Surface prediction and control algorithms for anti-lock brake system

Anti-lock brake system (ABS) has been designed to achieve maximum deceleration by preventing the wheels from locking. The friction coefficient between tyre and road is a nonlinear function of slip ratio and varies for different road surfaces. In this paper, methods have been developed to predict these different surfaces and accordingly control the wheel slip to achieve maximum friction coefficient for different road surfaces. The surface prediction and control methods are based on a half car model to simulate high speed braking performance. The prediction methods have been compared with the results available in the literature. The results show the advantage of ABS with surface prediction as compared to ABS without proper surface identification. Finally, the performance of the controller developed in this paper has been compared with four different ABS control algorithms reported in the literature. The accuracy of prediction by the proposed methods is very high with error in prediction in a range of 0.17-2.4%. The stopping distance is reduced by more than 3% as a result of prediction for all surfaces.     

Decision tree method for modeling travel mode switching in a dynamic behavioral process

As road congestion is exacerbated in most metropolitan areas, many transportation policies and planning strategies try to nudge travelers to switch to other more sustainable modes of transportation. In order to better analyze these strategies, there is a need to accurately model travelers’ mode-switching behavior. In this paper, a popular artificial intelligence approach, the decision tree (DT), is used to explore the underlying rules of travelers’ switching decisions between two modes under a proposed framework of dynamic mode searching and switching. An effective and practical method for a mode-switching DT induction is proposed. A loss matrix is introduced to handle class imbalance issues. Important factors and their relative importance are analyzed through information gains and feature selections. Household Travel Survey data are used to implement and validate the proposed DT induction method. Through comparison with logit models, the improved prediction ability of the DT models is demonstrated.     

基于半车模型的ABS滑模变结构控制仿真研究

由于汽车轮胎动力学的非线性,对防抱死制动系统采用了滑模变结构控制方法进行控制,滑模控制能较好地解决非线性系统的控制问题,但其固有的抖动会影响控制效果。文章介绍采用一种基于指数趋近律的滑模控制方法,针对两轮车辆的防抱死制动系统设计了滑模控制器,并在Matlab/Simulink里进行了仿真。仿真结果表明了该策略的可行性和有效性,并且能较好的抑制控制系统的抖动现象。     

Coordinated cruise control for high-speed train movements based on a multi-agent model

This paper investigates the coordinated cruise control strategy for multiple high-speed trains’ movement. The motion of an ordered set of high-speed trains running on a railway line is modeled by a multi-agent system, in which each train communicates with its neighboring trains to adjust its speed. By using the potential fields and LaSalles invariance principle, we design a new coordinated cruise control strategy for each train based on the neighboring trains’ information, under which each train can track the desired speed, and the headway distances between any two neighboring trains are stabilized in a safety range. Numerical examples are given to illustrate the effectiveness of the proposed methods.     

Development of control models for the planning of sustainable transportation systems

Planning of sustainable transportation systems requires integration of multiple systems while considering a holistic approach. A limited amount of research has been conducted that simultaneously considers all the transportation, economic activity, environmental and social effects. The proposed research envisages incorporating considerations related to sustainability and providing solutions to stakeholders in policy making. In this paper, a dynamic model for planning and development of sustainable transportation systems is presented. This is given by a system of three nonlinear differential equations representing the dynamics of the three independent states, namely, transportation, activity, and environmental systems. A policy scenario considering investment in energy efficient technologies and its effects on the states is discussed to assist making investment decisions. Optimal control techniques are used to design the controls. The results show that it is possible to formulate an optimal control to achieve the desired target. Numerical results, based on actual parameters, are presented to illustrate the long-term trends of the states. The methodology discussed in this paper will be helpful to decision makers in making optimal decisions. The contribution of this research work is the introduction of a systems and controls methodology to develop optimal policies for the design of sustainable systems.     

Quasi-optimal feedback control for a system of oversaturated intersections

Oversaturated intersection control is a long-standing problem in traffic science and engineering. The problem becomes even harder when we consider a system of oversaturated intersections. Most of the research works in this area are off-line studies that require fully knowledge of origin–destination demand, which would be difficult to obtain in reality. Although several on-line feedback control methods are proposed, they only aim at preventing queue spillover, not able to minimize vehicular delay time. Moreover, these on-line control strategies are not theoretically evaluated how optimal (or sub-optimal) they are. We propose in this paper a quasi-optimal decentralized QUEUE-based feedback (abbreviated as QUEUE) control strategy for a system of oversaturated intersections. The QUEUE strategy is applied cycle-by-cycle based on measurement of current queue sizes, but its overall result is able to approximate the optimal one derived from off-line studies. Details of the feedback control laws for upstream and downstream intersections, in the queueing period and the queue dissipation period, are discussed. Superior to the existing feedback control strategies, the upper bounds of sub-optimality of the QUEUE strategy generating from demand fluctuation and coupling of intersections are specified quantitatively. It is also theoretically proved that the queue measurement error or demand estimation error would not be amplified by the QUEUE strategy. Numerical examples show that the QUEUE strategy performs very well and is robust to errors.     

