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Empirical investigation of multiclass vehicle behaviour under heterogeneous traffic flow conditions
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Empirical data is the basis for development and validation of traffic flow models. From the videographic data collected on two urban arterial sections, the temporal evolution of macroscopic variables such as speed, flow and density have been established. Speed data has been obtained to determine the class specific behaviour with respect to varying flow conditions and lane positions. From the statistical analysis, it is inferred that modelling using mean speed values of the vehicles alone is not sufficient, standard deviation of speeds for changing density also needs to be taken in to account.High degree of variation in speeds are observed for fast moving vehicles(Cars and Motorised Two Wheelers) compared to slow moving vehicles (Heavy vehicles and Motorised Three Wheeler) which shows that heterogeneous traffic on arterial consists multiple classes of drivers, where the distribution of these driver classes are more in stationary condition than in non-stationary condition.The analysis also revealed that lane positioning has a significant effect on vehicular speeds. However, thereis a little variance of speeds across the lanessuggestvehicle dynamics can be described by considering the whole width of the road as a single unit instead of separate entities. The results of the study show that,if multiple classes of vehicle-driver units and their interactions are considered, then the macroscopic models would be able torepresent traffic dynamics in a better way. © 2018 Institute for Transport Studies in the European Economic Integration. All Rights Reserved.
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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/downloads. The traffic flow characteristics of these trajectories, such as speed, acceleration and deceleration, and longitudinal spacing, were investigated. 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. The results further indicate that vehicles, particularly motorcycles, move substantially in the lateral direction and that in a substantial fraction of the observations, drivers are not strictly following their leaders. The results suggest directions for development of a driving behavior model for mixed traffic streams.
This paper attempts to model vehicular time gap, which is defined as the time interval between any two successive arrivals of vehicles at a reference point of measurement on a road segment. Such an approach is justified under the non-lane-based heterogeneous traffic conditions prevailing in developing countries such as India, characterized by many “zero” time gaps due to simultaneous arrivals within a given road width. In addition, time gap data are characterized by a significant amount of data in the tail region due to long headways. Nevertheless, many researchers of time gap modeling have used light-tailed distributions that modeled time gaps satisfactorily due to two reasons: (a) The tail data was merged into a single bin; and (b) goodness-of-fit tests such as the Chi-square test, which has many limitations, were used. Further, some researchers have suggested different distributions for the same range of traffic flows, leading to ambiguity in distribution selection. In addition, bin size, which dictates the degree of fit of any distribution, has been ascribed very less importance in time gap modeling. Hence, this paper tries to consolidate and standardize the existing research in time gap modeling research by addressing all these issues. Two new distributions, namely Generalized Pareto (GP) and Generalized Extreme Value (GEV) with better tail modeling properties, have been proposed along with other conventional distribution to model vehicular time gaps over a wide range of flow from 550 vph to 4,100 vph. Two types of goodness-of-fit tests, namely Area-based and Distance-based tests, have been used. It has been found from the study that GP distribution fits the time gap data well (overall and tails) up to a flow range of 1,500 vph based on both kinds of tests, and GEV fits the data well for the flow levels above 1,500 vph based on the area test only.
A continuum model that describes a disordered, heterogeneous traffic stream is presented. Such systems are widely prevalent in developing countries where classical traffic models cannot be readily applied. The characteristics of such systems are unique since drivers of smaller vehicles exploit their maneuverability to move ahead through lateral gaps at lower speeds. At higher speeds, larger vehicles press their advantage of greater motive power. The traffic stream at the microscopic level is disordered and defines a porous medium. Each vehicle is considered to move through a series of pores defined by other vehicles. A speed-density relationship that explicitly considers the pore space distribution is presented. This captures the considerable dynamics between vehicle classes that are overlooked when all classes are converted to a reference class (usually Passenger Car Equivalents) as is traditionally done. Using a finite difference approximation scheme, traffic evolution for a two-class traffic stream is shown.
A new first-order traffic flow model is introduced that takes into account the fact that various types of vehicles use the roads simultaneously, particularly cars and trucks. The main improvement this model has to offer is that vehicles are differentiated not only by their lengths but also by their speeds in a free-flow regime. Indeed, trucks on European roads are characterized by a lower speed than that of cars. A system of hyperbolic conservation equations is defined. In this system the flux function giving the flow of heavy and light vehicles depends on total and partial densities. This problem is partly solved in the Riemann case in order to establish a Godunov discretization. Some model output is shown stressing that speed differences between the two types of vehicles and congestion propagation are sufficiently reproduced. The limits of the proposed model are highlighted, and potential avenues of research in this domain are suggested.
Traffic data collection under mixed traffic conditions is one of the major problems faced by researchers as well as traffic regulatory authorities. Study and analysis of traffic behavior is critically dependent on the availability of observed traffic data. For mixed traffic observed in developing countries, no suitable tool is available for this purpose. Keeping in view these necessities and problems in data collection, a novel offline image processing-based data collection system, suitable for mixed traffic conditions, is developed. Its underlying ability to detect, track, and classify vehicles makes it useful in collecting traffic data under varying traffic conditions. This system can automatically analyze traffic videos and provide macroscopic traffic characteristics such as classified vehicle flows, average vehicle speeds and average occupancies, and microscopic characteristics such as individual vehicle trajectories, lateral, and longitudinal spacing. It is observed that this new system is working well even under congested mixed traffic conditions.
In this paper, a multi-class traffic flow model as an extension of the Lighthill, Whitham and Richards (LWR) model with heterogeneous drivers is formulated. The model takes into account the distribution of heterogeneous drivers characterized by their choice of speeds in a traffic stream, which models the dynamic behavior of heterogeneous users whereby faster vehicles could overtake slower ones under uncongested condition as well as congested condition (though less easily), and slower vehicles would slow down the faster ones. Numerical simulations show that the model can predict many of the puzzling traffic flow phenomena such as the two-capacity (or reverse-lambda) regimes occurred in the fundamental diagram, hysteresis and platoon dispersion.
This paper discusses a model in the macroscopic category that is particularly useful for evaluating freeway operations. The model is described in mathematical detail for basic flow simulation, ramp metering and diversion, surveillance, and representation of freeway incidents. Computation of performance measures is also detailed. The simulation model, FREFLO, which is based on the model equations presented, is described and illustrated with a sample run.
Most of the Studies available in the literature indicate that the speed data on a highway follow the normal distribution. The present study indicates that the speed distribution may be a unimodal or a bimodal curve depending upon the variation in the speed of different categories of vehicles moving on the highway. The mathematical equations are suggested for unimodal and bimodal speed distribution curves. and their parameters are evaluated at 17 different sections of two-lane roads in India. It is observed that the proportion of slow moving vehicles is not a true indicator of bimodality in the speed data. The spread ratio defined as the ratio of the difference between 85th percentile and mean stream speed to the difference in mean stream speed and 15th percentile speed, is better correlated with the shape of the speed distribution Curve. The speed data follow the Unimodal curve only when the spread ratio is in the range of 0.69-1.35.
Based on the property of heterogeneous traffic flow, we in this Letter present a new car-following model. Applying the relationship between the micro and macro variables, a new dynamic model for heterogeneous traffic flow is obtained. The fundamental diagram and the jam density of the heterogeneous traffic flow consisting of bus and car are studied under three different conditions: (1) without any restrictions, (2) under the action of the traffic control policy that restrains some private cars and (3) using bus to replace the private cars restrained by the traffic control policy. The numerical results show that our model can describe some qualitative properties of the heterogeneous traffic flow consisting of bus and car, which verifies that our model is reasonable.