Mobility Compass

Discover mobility and transportation research. Find experts, partners, networks.

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The Mobility Compass is an open tool for improving networking and interdisciplinary exchange within mobility and transport research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Maji, Avijit

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (17/17 displayed)

  • 2023Optimizing Points of Intersection for Highway and Railway Alignment—Using Path Planner Method and Ant Algorithm-Based Approach1citations
  • 2023Decision Tree Analyses of Safety and Comfort Perceptions for Public Transportation in Kalyan-Dombivli Region of Maharashtracitations
  • 2023A Review of Key Socio-economic Factors Affecting High-Speed Rail Station Location Selection1citations
  • 2022Speed-Based Safety Evaluation of Horizontal Curves in Rural Highways4citations
  • 2022Analysis of Drivers’ Speed Behavior Along Horizontal Curves of Two-Lane Rural Highways Using Driving Simulator1citations
  • 2022A Global Perspective of Railway Securitycitations
  • 2022BPNN (ANN) Based Operating Speed Models for Horizontal Curves Using Naturalistic Driving Datacitations
  • 2022Calibration and Validation of VISSIM Parameters in Mixed Trafficcitations
  • 2020Risk Assessment of Horizontal Curves Based on Lateral Acceleration Index: A Driving Simulator-Based Study14citations
  • 2019Effect of Horizontal Curve Geometry on the Maximum Speed Reduction: A Driving Simulator-Based Study23citations
  • 2019Multivariate Analysis on Dynamic Car-Following Data of Non-lane-Based Traffic Environments11citations
  • 2019Optimization of high-speed railway station location selection based on accessibility and environmental impactcitations
  • 2019Operating speed prediction model as a tool for consistency based geometric design of four-lane divided highways34citations
  • 2018Speed prediction models for car and sports utility vehicle at locations along four-lane median divided horizontal curves33citations
  • 2016Vehicle Speed Characteristics and Alignment Design Consistency for Mountainous Roads42citations
  • 2016Developing probabilistic approach for asphaltic overlay design by considering variability of input parameters9citations
  • 2015Performance-based intersection layout under a flyover for heterogeneous traffic8citations

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Chart of shared publication
Roy, Sandeepan
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Sushma, M. B.
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Jayakumar, Malavika
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Sil, Gourab
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Choudhari, Tushar
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Joshi, Aparna
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Sahu, Prasanta K.
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Nama, Suresh
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Maurya, Akhilesh Kumar
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Budhkar, Anuj Kishor
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Das, Sanhita
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Budhkar, Anuj
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Tyagi, Ayush
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Edara, Praveen
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Maurya, Akhilesh K.
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Singh, Dharamveer
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Chawla, Hitesh
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Co-Authors (by relevance)

  • Roy, Sandeepan
  • Sushma, M. B.
  • Jayakumar, Malavika
  • Sil, Gourab
  • Choudhari, Tushar
  • Joshi, Aparna
  • Sahu, Prasanta K.
  • Nama, Suresh
  • Maurya, Akhilesh Kumar
  • Budhkar, Anuj Kishor
  • Das, Sanhita
  • Budhkar, Anuj
  • Tyagi, Ayush
  • Edara, Praveen
  • Maurya, Akhilesh K.
  • Singh, Dharamveer
  • Chawla, Hitesh
OrganizationsLocationPeople

document

Speed prediction models for car and sports utility vehicle at locations along four-lane median divided horizontal curves

  • Tyagi, Ayush
  • Maji, Avijit
Abstract

Sites with varying geometric features were analyzed to develop the 85th percentile speed prediction models for car and sports utility vehicle (SUV) at 50 m prior to the point of curvature (PC), PC, midpoint of a curve (MC), point of tangent (PT) and 50 m beyond PT on four-lane median divided rural highways. The car and SUV speed data were combined in the analysis as they were found to be normally distributed and not significantly different. Independent parameters representing geometric features and speed at the preceding section were logically selected in stepwise regression analyses to develop the models. Speeds at various locations were found to be dependent on some combinations of curve length, curvature and speed in the immediately preceding section of the highway. Curve length had a significant effect on the speed at locations 50 m prior to PC, PC and MC. The effect of curvature on speed was observed only at MC. The curve geometry did not have a significant effect on speed from PT onwards. The speed at 50 m prior to PC and curvature is the most significant parameter that affects the speed at PC and MC, respectively. Before entering a horizontal curve, drivers possibly perceive the curve based on its length. Longer curve encourages drivers to maintain higher speed in the preceding tangent section. Further, drivers start experiencing the effect of curvature only after entering the curve and adjust speed accordingly. Practitioners can use these findings in designing consistent horizontal curve for vehicle speed harmony.

Topics
  • data
  • driver
  • automotive engineering
  • regression analysis
  • highway
  • hydraulics
  • foundation
  • forecasting model
  • sport
  • curvature
  • tangent
  • speed data
  • rural highway
  • 85th percentile speed
  • utility vehicle

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