New Micromobility Means of Transport: An Analysis of E-Scooter Users' Behaviour in Trondheim

Negative effects of a massive use of cars, such as congestion, air pollution, noise, and traffic injuries, are affecting the cities everywhere. Recently introduced shared vehicles, such as e-scooters and electric bicycles, could potentially accelerate the transition towards sustainable mobility. Although these vehicles are becoming increasingly common and accepted within regulatory frameworks, some local governments are not yet ready to integrate e-scooters into their transport systems. Indeed, the legislation is unclear as it is not easy to determine whether the e-scooter is more like a bicycle or a vehicle. Moreover, it is difficult to predict the impact of e-scooters on road traffic, as well as the type of road infrastructure chosen by e-scooter drivers or the possible interaction of such vehicles with weak road users, such as pedestrians or cyclists. This study showed an analysis of speed and behaviour of e-scooter drivers in the city of Trondheim (Norway) to investigate how to manage this mode of transport. A total of 204 e-scooters were observed on six different roads in the city centre. The speed of e-scooter drivers was measured by a speed tracker (average value 15.4 km/h) and their behaviour recorded by a hidden observer in the field. Gender, age, distance from pedestrians, speed adaptation to the environment, and type of vehicle used were registered for each e-scooter. Through a Binomial Logit analysis, the data obtained were used to analyse the type of road infrastructure preferred by e-scooter drivers. Results showed that the cycle path is more widely used with percentage value from 60% to 90% of users. In addition, the probability of choice depended mainly on the road environment. The aim of this analysis was to assist local authorities in regulating the safe use of e-scooters and developing appropriate policies for their integration into cities.

Designing Two Secure Keyed Hash Functions Based on Sponge Construction and the Chaotic Neural Network

In this paper, we propose, implement, and analyze the structures of two keyed hash functions using the Chaotic Neural Network (CNN). These structures are based on Sponge construction, and they produce two variants of hash value lengths, i.e., 256 and 512 bits. The first structure is composed of two-layered CNN, while the second one is formed by one-layered CNN and a combination of nonlinear functions. Indeed, the proposed structures employ two strong nonlinear systems, precisely a chaotic system and a neural network system. In addition, the proposed study is a new methodology of combining chaotic neural networks and Sponge construction that is proved secure against known attacks. The performance of the two proposed structures is analyzed in terms of security and speed. For the security measures, the number of hits of the two proposed structures doesn't exceed 2 for 256-bit hash values and does not exceed 3 for 512-bit hash values. In terms of speed, the average number of cycles to hash one data byte (NCpB) is equal to 50.30 for Structure 1, and 21.21 and 24.56 for Structure 2 with 8 and 24 rounds, respectively. In addition, the performance of the two proposed structures is compared with that of the standard hash functions SHA-3, SHA-2, and with other classical chaos-based hash functions in the literature. The results of cryptanalytic analysis and the statistical tests highlight the robustness of the proposed keyed hash functions. It also shows the suitability of the proposed hash functions for the application such as Message Authentication, Data Integrity, Digital Signature, and Authenticated Encryption with Associated Data.