvng at hgher speeds; travellng n platoons; on steep downgrades; facng relatvely long amber ndcatons; and beng closely followed. Many studes on drvers behavour relatng to sgnal change at ntersectons showed that the probablty of drver to stop or cross the ntersecton at the onset of amber was modelled usng bnary logstc regresson technque [12-17]. One of these studes [15] developed a bnary choce model whch relates the probablty of a stoppng at the stop-lne or crossng t as a functon of approach speed, dstance from ntersecton, gender, age group and the exstence or not of a dlemma zone (an area approachng the stop lne wthn whch a drver fnds hmself s too close to stop safely and yet too far away to pass completely through the ntersecton at a legal speed before the red phase commences). Fndngs from the study revealed that a substantal proporton of drvers facng the amber sgnal were caught n a dlemma zone due to hgh approachng speeds and exercse an aggressve behavour. Numerous studes reported that an area wthn the upstream of ntersectons at the onset of amber phase s assocated wth larger varablty n drvers stop-go decsons [18-21]. 3.0 METHODOLOGY Sx solated ntersectons nstalled wth a fxed tme traffc sgnal system drawn from Johor and Melaka States, Malaysa were used for the data samplng. The ntersectons used are summarsed n Table 1. The layout of one of the ntersectons studed s as shown n Fgure 1. The selecton of the ntersectons and the data collecton perod are based on the followng consderatons: The ntersectons were sampled from dfferent suburban areas to ensure that sampled drvers were from dfferent groups of populaton; Each ntersecton was chosen relatvely further away from ts neghbourng ntersectons to ensure that drvers behavour and decsons are not nfluenced by the ntersectons n the upstream and downstream of the ntersecton studed; Fgure 1 Layout one of the ntersectons studed (Ste B) The surveys were carred out under good weather condtons to mnmse the effect of weather on drvers behavour; The analyss were based on the data collected durng off peak hour traffc to mnmze the nfluence of congeston on drver s decson and behavour; and Stes wth relatvely good vsblty were chosen to ensure that drvers have a good vson of the traffc sgnal ndcators. Data pertanng to the analyss of the factors that nfluence the decson of the drvers at the onset of amber was collected usng a vdeo recordng technque. The advantages of usng a vdeo recordng technque for traffc data collecton has been descrbed by varous researchers [22, 23]. The observaton of each drver s behavour was made when the vehcle was about 150m from the stop lne. Parameters extracted from vdeo playbacks are vehcle s approachng speed, poston of vehcle n the platoon, types of vehcle drven and dstance from the stop lne. Data extracton process was carred out usng an event recorder computer program. The analyss of the data was carred out usng SPSS computer program. The number of drvers observed for the analyss was estmated based on Equaton (1) [15, 24]. In ths study, only drvers approachng the stop lne at onset of amber perod and durng the entre amber perod were consdered. 2 K pq N 2 2 E (1)

3 Author et al. / Jurnal Teknolog (Scences & Engneerng) xx (201x) xx xx where, N s the sample sze, p s the proporton of vehcles facng an amber sgnal that passed, q s the proporton of vehcles facng an amber sgnal that stopped, K s the standard devaton correspondng to the desred level of confdence,, and E s the permtted error n the proporton estmate. In ths study, p = q = 0.5, K = 1.96 and E = Therefore, the mnmum number of drvers arrvng at the stop lne observed at each ntersecton was approxmated as 384. The observaton at each ntersecton was conducted for several days n order to obtan the requred sample sze. 4.0 RESULTS AND ANALYSIS 4.1 Drvers Decson on Approachng an Amber Perod A total of 2,793 drvers were observed at sx ntersectons. It must be ponted here agan that only drvers approachng the stop lne durng the amber perod were consdered n the study. The frequences of drvers decded not to stop at the onset of amber perod and run the red lght at each ntersecton are tabulated n Table 2. Ste No. Table 2 Drvers Decsons at Studed Intersectons Sample Sze (N) Accelerate through Amber Red lght Runnng Total Drvers Decded Not to Stop A (3.88%) 124 (30.10%) 140 (33.98%) B (36.67%) 174 (41.43%) 328 (78.10%) C (3.62%) 196 (35.51%) 216 (39.13%) D (32.08%) 0 (0%) 136 (32.08%) E (16.95%) 65 (15.97%) 134 (32.92%) F (6.23%) 176 (30.45%) 212 (36.68%) Total (15.43%) 735 (26.32%) 1166 (41.75%) In general, about 41.75% of the drvers sampled at sx ntersectons decded not to stop at onset of amber perod. About 15.43% of them managed to accelerate and clear the ntersectons before the red lght commences. However, about 26.32% of the total drvers observed ended up wth runnng the red lght. A study across some States n USA [25] reported about 55.8% of observed drvers were red lght runners. However, fndng from another recent study conducted n central Florda [3] reported 227 drvers out of 1292 observed populaton as red lght runners; ths represents an approxmate of 18%. The proporton of red lght runnng volaton (26.32%) from the observed drvers n ths study s seems to fall wthn the values n the exstng lterature. Varatons n the reported fgures from the varous studes could be due to dfferences n drvers behavour from the countres where the studes were conducted. 4.2 Formulaton of a Bnary Logstc Model There are many factors that could contrbute to the decson makng by drvers. In ths study, only factors such as vehcle s approachng speed (SPD), type of vehcle drven (VEH), vehcle poston n the platoon (POS) and dstance from the stop lne (DIST) when amber lght appears were consdered. Gender and age of the drver were not consdered n the analyss because of the dffculty n obtanng the data accurately. The frst part of the modellng approach s the development of a lnear functon of the ndependent varables. The method of maxmum lkelhood (β) was used to estmate the parameters of the logstc response functon. Ths method s well suted a problem assocated wth a response beng bnary [26]. A general form of the model s as gven n Equaton (2). z a aveh a POS a SPD a DIST (2) Where z s a lnear functon of the ndependent varables and a s the estmated regresson coeffcent. In the modellng process, the dstance of the vehcle from the stop lne (DIST) s expressed n terms of group of dstances. The dstances observed were dvded nto 19 groups,.e., dstance between 0 10m as group 1, 10 15m as group 2, 15 20m as group 3, 20 25m as group 4, 25 30m as group 5, 30 35m as group 6, 35 40m as group 7, 40 45m as group 8, 45 50m as group 9, 50 55m as group 10, 55 60m as group 11, and so forth. Vehcle poston s based on ts poston n a queung system,.e. 1 for the leader, 2 for the second vehcle n the platoon, 3 for the thrd and so forth. The movement of a platoon of vehcles s consdered end when green perod ends. The followng vehcle that contnues to clear the ntersecton or to stop at the stop lne s regarded as the leader of the next platoon f exst. Vehcles were classfed as 1 for passenger cars, 2 for trucks, 3 for buses, 4 for heavy trucks, and 5 for motorcycles. Speed was dvded nto 7 groups,.e., speed between as group 1, as group 2, as group 3, as group 4, as group 5, as group 6, and as group 7. The prelmnary statstcal assessment on the nfluence of each varable on the lnear functon, z, ndcates that the type of vehcle drven and the approach speed have a p value of much greater than 0.05 and, therefore, can be excluded from the model. The statstcal measures of other varables are summarsed n Table 3. Table 3 Summary of Statstcal Analyss of Model Varables and Parameter Estmaton Varables A S.E Wald p value Exp (β) Constant POS DIST As can be seen from Table 3, both varables POS and DIST have a p values of less than 0.05 whch may be nferred that the two varables (poston of vehcle n the platoon (POS) and dstance from the stop lne (DIST)), are the sgnfcant factors to nfluence the decson of a drver at the onset of amber perod. Ths leads to the form of lnear functon descrbng drver s decson as shown n Equaton (3). z 1.40 POS 0.23DIST (3) All varables are as defned earler. As descrbed earler, a drver only has two choces at the onset of amber perod,.e., ether to stop or to proceed. Mathematcally, these choces of drver s decson can be modelled usng a bnary logstc regresson approach as shown n Equaton (4) [26].

