This article presents a novel nonlinear adaptive proportional-integral (PI)-like tracking control approach designed for a class of uncertain nonlinear systems. The proposed method offers several key advantages: 1) it maintains a simple PI structure while incorporating nonlinear elements; 2) unlike traditional PI control, which typically employs fixed PI gains and is susceptible to integration saturation, this approach utilizes self-tuning PI gains to effectively eliminate saturation issues, thereby addressing the long-standing windup problem; and 3) it skillfully manages both the transient behavior and steady-state accuracy of the tracking error through a new prescribed performance function that is independent of initial conditions. This ensures that, for any unknown bounded initial tracking errors, the proposed PI-like control can uniformly confine the tracking error to a specified boundary (accuracy) within a predetermined time rather than over an infinite duration. The effectiveness and advantages of this method are validated through simulation results.

Urban Air Mobility (UAM) offers a solution to current traffic congestion by using electric Vertical Takeoff and Landing (eVTOL) vehicles to provide on-demand air mobility in urban areas. Effective traffic management is crucial for efficient operation of UAM systems, especially for high-demand scenarios. In this paper, we present a centralized framework for conflict-free takeoff scheduling of eVTOLs in on-demand UAM systems. Specifically, we provide a scheduling policy, called VertiSync, which jointly schedules UAM vehicles for servicing trip requests and rebalancing, subject to safety margins and energy requirements. We characterize the system-level throughput of VertiSync, which determines the demand threshold at which the average waiting time transitions from being stable to being increasing over time. We show that the proposed policy maximizes throughput for sufficiently large fleet size and if the UAM network has a certain symmetry property. We demonstrate the performance of VertiSync through a case study for the city of Los Angeles, and show that it significantly reduces average passenger waiting time compared to a first-come first-serve scheduling policy.

Intersections are major sources of traffic delays and accidents. Unlike traffic light controlled intersections, signal free intersections coordinate safe vehicle crossings individually, offering greater flexibility and the potential to eliminate unnecessary stops. Moreover, the high cost of installing and maintaining physical traffic signals motivates the adoption of virtual infrastructure enabled by connectivity and fast computation. This paper proposes a centralized intersection controller for real-time coordination of the dynamic traffic demands of Connected and Autonomous Vehicles (CAVs) at a signal free intersection. The proposed method consists of a centralized intersection controller that, in real time, assigns each CAV a safe reference path and desired speed based on its location, current speed, onboard vehicle controller transient response, and origin–destination pair, rather than transmitting direct acceleration commands to individual vehicles. Due to errors in measuring acceleration and susceptibility to delays, tracking a reference speed is a more robust and practical approach as it takes into account the ability of onboard vehicle controllers to track speeds in a more accurate way than accelerations. Moreover, the proposed approach allows vehicles to be in any lane when making left and right turns, which has been shown to improve efficiency. The proposed method was evaluated via multi-agent simulations in the open-source CARLA environment. It was compared against four benchmark methods: fixed- and variable cycle time traffic light controlled intersections (with variable cycle and phase plan optimized using Webster’s method) and two state-of-the-art signal free coordination methods. Results show that the proposed approach reduces average delays and increases the average lane flow rate compared to these benchmarks. As expected, the magnitude of improvement depends on the demand.

With the growth of cities and the expansion of urban populations, traffic congestion has become a major challenge in urban areas. Congestion significantly worsens economic and environmental conditions, and is particularly problematic in areas with heavy truck traffic. In this paper, we introduce a coordinated freight routing system aimed at optimizing the overall utility of the network and alleviating congestion through personalized routing instructions and incentives. This system specifically tailors the allocation of incentives and payments to individual drivers, considering both current traffic conditions and their specific routing pReferences. We employ a mixed logit model with a linear utility specification to model drivers’ route choice preferences and decisions. Participation in the system is voluntary, and the system ensures that for most drivers, the combined expected utility, including incentives, surpasses their anticipated utility under User Equilibrium (UE), thereby motivating a substantial number of drivers to follow the assigned routes. The system collects data on the drivers’ routing choices, subsequently updating estimates of utility parameters based on their recent decisions. Ahierarchical Bayes estimator is used for the estimation of individual-specific utility parameters. By integrating driver behavior into the routing process, our system actively adapts and updates parameters in response to real driver actions, offering a dynamic and accurate representation of evolving driver preferences. Numerical experiments on the Sioux Falls network demonstrate the effectiveness of the proposed method.

The control of a humanoid robot equipped with one arm and multiple fingers, designed primarily for grasping and manipulating various objects, is investigated. Synchronizing the movements of the fingers is a challenging task, as each joint must reach the desired angle simultaneously to ensure a firm grasp. The success of this task hinges on the synchronization of convergence times for each finger joint; otherwise, the object may slip or escape. This challenge is further intensified by uncertainties in the dynamics of the hand or the object. We present decentralized prescribed-time tracking control strategies for the dynamical system comprising the arm-finger combination. In this system, the fingers are primarily used for grasping the object while the arm is responsible for moving, tilting, or flipping it. To streamline the controller structure and simplify the stability analysis, we design a linear controller based on the maximum eigenvalue of a parameter matrix and establish a new technical lemma, which paves the way for the stability analysis of the prescribed-time tracking and the reduction of the input efforts of the actuator. We develop robust and decentralized adaptive control schemes separately for the arm and fingers, achieving better transient performance with less prior knowledge and lower computation costs. Finally, we validate the proposed controller's performance through kinematic simulations of grasping and moving tasks in 3-D, alongside numerical simulations that demonstrate the tracking performance of our algorithm in the joint space.

