Can segregating vehicles in mixed-traffic stream improve safety and throughput? implications using simulation

Paul, Geetashree; Raju, Narayana; Arkatkar, Shriniwas; Easa, Said M.

doi:10.1080/23249935.2020.1826595

Cited by 8 publications

(5 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimized values of driving behavior parameters were in close approximation to those reported by Chepuri et al ( 42 ) and Paul et al ( 43 ). The developed simulation model was run using the 21 h collected field data.…”

Section: Simulation Modelingsupporting

confidence: 84%

See 1 more Smart Citation

Modified Bureau of Public Roads Link Function

Gore

Arkatkar

Joshi

et al. 2022

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

The Bureau of Public Roads (BPR) function is a widely used link cost function in transportation planning because of its simple mathematical form, easily measurable field variables, and consistent performance. However, the BPR function is deterministic and does not capture the stochastic relation between travel time and traffic flow. The present study develops a modified BPR (MBPR) function by incorporating travel time uncertainty (TTU) in the deterministic BPR function. In the MBPR function, the effect of TTU is incorporated using two parameters, [Formula: see text] and [Formula: see text]. A nonlinear optimization problem is formulated, and the generalized reduced gradient method is used to calibrate the BPR and the MBPR function. The applicability of the proposed MBPR function is demonstrated using empirical data collected for an urban arterial in India and simulated data developed for a real-world urban road network. The proposed MBPR function captures the heterogeneity in travel time for different traffic flow values. The function (a) captures the variability in travel time under oversaturated conditions and (b) captures the time-dependent relation between traffic volume and delay. The physical meaning of [Formula: see text] and [Formula: see text] in the context of inter-day heterogeneity, infrastructure potential, and traffic flow heterogeneity are discussed. The practical application of MBPR as an analytical tool for system-wide performance evaluation is demonstrated by investigating the impact of traffic signal control on travel times using a before–after perspective. Compared with the before case, a 13% reduction in travel time is observed for the after case. Therefore, the installation of traffic signal control has reduced congestion.

show abstract

Section: Simulation Modelingsupporting

confidence: 84%

“…In the case of the vehicle-following behavior, the Wiedemann-74 model was calibrated. The selection of the Wiedemann-74 model was appraised based on past literature ( 42 , 43 ).…”

Section: Simulation Modelingmentioning

confidence: 99%

Modified Bureau of Public Roads Link Function

Gore

Arkatkar

Joshi

et al. 2022

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

show abstract

“… Similar variation in the two-wheeler proportion does not significantly affect the entry capacity. [ 147 ] Microscopic traffic simulation is employed to study the effects of dedicated lanes for motorised two-wheelers (MTW). Examine the effects of establishing distinct lanes for (MTW) in heterogeneous traffic.…”

Section: Vissim Application Literature Reviewmentioning

confidence: 99%

“…In Ref. [ 147 ], using microscopic traffic simulations, the effects of constructing distinct lanes for motorised two-wheelers were assessed [ 148 ]. used VISSIM to evaluate and analyse CAV's influence and performance on fuel consumption in a nonhomogeneous environment at various market penetration rates.…”

Section: Vissim Application Assessment and Evaluationmentioning

confidence: 99%

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Al-Msari,

Koting,

Ahmed

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…The developed trajectory data for each of the individual sections was then decided to be stitched to obtain an extended trajectory data using a suitable algorithm, coded using MATLAB. The complete details of trajectory data can be found in Paul et al [7] and Raju et al [8].…”

Section: Study Areamentioning

confidence: 99%

Data-Driven Approach for Modeling the Mixed Traffic Conditions Using Supervised Machine Learning

Raju

Arkatkar

Joshi

et al. 2021

Studies in Infrastructure and Control

Self Cite

View full text Add to dashboard Cite

The article describes modeling vehicular movements using supervised machine learning algorithms with trajectory data from heterogeneous non-lane-based traffic conditions. The trajectory data on the mid-block road section of around 540 m is used in the study. Supervised machine learning algorithms are employed to model the vehicular positions. A set of parameters were identified for modeling the longitudinal and lateral positions. With the set of parameters, the algorithm's potentiality for mimicking vehicular positions is evaluated. It was identified that supervised machine learning algorithms would model the vehicles' positions with accuracy in the range of 20-60 mean absolute percentage error. The k-NN algorithm was marginally edging past all algorithms and acted as a promising candidate for modeling vehicular positions. Keywords Extended trajectory data • Machine learning • Data driven 1 BackgroundExamining driving behavior on a given road section is one of the complex phenomena. Additionally, it is one of the demanding elements in understanding the road network performance, particularly from a road safety and efficiency point of view. Since its inception, different behavior models have explained vehicular behavior. Under lane-based traffic conditions (prevailing in the USA), through NGSIM datasets [1], extended vehicular trajectory over the road space (say the length of about 600-800 m) turns out to be a prime data source in understanding the driving behavior throughout N. Raju Transportation, Planning, T.U.

show abstract

Can segregating vehicles in mixed-traffic stream improve safety and throughput? implications using simulation

Cited by 8 publications

References 54 publications

Modified Bureau of Public Roads Link Function

Modified Bureau of Public Roads Link Function

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Data-Driven Approach for Modeling the Mixed Traffic Conditions Using Supervised Machine Learning

Contact Info

Product

Resources

About