Modeling following behavior of vehicles using trajectory data under mixed traffic conditions: an Indian viewpoint

Raju, Narayana; Arkatkar, Shriniwas; Joshi, Gaurang

doi:10.1080/19427867.2020.1751440

Cited by 17 publications

(13 citation statements)

References 30 publications

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“…Parameter sets 1, 2, and 3 (cf. Figure 2) are obtained by applying heuristics based on macroscopic criteria as reported in the literature (6,7,30). The vehicular composition and vehicle dimensions (average length and width of each vehicle class) are taken from Kanagaraj et al (31).…”

Section: Rationale For Calibrating the W-99 Model Using Trajectory Datamentioning

confidence: 99%

“…The W-99 (3) model has been widely used in traffic micro-simulation for both lane-based and non-lane-based conditions (1,(4)(5)(6)(7)(8)(9)(10)(11)(12). However, this model's use in nonlane-based states will be substantially different from lanebased conditions and requires careful calibration.…”

mentioning

confidence: 99%

“…One should note that the model parameters may vary based on leader, follower, and surrounding vehicle types and their speeds and positions (16). There is a growing body of work on the calibration of various microscopic models for mixed traffic using multiple models (5,6,(8)(9)(10)(11)(12)(13)(16)(17)(18)(19)(20). The analytical forms of some of the Wiedemann model equations are not easily accessible in the literature, making the calibration at the trajectory level difficult.…”

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confidence: 99%

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Calibrating Wiedemann-99 Model Parameters to Trajectory Data of Mixed Vehicular Traffic

Chaudhari

Srinivasan

Chilukuri

et al. 2021

Transportation Research Record: Journal of the Transportation R

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We propose a new methodology for calibrating Wiedemann-99 vehicle-following parameters for mixed traffic (different conventional vehicle classes) based on trajectory data. The existing acceleration equations of the Wiedemann model are modified to represent more realistic driving behavior. Exploratory analysis of simulation data revealed that different Wiedemann-99 model parameters could lead to similar macroscopic behavior, highlighting the importance of calibration at the microscopic level. Therefore, the proposed methodology is based on optimizing performance measures at the microscopic level (acceleration, speed, and trajectory profiles) to estimate suitable calibration parameters. Further, the goodness of fit for the observed data is sensitive to the numerical integration method used to compute vehicles’ velocity and position. We found that the calibrated parameters using the proposed methodology perform better than other approaches for calibrating mixed traffic. The results reveal that the calibrated parameter values and, consequently, the thresholds that delineate closing, following, emergency braking, and opening regimes, vary between two-wheelers and cars. The window (in the relative speed versus gap plot) for the unconscious following is larger for cars while the free-flow regime is more extensive for two-wheelers. Moreover, under the same relative speed and gap stimulus, two-wheelers and cars may be in different regimes and display different acceleration responses. Thus, accurate calibration of each vehicle’s parameters is essential for developing micro-simulation models for mixed traffic. The calibration analysis results of strict and overlapping staggered car following signify an impact of staggered car following compared with strict car following which demands separate calibration for strict and staggered following.

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Section: Rationale For Calibrating the W-99 Model Using Trajectory Datamentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Calibrating Wiedemann-99 Model Parameters to Trajectory Data of Mixed Vehicular Traffic

Chaudhari

Srinivasan

Chilukuri

et al. 2021

Transportation Research Record: Journal of the Transportation R

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“…From raw trajectory data, speed and acceleration are calculated, and leader-follower pairs are extracted. The identification of leader and follower is based on the study for heterogeneous traffic conditions in a weak lane-based environment [2,3].…”

Section: Data Collection and Extractionmentioning

confidence: 99%

“…As a result, traffic tends to display weak lane discipline. The driving behavior studies [2,3] from mixed traffic demonstrates smaller vehicles' lateral behavior impacting mixed traffic performance. Simultaneously, given the variation in the vehicles' physical properties, earlier studies [4,5] reported the underperformance of automated traffic tools in monitoring mixed traffic conditions.…”

Section: Introductionmentioning

confidence: 97%

Stability Analysis of Mixed Traffic Flow using Car-Following Models on Trajectory Data

Surya

Raju

Arkatkar

2021

2021 International Conference on COMmunication Systems &Amp; NETworkS (COMSNETS)

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There is an increase in the need to understand traffic flow behavior through multiple fronts, especially in developing countries like India, where a high degree of heterogeneity, non-lane-discipline, and non-compliance to traffic rules prevail in the road sections. As a result, it can impact the stability of the traffic stream. Four car-following equations to analyze six vehicle categories using various stability criteria are evaluated for the mixed traffic conditions. With the help of processed trajectory data of the western expressway, consisting of multiple types of flows, the parameters of four car-following equations are calibrated for all vehicle categories separately to address heterogeneity. Final stability equations are derived from the available criteria in the literature and analyzed accordingly. To address non-lane-discipline, a straightforward methodology to examine the stability of a model which considers lateral movement is developed and validated, and proven to provide more stability to the traffic. Comparisons between vehicle categories and between car-following models are made. Various interpretations about the impact of parameters in car-following models on the stability of traffic are provided.

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Assessment of Multilane Highway Capacity Through Simulation Process by Considering the Effect of Behavior of Driver of a Vehicle

Ghosh

Roy

Gangopadhyay

2020

J. Inst. Eng. India Ser. A

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Modeling following behavior of vehicles using trajectory data under mixed traffic conditions: an Indian viewpoint

Cited by 17 publications

References 30 publications

Calibrating Wiedemann-99 Model Parameters to Trajectory Data of Mixed Vehicular Traffic

Calibrating Wiedemann-99 Model Parameters to Trajectory Data of Mixed Vehicular Traffic

Stability Analysis of Mixed Traffic Flow using Car-Following Models on Trajectory Data

Assessment of Multilane Highway Capacity Through Simulation Process by Considering the Effect of Behavior of Driver of a Vehicle

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