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Urban commuters have been suffering from traffic congestion for a long time. In order to avoid or mitigate the congestion effect, it is significant to know how the introduction of autonomous vehicles (AVs) influence the road capacity. The effects that AVs bring to the macroscopic fundamental diagram (MFD) were investigated through microscopic traffic simulations. This is a key issue as the MFD is a basic model to describe road capacity in practical traffic engineering. Accordingly, the paper investigates how the different percentage of AVs affects the urban MFD. A detailed simulation study was carried out by using SUMO both with an artificial grid road network and a real-world network in Budapest. On the one hand, simulations clearly show the capacity improvement along with AVs penetration growth. On the other hand, the paper introduces an efficient modeling for MFDs with different AVs rates by using the generalized additive model (GAM). [ABSTRACT FROM AUTHOR]

An agent-based transport simulation model is used to examine the impacts of Autonomous Vehicles (AVs) on the mobility of certain groups of people. In the state of the art, it has been found that the researchers primarily have simulation studies focusing on the impacts of AVs on people regardless of certain groups. However, this study focuses on assessing the impacts of AVs on different groups of users, where each group is affected variously by the introduction of different penetration levels of AVs into the market. The Multi-Agent Transport Simulation (MATSim) software, which applies the co-evolutionary algorithm and provides a framework to carry out large-scale agent-based transport simulations, is used as a tool for conducting the simulations. In addition to the simulation of all travellers, 3 groups of users are selected as potential users of AVs, as follow: (1) long commuters with high-income, (2) elderly people who are retired, and (3) part-time workers. Budapest (Hungary) is examined in a case study, where the daily activity plans of the households are provided. Initially, the existing daily activity plans (i.e., the existing condition) of each group are simulated and assessed before the introduction of AVs into the market. After that, the AVs are inserted into the road network, where different fleet sizes of AVs are applied based on the demand of each group. The marginal utility of the travel time spent in case of a transport mode, the AV fleet size, and the cost of the travel are the key variables that determine the use of a transport mode. The key variables are set based on the characteristics of the case study (i.e., demand) and the AVs. The results of the simulations suggest that the AVs have different degrees of influences on certain groups as demonstrated in the occurred changes on the modal share. The value of changes depends on the Value of Travel Time (VOT) of people and the used fleet size of AVs. Moreover, the influence of the traveller's characteristics on the AVs is manifested, such as different values of fleet utilization. Furthermore, the study demonstrates that an increase in the fleet size of AVs beyond 10% of the demand does not significantly raise the modal share of AVs. The outcome of this paper might be used by decision-makers to define the shape of the AVs' use and those groups who are interested in using AVs. [ABSTRACT FROM AUTHOR]

Travelers choose transport modes on the basis of their preferences in which the features onboard vehicles affect the choice of transport modes. In economic theory, it is found that travelers seek to maximize their travel utilities. Autonomous vehicles (AVs) are to be on the market when machines will replace human drivers. AVs are to be used for different purposes, such as transporting passengers and goods. In literature, some research studied AVs in combination with other transport modes, whereas fewer studies have considered the impact of a camera onboard carsharing and privately shared autonomous vehicles (PSAVs) on traveler choices. This research was developed to predict the impact of traveler preferences on the availability of PSAV and carsharing considering certain variables, such as travel time, travel cost, waiting/walking time, and the availability of camera onboard (i.e., as an indication of comfort and privacy factors). A stated preference (SP) survey was designed in which sociodemographic and travel variables were included. A discrete choice experiment (DCE) was designed and included in the SP survey. The SP survey was distributed in Budapest, Hungary, where 313 respondents were collected. The discrete choice modeling approach is used, in which the multinomial logit (MNL) model was applied in developing a transport mode choice model. The developed model estimated the impact of different variables on transport modes as well as the willingness of travelers to use carsharing and PSAVs. The model explained the effects of trip length, income, gender, education, occupation, frequent current transport mode, and trip purpose on travelers' selection of transport mode. In conclusion, it demonstrated that people tend to use carsharing more than PSAVs, and the availability of cameras onboard decreases the probability of using a transport mode. [ABSTRACT FROM AUTHOR]

Traffic and transport researchers, policymakers, and vehicle manufacturers are interested in investigating the implications and the influence of autonomous vehicles (AV) because a gradual deployment of such new technologies is expected to take place in the future. This research investigates the impacts of the AVs emergence on traffic performance for the city of Budapest in three future traffic scenarios with different AV market replacement rates for the year 2030. The network was modeled using simulation-based dynamic traffic assignment in PTV Visum software. Four traffic performance parameters were analyzed to explore the impacts of AV's emergence on the network. The results showed noticeable improvements among the four investigated traffic performance parameters. [ABSTRACT FROM AUTHOR]