In this article, a double-channel event-triggered control method is developed for nonlinear uncertain interconnected systems using backstepping techniques, which introduces event-triggering mechanisms at both the sensor and controller sides. Using event-triggering mechanism at the sensor side presents a challenge to the backstepping control design as the discontinuous state/output signals received at the controller side result in nondifferentiable virtual control signals. This challenge becomes more pronounced when considering more general types of event-triggering mechanisms. Compared with existing methods, this article proposes a different idea with three innovative features: 1) the proposed event-triggering mechanism does not require the calculation of virtual control signals at the sensor side before transmitting them to the controller side; 2) the output triggering is considered directly, and there is no need to design separate controllers for the two communication scenarios without and with event-triggering, thereby avoiding the effect of errors caused by processing substitutions; and 3) it necessitates the online update of only one parameter estimator, avoiding the issue of over-parameterization. Finally, we validate the effectiveness and advantages of the proposed decentralized event-triggered control approach through a numerical case study.

Numerous studies have shown the effectiveness of intelligent transportation system techniques such as variable speed limit (VSL), lane change (LC) control, and ramp metering (RM) in freeway traffic flow control. The integration of these techniques has the potential to further enhance the traffic operation efficiency of both freeway and adjacent arterial networks. In this regard, we propose a freeway traffic control (FTC) strategy that coordinates VSL, LC, RM actions using a Q-learning (QL) framework which takes into account arterial traffic characteristics. The signal timing and demands of adjacent arterial intersections are incorporated as state variables of the FTC agent. The FTC agent is initially trained offline using a single-section road network, and subsequently deployed online in a connected freeway and arterial simulation network for continuous learning. The arterial network is assumed to be regulated by a traffic-responsive signal control strategy based on a cycle length model. Microscopic simulations demonstrate that the fully-trained FTC agent provides significant reductions in freeway travel time and the number of stops in scenarios with traffic congestion. It clearly outperforms an uncoordinated FTC and a decentralized feedback control strategy. Even though the FTC agent does not control the arterial traffic signals, it leads to shorter average queue lengths at arterial intersections by taking into account the arterial traffic conditions in controlling freeway traffic. These results motivate a future research where the QL framework will also include the control of arterial traffic signals.

Advances in technologies such as sensors, communications, robotics, computer software and hardware are paving the way of imagining the future of transportation system as well as the roadblocks and challenges that need to be overcome. While there have been considerable technology advancements on the vehicle level the objectives of these successes is driver comfort and marketing rather than solving the problem of congestion and mobility on the system level in general. Vehicle automation is attractive to a small group of users, it opens the way to re-imagine the ownership and use of vehicles but also raises safety concerns and doubts as to whether it will have any benefit to alleviating congestion. On the infrastructure level the traffic network system operates as an open loop system with limited feedback and control leading to a highly unbalanced system in space and time. As a result hidden capacities cannot be utilized by planning in a centralized manner. In order to achieve effective management and control of the transportation system information and data are necessary and can only be generated by connecting the infrastructure with transportation users. In this talk we present examples how this connectivity can be achieved, what is the benefit of making decisions in a centralized coordinated manner versus decentralized approaches involving individual greedy users. These examples will identify the potential benefits in moving from the current system to a more coordinated system where connectivity can be used to improve mobility, reduce costs and the impact on the environment.

Setting up guidance equipment and leaders is widely used as an effective measure to improve the operation and evacuation efficiency of subway stations for the safety of passengers. Cooperation among different kinds of guidance modals can benefit the passenger evacuation process and reduce the operating and management costs as well as the risk of injury to people in subway stations. This article proposes a framework of multimodal cooperative guidance (MMCG) systems for commanding the crowd evacuation in case of emergency, where three types of guidance modes are considered. The bi-level MMCG optimization models are constructed to determine the optimal quantities and initial locations of multimodal guidance. The MMCG schemes are designed by minimizing cost functions taking into account the constraints of the number of guidance, valid coverage, and guiding expectation. An extended social force (SF) model is proposed to study crowd evacuation dynamics with multimodal guidance. The computational experiments are conducted to evaluate the performance of the proposed cooperative guidance schemes at the subway platform scenario. Three unimodal guidance schemes and a contrasted scheme without guidance are also proposed as comparison schemes. The results show that the crowd evacuation efficiency and the utilization ratio of exits are improved by taking into account the cooperation among different guidance modals.

The vast majority of Variable Speed Limit (VSL) control strategies developed in the literature are based on the well-known macroscopic Cell Transmission Model (CTM), where the traffic density can vary in the longitudinal direction, but it is assumed to be constant in the lateral direction. That is, all lateral flows between lanes due to lane changes are ignored. This assumption does not accurately reflect the traffic behavior since every lane behaves differently depending mainly on the intensity of lane changes, especially close to bottleneck locations. Treating a multi-lane roadway as a single lane by the VSL controllers may fail to utilize the full motorway capacity when the bottleneck is active. In this paper, every lane is treated as a separate stream using a multi-lane CTM, where the net lane-changing flow is modeled as an additional unknown term in the conservation equation. The unknown net flow is estimated in real-time, and its estimate at each time is used in calculating the VSL command for each lane. Then, a Lane Change (LC) controller is combined with the VSL to prevent creating a queue in the vicinity of the bottleneck. The stability properties of the closed-loop system are analyzed, where the integrated control scheme guarantees that the lane traffic density operates within the free-flow region of the fundamental diagram. Microscopic simulations based on the commercial software PTV Vissim are used to demonstrate the effectiveness of the proposed control scheme.

Traffic congestion has a negative economic and environmental impact. Traffic conditions become even worse in areas with high volume of trucks. In this paper, we propose a coordinated pricing-and-routing scheme for truck drivers to efficiently route trucks into the network and improve the overall traffic conditions. A basic characteristic of our approach is the fact that we provide personalized routing instructions based on drivers’ individual routing preferences. In contrast with previous works that provide personalized routing suggestions, our approach optimizes over a total system-wide cost through a combined pricing-and-routing scheme that satisfies the budget balance on average property and ensures that every truck driver has an incentive to participate in the proposed mechanism by guaranteeing that the expected total utility of a truck driver (including payments) in case he/she decides to participate in the mechanism, is greater than or equal to his/her expected utility in case he/she does not participate. Since estimating a utility function for each individual truck driver is computationally intensive, we first divide the truck drivers into disjoint clusters based on their responses to a small number of binary route choice questions and we subsequently propose to use a learning scheme based on the Maximum Likelihood Estimation (MLE) principle that allows us to learn the parameters of the utility function that describes each cluster. The estimated utilities are then used to calculate a pricing-and-routing scheme with the aforementioned characteristics. Simulation results in the Sioux Falls network demonstrate the efficiency of the proposed pricing-and-routing scheme.

