Добірка наукової літератури з теми "Heterogeneous, disordered traffic"

Precise travel time prediction allows travelers and system controllers to be aware of the future conditions on roadways and helps in pre-trip planning and traffic control strategy formulation to lessen the travel time and mitigate traffic congestion problems. This research investigates the possibility of using the GPS trajectory dataset for travel time prediction in Indian traffic conditions having heterogeneous disordered traffic and improvement in prediction accuracy by shifting from the traditional historical average method to modern machine learning algorithms such as linear regressions, decision tree, random forest, and gradient boosting regression. The present study uses massive location data consisting of historical trajectories that were collected by installing GPS devices on the probe vehicles. A 3.6 km long stretch of the Delhi–Noida Direct (DND) flyway is selected as a case study to predict the travel time and compare the performance as well as the efficiency of various travel time prediction algorithms.

Investigating travel time variability is critical for pre-trip planning, reliable route selection, traffic management, and the development of control strategies to mitigate traffic congestion problems cost-effectively. Hence, a large number of studies are available in the literature which determine the most suitable distribution to fit the travel time data, but these studies recommend different distributions for the travel time data, and there is a disagreement on the best distribution option for fitting to the travel time data. The present study proposes a novel framework to determine the best distribution to represent the travel time data obtained from probe vehicles by using the modern machine learning technique. This study employs vast travel time data collected by fitting GPS tracking units on the probe vehicles and offers a comprehensive investigation of travel time distribution in different scenarios generated due to spatiotemporal variation of the travel time. The study also considers the effect of weather and uses the three most commonly used non-parametric goodness-of-fit tests (namely, Kolmogorov–Smirnov test, Anderson–Darling test, and chi-squared test) to fit and rank a comprehensive set of around 60 unimodal statistical distributions. The framework proposed in the study can determine the travel time distribution with 91% accuracy. Additionally, the distribution determined by the framework has an acceptance rate of 98.4%, which is better than the acceptance rates of the distributions recommended in existing studies. Because of its robustness and applicability in many different traffic situations, the proposed framework can also be used in developing countries with heterogeneous disordered traffic conditions to evaluate the road network’s performance in terms of travel time reliability.

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The groundwork for much of the existing traffic flow theory was laid in the 1950s and 1960s by researchers from developed economies like the United States. The car-following modelling approach, which is arguably the most popular type of microscopic traffic flow models, and the kinematic wave modelling approach, which is still the basis for much of the macroscopic traffic flow theory, were first introduced during this period. Because the object of this and much of the subsequent research it has spawned is the traffic of developed economies, the classical traffic flow theory deals almost exclusively with this specific kind of traffic: lane-disciplined with relatively homogeneous passenger car traffic. However, the traffic in many developing economies have a significant share of two and three-wheeler motorized vehicles and non-motorized vehicles with different static and dynamic characteristics, resulting in a fundamentally different traffic stream. Because of the differences in sizes and manoeuvrabilities, the vehicles in this traffic stream do not follow the lane-discipline. This kind of traffic has been described in the literature as “heterogeneous, disordered (HD)” or “mixed” traffic. These developing economies, mostly from Asia and Africa, are rapidly urbanizing and the need to deal with the challenge of traffic congestion is more urgent than ever. Given that these countries are already struggling to keep pace with the rising urban population for providing the necessary road infrastructure, it may be more effective to make the most efficient use of the existing infrastructure through the use of traffic management solutions like real-time traffic monitoring and signal control. As such solutions cannot be formulated using the traditional traffic flow theory, an alternative theory that explicitly considers the characteristics of the HD traffic is urgently needed. In the present study, HD traffic is characterised by the division of its component vehicle classes into car-following and gap-filling types. Flow-density relationships are derived for each vehicle type based on first principles. A new multiclass cell transmission model (CTM) is then proposed that can accommodate these vehicle classes of these two types, and its properties are analysed. This is followed by the development of a new node-based dynamic traffic assignment (DTA) framework embedded with a single class CTM satisfying the link-level first-in-first-out principle. Both the dynamic user equilibrium (DUE) and dynamic system optimum (DSO) problems are formulated within this framework as complementarity problems with guaranteed solution existence. Algorithms are developed to efficiently compute all the relevant travel costs and marginal costs needed in the determination of the DUE and DSO solutions. This DTA framework is then combined with the CTM for HD traffic proposed previously, followed by the extension of the DUE and DSO problem formulations and all the relevant algorithms to the context of HD traffic. Other contributions of this work include a comprehensive review of the studies conducted in the context of HD traffic, the proposal of a new methodology for the con- struction of driving cycles for cities with HD traffic, and the introduction of a backpropagation technique to efficiently compute the costs needed to calculate the DTA solutions.

