Analytical studies on the instabilities of heterogeneous intelligent traffic flow

It has been widely reported in literature that a small perturbation in traffic flow such as a sudden deceleration of a vehicle could lead to the formation of traffic jams without a clear bottleneck. These traffic jams are usually related to instabilities in traffic flow. The applications of intelligent traffic systems are a potential solution to reduce the amplitude or to eliminate the formation of such traffic instabilities. A lot of research has been conducted to theoretically study the effect of intelligent vehicles, for example adaptive cruise control vehicles, using either computer simulation or analytical method. However, most current analytical research has only applied to single class traffic flow. To this end, the main topic of this paper is to perform a linear stability analysis to find the stability threshold of heterogeneous traffic flow using microscopic models, particularly the effect of intelligent vehicles on heterogeneous (or multi-class) traffic flow instabilities. The analytical results will show how intelligent vehicle percentages affect the stability of multi-class traffic flow.     

基于动态综合评价模型的街区型住区开放对城市交通拥堵影响的研究（英文）

The growing use of vehicles with the development of China brings greater pressure on the existing road traffic network. Based on the domestic and international research experiences and the domestic congestion situations, this paper selected six indicators that affect the traffic conditions. As each single indicator cannot reflect the detailed traffic quality separately, the dynamic comprehensive evaluation principle is adopted.Then we used the principle of "Variation Driven" to determine the weight vector of each sub-indicator, and established a multi-indicator comprehensive evaluation system through the dynamic weight of each sub-indicator. Finally, the rationality and feasibility of the system is demonstrated by case analysis. This provides a strong theoretical foundation and basis to mitigate traffic congestion.     

Optimal Control in Autonomous Driving

Autonomous driving is no longer a subject of science fiction. Instead it has become a field of highly topical developments and has already reached numerous milestones. The Audi Autonomous Driving Cup provides a stage for students to participate in this development process. This competition, carried out in Germany, Austria and Switzerland, provides miniature vehicles of normed hardware to the participants which have the task of implementing algorithms for autonomous handling of problems such as lane tracking, obstacle detection, adaptive cruise control, overtaking, turning as well as entering and exiting parking spaces. To present the concept of optimal control based on a single track model in combination with image processing methods we focus in this paper on the parking manoeuvre only. Thereby we show that optimization and control techniques can play a key role in the design of autonomous vehicles. (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Discrete time dynamic traffic assignment models and solution algorithm for managed lanes

“Managed” lanes of highways usually refer to lanes that are not open to all types of vehicles, such as “High Occupancy Vehicles” (HOV) lanes and “High Occupancy Toll” (HOT) lanes, etc. The HOV lanes of highways are reserved only for vehicles with a driver and one or more passengers. Whereas, HOT lanes allow all vehicles but require tolls from the vehicles with no passenger except the driver. In this paper, we present a discrete-time traffic assignment system optimum model to predict the optimal traffic flows on managed lanes at various times in the entire planning horizon. This model minimizes the overall delay (travel time) and belongs to the class of dynamic traffic assignment (DTA) problems. When applied to general networks, DTA problems can be large and difficult to solve, but the problem is manageable when it is applied to a network with managed lanes. In particular, the DTA model in this paper for managed lanes is reduced to a mixed integer program for which several efficient heuristic algorithms exist. This paper also discusses the special properties of the discrete-time DTA model, based upon which a heuristic algorithm is proposed. Numerical results show that this algorithm is efficient for many cases of the managed lane problems.     

Optimal routing of vehicles with communication capabilities in disasters

Major emergencies and disasters such as acts of terrorism, acts of nature, or human-caused accidents may lead to disruptions in traffic flow. Minimizing the negative effects of such disruptions is critical for a nation’s economy and security. A decision support system that is capable of gathering (real-time) information about the traffic conditions following a disaster and utilizing this information to generate alternative routes for vehicles would benefit the government, industry, and the public. For this purpose, we develop a mathematical programming model to minimize the delay for vehicles with communication capabilities following a disaster. Most commercial trucks and public buses utilize QUALCOMM as a communication tool. We also develop a prediction model for vehicles that do not have any communication capabilities. Although the problem is inherently integer we developed a linear program to reduce the computational burden caused by the large size of the problem. An algorithm is proposed to update the parameters of the linear program based on a duality analysis in order to obtain better results. A monotonic speed–density relationship is embedded in the model to capture high traffic congestion that occurs after a disaster. The model and the algorithm are tested using a simulated disaster scenario. The results indicate that the proposed model improves system performance measures such as mobility and average speed.     

Multiscale Analysis and Data Networks

The empirical finding of self-similarity in data network traffic over many time scales motivates the need for analysis tools that are particularly well adapted for identifying structures in network traffic. These structures span a range of time scales or are scale-dependent. Wavelet-based scaling analysis methods are especially successful, both collecting summary statistics from scale to scale and probing the local structure of packet traces. They include both spectral density estimation to identify large time-scale features and multifractal estimation for small time-scale bursts. While these methods are primarily statistical in nature, we may also adapt them to visualize the “burstiness” or the instantaneous scaling features of network traffic. This expository paper discusses the theoretical and implementation issues of wavelet-based scaling analysis for network traffic. Because data network traffic research does not consist solely of analysis, we show how these wavelet-based methods may be used to monitor and infer network properties (in conjunction with on-line algorithms and careful network experimentation). More importantly, we address what types of networking questions we can and cannot investigate with such tools.     