In 2014, IEEE Intelligent Transportation Systems Society established a Technical Committee on Transportation 5.0 with the mission of promoting and transforming the deployment of advanced and innovative technologies, especially Artificial Intelligence in transportation. This paper briefly summarizes our main research and findings over the last decade. Transportation Foundation Models, Transportation Scenarios Engineering, and Transportation Operating Systems have been identified as the main directions for the research and development of next-generation intelligent transportation systems.

The current freight transportation network is highly unbalanced as routing decisions are made by individual users without coordination. Certain routes may become congested when chosen based on current traffic information without any anticipation that if other users do the same, these routes may become congested. In this paper we show how a centrally coordinated load balancing system that considers all vehicles to be diesel can take into account electric trucks as mixed fleets. The electric trucks impose additional constraints due to the limitation of range, charging time of batteries as well as the dependency of the battery charge on traffic conditions. The use of a co-simulation approach as part of the system accounts for these nonlinear dependencies and provides more realistic cost estimates for the optimization part. Traffic simulation results using a realistic road network reveal the benefits of applying load balancing and show that as the number of electric trucks increases, the emissions reduce; however, due to the cost of charging, their operational costs are not necessarily less than those of the corresponding diesel trucks. For the electric trucks to compete with diesel, charging should occur when drivers are off duty since the cost of charging includes the labor cost of the waiting driver. It is also shown that a centrally coordinated truck routing system that considers the characteristics of electric trucks in mixed fleets can reduce the operational cost of trucks and encourage the deployment of electric trucks in order to reduce emissions and improve air quality.

Variable Speed Limit (VSL) control has been one of the most popular techniques with the potential of smoothing traffic flow, maximizing throughput at bottlenecks, and improving mobility and safety. Despite the substantial research efforts in the application of VSL control, few studies have looked into the effect of the VSL sign distance from the point of an accident or a bottleneck. In this paper, we show that this distance has a significant impact on the effectiveness and performance of VSL control. We propose a rule-based VSL strategy that matches the outflow of the upstream VSL zone with the bottleneck capacity based on a multi-section Cell Transmission Model (CTM). Then, we consider the distance of the upstream VSL zone as a control variable and perform a comprehensive analysis of its impact on the performance of the closed-loop traffic control system based on the multi-section CTM. We develop a lower bound that this distance needs to satisfy in order to guarantee homogeneous traffic density across sections and reduce bottleneck congestion. The bound is verified analytically and demonstrated using microscopic simulation of traffic on I-710 in Southern California. The simulations are used to quantify the benefits on mobility, safety and emissions obtained by selecting the upstream VSL zone distance to satisfy the analytical lower bound. The developed lower bound is a design tool which can be used to tune and improve the performance of VSL controllers.

The automotive industry is at the forefront of the smart, connected and autonomous vehicle (SCAV) revolution to improve social mobility with safety and road utilization. Also, more and more countries and cities have announced restrictions on future internal combustion vehicle sales or use for cleaner transport. Hence, together with urgent demands for highly energy-efficient transport systems, the need to make them safer, greener, and smarter has been increasing rapidly in recent years. As a result, the development of intelligent transportation system (ITS) underpinned by advanced propulsion and innovative control systems is known as a feasible solution to address all of these challenges.

Increasing traffic volume with respect to physical space motivates explicit consideration of space constraint in traffic system analysis. We study dynamics of a system of homogeneous vehicles executing safe vehicle following on a closed single lane ring road. Dynamics of each vehicle is governed by a standard second order model and a two mode vehicle following controller. One mode is cruise control and the other is a constant time headway control for safety; switching between modes is determined by a linear combination of relative distance and speed. We show that there exists a threshold value for the number of vehicles at which the equilibria for inter-vehicle configurations transition from being infinite to being unique. We explicitly characterize the unique equilibrium in the latter case as well as the threshold value for transition in terms of system parameters (road length, constant time headway and free flow speed). We also show that, starting from any initial condition, the inter-vehicle configuration converges to an equilibrium. The threshold value for the number of vehicles is also shown to define the boundary of when the transfer function from external disturbance to error in relative spacing changes.

The efficient use of the road network for freight transport has a big impact on travel times, pollution, and fuel consumption, as well as on the mobility of passenger vehicles. In today's road network, truck drivers make uncoordinated selfish routing decisions, which may easily congest an initially uncongested route as many truck drivers make the same selfish decision by choosing the same route in an effort to minimize their travel time without accounting for the fact that others do the same, given the same available traffic information. In this paper, we propose a coordinated system for truck drivers, using monetary incentives and fees, to balance the traffic load and improve the overall traffic conditions and time delays experienced by both truck and passenger vehicle drivers. The basic characteristics of the mechanisms presented are that they are budget balanced, do not penalize the truck drivers compared to the user equilibrium, and they assume voluntary participation. Two models of voluntary participation are considered: weak and strong voluntary participation. In the first, each one of the drivers prefers all the drivers (including self) to participate in the mechanism than not. In the second model, each one of the truck drivers prefers to participate in the system, provided that all the others do. For each model of voluntary participation, an incentive mechanism is designed. A special emphasis is given to the fairness of the proposed mechanisms. The numerical examples are used to demonstrate the results and the efficiency of the solution techniques.

