Truck platooning is an innovative logistics approach to lower operational costs, particularly fuel consumption, while addressing contemporary transportation challenges. While recent studies on truck platooning have emphasized platoons’ energy savings, stability, and safety, there has been limited exploration of platoon formation and control. This paper uses optimal control theory to address the dispatching control of trucks with arriving platoons. In particular, trucks arrive at a highway station while platoons arrive alongside it. The station controls the truck holding and dispatching, where trucks are sent out with or without a platoon. Dispatching trucks with an arriving platoon reduces fuel consumption while waiting for a platoon to arrive increases the dwell time (i.e., transportation delay). We assume that an arriving platoon determines the number of trucks (i.e., the batch size) it can accept. Only a single truck can be dispatched if a platoon is absent. Hence, we formulate the dispatching control problem and derive the optimal policy for the discounted costs and the average cost governing the dispatch of trucks alongside platoons. We proved the optimality of threshold policies. Numerical results for the average cost case are presented. They are consistent with the optimal ones.

Truck platooning is a promising technology that can reduce costs (fuel consumption) and enhance the overall transportation productivity. While recent research has focused on platoons' network and stability, few studies have tackled platooning formation and control. This paper uses Reinforcement Learning (RL) to study the dispatching control of trucks with arriving platoons, a problem first proposed in [1]. This work builds on [1] by considering the lack of the cost function and statistical knowledge. In particular, we employ Q-learning to compute the optimal dispatch control policy at a highway hub. Given the unbounded state space of the model, traditional Q-learning may converge slowly or even get stuck in sub-optimal policies. We improve Q-learning by confining the agent to transition in a finite subset of the state space. For this purpose, we use the switching condition property of the optimal policy (derived in [1]), the underlying random walk model, and a sensitivity analysis of the cost function. Our numerical results demonstrate that our Enhanced Q-learning converges significantly faster (up to 97%) in terms of CPU time and number of interactions.

As the automotive industry is developing autonomous driving systems and vehicular networks, attention to truck platooning has increased as a way to reduce costs (fuel consumption) and improve efficiency in the highway. Recent research in this area has focused mainly on the aerodynamics, network stability, and longitudinal control of platoons. However, the system aspects (e.g., platoon coordination) are still not well explored. In this paper, we formulate a platooning coordination problem and study whether trucks waiting at an initial location (station) should wait for a platoon to arrive in order to leave. Arrivals of trucks at the station and platoons by the station are modelled by independent Bernoulli distributions. Next we use the theory of Markov Decision Processes to formulate the dispatching control problem and derive the optimal policy governing the dispatching of trucks with platoons. We show that the policy that minimizes an average cost function at the station is of threshold type. Numerical results for the average cost case are presented. They are consistent with the optimal ones.

Traffic jams are an essential and continuous challenge in our cities, responsible for socioeconomic and environmental concerns and an ambitious traffic jams management agenda is urgent. The distributed solutions in the literature for Traffic Management Systems (TMS) are heavily based on beacon messages or proactive communication protocols to share vehicular traffic information among vehicles. Thus, these solutions are not scalable when the number of vehicles increases in the network — when there are traffic jams. To overcome these problems, we propose a new VANET-based traffic management system named CoNeCT: Predictive Congestion Control based on Collaborative Information Sharing for Vehicular Ad hoc Networks. CoNeCT's primary goal is to support vehicles' collaboration in analyzing, predicting, and managing congestion. The proposed system was designed to decrease the number of messages by using a novel road segment load assessment that improves traffic flow classification. Vehicles aware of traffic conditions share it with their neighbors, and they can also request traffic views whenever necessary. Additionally, vehicles can detect significant traffic variations and predict future traffic conditions to improve roads' overall traffic conditions, mitigating the congestion before it arises. Results obtained from an extensive performance analysis show CoNeCT's ability to reduce traffic congestion with a low impact on the wireless communication medium, outperforming the state-of-art systems.