Ensemble based traffic light control for city zones using a reduced number of sensors

Rapid advances in computing, sensing and telecommunication technology offer unprecedented opportunities for artificial intelligence concepts to expand their applications in the field of traffic management and control. Our methodology gravitates around a powerful decision-making method: ensemble-based systems. This technique is used to accurately classify the near future traffic conditions and to make efficient decisions for adapting the traffic lights sequences within an urban area to optimize the traffic flows. The proposed approach requires only measurements provided by traffic sensors located along the principal roads entering the zone. This reduced number of sensors are considered to be enough relevant for classifying the near future state of the traffic and moreover, their measurements can be validated through analytical/hardware redundancy. Our methodology is meant to be implemented within the framework of a wireless sensor and actuator network and is confirmed by computer simulation, including normal or abnormal traffic conditions, for the central part of the city of Timisoara-Romania.     

Distributed model predictive control for vessel train formations of cooperative multi-vessel systems

Recently, the cooperative control of multiple vessels has been gaining increasing attention because of the potential robustness, reliability and efficiency of multi-agent systems. In this paper, we propose the concept of Cooperative Multi-Vessel Systems (CMVSs) consisting of multiple coordinated autonomous vessels. We in particular focus on the so-called Vessel Train Formation (VTF) problem. The VTF problem considers not only cooperative collision avoidance, but also grouping of vessels. An MPC-based approach is proposed for addressing the VTF problem. A centralized and a distributed formulation based on the Alternating Direction of Multipliers Method (ADMM) are investigated. The distributed formulation adopts a single-layer serial iterative architecture, which gains the benefits of reduced communication requirements and robustness against failures. The impacts of information updating sequences and responsibility parameters are discussed. We furthermore analyze the scalability of the proposed method. Simulation experiments of a CMVS navigating from different terminals in the Port of Rotterdam to inland waterways are carried out to illustrate the effectiveness of our method. The proposed method successfully steers the vessels from different origins to form a vessel train. Due to the effective communication, vessels can timely respond to the velocity changes that others make. After the formation is formed, the distances between vessels become constant. The results show the potential to use CMVSs for inland shipping with enhanced safety.     

Analytic hierarchy process application in selecting the mode of transport for a logistics company

As a multi‐criteria decision‐making (MCDM) method, the analytic hierarchy process (AHP) has been used considerably to solve hierarchical or network‐based decision problems in socio‐economic fields. Following an in‐depth explanation of the transport function in logistics and an overview of the MCDM methods, the AHP model is employed in the paper for a logistics company in selecting the most suitable way of transportation between two given locations in Turkey. The criteria used in the selection of transportation modes are identified as the cost, speed, safety, accessibility, reliability, environmental friendliness, and flexibility. Several cost parameters (transportation, storage, handling, bosphorus crossover) are incorporated into the decision‐making process. The application is carried out in instructional character. The results of the study indicate that the railway transportation, which is not widely used in Turkey, is also an alternative and suitable means of transportation. Copyright © 2013 John Wiley & Sons, Ltd.     

International private and public reinforcing dependencies for the innovation of automotive emission control systems in Japan and USA

In the beginning of the 1970s, the economies of USA and Japan were growing fast and environmental pollution was increasing to alarming levels. As passenger car emissions were found to be significant and rapidly increasing, their reduction was specially targeted. Following a bill passed by US Congress in 1968, requirements were set in 1970 for the vehicle manufacturers to reduce the emissions of carbon monoxide (CO) and hydrocarbons (HC) with 90% by 1975, and nitrogen oxides (NO_x) with 90% by 1976. These requirements were soon adapted to the Japanese regulatory framework, and were known in both countries as the “Muskie Act” or “Muskie Law” after the senator who developed the original bill.The new requirements spurred tremendous research and development efforts. Car manufacturers and research institutions in USA, Japan and Europe investigated and developed alternative solutions, including gas turbine and steam engine vehicles. California, the USA state with the most severe air quality problems and the only state at the time allowed to establish more strict requirements than federal regulation, established requirements implying the use of oxidation catalysts in 1975 and three-way catalysts (TWC’s) in 1977. Japan as a nation adopted similar requirements 1976 and 1978. Export of cars from Japan to USA increased rapidly. The rest of USA adopted emission standards similar to California’s only in 1981, timing USA vehicle sales rebound after the energy crisis and grave economic downturn. Strict requirements were thus established only after more than a decade of civic and legal processes between federal authorities, the car manufacturers and NGO’s.The history of vehicle development is one of cooperation and competition. This paper argues that the international cooperation on different levels of society (government, industry and science) together with commercial competition between the two countries was strong, continuous and instrumental in enabling the development of technology, appropriate regulation and infrastructural changes and thus created a market for cleaner cars and effectively reduced emissions from the growing vehicle fleet. In other words, the introduction of TWCs was reinforced by the simultaneous development of mitigating technology in two car producing countries competing for market space.     

