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Traffic engineering aims to control infrastructure and population behavior to achieve optimal usage of road networks. Fairness is fundamental to stimulate cooperation in large populations, and plays an important role in traffic engineering, as it increases the well-being of users, improves driving safety by rule-adherence, and overcomes public resistance at legislative implementation. Despite the importance of fairness, only a few works have translated fairness into the transportation domain, with a focus on transportation planning rather than traffic engineering. This work highlights the importance of fairness when solving conflicts of large populations for scare, public good, road-network resources with traffic engineering, and establishes a connection to the modern fairness theories. Moreover, this work presents a fairness framework that serves when designing traffic engineering solutions, when convincing in public debates with a useful, argumentative tool-set to confront equity considerations, and enables systematic research and design of control systems.

City road infrastructure is a public good, and over-consumption by self-interested, rational individuals leads to traffic jams. Congestion pricing is effective in reducing demand to sustainable levels, but also controversial, as it introduces equity issues and systematically discriminates lower-income groups. Karma is a non-monetary, fair, and efficient resource allocation mechanism, that employs an artificial currency different from money, that incentivizes cooperation amongst selfish individuals, and achieves a balance between giving and taking. Where money does not do its job, Karma achieves socially more desirable resource allocations by being aligned with consumers' needs rather than their financial power. This work highlights the value proposition of Karma, gives guidance on important Karma mechanism design elements, and equips the reader with a useful software framework to model Karma economies and predict consumers' behaviour. A case study demonstrates the potential of this feasible alternative to money, without the burden of additional fees.

Monetary markets serve as established resource allocation mechanisms, typically achieving efficient solutions with limited information. However, they are susceptible to market failures, particularly under the presence of public goods, externalities, or inequality of economic power. Moreover, in many resource allocating contexts, money faces social, ethical, and legal constraints. Consequently, research increasingly explores artificial currencies and non-monetary markets, with Karma emerging as a notable concept. Karma, a non-tradeable, resource-inherent currency for prosumer resources, operates on the principles of contribution and consumption of specific resources. It embodies fairness, near incentive compatibility, Pareto-efficiency, robustness to population heterogeneity, and can incentivize a reduction in resource scarcity. The literature on Karma is scattered across disciplines, varies in scope, and lacks of conceptual clarity and coherence. Thus, this study undertakes a comprehensive review of the Karma mechanism, systematically comparing its resource allocation applications and elucidating overlooked mechanism design elements. Through a systematic mapping study, this review situates Karma within its literature context, offers a structured design parameter framework, and develops a road-map for future research directions.

Automated vehicle technologies offer a promising avenue for enhancing traffic efficiency, safety, and energy consumption. Among these, Adaptive Cruise Control (ACC) systems stand out as a prevalent form of automation on today's roads, with their time gap settings holding paramount importance. While decreasing the average time headway tends to enhance traffic capacity, it simultaneously raises concerns regarding safety and string stability. This study introduces a novel variable time gap feedback control policy aimed at striking a balance between maintaining a minimum time gap setting under equilibrium car-following conditions, thereby improving traffic capacity, while ensuring string stability to mitigate disturbances away from the equilibrium flow. Leveraging nonlinear $H_\infty$ control technique, the strategy employs a variable time gap component as the manipulated control signal, complemented by a constant time gap component that predominates during car-following equilibrium. The effectiveness of the proposed scheme is evaluated against its constant time-gap counterpart calibrated using field platoon data from the OpenACC dataset. Through numerical and traffic simulations, our findings illustrate that the proposed algorithm effectively dampens perturbations within vehicle platoons, leading to a more efficient and safer mixed traffic flow.

