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Journal ArticleDOI

Identification of different vehicle-following manoeuvres for heterogeneous weak-lane disciplined traffic condition from vehicle trajectory data

01 Jun 2020-Vol. 491, Iss: 1, pp 012052
About: The article was published on 2020-06-01 and is currently open access. It has received 1 citation(s) till now. The article focuses on the topic(s): Trajectory.
Topics: Trajectory (51%)
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Journal ArticleDOI
TL;DR: Empirical evidence of qualitative and quantitative differences among following behaviors that can help in developing better microscopic traffic flow models for mixed traffic conditions is provided.
Abstract: In mixed traffic streams without lane discipline, driving behaviors are complex and difficult to model. However, limited attempts have been made to study the characteristics of these maneuvers usin...

Cites background from "Identification of different vehicle..."

  • ...Previously the following behavior was identified based on the percentage of lateral overlap (34) and lateral gap (35) between the leader and the follower....


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Journal ArticleDOI
TL;DR: The simulation framework for the traffic-flow model was prepared in such a way that the absence of lane discipline in mixed traffic flow conditions is taken into account and a detailed structure of the proposed model is presented.
Abstract: This paper describes a modeling methodology adopted to simulate the flow of heterogeneous traffic with vehicles of wide ranging static and dynamic characteristics. The simulation framework for the traffic-flow model was prepared in such a way that the absence of lane discipline in mixed traffic flow conditions is taken into account. A detailed structure of the proposed model is presented. Common issues related to traffic simulation such as vehicle generation, logics for vehicular movement, etc., are described in detail in the context of heterogeneous traffic conditions. The paper also discusses the procedures adopted for validation of the proposed model and their outcomes. Finally, the details of application of the model to study the traffic flow characteristics on urban roads are also presented.

184 citations

Journal ArticleDOI
Banihan Gunay1
Abstract: A car following model has been developed with particular reference to weak discipline of lane-based driving. The theory is based on the discomfort caused by lateral friction between vehicles. The movement of the following vehicle was formulated as a function of the off-centre effects of its leader(s). This incorporation of lateral friction offers a potential breakthrough in the fields of car following theory and microscopic simulation of traffic flow. Using a stopping-distance car following approach, the simulation presented in the paper pointed out the effect of the travel path width on the speed of the following vehicle, and the reduced following distance with increased lateral separation between the leader and follower. It was also shown that a special case of the proposed model (i.e. when the maximum escape speed is zero) produced the same results as the base model did for the conventional car following case. The simulation behaved rationally giving credibility to the author's staggered car following theory.

153 citations

Journal ArticleDOI
23 Feb 2007-Transport Reviews
TL;DR: 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.
Abstract: Driving behaviour models capture drivers’ tactical manoeuvring decisions in different traffic conditions. These models are essential to microscopic traffic simulation systems. The paper reviews the state‐of‐the‐art in the main areas of driving behaviour research: acceleration, lane changing and gap acceptance. Overall, the main limitation of current models is that in many cases they do not adequately capture the sophistication of drivers: they do not capture the interdependencies among the decisions made by the same drivers over time and across decision dimensions; they represent instantaneous decision‐making, which fails to capture drivers’ planning and anticipation capabilities; and only capture myopic considerations that do not account for extended driving goals and considerations. Furthermore, most models proposed in the literature were not estimated rigorously. In many cases, this is due to the limited availability of detailed trajectory data, which are required for estimation. Hence, data a...

138 citations

Journal ArticleDOI
Abstract: This paper presents the methodology and results of estimation of an integrated driving behavior model that attempts to integrate various driving decisions. The model explains lane changing and acceleration decisions jointly and so, captures inter-dependencies between these behaviors and represents drivers' planning capabilities. It introduces new models that capture drivers' choice of a target gap that they intend to use in order to change lanes, and acceleration models that capture drivers' behavior to facilitate the completion of a desired lane change using the target gap. The parameters of all components of the model are estimated simultaneously with the maximum likelihood method and using detailed vehicle trajectory data collected in a freeway section in Arlington, Virginia. The estimation results are presented and discussed in detail.

84 citations

Journal ArticleDOI
TL;DR: The results show statistically significant differences between the various vehicle types in travel speeds, accelerations, distance keeping, and selection of lateral positions on the roadway and suggest directions for development of a driving behavior model for mixed traffic streams.
Abstract: Models of driving behavior (e.g., car following and lane changing) describe the longitudinal and lateral movements of vehicles in the traffic stream. Calibration and validation of these models require detailed vehicle trajectory data. Trajectory data about traffic in cities in the developing world are not publicly available. These cities are characterized by a heterogeneous mix of vehicle types and by a lack of lane discipline. This paper reports on an effort to create a data set of vehicle trajectory data in mixed traffic and on the first results of analysis of these data. The data were collected through video photography in an urban midblock road section in Chennai, India. The trajectory data were extracted from the video sequences with specialized software, and the locally weighted regression method was used to process the data to reduce measurement errors and obtain continuous position, speed, and acceleration functions. The collected data were freely available at

69 citations

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