Developing a micro-simulation tool for autonomous connected vehicle platoons used in city logistics

Exploring parking choices under the coexistence of autonomous and conventional vehicles

Given the rapid technological advances in developing autonomous vehicles (AV), the key question appears not so much anymore how, but when AVs would be ready to be commercially introduced. Therefore, it is very timely to explore how the new way of travelling will shape the traffic environment in the future. Questions regarding the environmental impact, changes in infrastructure and policy measures are widely discussed. Most likely, the introduction of AVs will not only add an option to the traveller’s choice of means of transport, but also shape how people interact with the traffic environment. From a transport planning point of view, key questions concerning the introduction of AVs as a new means of transport are how it will influence travel behaviour, how supply and demand for AV will balance, how it impacts the viability of existing public transport services and how AVs will impact congestion and demand for parking. In this report, a new simulation framework based on MATSim is presented, allowing for the simulation of AVs in an integrated, network- and population-based traffic environment. The demand evolves dynamically from the traffic situation rather than being a static constraint as in numerous previous studies. This allows for the testing of various scenarios and concepts around the introduction of AVs while taking into account their feedback on the travellers’ choices and perceptions. Using a realistic test scenario, it is shown that even under conservative pricing a large share of travellers is attracted by autonomous vehicles, though it is highly dependend on the provided fleet size. For sufficiently large supplies it has been found that for the autonomous single-passenger taxis in this report the vehicle miles travelled increase up to 60%.

When people pick routes to minimize their travel time, the total experienced delay, or social cost, may be significantly greater than if people followed routes assigned to them by a social planner. This effect is accentuated when human drivers share roads with autonomous vehicles. When routed optimally, autonomous vehicles can make traffic networks more efficient, but when acting selfishly, the introduction of autonomous vehicles can actually worsen congestion. We seek to mitigate this effect by influencing routing choices via tolling. We consider a network of parallel roads with affine latency functions that are heterogeneous, meaning that the increase in capacity due to to the presence of autonomous vehicles may vary from road to road. We show that if human drivers and autonomous users have the same tolls, the social cost may be arbitrarily worse than when optimally routed. We then prove qualities of the optimal routing and use them to design tolls that are guaranteed to minimize social cost at equilibrium. To the best of our knowledge, this is the first tolling scheme that yields a unique socially optimal equilibrium for parallel heterogeneous network with affine latency functions.

This study employs the PTV VISSIM simulation software to investigate the impact of increasing traffic volumes on conventional vehicles and autonomous vehicles (AVs) with distinct behavioural traits: cautious, normal, and aggressive. The simulations cover a range of traffic volumes, from 100 to 600 vehicles, and measure the effects on travel time, emissions (CO, NOX, VOC), and fuel consumption. The results show that with increasing penetration rates of AVs, travel times generally decrease, with aggressive AVs achieving the shortest times, followed by normal, then cautious AVs. Emissions and fuel consumption also tend to decrease as the penetration rate of AVs increases. Notably, the results demonstrate that aggressive AVs excel in reducing travel time, while normal AVs consistently balance between efficiency and reduced emissions, and cautious AVs emphasize safety and lower emissions. Despite the differing behavioural traits, all AV types exhibit a marked improvement over conventional vehicles in terms of travel time, emissions, and fuel consumption. At every penetration rate, AVs lead to shorter travel times and lower emissions, with aggressive AVs being the most efficient, followed by normal and then cautious AVs. These findings emphasize the potential benefits of integrating autonomous vehicles into transportation networks. They suggest that optimizing AV behaviour, depending on the context and objectives, can lead to more efficient, environmentally friendly traffic systems. The study offers valuable insights for policymakers, urban planners, and researchers aiming to leverage the distinct strengths of each AV behaviour to create a more sustainable and efficient future for autonomous transportation.

A Model-to-Decision Approach for the Autonomous Vehicle (AV) Ethical Dilemma: AV Collision with a Barrier/Pedestrian(s)

The introduction of autonomous vehicles (AVs) in the near future will have a significant impact on road traffic. AVs may have advantages in efficiency and convenience, but safety can be compromised in mixed operations of manual vehicles and AVs. To deal with the issues associated with mixed traffic and to avoid its negative effects, a special purpose lane reserved for AVs can be proposed to segregate AVs from manual vehicles. In this research, we analyze the effect on efficiency and safety of AVs in mixed traffic and in a situation where an AV-only lane is deployed. In the analysis, we investigate the average speed, the throughput, and the inverse time-to-collision (ITTC). We differentiate the behaviors of manual vehicles and AVs through the reaction time, desired speed, and car-following models. As a result, we observe that the efficiency is improved when the market penetration rate of AVs increases, especially when the highway throughput increases by up to 84% in the case of mixed traffic. However, safety worsens when the market penetration of AVs is under 40%. In this case, the average speed can be improved and the frequency of dangerous situations (ITTC > 0.49) can be reduced drastically in the merging section by making the innermost lane AV-only. Accordingly, we conclude that AV-only lanes can have a significant positive impact on efficiency and safety when the market penetration rate of AVs is low.

