Journal ArticleDOI
Driving Behaviour: Models and Challenges
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The paper reviews the state‐of‐the‐art in the main areas of driving behaviour research: acceleration, lane changing and gap acceptance, and finds that current models do not adequately capture the sophistication of drivers.About:
This article is published in Transport Reviews.The article was published on 2007-02-23. It has received 170 citations till now. The article focuses on the topics: Poison control.read more
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Recent developments and research needs in modeling lane changing
TL;DR: In this paper, the major lane changing models in the literature are categorized into two groups: models that capture the lane changing decision-making process, and models that aim to quantify the impact of lane changing behavior on surrounding vehicles.
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Incorporating human-factors in car-following models: A review of recent developments and research needs
Mohammad Saifuzzaman,Zuduo Zheng +1 more
TL;DR: In this paper, the authors provide a comprehensive review of car-following models from both the engineering and human behavior points of view, analyzing the benefits and limitations of various models and highlighting future research needs in the area.
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Modeling Duration of Lane Changes
Tomer Toledo,David Zohar +1 more
TL;DR: Models of the duration of lane changes are presented using detailed vehicle trajectory data collected in naturalistic driving with high-mounted video cameras for passenger cars and heavy vehicles and statistical tests are conducted for the similarity between the lane-change durations of the two vehicle types.
Journal ArticleDOI
Review of Microscopic Lane-Changing Models and Future Research Opportunities
TL;DR: A detailed review and systematic comparison of existing microscopic lane- changing models that are related to roadway traffic simulation is conducted to provide a better understanding of respective properties, including strengths and weaknesses of the lane-changing models, and to identify potential for model improvement using existing and emerging data collection technologies.
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A car-following model considering asymmetric driving behavior based on long short-term memory neural networks
Xiuling Huang,Jie Sun,Jian Sun +2 more
TL;DR: A long short-term memory (LSTM) neural networks (NN) based car-following model to capture realistic traffic flow characteristics by incorporating the driving memory reveals that LSTM-NN model can capture the asymmetric driving behavior well and outperforms other models.
References
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Journal ArticleDOI
A cellular automaton model for freeway traffic
Kai Nagel,Michael Schreckenberg +1 more
TL;DR: A stochastic discrete automaton model is introduced to simulate freeway traffic and shows a transition from laminar traffic flow to start-stop- waves with increasing vehicle density, as is observed in real freeway traffic.
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Congested traffic states in empirical observations and microscopic simulations
TL;DR: It is shown that the results of the microscopic model can be understood by formulating the theoretical phase diagram for bottlenecks in a more general way, and a local drop of the road capacity induced by parameter variations has essentially the same effect as an on-ramp.
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Dynamical model of traffic congestion and numerical simulation
TL;DR: In this model, the legal velocity function is introduced, which is a function of the headway of the preceding vehicle, and the evolution of traffic congestion is observed with the development of time.
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A behavioural car-following model for computer simulation
TL;DR: A new model is constructed for the response of the following vehicle based on the assumption that each driver sets limits to his desired braking and acceleration rates and it is shown that when realistic values are assigned to the parameters in a simulation, the model reproduces the characteristics of real traffic flow.