4 Probablty to stop Author et al. / Jurnal Teknolog (Scences & Engneerng) xx (201x) xx xx y P y (4) Where y are ndependent Bernoull random varables wth P y, P s the probablty for a drver to stop expected values and s an error term. Snce the drver s decson s consdered as the bnary response, the outcomes of y are coded as 1 or 0 as ndcated n Equaton (5). In the analyss, s assumed to be normally dstrbuted wth mean equals to zero. Therefore, the logstc regresson model to descrbe the drver s response s smplfed as shown n Equaton (6). y 1 f decson to stop (5) 0 f decson to proceed z e y Py P y 1 z (6) z 1 e Substtutng Equaton (3) nto Equaton 6, the model to explan the probablty for a drver to stop havng a specfc poston n the queue and dstance from the stop lne can be wrtten as n Equaton (7) POS 0.23DIST e y P y 1 z (7) 1.40 POS0.23 DIST 1 e All varables are as defned earler. To llustrate the nterpretaton of the model gven n Equaton (7), a graph showng the probablty of a drver to stop at an onset amber perod for four leadng vehcles n a platoon s plotted as shown n Fgure st veh n platoon 2nd veh n platoon 3rd veh n platoon 4th veh n platoon Dstance from the stop lne (meter) Fgure 3 Probablty of a Drver Stoppng for a Gven Dstance from Stop Lne and Poston n Platoon It may be nferred from Fgure 3 that the probablty of a leadng vehcle to stop ncreases as ts dstance s further away from the stop lne. The probablty of the followng vehcles n a platoon to stop derved above s, however, based on the assumpton that they can only have a choce to proceed f ther respectve leadng vehcles have decded not to stop. 5.0 CONCLUDING REMARKS Ths study examned the applcaton of logstc regresson modelng approach to evaluate the factors nfluencng drver s decson at sgnalsed ntersecton at the onset of amber lght on ether to stop or crosses to clear the ntersecton. The most mportant fndngs from ths nvestgaton are summarsed as follows: a) An analyss of the results revealed that about 13.4% of the drvers tend to accelerate to clear the ntersecton at the onset of amber perod and 26.3% of the drvers ended up as red lght runners. b) The probablty of a drver to stop or proceed to clear the ntersecton at the onset of amber lght at sgnalsed ntersectons may be descrbed n form of bnary logstc model. c) Drver s dstance from the stop lne and vehcle s poston n platoon were found to be the major factors nfluencng the drver s decson at the onset of amber lght. However, f the subject drver s not the platoon leader, hs decson to proceed to clear the ntersecton s nfluenced by the leadng vehcle s drver decson. d) Fndngs from ths study also suggest that there s a possblty that the red lght runners are due to the exstence of dlemma zone at the sgnalsed ntersecton. Thus, the desgn of a traffc sgnal control system to regulate traffc movements at an ntersecton needs to re-evaluate the mplcaton of such a zone n order to reduce the red lght runnng volatons and hence mprove the level of safety at ntersectons. Acknowledgment The authors would lke to express deep apprecatons to the Mnstry of Hgher Educaton, Malaysa, through Research Management Centre (RMC), Unverst Teknolog Malaysa (UTM) and UTM for provdng a research grant (Q.J L100), opportunty and necessary facltes to support ths research work. References [1] Royal Malaysan Polce Statstcal Report on Road Accdents. Royal Malaysan Polce, Kuala [2] Royal Malaysan Polce Statstcal Report on Road Accdents. Royal Malaysan Polce, Kuala [3] Elmtny, N., Yan, X., Radwan, E., Russo, C. and Nashar, D Classfcaton analyss of drver's stop/go decson and red-lght runnng volaton. Accdent Analyss & Preventon. 42(1): [4] Hawa, M. J., Shabadn, A. and Rahm, S. A. S. M The Effectveness of Automated Enforcement System n Reducng Red Lght Runnng Volatons n Malaysa: Plot Locatons. Research Report MRR No 146. Malaysan Insttute of Road Safety Research (MIROS), Kuala

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