As we move more deeply into information-oriented services and systems, we clearly observe the importance and impact of smart and connected vehicles for urban computing. New Cloud-enabled paradigms have boosted information and service sharing. However, such paradigms rely heavily on the underlying communication layer, inheriting the challenges originated from the high mobility of vehicles. Several works have been devised to cope with highly dynamic vehicular environments in support of effective resource management and allocation, which we discuss in the paper. Moreover, we propose a Fog paradigm solution to resource allocation using a hierarchical method in vehicular clouds. Our method is based on the Multiplicative Analytic Hierarchy Process (MAHP) proposed by Lootsma. MAHP is a branch of another method called Analytic Hierarchy Process proposed by Saaty. Therefore, we used MAHP in the decision-making of the resource allocation process using a Fog paradigm to select the best Fog to allocate certain services. We evaluated the proposed solution comparing to three other decision methods, GREEDY, RANDOM, and RELIABLE. The proposed Fog-oriented Hierarchical Resource Allocation Policy in Vehicular Clouds (FRACTAL) performed better than the other decision methods, fulfilling more services and consequently denying fewer services.

The growth in the number of traffic accidents has become a cause for concern in urban centers. As a result of the increase in population in large cities and the number of vehicles, the consequences of accidents and congestion can be even more significant, considering the impacts on the economy, environment and people's quality of life. Therefore, aiming to minimize these impacts, we present ALIVE, a distributed and Vanet-based solution that reduces congestion caused by different sources and, especially, from accident sources, contributing to efficient urban mobility in smart cities. The solution performs the detection of accidents and the dissemination of warning messages in multiple hops. Besides, the system can share the road traffic information with the nearby streets to improve traffic efficiency. We evaluated the proposed solution with PANDORA and NRR Traffic Management solutions. Simulation results indicate that the proposed solution reduces the average travel time, time lost, and the number of transmitted messages.

Road capacity infrastructure and temporary interruptions in trips constitute the main reasons behind the traffic jam phenomenon. City urbanization and growth further intensify these two reasons through the increase of work area and the demand for mobility. In such a scenario, several issues can emerge, such as higher mobility costs, more frequent traffic jams, more significant environmental damage, reduced quality of life, and more pollution. Therefore, this work presents a Proactive and Reactive Approach for Urban Traffic Management in Vehicular Networks, RIDER, to minimize traffic congestion. RIDER is a fully-distributed protocol that can assume proactive and reactive behaviors for sharing traffic condition information. Vehicles with traffic condition information can organize them-selves to improve traffic flow and reduce traffic congestion. In the proposed solution, vehicles monitor the road traffic condition and proactively share this information when needed, considering adaptive multi-hop communication. If vehicles do not have nearby road traffic information, they executed a reactive traffic information discovery. RIDER was evaluated and compared to previous works, regarding the number of transmitted messages, packet collisions, and traffic congestion metrics.

The economic and environmental impacts caused by traffic congestion are increasing. Improvements in the cities road infrastructure for minimizing these impacts are pricey and do not happen immediately. Thus, in order to improve vehicular traffic flow in dense urban centers, we present REACT, a traffic management system to minimize vehicle congestion in Smart Cities. REACT is a traffic management system based on Vehicular communication, and it is divided into Request and Response phases. The Request phase allows vehicles to request traffic information from neighbor road segments. The Response supports vehicles to respond to the request with current road traffic information. The performance evaluation shows the ability of our solution to reduce traffic jams with a low communication overhead.

Traffic congestion in large cities became more intense considering the last years. Basically, this growth is attributed to the wide use of a single mode of transport due to the lack of alternatives capable of efficiently supplying urban traffic demand. In this sense, in economic terms, it is estimated that billions of dollars are wasted every year due to the extra expenses with fuels and maintenance caused by traffic. In order to minimize the economic and environmental damages caused by congestion, this work presents FIRE-NRD: A fully-distributed Traffic Management System for Next Road Decision. In the proposed solution, vehicles, while moving, are able to analyze and share a study of the traffic flow and thus provide sufficient knowledge in order to resolve "the next road decision", i.e., the next road choice to decrease its travel time. FIRE-NRD is based only on local data about traffic information and totally collaborative, where neighbor vehicles share their knowledge. FIRE-NRD is compared to literature solutions and present better results considering network and traffic metrics, such as number of messages and average travel time.