Driver behaviour models are widely used in the traffic engineering literature and practice. They are used for understanding drivers’ manoeuvring decisions in traffic streams. They also form the building blocks of microscopic traffic simulation tools, which are employed for traffic flow analysis and capacity estimation necessary for the design and operation of traffic facilities and evaluation of operational strategies. Most driver behaviour models in the literature assume homogeneous and orderly traffic conditions, characterised by homogeneity (i.e., only passenger cars comprise the traffic streams) and orderliness (i.e., vehicles move only in the longitudinal direction, except when changing lanes). Models developed with such assumptions cannot be applied to analyse heterogeneous, disorderly (HD) traffic conditions. This is because HD traffic streams, unlike homogeneous traffic streams, comprise a wide variety of vehicle classes with considerably different physical and operational characteristics. Moreover, driving in HD traffic streams is characterised by weaker lane discipline due to a greater extent of lateral movements than that in homogeneous traffic streams. This dissertation aims to formulate and apply driver behaviour models for HD traffic streams on uninterrupted traffic facilities while considering the following aspects – (1) the multi-vehicle anticipation (MVA) behaviour, where drivers’ manoeuvring decisions are influenced by multiple vehicles around them, as opposed to a single lead vehicle ahead, (2) the treatment of driver behaviour as a combination of different manoeuvring decisions, such as the decision of whether to accelerate, decelerate, or remain in same speed (represented by a discrete variable) and the decision of the extent of acceleration or deceleration (represented by continuous variables) – as opposed to a single, continuous variable representing all these facets of driver behaviour, (3) the incorporation of stochasticity due to the errors drivers make in perceiving the traffic environment, and (4) the consideration of drivers’ intentions (which are typically latent to the analyst) and two-dimensional movements of vehicles simultaneously while also incorporating MVA behaviour. Specifically, the following driver behaviour models are formulated and applied to understand driver behaviour in empirical trajectory datasets from Chennai (HD traffic) and California (homogeneous traffic): 1. The first model presented in this dissertation is an MVA-based discrete-continuous choice modelling framework to model vehicles’ longitudinal movements in HD traffic streams. In this model, driver behaviour at a given time instance is treated as a combination of (a) the driver’s choice of whether to accelerate, decelerate, or maintain a constant speed – represented by a discrete variable – and (b) the extent of acceleration or deceleration – represented by continuous variables. The discrete and continuous variables representing driver behaviour are modelled using a simultaneous econometric framework. The proposed model is used to examine driver behaviour in the HD traffic dataset from Chennai. The empirical analysis reveals the significance of the MVA effect on driver behaviour. Specifically, drivers consider the relative speeds and space gaps with respect to multiple vehicles within an influence zone around their vehicle. In addition, it is found that the influence of the traffic environment on drivers’ discrete choices (whether to accelerate, decelerate, or maintain a constant speed) is not the same as that on their choices of how much to accelerate or decelerate. 2. The second model is an extension of the above model to recognise the panel data nature of vehicle trajectory datasets typically used for estimating the parameters of driver behaviour models. This model recognises the role of vehicle- and driver-specific unobserved factors (latent to the analyst), such as aggressiveness that influence driving behaviour, and such influence persists across all observations of a vehicle. Doing so helps in reducing the confounding effects of unobserved factors when the proposed model is applied to different datasets to compare driving behaviour in different traffic streams. The panel data model is used to understand and compare longitudinal driving behaviour between the HD traffic dataset of Chennai and the homogeneous traffic dataset of California. The empirical analysis reveals the presence of MVA effect on driving behaviour in the homogeneous traffic setting, too. However, drivers in the HD traffic stream are influenced by more vehicles in their vicinity than those in the homogeneous traffic stream. 3. In the third model formulation, a mixed multinomial logit-based framework is developed to recognise stochasticity in driver behaviour models due to drivers’ errors in perceiving the traffic environment. For this model, an econometric analysis is undertaken to evaluate two different ways of specifying errors in variables in discrete choice models – additive errors and multiplicative errors. It is shown that the multiplicative specification of errors has a better behavioural basis and allows better identification of parameters representing variability due to drivers’ perception errors. An application of this model to the HD traffic dataset reveals different levels of variability due to errors in the perception of different traffic environment variables. It is found that drivers may pay greater attention to (which results in lower variability in) perceiving space gaps and relative speeds with respect to vehicles directly ahead of them than those not directly ahead. 4. The fourth and final model formulation is a two-dimensional, MVA, and multi-stimuli-based latent class framework to analyse motorcyclists’ two-dimensional movements in HD traffic streams. This formulation conjectures that drivers manage their cognitive load by dividing their driving decisions into two steps – (a) higher-level, strategic intentions (of whether to accelerate, decelerate, or maintain a constant speed and whether to steer to the left of, right of, or keep straight along the longitudinal direction), which are not fully observable from vehicle trajectories (hence latent to the analyst), and (b) lower-level, tactical decisions that can be observed in vehicle trajectories, such as the specific angle of movement and the specific extent of acceleration or deceleration executed. When applied to the HD traffic dataset of Chennai, the proposed model suggests that drivers’ higher-level intentions are more strongly influenced by the microscopic traffic environment variables than their lower-level decisions, perhaps because drivers invest a greater extent of cognitive resources for making higher-level intentions than that for lower-level decisions. Finally, a traffic simulator is developed to simulate traffic streams using the models developed in this dissertation. The simulation experiments using this simulator demonstrate that all the microscopic driver behaviour models developed in this dissertation reflect the typically observed macroscopic properties of vehicular traffic steams.