Several transport policies reduce pollution levels caused by private vehicles by introducing autonomous or electric vehicles and encouraging mode shift from private to public transport through park and ride (P&R) facilities. However, combining the policies of introducing autonomous vehicles with the implementation of electric vehicles and using the P&R system could amplify the decrease of transport sector emissions. The COPERT software has been used to calculate the emissions. This article aims to study these policies and determine which combinations can better reduce pollution. The result shows that each combination of autonomous vehicles reduces pollution to different degrees. In conclusion, the shift to more sustainable transport modes through autonomous electric vehicles and P&R systems reduces pollution in the urban environment to a higher percentage. In contrast, the combination of autonomous vehicles has lower emission reduction but is easier to implement with the currently available infrastructure.

The increasing worldwide demand on urban road transportation systems requires more restrictive measures and policies to reduce congestion, time delay and pollution. Autonomous vehicle mobility services, both shared and private, are possibly a good step towards a better road transportation future. This article aims to study the expected impact of private autonomous vehicles on road traffic parameters from a macroscopic level. The proposed methodology focuses on finding the different effects of different combinations of autonomous vehicle penetration and Passenger Car Units (PCU) on the chosen road traffic model. Four parameters are studied: traveled daily kilometers, daily hours, total daily delay and average network speed. The analysis improves the four parameters differently by implementing autonomous vehicles. The parameter total delay has the most significant reduction. Finally, several mathematical models are developed for the percentage of improvement for each chosen parameter.

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.

This study presents a method to analyze the most critical elements of the public road system concerning outer effects which hinder the normal operation of the whole system. The surveyed public road network in Budapest, Hungary is studied as a graph: Dijkstra’s algorithm is applied to find the shortest path, and the Boykov-Kolmogorov method is used to calculate the maximum flow of the network. Those elements are identified whose damage can critically influence the operation of the network, and where the infrastructure available for rescue teams has a bottleneck. Finally, the Wilcoxon post hoc test was applied with Bonferroni correction. The tests have proven that the new method can successfully identify the critical vulnerabilities of the network to determine its weak points by considering reduced road capacities and the increased needs for transportation arising due to a disaster. This pilot study confirmed that after the elimination of the problems in statistical methods, the new framework can robustly identify those road network elements whose development is of key importance from a disaster management perspective.

This paper studies the effect of automated vehicle implementation on transport system emission from a macroscopic point of view. The paper considers several scenarios differing in passenger car unit (PCU) and the penetration share percent of automated vehicles in the system using PTV Visum software. The study presents that automated vehicles reduce total emission by both the effect of smooth driving of each automated vehicle independently and the spread of automated vehicles in the network. Furthermore, apart from considering the effect of different PCU values and penetration levels, the developed model takes into account three different types of emissions and seven different vehicle classes.