Purpose: This study first presents a method to identify the parameters increasing road vulnerability on a macroscopic road network model. The second part exploresthe effect size difference of the analyzed attributes on network vulnerability through the implementation of different autonomous vehicles (AVs) penetrations and automation levels. Methods: The road traffic network of Budapest, Hungary on PTV VISUM is studied by adopting a passenger car unit factor procedure to simulate the effect of AVs on road saturation. Five link parameters were used: length, distance from the centre, speed, number of lanes, and number of connectors. Network vulnerability was studied by simulating a combination of road elimination process with different passenger car unit values for AVs. Results: The analysis found the number of road lanes is the most significant parameter, affecting the link criticality; followed by road length and distance from the centre. The analysis of four AV scenarios with different AV penetration and level of automation showed huge effect differences ranging from 3.50% for a simple AV automation level with low AV percentage to as large to 28.53% for a fully automated fleet. Conclusions: AV implementation has proved efficient in reducing the amount of travel delays in the case of road failure. Finally, it was found that the number of lanes remained the most significant influencing parameter on travel delay. The main question is to discover the effect size difference of the analyzed attributes on network vulnerability through the implementation of different AVs penetrations and automation levels. [ABSTRACT FROM AUTHOR]

The collapse of the Morandi Bridge in Genoa has pointed out the importance of the structural health monitoring of large structures, like bridges, skyscrapers, etc. Although GNSS sensors have been used in these monitoring tasks, the spread of the technology was limited due to the high costs of the GNSS receivers. Recently, U-blox has marketed a new, low-cost multi-constellation, multi-frequency RTK-capable GNSS receiver mainly for the autonomous driving industry. Nevertheless, this receiver opens up new applications as well as in the civil engineering as in the surveying practice. At the Department of Geodesy and Surveying of the Budapest University and Technology a low-cost GNSS displacement and deformation monitoring system was designed and realized. The system consists of field modules and server-side components. The field module contains a U-blox F9P GNSS receiver, a Raspberry Pi 4 computer, a GSM WiFi modem and a battery. The open source RTKLIB program is used to send the NTRIP correction to the GNSS receiver and it also relays the calculated positions to the server components. On the server side the positions and related information are collected in a PostgreSQL database using a Python program developed at our department. The collected data is published using thin web clients in a normal web browser. Server-side components are written in PHP while Javascript code runs on the client side. AJAX (Asynchronous JavaScript and XML) technology makes the solution dynamic. After few weeks of testing and bug fixing the developed monitoring system was used in the construction of the new triple railway bridge above the Danube at Budapest. The individual bridges consist of six 80-90-meter-long parts which are lifted from two barges. Six field modules were installed, four on the lifted element and two on the fixed one. Our monitoring system is responsible to track the deformation of the bridge element caused by the changes of the water level of the Danube. The system is designed to monitor cm level displacement in real-time, and initiate alerts in case of large displacements. Its application is not limited to structural monitoring, other application areas are also feasible, where the real-time cm accurate GNSS positioning is an asset, such as subsidence monitoring over underground caves or mines or landslide monitoring. [ABSTRACT FROM AUTHOR]

Introducing autonomous vehicles (AVs) on the market is likely to bring changes in the mobility of travelers. In this work, extensive research is conducted to study the impact of different levels of automation on the mobility of people, and full driving automation needs further study because it is still under development. The impacts of AVs on travel behavior can be studied by integrating AVs into activity-based models. The contribution of this study is the estimation of AVs' impacts on travelers' mobility when different travel demands are provided, and also the estimation of AVs' impact on the modal share considering the different willingness of pay to travel by AVs. This study analyses the potential impacts of AVs on travel behavior by investigating a sample of 8500 travelers who recorded their daily activity plans in Budapest, Hungary. Three scenarios are derived to study travel behavior and to find the impacts of the AVs on the conventional transport modes. The scenarios include (1) a simulation of the existing condition, (2) a simulation of AVs as a full replacement for conventional transport modes, and (3) a simulation of the AVs with conventional transport modes concerning different marginal utilities of travel time in AVs. The simulations are done by using the Multi-Agent Transport Simulation (MATSim) open-source software, which applies a co-evolutionary optimization algorithm. Using the scenarios in the study, we develop a base model, determine the required fleet size of AVs needed to fulfill the demand of the different groups of travelers, and predict the new modal shares of the transport modes when AVs appear on the market. The results demonstrate that the travelers are exposed to a reduction in travel time once conventional transport modes are replaced by AVs. The impact of the value of travel time (VOT) on the usage of AVs and the modal share is demonstrated. The decrease in the VOT of AVs increases the usage of AVs, and it particularly decreases the usage of cars even more than other transport modes. AVs strongly affect the public transport when the VOT of AVs gets close to the VOT of public transport. Finally, the result shows that 1 AV can replace 7.85 conventional vehicles with acceptable waiting time. [ABSTRACT FROM AUTHOR]