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.

The research presented in this paper is focused on the definition of a new methodology for evaluating how illuminated crosswalks influence drivers’ behavior when approaching the zebra in nighttime conditions. The proposed methodology is based on in situ speed measurements, and cars’ speed was detected in an urban road segment of the city of Rome with a Telelaser instrument. Vehicles speed profiles are measured in the same road segment both in LED-illuminated conditions and in non-illuminated conditions. Results have shown a promising impact of the LED lighting system on pedestrian safety. In fact, cars’ mean speed decreases by 19.3% at the crosswalk section in illuminated conditions. Moreover, a positive effect on safety, in terms of mean speed reduction (−16.4%), was found even in the absence of pedestrians.

Attentional deficits have tragic consequences on road safety. These deficits are not solely caused by distraction, since they can also arise from other mental impairments such as, most frequently, mental fatigue. Fatigue is among the most prevalent impairing conditions while driving, degrading drivers’ cognitive and physical abilities. This issue is particularly relevant for professional drivers, who spend most of their time behind the wheel. While scientific literature already documented the behavioral effects of driving fatigue, most studies have focused on drivers under sleep deprivation or anyhow at severe fatigue degrees, since it is difficult to recognize the onset of fatigue. The present study employed an EEG-driven approach to detect early signs of fatigue in professional drivers during a simulated task, with the aim of studying visual attention as fatigue begins to set in. Short-range and long-range professional drivers were recruited to take part in a 45-min-long simulated driving experiment. Questionnaires were used to validate the experimental protocol. A previously validated EEG index, the MDrow, was adopted as the benchmark measure for identifying the “fatigued” spans. Results of the eye-tracking analysis showed that, when fatigued, professional drivers tended to focus on non-informative portions of the driving environment. This paper presents evidence that an EEG-driven approach can be used to detect the onset of fatigue while driving and to study the related visual attention patterns. It was found that the onset of fatigue did not differentially impact drivers depending on their professional activity (short- vs. long-range delivery).

This paper presents the framework for a dynamic Advanced Traveler Information System (ATIS). The ATIS currently in use provides users with stereotyped travel options, but the set of available modes in a given place and time is not the same for each traveler, and such a personal choice set varies within the context of daily trip chains. The research presented in this paper addressed these limitations by including dynamic features in the proposed system. The activity chain that the user performs as well as the personal mode availabilities are modelled simultaneously to define the logical architecture of an innovative information system. Such a technology was intended to assist travelers in performing their daily trip chaining. In order to provide some insight regarding the efficacy of the proposed procedure, a pilot test was performed using real travel time information. Results have shown that the ATIS proposed in this study might generate a significant reduction in travel times.