Providing ride-hailing services with electric vehicles can help reduce greenhouse gas emissions and solve the last mile problem. This paper develops a reinforcement learning based algorithm to operate a community owned electric vehicle fleet, which provides ride-hailing services to local residents. The goals of operating the electric vehicle fleet are to minimize customer waiting time, electricity cost, and operational costs of the vehicles. A novel framework characterized by decentralized learning and centralized decision making is proposed to solve the electric vehicle fleet dispatch problem. The decentralized learning process allows the individual vehicles to share their operating experiences and deep neural network model for state-value function estimation, which mitigates the curse of dimensionality of state and action domains. The centralized decision making framework converts the vehicle fleet coordination problem into a linear assignment problem, which has polynomial time complexity. Numerical study results show that the proposed approach outperforms the benchmark algorithms in terms of societal cost reduction.

In 2015, the Board of Governors of IEEE Intelligent Transportation Systems Society had approved the proposal to name the Best Paper Award in IEEE Transactions on Intelligent Transportation Systems as the George N. Saridis Best Transactions Paper Award. After nearly five years of preparation and planning, and almost one year of hard and concentrated effort by the Award Committee, we are pleased to announce the 2014–2017 George N. Saridis Best Transactions Paper Award for papers published in the IEEE Transactions on Intelligent Transportation Systems.

According to the U.S. Department of Transportation, 36 states are experiencing shortages in rest areas, affecting the truck drivers' ability to comply with working hour regulations. Since expanding the infrastructure would require significant capital investment, this issue points to the need for better utilization of the existing truck parking capacity. In this paper, we present a mixed integer programming (MIP) model for a variant of the truck driver scheduling problem which considers the parking availability of the rest areas along the route, as well as the USA's hours-of-service (HOS) constraints for long trips. The parking availability of each rest area is modeled as a set of time-windows which is enforced only if the driver needs to stop at that location. Computational experiments using CPLEX were performed to test the impact of these constraints. The results indicate that by including parking availability constraints we can generate more realistic schedules, which will not send drivers to busy rest areas, without increasing the cost for the driver/company.

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The sharp increase in e-commerce over the last few years has led to an increase in the volume of trucks both in ports and in commercial areas. Truck traffic has a negative impact on traffic flow in general due to the size of trucks and their slower dynamics. The continuously increasing use of navigation apps has led drivers to make their routing decisions in an independent manner in an effort to minimize their own individual travel time, with possible significant deviation from a socially optimum solution. In this paper, we consider the use of coordinated routing in order to achieve load balancing. Users send their OD matrices as well as their preferred departure time to the coordinator who gives them routing instructions based on a socially optimum cost. This design enables us to derive sufficient conditions under which we prove the existence of mechanisms which are truthful in equilibrium, budget balanced on average and create individual incentives for voluntary participation of the truck drivers. Subsequently, we design our mechanism in a way that only uses a minimal set of sufficient conditions in order to guarantee the existence of a solution and maximize its efficiency. Finally, the extensive simulation results of our approach in the Braess and the Sioux Falls networks demonstrate that the proposed mechanism can approach the system optimum solution.

The complexity and dynamics of multimodal freight transportation networks make the optimum routing of freight demand a challenging task. Route decision-making in a dynamical and complex urban multimodal transportation environment aims to minimize a certain objective cost relying on the accurate prediction of the traffic network states and the estimation of the route costs that are not readily available. The purpose of this paper is to develop a methodology to be used by a central coordinator who generates individual routing decisions for shippers by minimizing overall cost, assuming that all participating shippers send their demands to this central coordinator. We propose, analyze, and evaluate a multimodal freight routing system with hard vehicle availability and capacity constraints based on a hierarchical Co-Simulation Optimization (COSMO) approach. The COSMO approach consists of a simulation layer that provides traffic state predictions and cost estimations to an upper optimization layer that incorporates a load balancing methodology to speed up the convergence of the optimization algorithm. A simulation test bed consisting of a road traffic simulation and a rail simulation model for the Los Angeles/Long Beach Ports regional area is developed and is used to demonstrate the efficiency of the proposed approach.

The adoption of passenger leaders could make crowd evacuation in urban railway transit (URT) stations more efficient. The number, location, and the actions of the leaders are the most important elements in an evacuation strategy and have a great impact on evacuation efficiency. This paper proposes a hybrid bi-level model to optimize the number and initial locations of leaders as well as the routes of leaders during the evacuation, which explicitly incorporates the passengers' guidance demand and multi-leader coordination mechanism. The leaders' initial locations are generated by solving the maximal covering location problem (upper level model) and their evacuation routes are determined by a co-simulation heuristic approach (lower level model). The social force model and its modifications are used to model the dynamics of common evacuees, leaders, and followers in simulation models. The convergence performance of the proposed co-simulation heuristic approach and the effectiveness of the optimal evacuation strategy have been investigated and demonstrated using a case study of a typical island platform of Beijing's URT station. Three other evacuation strategies are considered for comparison purposes in order to show the influence of the number and initial locations of leaders as well as the multi-leader coordination mechanism during the evacuation process. Our analysis supported by simulations shows the following: 1) the optimal number of leaders exists for a given human cost and guidance demand constraints; 2) the distribution of leaders for maximal covering makes the evacuation of the followers more efficient; and 3) the proposed optimal evacuation strategy has better performance in terms of shorter evacuation time and higher utilization of exits compared with other considered strategies.

Welcome to the third issue of the IEEE Transactions on Computational Social Systems (TCSS) for 2018.

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Variable speed limit (VSL) control of highway traffic is expected to improve traffic mobility, safety, and environment, especially during incidents. However, most existing VSL controllers show significant benefits in macroscopic analysis but little improvement in microscopic simulations in terms of traffic mobility. We demonstrate that the lack of improvement for travel time in many incident cases is due to lane changes that are taking place close to the bottleneck leading to severe capacity drop, which is not adequately captured by most macroscopic models. In this paper, we develop a combined lane change and VSL control scheme, which generates consistent improvements both with macroscopic and microscopic models. The lane change controller generates lane change recommendations upstream the incident or bottleneck in order to reduce the effect of the capacity drop. The VSL controller is developed using a feedback linearization approach based on the cell transmission macroscopic model and is shown analytically to guarantee exponential convergence to the optimum equilibrium point. Microscopic Monte Carlo simulations of traffic on the I-710 freeway were used to demonstrate that this combined control strategy is able to generate consistent improvements with respect to travel time, safety, and environmental impact under different traffic conditions and incident scenarios.

