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 7 citations till now. The article focuses on the topics: Trajectory.
Citations
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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...
4 citations
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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08 Oct 2022
TL;DR: In this paper , the authors provide an overview of the available literature on the current non-lane based driving and future LFT strategies, as well as the taxonomy used in literature to clearly distinguish the two traffic modes.
Abstract: Connected and automated vehicles (CAVs), with the capability to exchange information with other CAVs and the infrastructure, open up new opportunities in road traffic systems including the recently introduced concept of lane-free traffic (LFT). In an LFT environment, vehicles move without the need for traditional fixed lanes. It is an emerging research area where many forms of LFT strategies are being investigated that can benefit road capacity optimization, thus reducing traffic congestion and increasing traffic efficiency. LFT strategies can also benefit from current research on non-lane based driving behavior observed in some countries. Thus, this paper overviews the available literature on the current non-lane based driving and future LFT. It also studies the taxonomy used in literature to clearly distinguish the two traffic modes. It details the current developments in LFT research and the underlying technologies. From these developments, the paper provides an overview of currently available approaches for realizing LFT. Finally, it discusses the policy considerations needed for the seamless integration of LFT into the current infrastructure once CAVs become mainstream.
3 citations
TL;DR: In this article , a comprehensive model consisting of a real-time lane identification model and a realtime queue length estimation model is developed based on the traffic shockwave theory using GPS data.
Abstract: Nowadays more private car trips have caused worse congestion due to the Covid-19 pandemic in many cities. Congestion charging is one of the taxes that is levied on vehicle owners to reduce urban traffic congestion. One of the most important reasons congestion charging is not accepted by the public is the high cost. Monitoring the state of traffic congestion in real time requires a lot of expensive installations. The purpose of this paper is to make congestion charging more accurate and acceptable using artificial intelligent algorithm. Massive real-time Global Positioning System (GPS) data provides new data for road congestion charging. The queuing length at intersections is an important measurement for the degree of traffic congestion, and it is also the basis for road congestion pricing. GPS positioning cannot provide sufficient position accuracy for lane identification of vehicles. In this study, a comprehensive model consisting of a real-time lane identification model and a real-time queue length estimation model is developed based on the traffic shockwave theory using GPS data. The comprehensive model can identify the lane where the queuing vehicle is located and estimate the real-time queue length of the lane. The proposed models were evaluated using field-collected data in Guangzhou, China. The testing results show that the proposed comprehensive model can identify lanes and estimate queue lengths with satisfactory accuracy. The model proposed in this paper provides real-time data for road dynamic pricing in a cost-effective way, which can promote the implementation of congestion charging in cities.
3 citations
08 Oct 2022
TL;DR: In this article , the authors provide an overview of the available literature on the current non-lane based driving and future LFT strategies, as well as the taxonomy used in literature to clearly distinguish the two traffic modes.
Abstract: Connected and automated vehicles (CAVs), with the capability to exchange information with other CAVs and the infrastructure, open up new opportunities in road traffic systems including the recently introduced concept of lane-free traffic (LFT). In an LFT environment, vehicles move without the need for traditional fixed lanes. It is an emerging research area where many forms of LFT strategies are being investigated that can benefit road capacity optimization, thus reducing traffic congestion and increasing traffic efficiency. LFT strategies can also benefit from current research on non-lane based driving behavior observed in some countries. Thus, this paper overviews the available literature on the current non-lane based driving and future LFT. It also studies the taxonomy used in literature to clearly distinguish the two traffic modes. It details the current developments in LFT research and the underlying technologies. From these developments, the paper provides an overview of currently available approaches for realizing LFT. Finally, it discusses the policy considerations needed for the seamless integration of LFT into the current infrastructure once CAVs become mainstream.
2 citations
TL;DR: In this paper , a comprehensive model consisting of a real-time lane identification model and a realtime queue length estimation model is developed based on the traffic shockwave theory using GPS data.
Abstract: Nowadays more private car trips have caused worse congestion due to the Covid-19 pandemic in many cities. Congestion charging is one of the taxes that is levied on vehicle owners to reduce urban traffic congestion. One of the most important reasons congestion charging is not accepted by the public is the high cost. Monitoring the state of traffic congestion in real time requires a lot of expensive installations. The purpose of this paper is to make congestion charging more accurate and acceptable using artificial intelligent algorithm. Massive real-time Global Positioning System (GPS) data provides new data for road congestion charging. The queuing length at intersections is an important measurement for the degree of traffic congestion, and it is also the basis for road congestion pricing. GPS positioning cannot provide sufficient position accuracy for lane identification of vehicles. In this study, a comprehensive model consisting of a real-time lane identification model and a real-time queue length estimation model is developed based on the traffic shockwave theory using GPS data. The comprehensive model can identify the lane where the queuing vehicle is located and estimate the real-time queue length of the lane. The proposed models were evaluated using field-collected data in Guangzhou, China. The testing results show that the proposed comprehensive model can identify lanes and estimate queue lengths with satisfactory accuracy. The model proposed in this paper provides real-time data for road dynamic pricing in a cost-effective way, which can promote the implementation of congestion charging in cities.
2 citations
References
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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.
194 citations
TL;DR: In this paper, a car following model was developed with particular reference to weak discipline of lane-based driving, based on the discomfort caused by lateral friction between vehicles, and the movement of the following vehicle was formulated as a function of the off-centre effects of its leader(s).
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.
173 citations
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.
170 citations
TL;DR: In this paper, the authors present the methodology and results of estimation of an integrated driving behavior model that attempts to integrate various driving decisions, such as lane changing and acceleration decisions jointly and so, captures interdependencies between these behaviors and represents drivers' planning capabilities.
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.
96 citations
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 http://toledo.net.technion.ac.il/down...
95 citations