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As the advancement of driverless technology, together with information and communication technology moved at a fast pace, autonomous vehicles have attracted great attention from both industries and academic sectors during the past decades. It is evident that this emerging technology has great potential to improve the pedestrian safety on roads, mitigate traffic congestion, increase fuel efficiency, and reduce greenhouse gas emissions. However, there is limited systematic research into the applications and public perceptions of autonomous vehicles in road transportation. The purpose of this systematic literature review is to synthesise and analyse existing research on the applications, implications, and public perceptions of autonomous vehicles in road transportation system. It is found that autonomous vehicles are the future of road transportation and that the negative perception of humans is rapidly changing towards autonomous vehicles. Moreover, to fully deploy autonomous vehicles in a road transportation system, the existing road transportation infrastructure needs significant improvement. This systematic literature review contributes to the comprehensive knowledge of autonomous vehicles and will assist transportation researchers and urban planners to understand the fundamental and conceptual framework of autonomous vehicle technologies in road transportation systems. [ABSTRACT FROM AUTHOR]

With the growing rate of urban population and transport congestion, it is important for a city to have bike riding as an attractive travel choice but one of its biggest barriers for people is the perceived lack of safety. To improve the safety of urban cycling, identification of high-risk location and routes are major obstacles for safety countermeasures. Risk assessment is performed by crash data analysis, but the lack of data makes that approach less effective when applied to cyclist safety. Furthermore, the availability of data collected with the modern technologies opens the way to different approaches. This research aim is to analyse data needs and capability to identify critical cycling safety events for urban context where bicyclist behaviour can be recorded with different equipment and bicycle used as a probe vehicle to collect data. More specifically, three different sampling frequencies have been investigated to define the minimum one able to detect and recognize hard breaking. In details, a novel signal processing procedure has been proposed to correctly deal with speed and acceleration signals. Besides common signal filtering approaches, wavelet transformation and Dynamic Time Warping (DTW) techniques have been applied to remove more efficiently the instrument noise and align the signals with respect to the reference. The Euclidean distance of the DTW has been introduced as index to get the best filter parameters configuration. Obtained results, both during the calibration and the investigated real scenario, confirm that at least a GPS signal with a sampling frequency of 1 Hz is needed to track the rider's behaviour to detect events. In conclusion, with a very cheap hardware setup is possible to monitor riders' speed and acceleration. [ABSTRACT FROM AUTHOR]