Car following theory with lateral discomfort
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.
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TL;DR: In this paper, the authors proved that the vehicle trajectories predicted by a simple linear car-following model, CF(L), the kinematic wave model with a triangular fundamental diagram, KW(T), and two cellular automata models CA(L) and CA(M) match everywhere to within a tolerance comparable with a single jam spacing.
Abstract: This paper proves that the vehicle trajectories predicted by (i) a simple linear car-following model, CF(L), (ii) the kinematic wave model with a triangular fundamental diagram, KW(T), and (iii) two cellular automata models CA(L) and CA(M) match everywhere to within a tolerance comparable with a single “jam spacing”. Thus, CF(L) = KW(T) = CA(L, M).
167 citations
TL;DR: It is found that the proposed model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion and the lateral separation effects greatly enhance the realism of car following models.
Abstract: In order to describe car following behavior in real world, this paper presents a non-lane-based car following model by incorporating the effects of the lane width in traffic. The stability condition of the model is obtained by using the linear stability theory. And numerical simulation is carried out to validate the analytic results. The property of the model is investigated, and it is found that the proposed model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion. The results implied that incorporating the lane width effects in car following model not only stabilize traffic flow and suppress the traffic jam, but also lower critical headway and increase capacity. Thus, the lateral separation effects greatly enhance the realism of car following models.
133 citations
TL;DR: In this paper, a new non-lane-based lattice model is proposed by incorporating the lateral separation effects of the lane width in traffic flow and the stability condition of the extended model is obtained by using the linear stability theory.
119 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
Cites background from "Car following theory with lateral d..."
...Gunay developed a car following model based on safety distances that takes into account the lateral frictions between the subject and other vehicles (11)....
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TL;DR: In this paper, a car-following model for a non-lane-based system taking lateral separation into account is developed. But the authors do not consider the effect of lane width on the stability of the system.
Abstract: We develop a heterogeneous continuum model based upon a car-following model for a nonlane-based system taking lateral separation into account. The criterion for linear stability analysis and traveling wave solution of the homogeneous case is studied. The consideration of the lateral separation not only stabilizes the flow but also shrinks the critical region. For heterogeneous case, the fundamental diagram is examined for two different equilibrium speed-density functions and the effect of lane width is investigated for different compositions of heterogeneous traffic. The theoretical findings agree well with the results of numerical simulation which justifies the applicability of the model to a nonlane-based system.
93 citations
References
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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.
Abstract: The ability to predict the response of a vehicle in a stream of traffic to the behaviour of its predecessor is important in estimating what effect changes to the driving environment will have on traffic flow. Various proposed to explain this behaviour have different strengths and weaknesses. The paper constructs a new model for the response of the following vehicle based on the assumption that each driver sets limits to his desired braking and acceleration rates. The parameters in the model correspond directly to obvious characteristics of driver behaviour and the paper goes on to show that when realistic values are assigned to the parameters in a simulation, the model reproduces the characteristics of real traffic flow.
1,925 citations
Book•
07 Dec 1989
TL;DR: The remaining portion of the book, Chapters 8 through 13, is devoted to analytical techniques involving the total traffic flow situation; chapter subjects are, respectively, demand-supply analysis, capacity analysis, traffic stream models, shock wave analysis, queueing analysis, and computer simulation models.
Abstract: The purpose of this textbook is to provide an in-depth treatment of the fundamentals of traffic flow. Chapter 1 provides an introduction. Chapter 2, devoted to microscopic flow characteristics, is concerned with individual time headways between vehicles, with particular emphasis on mean values and distribution forms. The chapter presents empirical measurements, describes pertinent mathematical distributions, suggests evaluation procedures, and provides selected applications. Chapter 3 is concerned with macroscopic flow characteristics, which are expressed as flow rates, and attention is given to temporal, spatial, and modal patterns. Empirical evidence is provided, analytical techniques are described, and applications are presented. Chapter 4 describes microscopic speed characteristics of individual vehicles passing a point or short segment during a specified time period. Particular attention is given to vehicular speed trajectories over time and space as well as to statistical analysis of individual speed measurements. Chapter 5 is directed to macroscopic speed characteristics, which are concerned with the speed of groups of vehicles passing a point or short segment during a specified period. Particular attention is given to temporal, spatial, and modal variations as well as to statistical analysis of group speed measurements. Travel time and delay techniques are also included. Chapter 6 is devoted to microscopic density characteristics, which are concerned with individual distance headways between vehicles, with particular emphasis on minimum and average values. The chapter includes an extensive coverage of car-following theories and automatic data collection systems. Chapter 7 is concerned with macroscopic density characteristics, which are expressed as the number of vehicles occupying a section of roadway. Density measurement techniques are described and particular attention is given to density contour maps. Analytical techniques using density contour maps are described. The remaining portion of the book, Chapters 8 through 13, is devoted to analytical techniques involving the total traffic flow situation; chapter subjects are, respectively, demand-supply analysis, capacity analysis, traffic stream models, shock wave analysis, queueing analysis, and computer simulation models. An index is provided.
1,540 citations
TL;DR: In this article, the authors assess the range of options available in the choice of car-following model, and assess just how far work has proceeded in our understanding of what, at times, would appear to be a simple process.
Abstract: In recent years, the topic of car-following has become of increased importance in traffic engineering and safety research. Models of this phenomenon, which describe the interaction between (typically) adjacent vehicles in the same lane, now form the cornerstone for many important areas of research including (a) simulation modelling, where the car-following model (amongst others) controls the motion of the vehicles in the network, and (b) the functional definition of advanced vehicle control and safety systems (AVCSS), which are being introduced as a driver safety aid in an effort to mimic driver behaviour but remove human error. Despite the importance of this area however, no overview of the models availability and validity exists. It is the intent of this paper therefore to briefly assess the range of options available in the choice of car-following model, and assess just how far work has proceeded in our understanding of what, at times, would appear to be a simple process.
1,255 citations
TL;DR: A review of car-following and control-system models for traffic flow can be found in this article, where a new model which appears to have some merit is also presented.
228 citations
"Car following theory with lateral d..." refers background in this paper
...Pipes (1966) characterised the suggestion of the California Motor Vehicle code as ‘‘a good rule for following another vehicle at a safe distance is to allow yourself at least the length of a car between your vehicle and the vehicle ahead for every 10 miles per hour of speed at which you are travelling’’....
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TL;DR: In this paper, the authors proved that the vehicle trajectories predicted by a simple linear car-following model, CF(L), the kinematic wave model with a triangular fundamental diagram, KW(T), and two cellular automata models CA(L) and CA(M) match everywhere to within a tolerance comparable with a single jam spacing.
Abstract: This paper proves that the vehicle trajectories predicted by (i) a simple linear car-following model, CF(L), (ii) the kinematic wave model with a triangular fundamental diagram, KW(T), and (iii) two cellular automata models CA(L) and CA(M) match everywhere to within a tolerance comparable with a single “jam spacing”. Thus, CF(L) = KW(T) = CA(L, M).
167 citations