Applications of the urban traffic control strategy TUC

Despite the long-lasting research and developments in the field of urban traffic control systems, the continuously increasing mobility requirements urge for solutions that will release urban areas from the serious congestion problems and their consequences. From the control point of view, this may be translated into the employment of traffic-responsive systems that respond automatically to the prevailing traffic conditions. This is the aim of the signal control strategy TUC, whose basic philosophy, design methodology, characteristics and application results under both simulated and field conditions are presented in this paper. Based on a store-and-forward type of mathematical modelling and using well-known methodological tools from Automatic Control Theory, the TUC strategy addresses in a simple but efficient way, as demonstrated from the applications so far, the problem of co-ordinated, traffic-responsive signal control in large-scale urban networks.     

Instability of cooperative adaptive cruise control traffic flow: A macroscopic approach

This paper proposes a macroscopic model to describe the operations of cooperative adaptive cruise control (CACC) traffic flow, which is an extension of adaptive cruise control (ACC) traffic flow. In CACC traffic flow a vehicle can exchange information with many preceding vehicles through wireless communication. Due to such communication the CACC vehicle can follow its leader at a closer distance than the ACC vehicle. The stability diagrams are constructed from the developed model based on the linear and nonlinear stability method for a certain model parameter set. It is found analytically that CACC vehicles enhance the stabilization of traffic flow with respect to both small and large perturbations compared to ACC vehicles. Numerical simulation is carried out to support our analytical findings. Based on the nonlinear stability analysis, we will show analytically and numerically that the CACC system better improves the dynamic equilibrium capacity over the ACC system. We have argued that in parallel to microscopic models for CACC traffic flow, the newly developed macroscopic will provide a complete insight into the dynamics of intelligent traffic flow.     

Locating replenishment stations for electric vehicles: application to Danish traffic data

Environment-friendly electric vehicles have gained popularity and increased attention in recent years. The deployment of a network of recharging stations is essential given their limited travel range. This paper considers the problem of locating electronic replenishment stations for electric vehicles on a traffic network with flow-based demand. The objective is to optimize the network performance, for example to maximize the flow covered by a prefixed number of stations or to minimize the number of stations needed to cover traffic flows. Two integer linear programming formulations are proposed to model the problem. These models are tested on real-life traffic data collected in Denmark.     

Simulated annealing and tabu search approaches to unidirectional flowpath design for Automated Guided Vehicle Systems

In this paper, we address the flowpath design issue of Automated Guided Vehicle Systems (AGVSs). In particular, we concentrate on the design of unidirectional flowpaths (i.e. vehicles are restricted to travel only in one direction along a given segment of the flowpath). We have developed intelligent heuristics — simulated annealing and tabu search algorithms for the design of unidirectional AGVSs. Different versions of simulated annealing and tabu search algorithms are implemented. Our extensive computational results indicate that both simulated annealing and tabu search yield solutions of adequate quality for all practical purposes. A tabu search implementation with the use of a frequency-based memory structure dominates all tested heuristics in terms of solution quality (i.e. percent deviation from optimality), with an impressive average performance over 45 test problems of less than 0.85% deviation from optimality.     

Heuristic algorithms for a multi-period multi-stop transportation planning problem

We consider a multi-period multi-stop transportation planning problem (MPMSTP) in a one-warehouse multi-retailer distribution system where a fleet of homogeneous vehicles delivers products from a warehouse to retailers. The objective of the MPMSTP is to minimize the total transportation distance for product delivery over the planning horizon while satisfying demands of the retailers. We suggest two heuristic algorithms based on the column generation method and the simulated annealing algorithm. Computational experiments on randomly generated test problems showed that the suggested algorithms gave better solutions than an algorithm currently used in practice and algorithms modified from existing algorithms for vehicle routing problems.     

Bayesian semiparametric customer base segmentation of mobile phone users based on longitudinal traffic data

Customer segmentation is one of the most important purposes of customer base analysis for telecommunication companies. Because companies accumulate very large amounts of data on customer behavior, segmentation is typically achieved by profiling and clustering traffic behavior jointly with demographic data and contracts characteristics. Unfortunately, most algorithms and models used for segmentation do not take into account the longitudinal characteristics of data. In particular, in telecommunication traffic analysis, the importance of decreasing patterns of traffic in customers' lives is well known, and it is relevant to aggregate all clients with such a pattern, while other unknown clusters may be of interest for the marketing manager. Our approach to address this problem is based on specifying the distribution of functions as a mixture of a parametric hierarchical model describing the decreasing pattern segment and a nonparametric contamination that allows unanticipated curve shapes in subjects' traffic. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. Copyright © 2014 John Wiley & Sons, Ltd.     

