Autonomous vehicles and shared mobility represent two fields of strong innovation in transportation research, and Shared Autonomous Vehicle (SAV) services have been studied as a new promising mobility system. Such services offer a strong potential especially when integrated with the Public Transport (PT) system, increasing its attractiveness by providing first- and last-mile connections to low-demand areas. This paper performs a systematic review on a niche of SAV-related literature, selecting articles that address PT-SAV integrations, the issue of how SAVs should be implemented together with already existing transit systems to enhance collective mobility. Papers have been classified firstly according to the methodological and modelling approaches used by researchers, and then according to the different operational schemes with which SAV systems can be deployed. Common findings have been reported and commented on, identifying the lack of articles aimed at determining the most suitable SAV service typology for specific contexts and network configuration. Many authors indicate that if SAVs are operated as feeder modes to mass transit, they might improve public transport connectivity. However, further research is needed to explore the efficacy of SAV systems as an opportunity to address first/last-mile PT planning issues.

Encouraging older and newer mobility alternatives to standard privately owned cars, such as cycling and autonomous vehicles, is necessary to reduce pollution, enhance safety, increase transportation efficiency, and create a more sustainable urban environment. Implementing mobility plans that identify the use of different transport modes in their confidence intervals can lead to the development of smarter and more efficient cities, where all citizens can benefit from safe and environmentally friendly streets. This research aims to provide insights into designing urban streets that seamlessly integrate autonomous vehicles and cyclists, promoting sustainable mobility while ensuring urban transport efficiency. With this aim, the research identifies and prioritizes the factors that are relevant to street design as well as the appropriate strategies to address them. Our methodology combines Multi-Criteria Decision-Making (MCDM) with Game theory to identify and realize the most convenient conditions for this integration. Initially, the basic factors were identified using the value-interval fuzzy Delphi method. Following this, the factors were weighted with the interval-fuzzy Analytic Network Process (ANP), and the cause-and-effect variables were evaluated using the interval-fuzzy Decision-Making Trial and Evaluation Laboratory ANP (DANP). Finally, Game theory was employed to determine the optimal model for addressing these challenges. The results indicate that safety emerged as the most significant factor and two optimal strategies were identified; the integration of green infrastructure and smart technology.

Massive datasets of Floating Car Data (FCD) are collected and thereafter processed to estimate and predict traffic conditions. In the framework of short-term traffic forecasting, machine learning techniques have become very popular. However, the big datasets available today contain for the most part easily predictable data, that are data observed during recurrent conditions. Integration of different machine learning techniques with traffic engineering notions must contribute to obtain new transportation-oriented data-driven methods. In this paper we address traffic dynamics estimation by using individual FCD in order to develop an integrative framework able to recognize and select the suitable method for traffic forecasting. Taking into account the spatial distributions of individual FCD positions we retrieve a new spatial-based criterion for the integration of models.

This study investigates the use of deep reinforcement learning techniques to improve traffic signal control systems through the integration of deep learning and reinforcement learning approaches. The purpose of a deep reinforcement learning architecture is to provide adaptive control via a reinforcement learning interface and deep learning for the representation of traffic queues with regards to signal timings. This has driven recent research, which has reported success in the use of such dynamic approaches. To further explore this success, we apply a deep reinforcement learning algorithm over a grid of 21 interconnected traffic signalized intersections and monitor its effectiveness. Unlike previous research, which often examined isolated or idealized scenarios, our model is applied to the real-world traffic network of Via “Prenestina” in eastern Rome. We utilize the Simulation of Urban MObility (SUMO) platform to simulate and test the model. This study has two main objectives: ensure the algorithm’s correct implementation in a real traffic network and assess its impact on public transportation, incorporating an additional priority reward for public transport. The simulation results confirm the model’s effectiveness in optimizing traffic signals and reducing delays for public transport.

Variable Speed Limit (VSL) systems aimed at reducing congestion and improving safety performance have been implemented around the world in previous years. However, field studies have shown controversial results regarding traffic performance improvement. This study integrates statistical testing methods and clustering techniques for assessing the effect of a non-mandatory VSL system on traffic flow performances on a 14-km portion of the Padua–Mestre motorway in Italy. Statistical analysis is conducted on the observed speeds, collected for almost one year, to identify any significant differences provided by VSL activation. The changes in global motorway performances induced by the VSL in typical traffic patterns under recurring congestion are assessed using both statistical tests and two specific clustering algorithms, namely K-means and DBSCAN. The results indicate that the VSL system effectively affects the observed speeds and alleviates congested conditions: the observed reduction in mean travel time ranges is around 4% with the VSL system active across various lanes; the standard deviation of vehicular speeds witnessed a decrease of 12% to 20% in the most congested segments, while no notable distinction is observed in traffic flows.