Analysis of Lateral Distance Keeping Behaviour in Mixed Traffic Conditions With Little Lane Discipline

Bangarraju, V. S. H.; Ravishankar, K.V.R.; Mathew, Tom V.

doi:10.7708/ijtte.2016.6(4).06

Cited by 13 publications

(5 citation statements)

References 5 publications

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“…Researchers have attributed this driving behaviour to inadequate lane markings and lane width, poor visibility, poor road surface and deterioration in perfect driving attitudes (Gunay 2003(Gunay , 2004(Gunay , 2007Khan, Maini 1999). Further, weak lane discipline is predominant in developing countries (Mathew et al 2015;Mallikarjuna, Rao 2011;Mahapatra, Maurya 2013;Bangarraju et al 2016). Vehicles in weak lane discipline environment are affected by lead vehicles located in the adjacent lanes or positions (Mathew et al 2015;Gunay 2003Gunay , 2007Bangarraju et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Further, weak lane discipline is predominant in developing countries (Mathew et al 2015;Mallikarjuna, Rao 2011;Mahapatra, Maurya 2013;Bangarraju et al 2016). Vehicles in weak lane discipline environment are affected by lead vehicles located in the adjacent lanes or positions (Mathew et al 2015;Gunay 2003Gunay , 2007Bangarraju et al 2016). Researchers have opined that vehicles veer laterally to select appropriate lane position and speed based on the lead vehicle(s) (Drew 1968;Case et al 1953;May 1959).…”

Section: Introductionmentioning

confidence: 99%

“…For rural highways, several researchers have adopted the threshold headway value as 5 s (Fitzpatrick et al 2000a;Hashim et al 2016;Gong, Stamatiadis 2008;Poe et al 1996;Russo et al 2015Russo et al , 2016. However, weak lane discipline in multi-lane divided highways can influence vehicles (Mathew et al 2015;Mallikarjuna, Rao 2011;Gunay 2003Gunay , 2007Bangarraju et al 2016;Drew 1968;Case et al 1953;May 1959;Rawson 2015) prior to centre of the horizontal curve. Therefore, at selected speed measurement points consideration of lane based threshold headway value alone may not be appropriate to evaluate the free flow condition in multi-lane divided highways.…”

Section: Introductionmentioning

confidence: 99%

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Operating Speed Prediction Model as a Tool for Consistency Based Geometric Design of Four-Lane Divided Highways

et al. 2019

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Researchers have studied two-lane rural highways to predict the operating speed on horizontal curves and correlated it with safety. However, the driving characteristics of four-lane-divided highways are different. Weak lane discipline is observed in these facilities, which influences vehicle speed in adjacent lane or space. So, irrespective of its lane or lateral position, vehicles in four-lane divided highways are considered free flowing only when it maintains the minimum threshold headway from any lead vehicle. Examination of two conditions is proposed to ensure the free flow. Vehicles meeting both conditions, when tracked from the preceding tangent section till the centre of the horizontal curve, are considered as free flowing. The speed data of such free flowing passenger cars at the centre of eighteen horizontal curves on four-lane divided highways is analysed to develop a linear operating speed prediction model. The developed model depends on curve radius and preceding tangent length. The operating speed of passenger car in four-lane divided highways is influenced by horizontal curve of radius 360 m or less. Further, longer tangent would yield higher operating speed at the centre of the curve. Finally, two nomograms are suggested for conventional design, consistency based design and geometric design consistency evaluation of four-lane divided horizontal curves.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Operating Speed Prediction Model as a Tool for Consistency Based Geometric Design of Four-Lane Divided Highways

et al. 2019

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“…In addition, their study pointed out that driving trajectories are a promising surrogate measure of safety as highlighted by the correlation between the trajectories identified as dangerous and the radii of the curves. Another study analyzed driving trajectories with little lane discipline [7]. By studying lateral distance-keeping behavior, lane keeping behavior and the lateral behavior of vehicles, the article explored the nonlane-based behavior of traffic.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition of Lane Changing Maneuvers for Vehicle Driving Safety

Zhang

Lou

et al. 2023

Electronics

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The increasing number of vehicles has caused traffic conditions to become increasingly complicated in terms of safety. Emerging autonomous vehicles (AVs) have the potential to significantly reduce crashes. The advanced driver assistance system (ADAS) has received widespread attention. Lane keeping and lane changing are two basic driving maneuvers on highways. It is very important for ADAS technology to identify them effectively. The lane changing maneuver recognition has been used to study traffic safety for many years. Different models have been proposed. With the development of technology, machine learning has been introduced in this field with effective results. However, models which require a lot of physical data as input and unaffordable sensors lead to the high cost of AV platforms. This impedes the development of AVs. This study proposes a model of lane changing maneuver recognition based on a distinct set of physical data. The driving scenario from the natural vehicle trajectory dataset (i.e., HighD) is used for machine learning. Acceleration and velocity are extracted and labeled as physical data. The normalized features are then input into the k-nearest neighbor (KNN) classification model. The trained model was applied to another set of data and received good results. The results show that based on the acceleration features, the classification accuracy of lane keeping (LK), lane changing to the left (LCL) and lane changing to the right (LCR) is 100%, 97.89% and 96.19%.

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“…In order to reduce human errors during data extraction to a minimum, Chakradhar Reddy et al, (2017), supported the extracted vehicular trajectory data on homogeneous traffic conditions and proposed that Moving Average is the most appropriate approach. The lateral gap was observed by Bangarraju and Ravi Shankar (2016) Time to collision for longitudinal behavior of driver was also carried out by some researchers and is varies from 0.5 sec to 90 sec depending on relative distance and velocity. In the case of opening the mid-range section, rear and side swipe collisions are more common.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Driver Behavior in Vehicle Following and Lane Changing Parameters on Urban Mid Block Sections

Sowjanya¹

2021

ITII

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In mixed traffic situations, there is weak or no lane behavior of the driver much more complicated where vehicle and driver behavior show a huge difference between them. Road traffic driving behavior on urban midblock sections is one of the most complex phenomena to be examined particularly in heterogeneous traffic conditions. This is often attributed to the capacity of the road section and the traffic flow features at the macroscopic and microscopic level of a road section. Very few researchers have attempted to investigate these features in heterogeneous environments because of the lack of adequate information gathering methods and the amount of complexity involved. In this background, an access controlled mid block road section was selected for video data collection. The main objectives of this study include developing vehicular trajectory data and analyzing the lane changing and vehicle following behavior of driver on the mid block section considering the relative velocities and relative spacing between various types of vehicles under heterogeneous traffic conditions. The videos were collected from urban roadway in the Kurnool district of Andhra Pradesh. The length of the stretch is 120m and the width is 7.0 m. The data was extracted to know the variations in terms of longitudinal and lateral speeds, velocities, vehicle following and lane changing behavior of the drivers. The data extracted was smoothened by moving average method to minimize the human errors. Lateral amplitude of the vehicles of various types was analyzed. The study revealed that vehicles in the mixed stream, in general and in particular, Bikes and Autos particularly move substantially in the lateral direction.

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Analysis of Lateral Distance Keeping Behaviour in Mixed Traffic Conditions With Little Lane Discipline

Cited by 13 publications

References 5 publications

Operating Speed Prediction Model as a Tool for Consistency Based Geometric Design of Four-Lane Divided Highways

Operating Speed Prediction Model as a Tool for Consistency Based Geometric Design of Four-Lane Divided Highways

Recognition of Lane Changing Maneuvers for Vehicle Driving Safety

Analysis of Driver Behavior in Vehicle Following and Lane Changing Parameters on Urban Mid Block Sections

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