Simulation-Based Analysis of the Effect of Significant Traffic Parameters on Lane Changing for Driving Logic “Cautious” on a Freeway

Farooq, Danish; Juhász, János

doi:10.3390/su11215976

Cited by 13 publications

(16 citation statements)

References 47 publications

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“…For level 2, these main factors were further distributed into fourteen sub-factors and, for level 3, these were further distributed into eight sub-factors. Many researchers considered these specified factors significant in related studies, such as lanechange crashes [42,52], lane-change risks [53], lane-changing simulation [26], discretionary lane-changing [27,54] and road-safety evaluation [44].…”

Section: Lane-change Modelmentioning

confidence: 99%

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Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model

Farooq

Moslem

Jamal

et al. 2021

IJERPH

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Frequent lane changes cause serious traffic safety concerns for road users. The detection and categorization of significant factors affecting frequent lane changing could help to reduce frequent lane-changing risk. The main objective of this research study is to assess and prioritize the significant factors and sub-factors affecting frequent lane changing designed in a three-level hierarchical structure. As a multi-criteria decision-making methodology (MCDM), this study utilizes the analytic hierarchy process (AHP) combined with the best–worst method (BWM) to compare and quantify the specified factors. To illustrate the applicability of the proposed model, a real-life decision-making problem is considered, prioritizing the most significant factors affecting lane changing based on the driver’s responses on a designated questionnaire survey. The proposed model observed fewer pairwise comparisons (PCs) with more consistent and reliable results than the conventional AHP. For level 1 of the three-level hierarchical structure, the AHP–BWM model results show “traffic characteristics” (0.5148) as the most significant factor affecting frequent lane changing, followed by “human” (0.2134), as second-ranked factor. For level 2, “traffic volume” (0.1771) was observed as the most significant factor, followed by “speed” (0.1521). For level 3, the model results show “average speed” (0.0783) as first-rank factor, followed by the factor “rural” (0.0764), as compared to other specified factors. The proposed integrated approach could help decision-makers to focus on highlighted significant factors affecting frequent lane-changing to improve road safety.

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Section: Lane-change Modelmentioning

confidence: 99%

“…Researchers further developed an exponential probability model to examine the relationship between lane-changing probability and relative gap, as well as relative velocity. Furthermore, a recent study utilized a simulation model to evaluate the influence of significant factors on lane-changing [26].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model

Farooq

Moslem

Jamal

et al. 2021

IJERPH

Self Cite

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“…Microscopic traffic parameters are changing, in many cases because of lane change actions [20] that introduce challenges in finding the most appropriate traffic model [21,22]. Lane change behavior is a critical point in autonomous driving projects, especially in the case of mixed traffic scenarios that include both autonomous and nonautonomous vehicles [23].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection Based on Parity Equations in Multiple Lane Road Car-Following Models Using Bayesian Lane Change Estimation

Pop

Proştean

Proștean

2020

JSAN

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One of the current topics of interest in transportation science is the use of intelligent computation and IoT (Internet of Things) technologies. Researchers have proposed many approaches using these concepts, but the most widely used concept in road traffic modeling at the microscopic level is the car-following model. Knowing that the standard car-following model is single lane-oriented, the purpose of this paper is to present a fault detection analysis of the extension to a multiple lane car-following model that uses the Bayesian reasoning concept to estimate lane change behavior. After the application of the latter model on real traffic data retrieved from inductive loops placed on a road network, fault detection using parity equations was used. The standard car-following model applied separately for each lane showed the ability to perform a lane change action and to incorporate a new vehicle into the current lane. The results will highlight the advantages and the critical points of influence in the use of a multiple lane car-following model based on probabilistic estimated lane changes. Additionally, this research applied fault detection based on parity equations for the proposed model. The purpose was to deliver an overview of the faults introduced by the behavior of vehicles in adjacent lanes on the behavior of the target vehicle.

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“…More details about data verification of these two types of data shown below. (Thomas S. et al, 2002;Farooq et al, 2019) Videos have long recording time, excellent continuity, and a high number of videos.…”

Section: Verification Of Traffic and Meteorological Datamentioning

confidence: 99%

“…The weather has a significant impact on real road conditions and dynamics. (Benjamin et al, 2013;Bie et al, 2017) High precision; excellent continuity; high completeness Previous studies (Thomas Schnell et al, 2002;Farooq et al, 2019) (2017) stated that adverse weather indeed has a significant influence on road conditions and traffic dynamics because it lowers the free slow speed, shifts critical density, decreases flow capacity, and contributes to road congestion. In the study, recent historical weather and traffic data used as inputs into real-time traffic prediction and control.…”

Section: Historical Weathermentioning

confidence: 99%

Reliability Improvement On Feasibility Study For Selection Of Infrastructure Projects Using Data Mining And Machine Learning

Hu¹

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21. Temporal Distribution of Pedestrians (15, 30, and 60 minutes, respectively) 22. Temporal Distribution of Vehicles (15, 30, and 60 minutes, respectively) 23. Temporal Distribution of Trains (15, 30, and 60 minutes, respectively) 24. Residence Location of Participants in Map 25. Pie Chart of Residence Location of Survey Participants 26. Train Blocking Frequency and Block Time 27. Ballot for Four Types of Bridge 28. Four Types of Pedestrian Bridge 29. One Aspect of Social Impacts the Proposed Pedestrian Overpass Have 30. Sustainable and Environmental Impacts of the Pedestrian Bridge 31. EIA Process and Project Lifecycle 32. Application of ANN in MATLAB R2018b 33. Basic Layout of Time-Series Neural Network 34. The Layout of the Most Optimal Time-Series Neural Network 35. Training Performance of 15-Minute NOP Simulation 36. Error Histogram of 15-Minute NOP Simulation 37. Regressions of 15-Minute NOP Simulation 38. Time-Series Response and Error of 15-Minute NOP Simulation 39. Training Performance of 15-Minute NOV Simulation 40. Error Histogram of 15-Minute NOV Simulation 41. Regressions of 15-Minute NOV Simulation ix 42. Time-Series Response of 15-Minute NOV Simulation 43. Training Performance of 30-Minute NOP Simulation 44. Error Histogram of 30-Minute NOP Simulation 45. Regressions of 30-Minute NOP Simulation 46. Time-Series Response of 30-Minute NOP Simulation 47. Training Performance of 30-min NOV Simulation 48. Error Histogram of 30-min NOV Simulation 49. Regressions of 30-Minute NOV Simulation 50. Time-Series Response of 30-Minute NOV Simulation 51. Training Performance of 60-Minute NOP Simulation 52. Error Histogram of 60-Minute NOP Simulation 53. Regressions of 60-Minute NOP Simulation 54. Time-Series Response of 60-Minute NOP Simulation 55. Training Performance of 60-Minute NOV Simulation 56. Error Histogram of 60-Minute NOV Simulation 57. Regressions of 60-Minute NOV Simulation 58. Time-Series Response of 60-Minute NOV Simulation 59. Time-Series Response of NOP Simulation for Additional Testing 60. Time-Series Response of NOV Simulation for Additional Testing 61.

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Simulation-Based Analysis of the Effect of Significant Traffic Parameters on Lane Changing for Driving Logic “Cautious” on a Freeway

Cited by 13 publications

References 47 publications

Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model

Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model

Fault Detection Based on Parity Equations in Multiple Lane Road Car-Following Models Using Bayesian Lane Change Estimation

Reliability Improvement On Feasibility Study For Selection Of Infrastructure Projects Using Data Mining And Machine Learning

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