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Obstacle avoidance is a fundamental operation for automated driving and its formulation traditionally originates from robotics and decision making control fields. Given the high complexity required to compute an obstacle-free trajectory, this operation is usually demanded to a lower frequency planning layer that provides then a trajectory reference to be followed by a higher frequency control layer. As a result, whenever replanning is needed (for example, due to a new detected obstacle), the control layer must wait for a new planned trajectory to be generated. In this paper, we propose a novel methodology to approach obstacle avoidance already in the control layer, which allows a prompter response. In particular, we show how obstacle avoidance and reference tracking can be integrated, thus with no need to switch among different controllers, based on a null-space based behavioral control approach, implemented in a (possibly nonlinear) model predictive control scheme. We demonstrate practical implementation of the proposed methodology employing two different vehicle dynamic models and in four different (urban and highway) scenarios. Furthermore, we provide a sensitivity analysis to understand how parameters choice affects the automated vehicle behavior.

This paper deals with traffic control at motorway bottlenecks assuming the existence of an unknown, time-varying, Fundamental Diagram (FD). The FD may change over time due to different traffic compositions, e.g., light and heavy vehicles, as well as in the presence of connected and automated vehicles equipped with different technologies at varying penetration rates, leading to inconstant and uncertain driving characteristics. A novel methodology, based on Model Reference Adaptive Control, is proposed to robustly estimate in real-time the time-varying set-points that maximise the bottleneck throughput, particularly useful when the traffic is regulated via a feedback-based controller. Furthermore, we demonstrate the global asymptotic stability of the proposed controller through a novel Lyapunov analysis. The effectiveness of the proposed approach is evaluated via simulation experiments, where the estimator is integrated into a feedback ramp-metering control strategy, employing a second-order multi-lane macroscopic traffic flow model, modified to account for time-varying FDs.

Electric pedal-assist bikes (e-bikes) are an emerging technology that aims to enhance cycling by incorporating battery-powered motors activated while pedalling. To promote cycling effectively, it is crucial to understand the factors that influence cyclists’ route choice behaviour. This study investigates individual route choice behaviour among cyclists, taking into account their bike type (i.e., e-bikes and regular bikes). Data collected through a stated preference (SP) survey in Finland is analysed using discrete choice models to compare the differences between e-bike and regular bike users’ route choice behaviour. The study also compares the outputs of multinomial and mixed Logit models for both e-bike and regular bike users to address the impact of error correlation in SP data. Furthermore, by employing a classification approach, the study examines the differences between the expected and actual behavioural changes upon using e-bikes, referred to as the expectation–reality gap, in terms of route choice behaviour. Our research findings highlight certain factors that consistently promote cycling among both regular bike and e-bike users, specifically, low interaction with traffic, fewer intersections, and the presence of separated bike facilities. Also, our findings imply that the SP survey is well-designed to capture the preferences of the individuals. Hence, the observations are not severely correlated, i.e., errors can be assumed to be independently and identically distributed. Furthermore, we show that regular bike and e-bike users with similar characteristics do not share similar beliefs regarding the effects of e-bikes on their cycling habits.

While intelligent transport systems are preparing to welcome connected and automated vehicles (CAVs), it is uncertain which algorithms should be employed to manage them. In particular, even though remarkable improvements in telecommunication technologies, such as vehicle-to-everything (V2X), enable communication and computation sharing among different agents, e.g. vehicles and infrastructures, distributed control algorithms are still not fully exploited. However, such methodologies have the potential to perfectly blend with the very nature of CAVs and alleviate traffic management units from the centralized computational burden. In this paper, we propose a formation control algorithm to control vehicle trajectories for two scenarios: 1) overtaking, and 2) intersection control. We include a simulation experiment to show some preliminary results and discuss limitations and future steps.

This paper deals with the problem of controlling traffic at motorways bottlenecks in presence of an unknown, time-varying, Fundamental Diagram (FD). The FD may change over time due to traffic composition or to the presence of Connected and Automated Vehicles (CAVs) with varying driving characteristics and penetration rates. A novel methodology, based on Model Reference Adaptive Control, is presented to robustly estimate the time-varying set-points that maximise the bottleneck throughput. The proposed approach is integrated in a control scheme that includes a linear quadratic integral regulator designed to control traffic which comprises a percentage of CAVs. Simulation experiments, based on a first-order multi-lane macroscopic traffic flow model that also considers for the capacity drop phenomenon, are presented to illustrate the effectiveness of the proposed approach.

In this paper, we propose a novel cost function to be embedded in a distributed algorithm for cooperative rerouting of connected and automated vehicles (CAVs) in urban networks. The computation is performed by intersection units managing the portion of the network for which they are in charge by sending updated routes to follow to the CAVs there. Moreover, the intersection units communicate among each other to be updated on the situation of the (in general nonhomogeneous) links of the network. The proposed approach allows the decomposition of the problem into subproblems, which are resolved distributively with little information exchange. The problems are constructed to obtain a fair compromise between user equilibrium and system optimum, taking advantage of the strengths and coping with the limitations of both. We 1) show the results obtained by running simulations with CAVs on the Sioux Falls network compared with a baseline scenario; 2) analyze the results obtained with different penetration rates of CAVs; and 3) provide a sensitivity analysis to investigate how parameters in our approach affect the results of the experiments.

