Modeling lateral placement and movement of vehicles on urban undivided roads in mixed traffic: A case study of India

Kotagi, Punith B.; Raj, Pooja; Asaithambi, Gowri

doi:10.1016/j.jtte.2018.06.008

Cited by 17 publications

(5 citation statements)

References 13 publications

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“…In addition, the average lateral positions of slow-moving (compared to MTWs) auto rickshaws and heavy vehicles were observed at the left-hand side of the selected road section (4.51 and 5.85 m from the curb side, respectively) ( 38 ), which is considered as the underlying cause behind their higher rate of right-hand shifts. This is in congruence with the existing studies that highlighted that drivers mostly prefer to perform a right-hand shift as compared to a left-hand shift ( 40 – 43 ). Moreover, the total extracted data (considering both lateral shifting and no shifting maneuvers) indicated the percentages of left-hand shifts, right-hand shift, and no shifts as 8%, 12%, and 80%, respectively.…”

Section: Lateral Shifting Preferences Of Motorized Two-wheelerssupporting

confidence: 92%

Model for Predicting Lateral Shifts and Driving Style of Motorized Two-Wheelers

Dey,

Das

2023

Transportation Research Record: Journal of the Transportation R

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The compact sizes, lack of available physical protection, and complex maneuvering patterns of motorized two-wheeler (MTW) riders make them more vulnerable to crash risks and accidents. Considering the increased vulnerability of MTWs in dense urban mixed traffic environments, a proper evaluation and modeling of their lateral movement decisions and driving style can enhance safety associated with the riders and augment the reliability of the existing microsimulation models. Utilizing trajectory data of a six-lane divided urban arterial, the current study attempts to investigate the lateral movement (lateral shift) tendency of MTWs, addressing different influential variables that may affect riders’ decisions and describing how this choice is affected by prevailing traffic conditions. Based on several driving style parameters, such as change in speed, change in angular position, and longitudinal gap maintained with rear vehicles, this study further contributes to the existing literature by proposing a new index for identifying the driving style of riders during the lateral shifting process. Modeling results of a multinomial logit model indicated the importance of considering longitudinal and lateral gaps, vehicle speeds, and lateral fluctuations made by the subject MTW in the past trajectories in modeling the lateral shift decisions of riders. Considering the number of lateral fluctuations as an indicator of MTWs’ driving style, a new “aggressiveness index” is defined and, accordingly, a modeling approach is proposed to classify aggressive and non-aggressive driving styles of the riders. The results suggest that the history of past trajectories of the subject MTW during lateral shifting should be considered, and consideration of a non-linear relationship among the parameters can result in a better classification of driving style of MTW riders.

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Section: Lateral Shifting Preferences Of Motorized Two-wheelerssupporting

confidence: 92%

Model for Predicting Lateral Shifts and Driving Style of Motorized Two-Wheelers

Dey,

Das

2023

Transportation Research Record: Journal of the Transportation R

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“…This study explored the relationship between electricity consumption and influencing variables. Multiple linear regression analysis was applied as it has been widely used to analyze driving behaviors in many studies [21,[26][27][28]. The dependent variable was electricity consumption.…”

Section: Discussionmentioning

confidence: 99%

Factors Influencing the Real-World Electricity Consumption of Electric Motorcycles

Kusalaphirom,

Satiennam,

Satiennam

2023

Energies

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Currently, studies regarding the factors influencing the real-world electricity consumption of electric motorcycles are lacking. The objective of this study was to examine the factors influencing the real-world electricity consumption of electric motorcycles when driving along an uncongested road network. This study developed an onboard measurement device to collect on-road data, including instant speed data and electricity consumption, from the test electric motorcycle while it was driving on a real-world road. Overall, 105 participants (n = 105) drove the test motorcycle along the uncongested urban road network. Multiple linear regression analysis was applied to explore the effect of influencing variables on the electricity consumption of electric motorcycles. The analysis results revealed that the rider’s weight and average running speed positively influenced electricity consumption, whereas decelerating time negatively influenced electricity consumption. Noticeably, the rider’s weight affected electricity consumption more than other factors. The lightweighting of electric motorcycles was mainly recommended to lower electricity consumption. Subsequently, CO2 emissions from electricity generation could be reduced.

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“…Many studies have applied multivariate dependence methods to analyze driving behavior. Kotagi et al [38] developed a multiple linear regression model to predict the lateral distance and movement of vehicles on urban undivided roads with mixed traffic in India. Kadali et al [39] developed a multiple linear regression model for the analysis of pedestrian gap acceptance behavior at mid-block crosswalks under mixed traffic conditions.…”

Section: Discussionmentioning

confidence: 99%

Lane-Filtering Behavior of Motorcycle Riders at Signalized Urban Intersections

Promraksa

Satiennam

et al. 2022

Journal of Advanced Transportation

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In developing countries, motorcycle riders typically perform lane filtering at signalized urban intersections. This study aims to determine the factors that affect the lateral clearance of motorcycle riders as they travel between two lanes of mixed traffic at signalized urban intersections in developing countries. In this study, an onboard measurement device was developed to measure the lane-filtering behavior of motorcycle riders. It was installed on a test motorcycle to continuously record the lateral clearance, riding behavior, and surrounding traffic conditions. Thirty participants rode the test motorcycle through a signalized urban intersection. Multilevel linear regression was applied to analyze the relationship between lateral clearance and relevant variables at a significance level of 0.05. The instant speed and side of the filtering motorcycle, condition of the lateral vehicle, type of lateral vehicle, and riding frequency of the motorcycle rider significantly influenced the lateral clearance. The findings of this study can contribute to filtering lane management, connected autonomous vehicles, and microscopic traffic simulations for motorcycles traveling in mixed traffic at signalized urban intersections.

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Modeling lateral placement and movement of vehicles on urban undivided roads in mixed traffic: A case study of India

Cited by 17 publications

References 13 publications

Model for Predicting Lateral Shifts and Driving Style of Motorized Two-Wheelers

Model for Predicting Lateral Shifts and Driving Style of Motorized Two-Wheelers

Factors Influencing the Real-World Electricity Consumption of Electric Motorcycles

Lane-Filtering Behavior of Motorcycle Riders at Signalized Urban Intersections

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