The urban traffic network has temporal and spatial characteristics whose changing conditions have often unpredictable effects on the flow of loads that include passengers and freight. As a result, the current traffic network is unbalanced, leading to high and low peaks of traffic in both time and space. The freight transportation chain can utilize these high and low peaks in the road and rail network in order to utilize more effectively available capacity. The purpose of this paper is to develop a coordinated multimodal dynamic freight load balancing (MDFLB) system to balance freight loads across the rail and road network. The MDFLB system collects and updates information from all the shipping companies and assigns freight loads to the available carriers using an optimization model while taking into account current and predicted dynamical changes in the associated networks. Since the freight loads can change the assumed states of the network, namely, the link travel times, which could then render the solution of the optimization problem no longer optimum, an iterative approach is considered involving online network simulation models. The simulation models are used to test and modify the optimization-based load balancing solution and estimate the new states of the network used by the optimizer. This feedback iterative approach guarantees that the overall cost function is non-increasing and it stops when it converges to a minimum or when a stopping criterion is satisfied depending on the time horizon of interest. A simulation case study that focuses on distribution of freight in an area that includes the two major sea ports in Southern California is used to demonstrate the effectiveness of the proposed coordinated MDFLB.

Intersection crossing is a frequent driving maneuver in urban driving. Traffic lights and stop signs force the vehicles to stop and restart causing frustration to drivers. To help the driver in approaching and passing intersections, the vehicle can be equipped with an advanced driver assistance system (ADAS) that utilizes vehicle-to-infrastructure communication. As opposed to centralized traffic light optimization, the in-vehicle system uses information on traffic light location and timing to find an individual optimal driving pace, which is then conveyed to the driver. The conventional approach to the pace optimization problem is to consider such parameters as time of arrival, fuel consumption, and emissions. However, to be effective, the system should take into account another important factor-driver's preferences and driving characteristics to improve the acceptance of the system. In this paper, we propose a personalized pace optimization algorithm for approaching and passing signalized intersections that can be used in ADAS. It learns the personal driver's preferences and characteristics during the course of driving and uses this knowledge to calculate driving pace that improves fuel economy, reduces waiting time, and addresses the driver's preferences. We demonstrate the proposed methodology by comparing the system's recommendations for drivers with different preferences and driving styles. Drivers' features were extracted from data collected on an experimental vehicle. We utilize the drivers' profiles to find an optimal driving pace for a specified route for each particular driver. We validate the methodology by simulation to show fuel economy benefits of the generated speed profiles.

Presents the introductory editorial for this issue of the publication.

Presents the introductory editorial for this issue of the publication.

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Obtaining accurate information about current and near-term future traffic flows of all links in a traffic network has a wide range of applications, including traffic forecasting, vehicle navigation devices, vehicle routing, and congestion management. A major problem in getting traffic flow information in real time is that the vast majority of links is not equipped with traffic sensors. Another problem is that factors affecting traffic flows, such as accidents, public events, and road closures, are often unforeseen, suggesting that traffic flow forecasting is a challenging task. In this paper, we first use a dynamic traffic simulator to generate flows in all links using available traffic information, estimated demand, and historical traffic data available from links equipped with sensors. We implement an optimization methodology to adjust the origin-to-destination matrices driving the simulator. We then use the real-time and estimated traffic data to predict the traffic flows on each link up to 30 min ahead. The prediction algorithm is based on an autoregressive model that adapts itself to unpredictable events. As a case study, we predict the flows of a traffic network in San Francisco, CA, USA, using a macroscopic traffic flow simulator. We use Monte Carlo simulations to evaluate our methodology. Our simulations demonstrate the accuracy of the proposed approach. The traffic flow prediction errors vary from an average of 2% for 5-min prediction windows to 12% for 30-min windows even in the presence of unpredictable events.

Parking guidance and information (PGI) systems are becoming important parts of intelligent transportation systems due to the fact that cars and infrastructure are becoming more and more connected. One major challenge in developing efficient PGI systems is the uncertain nature of parking availability in parking facilities (both on-street and off-street). A reliable PGI system should have the capability of predicting the availability of parking at the arrival time with reliable accuracy. In this paper, we study the nature of the parking availability data in a big city and propose a multivariate autoregressive model that takes into account both temporal and spatial correlations of parking availability. The model is used to predict parking availability with high accuracy. The prediction errors are used to recommend the parking location with the highest probability of having at least one parking spot available at the estimated arrival time. The results are demonstrated using real-time parking data in the areas of San Francisco and Los Angeles.

Safety, capacity, and timely schedules are some of the most crucial objectives in railway operations. Positive train control (PTC) is a concept whose goal is to improve the safety and efficiency of railway operations by using advanced information technologies. Information technologies such as active communications enable the use of a dynamic headway policy, which can increase the track capacity and improve dispatching efficiency in addition to improving safety. In this paper, we propose a dynamic headway system for PTC based on active communications, which we integrate with a dynamic dispatching model in order to improve track capacity and safety in railway operations. We use a simulation model of a rail network in southern California to demonstrate the effectiveness of our proposed approach. The simulation results of different scenarios show reductions in train delays of at least 55% and reductions in travel time of at least 35% when using the dynamic headway versus using a fixed headway.

It is well known that not all drivers drive the same and that the same driver has different driving characteristics with different vehicles. Identifying the characteristics that are unique to each driver/vehicle response opens the way for more personalized and accurate driver assistance systems. In this paper, we consider the problem of identifying the driver/vehicle characteristics by processing real-time driving response data. We propose the use of a Gaussian mixture model combined with the knowledge of dynamic characteristics modeled as probability distributions together with additional logic and appropriate thresholds in order to implement a real-time driver/vehicle response diagnostics system. We focus our efforts on the vehicle-following part of driving. The system is tested on a customized vehicle using different drivers under different driving conditions. We demonstrated that the system can distinguish between different drivers and can classify driver aggressiveness during vehicle following.