We develop a predictor-based adaptive cruise control design with integral action (based on a nominal constant time-headway policy) for compensation of long actuator and sensor delays in vehicular systems utilizing measurements of the relative spacing as well as of the speed and the short-term history of the desired acceleration of the ego vehicle. Employing an input-output approach we show that the predictor-based adaptive cruise control law with integral action guarantees all of the four typical performance specifications of adaptive cruise control designs, namely, (1) stability, (2) zero steady-state spacing error, (3) string stability, and (4) non-negative impulse response, despite the long input delay. The effectiveness of the developed control design is illustrated in simulation considering various performance metrics.

A feedback control strategy is proposed for lane assignment at bottleneck locations. The strategy assumes that some vehicles equipped with vehicle automation and communication systems are capable of receiving and executing specific lane-changing orders or recommendations. From a previously proposed optimal control strategy based on a simplified multilane motorway traffic flow model and formulated as a linear quadratic regulator, a feedback control problem was designed. The aim was to maximize the throughput at bottleneck locations while distributing the total density at the bottleneck area over the lanes according to a given policy by optimal lane assignment of the vehicles upstream of the bottleneck. The feedback control decisions were based on real-time measurements of the traffic state and inflow. The proposed strategy was tested on a nonlinear first-order macroscopic multilane traffic flow model, which also accounted for the capacity drop phenomenon.

A model-based traffic state estimation approach is developed for per-lane density estimation as well as on-ramp and off-ramp flows estimation for highways in presence of connected vehicles, namely, vehicles that are capable of reporting information to an infrastructure-based system. Three are the basic ingredients of the developed estimation scheme: (1) a data-driven version of the conservation-of-vehicles equation (in its time- and space-discretized form); (2) the utilization of position and speed information from connected vehicles\x92 reports, as well as total flow measurements obtained from a minimum number (sufficient for the observability of the model) of fixed detectors, such as, for example, at the main entry and exit of a given highway stretch; and (3) the employment of a standard Kalman filter. The performance of the estimation scheme is evaluated for various penetration rates of connected vehicles utilizing real microscopic traffic data collected within the Next Generation SIMulation (NGSIM) program. It is shown that the estimation performance is satisfactory, in terms of a suitable metric, even for low penetration rates of connected vehicles. The sensitivity of the estimation performance to variations of the model parameters (two in total) is also quantified, and it is shown that, overall, the estimation scheme is little sensitive to the model parameters.

A feedback control strategy is proposed for lane assignment at bottleneck locations. The strategy assumes that some vehicles equipped with vehicle automation and communication systems are capable of receiving and executing specific lane-changing orders or recommendations. From a previously proposed optimal control strategy based on a simplified multilane motorway traffic flow model and formulated as a linear quadratic regulator, a feedback control problem was designed. The aim was to maximize the throughput at bottleneck locations while distributing the total density at the bottleneck area over the lanes according to a given policy by optimal lane assignment of the vehicles upstream of the bottleneck. The feedback control decisions were based on real-time measurements of the traffic state and inflow. The proposed strategy was tested on a nonlinear first-order macroscopic multilane traffic flow model, which also accounted for the capacity drop phenomenon.

This paper presents two case studies in which a macroscopic model-based approach for the estimation of traffic conditions, which the authors have recently developed, is employed and tested. The estimation method is developed for a mixed traffic scenario, in which traffic is composed of both ordinary and connected vehicles. Only average speed measurements, which may be obtained from connected vehicle reports, and a minimal number (sufficient to guarantee observability) of spot sensor–based total flow measurements are utilized. In the first case study, NGSIM microscopic data are used to test the capability of estimating traffic conditions on the basis of aggregated information retrieved from moving vehicles and considering various penetration rates of connected vehicles. In the second case study, a longer highway stretch with internal congestion is utilized to test the capability of the proposed estimation scheme to produce appropriate estimates for varying traffic conditions on long stretches. In both cases, the performances are satisfactory and the obtained results demonstrate the effectiveness of the method in both qualitative and quantitative terms.

The development and deployment of simple, yet efficient, coordinated and integrated control tools for motorway traffic control remains a challenge. A generic integrated motorway traffic flow control concept is proposed in this paper. It is based on the combination and suitable extension of control algorithms and tools proposed or deployed in other studies, such as ramp metering or VSL (Variable Speed Limit)-enabled cascade-feedback mainstream traffic flow control, and allows for consideration of multiple bottlenecks. The new controller enables coordination of ramp metering actions at a series of on-ramps, as well as integration with VSL control actions, towards a common control goal, which is bottleneck throughput maximisation. The approach enables a pre-specified (desired) balancing of the incurred delays upstream of the employed actuators, via a suitably designed knapsack algorithm. Despite the multitude of the offered configurations, options and possibilities, the overall control algorithm remains simple, efficient and suitable for field implementation. The control algorithm is evaluated and demonstrated using a validated macroscopic traffic flow model for a number of scenarios.