The optimal operation of transportation networks is often susceptible to unexpected disruptions, such as traffic incidents and social events. Many established control strategies rely on mathematical models that struggle to cope with real-world uncertainties, leading to a significant decline in effectiveness when faced with substantial disruptions. While previous research works have dedicated efforts to improving the robustness or resilience of transportation systems against disruptions, this paper applies the cutting-edge concept of antifragility to better design a traffic control strategy for urban road networks. Antifragility sets itself apart from robustness and resilience as it represents a system's ability to not only withstand stressors, shocks, and volatility but also thrive and enhance performance in the presence of such adversarial events. Hence, modern transportation systems call for solutions that are antifragile. In this work, we propose a model-free deep Reinforcement Learning (RL) scheme to control a two-region urban traffic perimeter network. The system exploits the learning capability of RL under disruptions to achieve antifragility. By monitoring the change rate and curvature of the traffic state with the RL framework, the proposed algorithm anticipates imminent disruptions. An additional term is also integrated into the RL algorithm as redundancy to improve the performance under disruption scenarios. When compared to a state-of-the-art model predictive control approach and a state-of-the-art RL algorithm, our proposed method demonstrates two antifragility-related properties: (a) gradual performance improvement under disruptions of constant magnitude; and (b) increasingly superior performance under growing disruptions., Comment: 32 pages, 13 figures

Major cities worldwide experience problems with the performance of their road transportation systems, and the continuous increase in traffic demand presents a substantial challenge to the optimal operation of urban road networks and the efficiency of traffic control strategies. The operation of transportation systems is widely considered to display fragile property, i.e., the loss in performance increases exponentially with the linearly increasing magnitude of disruptions. Meanwhile, the risk engineering community is embracing the novel concept of antifragility, enabling systems to learn from historical disruptions and exhibit improved performance under black swan events. In this study, based on established traffic models, namely fundamental diagrams and macroscopic fundamental diagrams, we first conducted a rigorous mathematical analysis to prove the fragile nature of the systems theoretically. Subsequently, we propose a skewness-based indicator that can be readily applied to cross-compare the degree of fragility for different networks solely dependent on the MFD-related parameters. At last, by taking real-world stochasticity into account, we implemented a numerical simulation with realistic network data to bridge the gap between the theoretical proof and the real-world operations, to reflect the potential impact of uncertainty on the fragility of the systems. This work aims to demonstrate the fragile nature of road transportation systems and help researchers better comprehend the necessity to consider explicitly antifragile design for future traffic control strategies., Comment: 44 pages, 15 figures

We propose a simple and effective method to derive the Fundamental Diagram (FD) from platoon vehicle trajectories. Average traffic state variables are computed using Edie's generalized definitions within time-dependent trapezoidal space-time areas. To obtain a clear FD, we employ a bivariate data aggregation technique to eliminate scatter. Our findings are as follows: (i) The proposed method demonstrates a remarkably consistent relation between the traffic variables and a clear triangular shape for autonomously-driven vehicles. (ii) The FDs are invariant to several factors of heterogeneity such as the platoon length, vehicle characteristics, road particularities, and data acquisition accuracy. (iii) ACC-driven vehicle platoons with minimum headway setting achieve much higher capacity, roughly 90\% than those with a large headway setting. (iv) Connectivity might increase capacity. (v) Human drivers have a wider near-capacity operation area, showing different behaviors at high speeds than low ones, and (vi) Safety concerns might arise due to high values of backward wave speed for ACC-driven vehicles. Comparative analysis with the state-of-the-art confirms the validity of our approach. The proposed method stands out due to its simplicity and accuracy, which paves the way for practical applications in real-time traffic flow monitoring and control within modern intelligent transportation systems., Comment: 14 pages, 8 figures, preprint

Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from probe vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, energy consumption estimation is largely ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction. The approach introduces a calibration algorithm for time-varying shockwave speed. The calibrated shockwave speed of the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionaly, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption.

Evidence in the literature shows that automated and human driving modes demonstrate different driving characteristics, i.e., headway policy, spacing policy, reaction time, comfortable acceleration, and others. These differences alter observed traffic dynamics and have an impact on energy consumption. This paper assesses the energy footprint of commercially implemented adaptive cruise control (ACC) systems and human drivers in car-following formation via different models using empirical observations on very similar driving cycles and/or routes. Most importantly, it initiates a critical discussion of the findings under the behavioral properties of each mode. Findings show that: ACC systems propagate an increasing energy consumption upstream, while human drivers do not; they succeed in maintaining a constant time-headway policy, operating very reliably; they develop a strong bond with their leader compared to their human counterparts; the two modes (humans and ACCs) are operating in different phase-space areas with room for improvement. Overall, findings show that ACC systems must be optimized to achieve a trade-off between functional requirements and eco-driving instructions., Comment: 10 pages, 7 figures, 2 Tables