A significant issue in vehicular ad hoc networks is the design of an effective broadcast scheme which can facilitate the fast and reliable dissemination of emergency warning messages in the vicinity of an expected event, such as a car accident. In this work we propose a novel solution to this problem, which we refer to as Speed Adaptive Probabilistic Flooding. The scheme employs probabilistic flooding to mitigate the effects of the broadcast storm problem, typical when using blind flooding, and its unique feature is that the rebroadcast probability is regulated adaptively based on the vehicle speed to account for varying traffic densities within the transportation network. The protocol enjoys a number of benefits relative to other approaches: it is simple to implement, it does not introduce additional communication burden, as it relies on local information only and it does not rely on the existence of a positioning system which may not always be available. The scheme is evaluated on different sections of the highway system in the City of Los Angeles using an integrated platform combining the OPNET Modeler and the VISSIM simulator. Simulation results indicate that the proposed scheme fulfills its design objectives as it achieves high reachability and low latency of message delivery in a number of scenarios. Its robustness with respect to changing number of hops and transmission ranges is also demonstrated.

Many congestion control protocols have been recently proposed in order to alleviate the problems encountered by TCP in high-speed networks and wireless links. Protocols utilizing an architecture that is in the same spirit as the ABR service in ATM networks require estimates of the effective number of users utilizing each link in the network to maintain stability in the presence of delays. In this paper, we propose a novel estimation algorithm that is based on online parameter identification techniques and is shown through analysis and simulations to converge to the effective number of users utilizing each link. The algorithm does not require maintenance of per-flow states within the network or additional fields in the packet header, and it is shown to outperform previous proposals that were based on pointwise division in time. The estimation scheme is designed independently from the control functions of the protocols and is thus universal in the sense that it operates effectively in a number of congestion control protocols. It can thus be successfully used in the design of new congestion control protocols. In this paper, to illustrate its universality, we use the proposed estimation scheme to design a representative set of Internet congestion control protocols. Using simulations, we demonstrate that these protocols satisfy key design requirements. They guide the network to a stable equilibrium that is characterized by high network utilization, small queue sizes, and max-min fairness. In addition, they are scalable with respect to changing bandwidths, delays, and number of users, and they generate smooth responses that converge quickly to the desired equilibrium.

Cruise controllers are used to automatically control the speed of motor vehicles. In order to maintain a desired vehicle speed the controller takes over the throttle in a cruise control system which proves particularly useful for long drives. Adaptive Cruise Control (ACC) systems have additional capabilities such as automatic braking or dynamic set-speed type controls and hence can accommodate changes in cruise speed required to adapt to changing road conditions. We propose that further improvement in fuel economy can be achieved by considering the vehicle's longitudinal dynamics as an input-constrained system and the wheel torque as the corresponding constrained input. We effectively address the resulting input saturation nonlinearity by employing our adaptive anti-windup compensator design. Simulation results are used to compare the performance of the original cruise control system allowing for the full-range of wheel torque and the ACC system where the wheel torque is forced to remain within the user-defined limits for improved fuel economy.

Route planning in uncertain and dynamic networks has recently emerged as an active and intense area of research, both due to industry needs and technological advances. This paper investigates methods to predict travel times along the arcs and estimate arrival times at the nodes of a stochastic and dynamic network in real time. It is shown that, under fairly mild conditions, the developed travel and arrival time estimators are unbiased and that the error variance of the arrival time estimator is bounded. Simulation results are used to demonstrate the efficiency of the proposed algorithm.

This introductory article overviews the progress of electrical, electronics, software, and other relevant technologies that shape the modern automobile. Some of these technologies are described in more detail in the articles that comprise this special issue.

Most existing methods for truck route planning assume known static data in an environment that is time varying and uncertain by nature, which limits their widespread applicability. The development of intelligent transportation systems such as the use of information technologies reduces the level of uncertainties and makes the use of more appropriate dynamic formulations and solutions feasible. In this paper, a truck route planning problem called stochastic traveling salesman problem with time windows (STSPTW) in which traveling times along roads and service times at customer locations are stochastic processes is investigated. A methodology is developed to estimate the truck arrival time at each customer location. Using estimated arrival times, an approximate solution method based on dynamic programming is proposed. The algorithm finds the best route with minimum expected cost while it guarantees certain levels of service are met. Simulation results are used to demonstrate the efficiency of the proposed algorithm

In this paper, longitudinal vehicle-following controllers for heavy trucks with different spacing policies are designed, analyzed, simulated, and experimentally tested, and their performance in mixed traffic with passenger vehicles is evaluated. A new vehicle-following controller for trucks, which has better properties than existing ones with respect to performance and impact on fuel economy and pollution during traffic disturbances, is developed. The response of trucks to disturbances caused by lead passenger vehicles is smooth due to the limited acceleration capabilities of trucks whether they are manual or equipped with adaptive cruise control (ACC) systems. Vehicles following the truck are therefore presented with a smoother speed trajectory to track. This filtering effect of trucks is shown to have beneficial effects on fuel economy and pollution. However, it creates large intervehicle gaps that invite cut-ins from neighboring lanes, creating additional disturbances. These cut-ins, under certain realistic scenarios, may reduce any benefits obtained by the smooth response of trucks as well as increase travel time. The results of this paper indicate possible benefits trucks may have in mixed traffic and also reinforces what is already known-that trucks could be detrimental to traffic flow