A macroscopic model-based approach for estimation of the traffic state, specifically of the (total) density and flow of vehicles, is developed for the case of "mixed" traffic, i.e., traffic comprising both ordinary and connected vehicles. The development relies on the following realistic assumptions: (i) The density and flow of connected vehicles are known at the (local or central) traffic monitoring and control unit on the basis of their regularly reported positions; and (ii) the average speed of conventional vehicles is roughly equal to the average speed of connected vehicles. Thus, complete traffic state estimation (for arbitrarily selected segments in the network) may be achieved by merely estimating the percentage of connected vehicles with respect to the total number of vehicles. A model is derived, which describes the dynamics of the percentage of connected vehicles, utilizing only well-known conservation law equations that describe the dynamics of the density of connected vehicles and of the total density of all vehicles. Based on this model, which is a linear parameter-varying system, an estimation algorithm for the percentage of connected vehicles is developed employing a Kalman filter. The estimation methodology is validated through simulations, using a second-order macroscopic traffic flow model as ground truth for the traffic state, as well as using real microscopic traffic data collected within the Next Generation SIMulation (NGSIM) program.

This paper presents two case studies in which a macroscopic model-based approach for the estimation of traffic conditions, which the authors have recently developed, is employed and tested. The estimation method is developed for a mixed traffic scenario, in which traffic is composed of both ordinary and connected vehicles. Only average speed measurements, which may be obtained from connected vehicle reports, and a minimal number (sufficient to guarantee observability) of spot sensor–based total flow measurements are utilized. In the first case study, NGSIM microscopic data are used to test the capability of estimating traffic conditions on the basis of aggregated information retrieved from moving vehicles and considering various penetration rates of connected vehicles. In the second case study, a longer highway stretch with internal congestion is utilized to test the capability of the proposed estimation scheme to produce appropriate estimates for varying traffic conditions on long stretches. In both cases, the performances are satisfactory and the obtained results demonstrate the effectiveness of the method in both qualitative and quantitative terms.

Vehicle automation and communication systems (VACS) are expected to appear in an increasing amount of vehicles within the next years. Among the wide range of proposed VACS, the ones that include vehicle-to-infrastructure communication capabilities may be exploited both as sensors and as actuators. This enables traffic control centres to obtain more accurate information on the current traffic state and to assign to each vehicle appropriate control tasks, so as to achieve a global traffic flow target. The concept employs and exploits the synergistic (integrated) action of a number of old and new control measures, including ramp metering, vehicle speed control, and lane changing control, at a macroscopic level. The problem is tackled through a Quadratic Programming optimisation problem used as the core of a model predictive control framework. The optimal control actions may be sent directly to vehicles equipped with adaptive cruise control (ACC), affecting directly their cruise speed and, in addition, their lane-changing behaviour. The effectiveness and the computational feasibility of the proposed approach are demonstrated via microscopic simulation for a variety of ACC settings and penetration rates of equipped vehicles.

The presence and exploitation of Vehicle Automation and Communication Systems (VACS) while defining optimal control strategies in motorway traffic flow control is addressed in this paper. VACS are supposed to act both as sensors (providing information on traffic conditions) and as actuators, allowing the application of ramp metering, variable speed limit control, and lane changing control. A quadratic programming problem is defined on the basis of a novel first-order traffic flow model for multi-lane motorways. An example is presented in order to illustrate the effectiveness of the proposed optimisation problem.

Mainstream traffic flow control (MTFC) enabled via variable speed limits (VSLs) has been investigated in previous studies, utilizing various control strategies. In this paper a new feedback control strategy is proposed for MTFC enabled via VSLs, considering multiple bottleneck locations. Results are evaluated using a validated macroscopic model. The feedback concept is robust and can be immediately implemented in the field as it considers practical and safety constraints.

A risk-based approach to control dangerous goods (DG) transport flows by roads is proposed, solving a real-time flow assignment problem. The model takes into account the planned scheduling of the DG fleets. The objective is to readapt the schedule in real time, controlling the DG flow to minimize both the risk on the network and the gap between the proposed modified delivery and the planned one. The innovative aspect of the proposed approach is to balance the social objective of a national authority, thus minimizing the risk on the road infrastructures, with the economical objective of the DG distribution companies that have to minimize the actual time, as defined by the planned deliveries. The proposed DG transport model is defined according to a system of systems view. Each subsystem (SS) represents either a regional area or, more commonly, a segment of a road. The proposed approach provides a useful tool for evaluating the optimal speed for DG vehicles in each SS and the optimal amount of DG flow that should transit from one SS to another, following the planned delivery schedule. The problem has been tackled in two different formulations. First, a nonlinear mathematical programming formulation is defined. Then, according to simplifying assumptions, the problem is solved as a discrete-time finite horizon linear quadratic optimal control problem with a state feedback control. An exemplificative case study is used to show a comparison between the two formulations, as well as the effects of a risk sudden change in the overall DG routing.