Traffic-responsive signal control is a cost-effective and easy-to-implement network management strategy with high potential in improving performance in congested networks with dynamic characteristics. Max Pressure (MP) distributed controller gained significant popularity due to its theoretically proven ability of queue stabilization and throughput maximization under specific assumptions. However, its effectiveness under saturated conditions is questionable, while network-wide application is limited due to high instrumentation cost. Perimeter control (PC) based on the concept of the Macroscopic Fundamental Diagram (MFD) is a state-of-the-art aggregated strategy that regulates exchange flows between regions, in order to maintain maximum regional travel production and prevent over-saturation. Yet, homogeneity assumption is hardly realistic in congested states, thus compromising PC efficiency. In this paper, the effectiveness of network-wide, parallel application of PC and MP embedded in a two-layer control framework is assessed with mesoscopic simulation. Aiming at reducing implementation cost of MP without significant performance loss, we propose a method to identify critical nodes for partial MP deployment. A modified version of Store-and-forward paradigm incorporating finite queue and spill-back consideration is used to test different configurations of the proposed framework, for a real large-scale network, in moderately and highly congested scenarios. Results show that: (i) combined control of MP and PC outperforms separate MP and PC applications in both demand scenarios; (ii) MP control in reduced critical node sets leads to similar or even better performance compared to full-network implementation, thus allowing for significant cost reduction; iii) the proposed control schemes improve system performance even under demand fluctuations of up to 20% of mean., Comment: Submitted to Transportation Research Part C: Emerging Technologies

Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite efforts to provide robust predictions for semi-actuated signal control systems, predicting signal phase timings for fully-actuated controls remains challenging. This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. The performance of a Linear Regression (LR), a Random Forest (RF), and a Long-Short-Term-Memory (LSTM) neural network are assessed against a naive baseline model. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods. Furthermore, tree-based decision models such as the RF perform best with an accuracy that meets requirements for practical applications.

In this article, a new notion of modal strong structural controllability is introduced and examined for a family of LTI networks. These networks include structured LTI subsystems, whose system matrices have the same zero/nonzero/arbitrary pattern. An eigenvalue associated with a system matrix is controllable if it can be directly influenced by the control inputs. We consider an arbitrary set \Delta, and we refer to a network as modal strongly structurally controllable with respect to \Delta if, for all systems in a specific family of LTI networks, every \lambda\in\Delta is a controllable eigenvalue. For this family of LTI networks, not only is the zero/nonzero/arbitrary pattern of system matrices available, but also for a given \Delta, there might be extra information about the intersection of the spectrum associated with some subsystems and \Delta. Given a set \Delta, we first define a \Delta-network graph, and by introducing a coloring process of this graph, we establish a correspondence between the set of control subsystems and the so-called zero forcing sets. We also demonstrate how with \Delta={0} or \Delta=C\{0}, existing results on strong structural controllability can be derived through our approach. Compared to relevant literature, a more restricted family of LTI networks is considered in this work, and then, the derived condition is less conservative.

Accurate estimation of the traffic state over a network is essential since it is the starting point for designing and implementing any traffic management strategy. Hence, traffic operators and users of a transportation network can make reliable decisions such as influence/change route or mode choice. However, the problem of traffic state estimation from various sensors within an urban environment is very complex for several different reasons, such as availability of sensors, different noise levels, different output quantities, sensor accuracy, heterogeneous data fusion, and many more. To provide a better understanding of this problem, we organized an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras by the city's authorities, processed video data, and the Google Distance Matrix. We assess the different data sources, and we propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources. Comparative results with ground-truth data (derived from video measurements) show the efficiency and robustness of the proposed methodology.