Almost every automobile company is producing vehicles with adaptive cruise control (ACC) system onboard that enables a vehicle to do automatic vehicle following in the longitudinal direction. The ACC system is designed for driver's comfort and safety and to operate with manually driven vehicles. These characteristics of ACC were found to have beneficial effects on the environment and traffic flow characteristics by acting as filters of a wide class of traffic disturbances. It has been argued that the smooth response of ACC vehicles to high-acceleration disturbances or large position errors creates large gaps between the ACC vehicle and the vehicle ahead inviting cut-ins and therefore generating additional disturbances that would not have been created if all vehicles had been manually driven. In this paper, we examine the effect of lane changes on the benefits suggested by Bose and Ioannou as well as the sensitivity of these benefits with respect to various variables such as the penetration of the ACC vehicles, level of traffic disturbances etc. We demonstrate, using theory, simulations, and experiments, that during lane changes, the smoothness of the ACC vehicle response attenuates the disturbances introduced by a cut-in or an exiting vehicle in a way that is beneficial to the environment when compared with similar situations where all vehicles are manually driven. We concluded that a higher number of possible cut-ins that may occur due to the larger gaps created during high-acceleration maneuvers by the vehicle in front of the ACC vehicle, will not annul the benefits obtained in the absence of such cut-ins when compared with the situation of similar maneuvers but with no cut-ins in the case of 100% manually driven vehicles.

The growing demand of computer usage requires efficient ways of managing network traffic in order to avoid or at least limit the level of congestion in cases where increases in bandwidth are not desirable or possible. In this paper we developed and analyzed a generic Integrated Dynamic Congestion Control (IDCC) scheme for controlling traffic using information on the status of each queue in the network. The IDCC scheme is designed using nonlinear control theory based on a nonlinear model of the network that is generated using fluid flow considerations. The methodology used is general and independent of technology, as for example TCP/IP or ATM. We assume a differentiated-services network framework and formulate our control strategy in the same spirit as IP DiffServ for three types of services: Premium Service, Ordinary Service, and Best Effort Service. The three differentiated classes of traffic operate at each output port of a router/switch. An IDCC scheme is designed for each output port, and a simple to implement nonlinear controller, with proven performance, is designed and analyzed. Using analysis performance bounds are derived for provable controlled network behavior, as dictated by reference values of the desired or acceptable length of the associated queues. By tightly controlling each output port, the overall network performance is also expected to be tightly controlled. The IDCC methodology has been applied to an ATM network. We use OPNET simulations to demonstrate that the proposed control methodology achieves the desired behavior of the network, and possesses important attributes, as e.g., stable and robust behavior, high utilization with bounded delay and loss, together with good steady-state and transient behavior.

With the development of near term automatic vehicles following concepts such as intelligent cruise control (ICC) and cooperative driving, vehicles will be able to automatically follow each other in the longitudinal direction. The modeling of traffic flow consisting of such vehicles is important for analyzing the effects of vehicle automation on the characteristics of traffic flow and for suggesting macroscopic control strategies to improve efficiency. Such analysis may also suggest ways for modifying the vehicle control characteristics in order to improve the macroscopic behavior of traffic. In this paper, we developed a mesoscopic and macroscopic model that describes the automated traffic-flow dynamics in a single highway lane. The mesoscopic model describes the speed and density continuously in time and space and at the same time retains the microscopic characteristics of traffic flow. The macroscopic model describes the average speed and density at each section of the lane and at each point in time. Even though the macroscopic model does not retain the microscopic characteristics of the vehicular traffic, computationally it is much simpler than the mesoscopic one. Simulations are used to demonstrate the effectiveness of these models in describing traffic-flow characteristics. The developed models indicate some similarities, but also some fundamental differences with existing traffic-flow models for manually driven vehicles.

The introduction of semiautomated vehicles designed to operate with manually driven vehicles is a realistic near-term objective. The purpose of this paper is to analyze the effects on traffic-flow characteristics and environment when semiautomated vehicles with automatic vehicle following capability (in the same lane) operate together with manually driven vehicles. We have shown that semiautomated vehicles do not contribute to the slinky effect phenomenon when the lead manual vehicle performs smooth acceleration maneuvers. We have demonstrated that semiautomated vehicles help smooth traffic flow by filtering the response of rapidly accelerating lead vehicles. The accurate speed tracking and the smooth response of the semiautomated vehicles designed for passenger comfort reduces fuel consumption and levels of pollutants of following vehicles. This reduction is significant when the lead manual vehicle performs rapid acceleration maneuvers. We have demonstrated using simulations that the fuel consumption and pollution levels present in manual traffic can be reduced during rapid acceleration transients by 28.5% and 1.5%-60.6%, respectively, due to the presence of 10% semiautomated vehicles. These environmental benefits are obtained without any adverse effects on the traffic-flow rates. Experiments with actual vehicles are used to validate the theoretical and simulation results.

Due to the boom in world trade, port authorities are looking into ways of making existing facilities more efficient. One way to improve efficiency, increase capacity, and meet future demand is to use advanced technologies and automation in order to speed up terminal operations. In this paper, we design, analyze, and evaluate four different automated container terminal (ACT) concepts. These concepts include automated container terminals based on the use of automated guidance vehicles (AGVs), a linear motor conveyance system (LMCS), an overhead grid rail system (GR), and a high-rise automated storage and retrieval structure (AS/RS). We use future demand scenarios to design the characteristics of each terminal in terms of configuration, equipment and operations. A microscopic simulation model is developed and used to simulate each terminal system for the same operational scenario and evaluate its performance. A cost model is used to evaluate the cost associated with each terminal concept. Our results indicate that automation could improve the performance of conventional terminals substantially and at a much lower cost. Among the four concepts considered the one based on automated guidance vehicles is found to be the most effective in terms of performance and cost.