Heuristic Search Algorithms for the Minimum Volume Ellipsoid

Abstract

A method of robust estimation of multivariate location and shape that has attracted a lot of attention recently is Rousseeuw's minimum volume ellipsoid estimator (MVE). This estimator has a high breakdown point but is difficult to compute successfully. In this article, we apply methods of heuristic search to this problem, including simulated annealing, genetic algorithms, and tabu search, and compare the results to the undirected random search algorithm that is often cited. Heuristic search provides several effective algorithms that are far more computationally efficient than random search. Furthermore, random search, as currently implemented, is shown to be ineffective for larger problems.     

Comparison of a Filter- and a Model Predictive Control Based Motion Cueing Algorithm

Motion Cueing Algorithms (MCA) include control strategies to take into account the motion-based driving simulator's restrictions concerning workspace limits and dynamic boundaries. A typical 6-DoF simulator consists of a motion system which exhibits three translational and rotational degrees of freedom. Its actuators are capable of realizing accelerations, velocities and positions in a limited range. Based on these facts MCAs aim to generating realistic simulations of the driving motion (such as a driving manoeuvre) in order to immerse persons in virtual environments provided by the simulator. Filter-based, classical MCAs belong to the most applied algorithms and mainly consist of linear transfer functions. Whereas, Model Predictive Control (MPC) algorithms rest upon a reduced model of the technical system's dynamics and, optionally, a model of the human motion perception system. An optimization problem subject to the restrictions of the motion system predicts the control variables over a time horizon. This paper addresses the strengths and weaknesses of the two stated approaches with focus on Motion Cueing errors. These errors describe discrepancies between the motion that is to be simulated (e.g. a driving manoeuvre) and the motion that is eventually provided by the combination of the MCA and the motion simulator. On basis of the results, it gives an outlook why optimization based algorithms have a higher potential to improve driving simulation. (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Heavy traffic analysis of controlled multiplexing systems

The paper develops the mathematics of the heavy traffic approach to the control and optimal control problem for multiplexing systems, where there are many mutually independent sources which feed into a single channel via a multiplexer (or of networks composed of such subsystems). Due to the widely varying bit rates over all sources, control over admission, bandwidth, etc., is needed to assure good performance. Optimal control and heavy traffic analysis has been shown to yield systems with greatly improved performance. Indeed, the heavy traffic approach covers many cases of great current interest, and provides a useful and practical approach to problems of analysis and control arising in modern high speed telecommunications. Past works on the heavy traffic approach to the multiplexing problem concentrated on the uncontrolled system or on the use of the heavy traffic limit control problem for applications, and did not provide details of the proofs. This is done in the current paper. The basic control problem for the physical system is hard, and the heavy traffic approach provides much simplification. Owing to the presence of the control, as well as to the fact that the cost function of main interest is “ergodic”, the problem cannot be fully treated with “classical” methods of heavy traffic analysis for queueing networks. A basic result is that the optimal average costs per unit time for the physical problem converge to the optimal cost per unit time for the limit stationary process as the number of sources and the time interval goes to infinity. This convergence is both in the mean and pathwise senses. Furthermore, a “nice” nearly optimal control for the limit system provides nearly optimal values for the physical system, under heavy traffic, in both a mean and pathwise sense. This revised version was published online in June 2006 with corrections to the Cover Date.     

Systematic Modelling Using Lagrangian DAEs

A treatment for formulating equations of motion for discrete engineering systems using a differential-algebraic form of Lagrange's equation is presented. The distinguishing characteristics of this approach are the retention of constraints in the mathematical model and the consequent use of dependent coordinates. A derivation of Lagrange's equation based on the first law of thermodynamics is featured. Nontraditional constraint classifications for Lagrangian differential-algebraic equations (DAEs) are defined. Model formulation is systematic and lays a foundation for developing DAE-based tools and algorithms for applications in dynamic systems and control.     

结点有约束的交通网络最短路径模型

结点有约束的网络是一类特殊的网络，如具有禁止通行限制信息的交通路网等，由于最短路径的求解是有后效性的，经典的Dijkstra算法等不能直接用来求解该问题，本文提出了一种结点有约束的交通网络最短路径建模方法，该方法所建模型为一般网络模型，可用任一传统高效的算法求其最短路径，从根本上降低了问题的复杂性，为很好地解决交通、通信等领域中的此类问题提供了有益的方法。     

驾驶者行为选择对十字路口通行能力的影响

考虑通过十字路口时左转车辆和直行车辆发生路权冲突时的不同驾驶者行为,将驾驶者分为跟随型驾驶者和谦让型驾驶者,并建立了左转车辆通过十字路口的模型,研究不同的驾驶者行为选择对十字路口通行能力的影响。结果表明,随着交通流量逐渐增加,驾驶者的行为越趋向于跟随时,十字路口的通行能力将会急剧降低;而当驾驶者的行为趋向于谦让时,十字路口的通行能力甚至优于有交警指挥的情形。该结果很好地解释了在行车高峰期中国十字路口堵塞的原因,即驾驶者的行为选择,可以通过改变驾驶者的行为选择和在交通流量达到一定的饱和度时派出交警来改善十字路口的通行能力。