Evaluation of a model predictive control framework for motorway traffic involving conventional and automated vehicles

A Model Predictive Control (MPC) strategy for motorway traffic management, which takes into account both conventional control measures and control actions executed by vehicles equipped with Vehicle Automation and Communication Systems (VACS), is presented and evaluated using microscopic traffic simulation. A stretch of the motorway A20, which connects Rotterdam to Gouda in the Netherlands, is taken as a realistic test bed. In order to ensure the reliability of the application results, extensive speed and flow measurements, collected from the field, are used to calibrate the site’s microscopic traffic simulation model. The efficiency of the MPC framework, applied to this real sizable and complex network under realistic traffic conditions, is examined for different traffic conditions and different penetration rates of equipped vehicles. The adequacy of the control application when only VACS equipped vehicles are used as actuators, is also considered, and the related findings underline the significance of conventional control measures during a transition period or in case of increased future demand.     

Development and evaluation of a knowledge-based system for traffic congestion management and control

This paper describes a real-time knowledge-based system (KBS) for decision support to Traffic Operation Center personnel in the selection of integrated traffic control plans after the occurrence of non-recurring congestion, on freeway and arterial networks. The uniqueness of the system, called TCM, lies in its ability to cooperate with the operator, by handling different sources of input data and inferred knowledge, and providing an explanation of its reasoning process. A data fusion algorithm for the analysis of congestion allows to represent and interpret different types of data, with various levels of reliability and uncertainty, to provide a clear assessment of traffic conditions. An efficient algorithm for the selection of control plans determines alternative traffic control responses. These are proposed to an operator, along with an explanation of the reasoning process that led to their development and an estimation of their expected effect on traffic. The validation of the system, which is one of only few examples of validation of a KBS in transportation, demonstrates the validity of the approach. The evaluation results, in a simulated environment demonstrate the ability of TCM to reduce congestion, through the formulation of traffic diversion and control schemes.     

Hierarchical control strategies for energy management of connected hybrid electric vehicles in urban roads

This paper presents a fuel efficient control strategy for a group of connected hybrid electric vehicles (HEVs) in urban road conditions. A hierarchical control architecture is proposed in this paper for every HEV, where the higher level and the lower level controller share information with each other and solve two different problems that aim at improving its fuel efficiency. The higher level controller of each HEV is considered to utilize traffic light information, through vehicle to infrastructure (V2I) communication, and state information of the vehicles in its near neighborhood, via vehicle to vehicle (V2V) communication. Apart from that, the higher level controller of each HEV uses the recuperation information from the lower level controller and provides it the optimal velocity profile by solving its problem in a model predictive control framework. Each lower level controller uses adaptive equivalent consumption minimization strategy (ECMS) for following their velocity profiles, obtained from the higher level controller, in a fuel efficient manner. In this paper, the vehicles are modeled in Autonomie software and the simulation results are provided in the paper that shows the effectiveness of the proposed control architecture.     

Development of a simulation platform for safety impact analysis considering vehicle dynamics,sensor errors,and communication latencies: Assessing cooperative adaptive cruise control under cyber attack

While safety is one of the most critical contributions of Cooperative Adaptive Cruise Control (CACC), it is impractical to assess such impacts in a real world. Even with simulation, many factors including vehicle dynamics, sensor errors, automated vehicle control algorithms and crash severity need to be properly modeled. In this paper, a simulation platform is proposed which explicitly features: (i) vehicle dynamics; (ii) sensor errors and communication delays; (iii) compatibility with CACC controllers; (iv) state-of-the-art predecessor leader following (PLF) based cooperative adaptive cruise control (CACC) controller; and (v) ability to quantify crash severity and CACC stability. The proposed simulation platform evaluated the CACC performance under normal and cybersecurity attack scenarios using speed variation, headway ratio, and injury probability. The first two measures of effectiveness (MOEs) represent the stability of CACC platoon while the injury probability quantifies the severity of a crash. The proposed platform can evaluate the safety performance of CACC controllers of interest under various paroxysmal or extreme events. It is particularly useful when traditional empirical driver models are not applicable. Such situations include, but are not limited to, cyber-attacks, sensor failures, and heterogeneous traffic conditions. The proposed platform is validated against data collected from real field tests and tested under various cyber-attack scenarios.