Understanding Driver Behavior at Intersection for Mixed Traffic Conditions Using Questionnaire Survey

Ingale, Ajinkya; Sahu, Prasanta K.; Bajpai, Rishabh; Maji, Avijit; Sarkar, Anish

doi:10.1007/978-981-32-9042-6_51

Cited by 10 publications

(11 citation statements)

References 10 publications

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“…This may due to a rise in the number of collisions at conflict points, intersections have become a vital place for motorists. As a result, understanding motorist behavior at intersections can aid in the creation of a better design to meet motorist expectations when negotiating an intersection, particularly in mixed traffic conditions [2].…”

Section: Resultsmentioning

confidence: 99%

“…Diverse models concerning the connection between attitude and behavior emphasize the significance of awareness of traffic risk or traffic law as an initial step to alter driver behavior and move forward to road safety [1,2]. The contention is that as long as drivers do not aware of what constitutes risky traffic behavior, they are unable to refrain from acting in a risky manner.…”

Section: Introductionmentioning

confidence: 99%

“…Situational stimuli to driver actions include road intersections [2] and traffic conditions such as turning movement and gap acceptance [4]. Besides, road safety study has focused on accident sites, main modes of transportation, accident times, and major causes of accidents [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Investigating Motorists Perceptions towards Road Safety

Dewi¹

2021

cea

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Exploring motorist attitudes and perceptions will aid in decision makers' awareness of road safety measures, allowing for the creation of policies and the introduction of effective steps to ensure that the road safety goals are met. This study aims to explore motorists' perceptions towards road safety and to come up with new ways to enhance road traffic safety in developing cities. The self-reporting questionnaires using a Likert scale were used to collect data from respondents in Denpasar, Bali Island. Using motorist perception data, the multinomial logit models were constructed to classify the factors that affect road traffic accidents. This study discovered that intersections are a high-risk area for road traffic accidents. Gaining a deeper understanding of motorist behavior at intersections will aid in the creation of a better design that meets motorist expectations when approaching an intersection. Furthermore, the ability to ‗read' roads to predict hazards is a vital component of driving abilities that has been related to road traffic accidents. The key causes of traffic accidents were also discovered to be disobedient conduct and reckless driving. Based on these results, some countermeasures for improving road traffic safety in Bali were discussed with an emphasis on engineering and education perspectives.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating Motorists Perceptions towards Road Safety

Dewi¹

2021

cea

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“…Three categories of data are most frequently considered: what the users scan [11]; the number of fixations [12]; how long the users scan [13]. Moreover, the visual behavior depends on the presence of signals at an intersection [14], the maneuver that drivers made [15], the time of the day [16], the surrounding objects [17], the driver's experience [18], and the lighting conditions [19]. Since eye movement and steering are linked, because the driver's choices stem from their visual information [20], these data contribute to modeling the driver's behavior when approaching an intersection.…”

Section: Introductionmentioning

confidence: 99%

“…This value describes the probability value that simulates a driver's risk appetite (risk propensity). Equation (14) gives the average time spent by drivers to analyze the approaching leg t d,a :…”

mentioning

confidence: 99%

Operating Times and Users’ Behavior at Urban Road Intersections

2020

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The safety of at grade road intersections is a relevant issue with social, economic, and environmental implications. It is related to the behavior of a driver approaching an intersection that, in its turn, is affected by kinematic and physiological variables. This study proposes a model to calculate the intersection operation time (IOT) for typical non-signalized 4-leg and 3-leg (or T-leg) urban intersections. Data available in the literature have been considered in order to identify the points of interest and assess the number and the time of a driver’s eye fixation on them. When approaching an intersection, the probability of glancing in a particular area changes with the distance to the yield or stop line; for this reason, a probabilistic approach was used to model the phenomenon. All possible maneuvers have been considered: left turning, right turning, and through-movement. The proposed model allowed an objective comparison between time spent by drivers for various maneuvers and layout conditions, and identification of the critical conditions. Indeed, significant differences in terms of IOT were found: they could lead to modification of the traffic management considering different needs of road users, traffic demand, and geometrical and functional constraints.

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