The growing demand of computer usage requires efficient ways of managing network traffic in order to avoid or at least limit the level of congestion in cases where increases in bandwidth are not desirable or possible. Using non-linear control theory we developed and analysed a generic integrated dynamic congestion control (IDCC) scheme for controlling traffic using information on the status of each queue in the network. The IDCC scheme is based on a nonlinear model of the network that is generated using fluid flow considerations. The methodology used is general and independent of technology, as for example TCP/IP or ATM. We assume a differentiated-services network framework and formulate our control strategy in the same spirit as IP Diff-Serv for three types of services: premium service, ordinary service, and best effort service. The three differentiated classes of traffic operate at each output port of a router/switch. An IDCC scheme is designed for each output port, and a powerful, simple to implement controller is designed and analysed. The IDCC methodology has been applied to an ATM network. We use OPNET simulations to demonstrate that the proposed control methodology achieves the desired behaviour of the network, and possesses important attributes, such as: stable and robust behaviour, high utilisation with bounded delay and loss performance, and good steady state and transient behaviour.

Designing effective congestion control strategies for broadband networks is known to be difficult because of the variety of dynamic parameters involved such as link speeds, burstiness of the traffic, and the distances between traffic sources and switching nodes. We propose a traffic management scheme, which is insensitive to the propagation delay between the sources and switching nodes. We achieve this by combining connection admission control with a bandwidth allocation strategy. By seeking the cooperation of the available bit rate (ABR) sources (to limit their peak cell rate, PCR) at the time of admission of a variable bit rate (VBR) source to the network, the scheme eliminates the continuous congestion control. Our simulations show that the strategy is able to eliminate the ABR cell losses, achieves effective server and buffer utilization, and provides bounded delays to the VBR traffic (unlike the case without any controls).

We extend earlier results on the combined control problem of connection admission, flow rate, and bandwidth allocation (capacity, service-rate) in ATM based networks under nonstationary conditions to the multiple node path case. We propose a nonlinear switching strategy, and show that it is effective in controlling a mix of ABR and guaranteed traffic. We derive bounds on the mix between ABR and guaranteed traffic to ensure that control strategy is effective. The derived strategy is insensitive to propagation delay along the path, and via the setting of the values of the control design variables it can be used to influence the delivered quality of service (QoS). The performance of the proposed scheme is evaluated using analysis and simulation. Simulation results show that the proposed scheme achieves effective server and buffer utilisation (as predicted by analysis) and achieves prescribed bounded delays (for guaranteed traffic only) and zero cell loss (even for ABR traffic demands exceeding server capacity, which if not controlled would cause cell loss). Thus proposed scheme delivers guaranteed QoS to users, avoids retransmissions, and as a result increases the network utilisation and customer satisfaction.

We consider the combined control problem of connection admission, flow rate, and bandwidth allocation (capacity, service-rate) under nonstationary conditions. A fluid flow model in state variable form describes the time varying mean behaviour of available bit rate (ABR) traffic, which competes with guaranteed traffic for network resources. Using nonlinear control we derive an integrated control strategy, for the finite buffer and finite server case, that is insensitive to any propagation delay. We also derive bounds on mix between ABR and guaranteed traffic to ensure that control strategy is effective, and select control design variables which can be used to influence the delivered QoS. The performance of proposed scheme is evaluated using analysis and simulation. Simulation results show that it achieves effective server and buffer utilisation (as predicted by analysis) and by appropriate choice of control design variables achieves prescribed bounded delays (for guaranteed traffic only) and zero cell loss (even for ABR traffic demands exceeding server capacity, which if not controlled would cause losses). Hence proposed scheme delivers guaranteed QoS to user and avoids retransmissions, thus increasing network utilisation.

Several continuous-time and discrete-time recurrent neural network models have been developed and applied to various engineering problems. One of the difficulties encountered in the application of recurrent networks is the derivation of efficient learning algorithms that also guarantee the stability of the overall system. This paper studies the approximation and learning properties of one class of recurrent networks, known as high-order neural networks; and applies these architectures to the identification of dynamical systems. In recurrent high-order neural networks, the dynamic components are distributed throughout the network in the form of dynamic neurons. It is shown that if enough high-order connections are allowed then this network is capable of approximating arbitrary dynamical systems. Identification schemes based on high-order network architectures are designed and analyzed.

A class of feedforward neural networks, structured networks, has recently been introduced as a method for solving matrix algebra problems in an inherently parallel formulation. A convergence analysis for the training of structured networks is presented. Since the learning techniques used in structured networks are also employed in the training of neural networks, the issue of convergence is discussed not only from a numerical algebra perspective but also as a means of deriving insight into connectionist learning. Bounds on the learning rate are developed under which exponential convergence of the weights to their correct values is proved for a class of matrix algebra problems that includes linear equation solving, matrix inversion, and Lyapunov equation solving. For a special class of problems, the orthogonalized back-propagation algorithm, an optimal recursive update law for minimizing a least-squares cost functional, is introduced. It guarantees exact convergence in one epoch. Several learning issues are investigated.

A complete tutorial review of the entire field is presented, beginning with simple instability examples to identify the causes of nonrobust behavior in adaptive control. Some of the mathematical groundwork is presented, and the theory for the design and analysis of adaptive laws is developed. Commonly used adaptive controller structures are discussed, highlighting their particular robustness properties. Particular attention is paid to model reference, pole placement, and linear quadratic controller structures. Designs and analyses of model reference, pole placement, and linear quadratic controllers, based on combining the corresponding controller structures with the various robust adaptive laws, are presented. Suggestions for future research are given.>

The robustness of adaptive controllers with respect to uncertainty is examined. The uncertainties include bounded input disturbances, unknown and time-varying plant parameters, and unmodeled dynamics. A simple example shows instability of a recent manipulator control scheme in the presence of bounded disturbances. The adaptive laws for updating the controller parameters are modified so that instabilities are counteracted and robustness is guaranteed.>

The problem of routing virtual circuits according to dynamical probabilities in virtual-circuit packet-switched networks is considered. Queueing network models are introduced and performance measures are defined. A decentralized asynchronous adaptive routing methodology based on learning automata theory is presented. Every node in the network has a stochastic learning automaton as a router for every destination node. The routing probabilities that are assigned to the network paths are updated asynchronously on the basis of current network conditions. A learning algorithm suitable for routing is used. Some initial simulation experiments, for a simple network, show convergence to optimal routing.>