In this paper, we study mixed traffic systems that move along a single-lane ring-road or open-road. The traffic flow forms a platoon, which includes a number of heterogeneous human-driven vehicles (HDVs) together with only one connected and automated vehicle (CAV) that receives information from several neighbors. The dynamics of HDVs are assumed to follow the optimal velocity model (OVM), and the acceleration of the single CAV is directly controlled by a dynamical output-feedback controller. The ultimate goal of this work is to present a robust control strategy that can smoothen the traffic flow in the presence of undesired disturbances (e.g. abrupt deceleration) and parametric uncertainties. A prerequisite for synthesizing a dynamical output controller is the stabilizability and detectability of the underlying system. Accordingly, a theoretical analysis is presented first to prove the stabilizability and detectability of the mixed traffic flow system. Then, two H-infinity control strategies, with and without considering uncertainties in the system dynamics, are designed. The efficiency of the two control methods is subsequently illustrated through numerical simulations, and various experimental results are presented to demonstrate the effectiveness of the proposed controller to mitigate disturbance amplification and achieve platoon stability.

In a future connected vehicle environment, an optimized route and motion planning should not only fulfill efficiency and safety constraints but also minimize vehicle motions and oscillations, causing poor ride comfort perceived by passengers. This work provides a framework for a large-scale and cost-efficient evaluation to address AV's ride comfort and allow the comparison of different comfort assessment strategies. The proposed tool also gives insights to comfort data, allowing for the development of novel algorithms, guidelines, or motion planning systems incorporating passenger comfort. A vehicle-road simulation framework utilizable to assess the most common ride comfort determination strategies based on vehicle dynamics data is presented. The developed methodology encompasses a road surface model, a non-linear vehicle model optimization, and Monte Carlo simulations to allow for an accurate and cost-efficient generation of virtual chassis acceleration data. Ride comfort is determined by applying a commonly used threshold method and an analysis based on ISO 2631. The two methods are compared against comfort classifications based on empirical measurements of the International Roughness Index (IRI). A case study with three road sites in Austria demonstrates the framework's practical application with real data and achieves high-resolution ride comfort classifications. The results highlight that ISO 2631 comfort estimates are most similar to IRI classifications and that the thresholding procedure detects preventable situations but also over- or underestimates ride comfort. Hence, the work shows the potential risk of negative ride comfort of AVs using simple threshold values and stresses the importance of a robust comfort evaluation method for enhancing AVs' path and motion planning with maximal ride comfort.

Traffic management by applying congestion pricing is a measure for mitigating congestion in protected city corridors. As a promising tool, pricing improves the level of service in a network and reduces travel delays. However, real-world implementations are restricted to static pricing, i.e., the price is fixed and not responsive to the prevailing regional traffic conditions. Dynamic pricing overcomes these limitations but also affects the users route choices. This work uses dynamic pricing's influence and predicts pricing functions to aim for a system optimal traffic distribution. The framework models a large-scale network where every region is considered homogeneous, allowing for the Macroscopic Fundamental Diagram (MFD) application. We compute Dynamic System Optimum (DSO) and a Quasi Dynamic User Equilibrium (QDUE) of the macroscopic model by formulating a linear optimization problem and utilizing the Dijkstra algorithm and a Multinomial Logit model (MNL), respectively. The equilibria allow us to find an optimal pricing methodology by training Multi-Layer-Neural (MLN) network models. We test our framework on a case study in Zurich, Switzerland, and showcase that (a) our neural network model learns the complex user behavior and (b) allows predicting optimal pricing functions. Results show a significant performance improvement when operating a transportation network in the DSO and highlight how dynamic pricing influences the user's route choice behavior towards the system optimal equilibrium.

Most optimal routing problems focus on minimizing travel time or distance traveled. Oftentimes, a more useful objective is to maximize the probability of on-time arrival, which requires statistical distributions of travel times, rather than just mean values. We propose a method to estimate travel time distributions on large-scale road networks, using probe vehicle data collected from GPS. We present a framework that works with large input of data, and scales linearly with the size of the network. Leveraging the planar topology of the graph, the method computes efficiently the time correlations between neighboring streets. First, raw probe vehicle traces are compressed into pairs of travel times and number of stops for each traversed road segment using a `stop-and-go' algorithm developed for this work. The compressed data is then used as input for training a path travel time model, which couples a Markov model along with a Gaussian Markov random field. Finally, scalable inference algorithms are developed for obtaining path travel time distributions from the composite MM-GMRF model. We illustrate the accuracy and scalability of our model on a 505,000 road link network spanning the San Francisco Bay Area.