A discrete-continuous multi-vehicle anticipation model of driving behaviour in heterogeneous disordered traffic conditions

Nirmale, Sangram Krishna; Pinjari, Abdul Rawoof; Sharma, Anshuman

doi:10.1016/j.trc.2021.103144

Cited by 14 publications

(5 citation statements)

References 62 publications

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“…Existing lane-changing models all take into account the effect of the multi-vehicle travel states in the original and target lanes on lane-changing drivers. Empirical studies have found that following drivers have the "multi-vehicle anticipation ability" to predict the future driving conditions of the vehicle in front of them in their lane [15]. Still, recent studies have shown that this ability is not limited to the single-lane category [27].…”

Section: Driver Multidirectional Multi-vehicle Anticipation Capabilit...mentioning

confidence: 99%

“…Therefore, when modeling and analyzing driving behavior in urban road scenarios, it is necessary to choose a basic model that can be considered to be highly scalable for research. Several classical models, such as the optimal velocity (OV) model and the Gipps model, are often extended into microscopic traffic flow models for various driving scenarios [15][16][17][18][19]. Since these models have different assumptions and dynamic characteristics, how to incorporate the influence of external factors on driver behavior into the model by using a reasonable method, in conjunction with the application scenarios, has always been one of the main concerns of micro traffic flow research scholars over the years [20].…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Driving Behavior Analysis and Unified Modeling in Urban Road Scenarios

Zhang,

Qu,

Zhang

et al. 2024

Sustainability

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To improve the simulation accuracy and efficiency of microscopic urban traffic, a unified modeling method considering the behavioral characteristics of vehicle drivers is proposed by considering the lane-changing vehicles on the inlet lanes of signalized intersections and their approach following vehicles on the target lanes as research objects. Based on the driver’s multidirectional, multi-vehicle anticipation ability and introducing lateral vehicle influence coefficients, the full velocity difference car-following model was extended to microscopic traffic models that consider the driver’s capacity for multi-directional, multi-vehicle anticipation. The extended model can describe longitudinal movements of lane changing and car followers using lateral vehicle influential parameters. The influences of traffic control signals and the type of lane change on drivers’ decisions were integrated into the model by reformulating the optimal velocity function of the basic car following the model. Similar modeling methods and components were applied to formulate four groups of experimental models and one group of test models. Vehicle trajectory data and manual observations were collected on urban arteries to calibrate and evaluate the research models, experimental models, and test models. The results show that the car-following behavior is more sensitive to the variation in the status of the lateral moving vehicle and change of lane-changing type compared to lane-changing behavior during the lane-changing process. In addition, when lane changing gradually encroaches on the target lane, the vehicle observes the driving conditions and adjusts its driving behaviors differently. This research helps to analyze travel characteristics and influence mechanisms of vehicles on urban roads, which is a guide for the future development of sustainable transportation and self-driving vehicles and promoting the efficient operation of urban transportation systems.

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Section: Driver Multidirectional Multi-vehicle Anticipation Capabilit...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle Driving Behavior Analysis and Unified Modeling in Urban Road Scenarios

Zhang,

Qu,

Zhang

et al. 2024

Sustainability

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“…The traditional flow and density need extra information like composition that defines the properties of vehicle classes in the traffic stream. To overcome this, passenger car equivalent (PCE) [15]- [19], area defined density [20], [21], and AO [22] concepts were developed. Multi-class models have been proposed as an alternative to traditional density measurements to address the challenges of PCE [23]- [25].…”

Section: Introductionmentioning

confidence: 99%

Empirical Investigation of Fundamental Diagrams in Mixed Traffic

Maiti

Chilukuri

2023

IEEE Access

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A thorough understanding of the fundamental relation of traffic flow variables is critical for the efficient operation of traffic systems. However, their relationships in mixed traffic are challenging to model due to the continuously changing vehicle composition. This paper proposes a composition-based approach for estimating the fundamental relationships between traffic flow variables using empirical data. The methodology seeks to eliminate the difficulties in class-specific ss identification by introducing a continuous wavelet transformation with oblique cumulative arrival and oblique occupancy time plots. We used machine learning (ML) algorithms to delineate regimes and showed the fundamental diagrams for a given location that has a composition-invariant free-flow branch but has distinct composition-specific branches in congestion. Also, it was observed that the congested regime (CR) has a wide scatter indicating possible stochastic inter-class interactions for varying vehicular composition. We proposed a distance optimization method to re-cluster the CR data and found that the proposed method improves the fit with the empirical observations. The inter-class interactions result illustrates that the heavy vehicles will dominate the high-speed vehicles with the increase of AO. It is found that beyond a critical level of AO in congestion, all vehicle class travel at the same speed. Finally, it is found that validation with different datasets shows that the proposed methodology is robust in estimating fundamental diagrams under mixed traffic conditions.

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“…Microscopic simulation models, including multianticipation, have shown to be suitable to simulate the human driver behavior based on data collected from a helicopter ( Hoogendoorn et al, 2006 ). Nirmale et al ( Nirmale et al, 2021 ) used trajectory data from Chennai, India, to show that human drivers react to many vehicles around and downstream. Multianticipation has proven to be a stabilizing factor for traffic flow ( Monteil et al, 2014 , Ngoduy, 2015 , Sun et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%