Recent developments in urban freight analytics for collaborative city logistics

Transportation planning models do not usually consider intersection delays explicitly in calculating path travel times. However, because intersection delays often make up a considerable portion of total travel time in urban areas, ignoring them will result in inaccuracy in model results. Incorporating intersection delays into a traffic assignment model presents several challenges. One is that future signal timing plans are unknown, and another is that introducing intersection delays into traffic assignment renders the necessary condition for convergence invalid. This paper proposes a pragmatic approach to considering intersection delays in traffic assignment by assuming that signal timing plans will be optimized for given traffic conditions and by demonstrating that a convergent solution can be obtained when intersection delays are explicitly accounted for. A large data set of traffic volume combinations representing the operation of an intersection and the corresponding intersection delays based on optimized signal timing are obtained from TRANSYT-7F. This data set is used to train an artificial neural network (ANN) model that provides delay estimates for given traffic conditions for certain types of intersection configurations. An iterative process of traffic assignment and delay estimation is carried out to obtain a stable assignment solution. The delay estimates from the ANN delay model are evaluated by the percentage root mean squared errors, which are less than 25.6%, with larger prediction errors typically associated with severely oversaturated conditions. An example is presented to show the use of the delay model in traffic assignment. The delay model allows the concurrent optimization of signal controls and traffic routing to obtain a local optimum network solution.

Automated driving systems are an emerging technology expected to transform the transportation industry due to reduced human involvement. While the introduction of autonomous vehicles (AVs) can significantly improve road safety, increase mobility, and reduce road crashes, there are also associated disadvantages. Policymakers have the ability to implement legislation that maximizes the benefits of AVs and minimizes their risks. Through an evaluation of barriers to AV adoption and regulations worldwide, this study aims to provide an overview of AV equity, liability, and safety policies globally. The results revealed that safety was the primary factor influencing public perception and hindering widespread adoption of autonomous vehicles. Furthermore, it was determined that governments have refrained from implementing stringent measures to encourage the advancement of AV technologies. Instead of enacting AV related laws, most countries have developed guidelines or formed councils to encourage further deliberation on safety and liability concerns associated with autonomous driving systems. Transportation professionals, AV consumers, and manufacturers will develop an understanding of AV accessibility, liability, and safety policies through this review. This paper can also help policymakers and drive future enacted legislation.

The rapid development of autonomous vehicles (AVs) has depicted a promising future of a safer and more efficient transportation system. To better induce this revolution, massive efforts have been spent on the technical competence of AVs. However, the human comfort in AVs has been an under-discussed yet important topic to the user acceptance of AVs. For the detection of human comfort, existing studies focus more on the physical influential factors of comfort such as sitting posture, vibration, and noise. With the introduction of AVs, psychological factors also have gained greater influence on human comfort. Despite existing studies of exploring correlations between human comfort and some physiological signals in automated driving contexts, there is few study on how human comfort level in AVs can be detected with these physiological signals. In this paper, we developed effective human comfort study approaches in autonomous vehicles with wearable sensors. We also proposed a machine learning based approach with adaptive feature selection to detect human comfort levels based on the wearable sensing data. The experimental results illustrated the effectiveness of the proposed approaches in studying human comfort in AVs.

As traffic congestion increases day by day, it becomes necessary to improve the existing roadway facilities to maintain satisfactory operational and safety performances. New vehicle technologies, such as Connected and Autonomous Vehicles (CAV) have a potential to significantly improve transportation systems. Using the advantages of CAVs, this study developed signalized intersection control strategy algorithm that optimizes the operations of CAVs and allows signal priority for connected platoons. The algorithm was tested in VISSIM microsimulation using a real-world urban corridor. The tested scenarios include a 2040 Do-Nothing scenario, and CAV alternatives with 25%, 50%, 75% and 100% CAV penetration rate. The results show a significant reduction in intersection delays (26% - 38%) and travel times (6% - 20%), depending on the penetration rate, as well as significant improvements on the network-wide level. CAV penetration rates of 50% or more have a potential to significantly improve all operational measures of effectiveness.

The introduction of autonomous vehicles (AVs) has the potential to drastically change society, planning, design, and development strategies. This study uses the PRISMA protocol to carry out a systematic literature review, focusing on the backcasting method as an analytic tool. By examining. 21 studies published between 2003 and 2024, this paper highlights the phases of backcasting: visioning, policy packaging, and appraisal, and identifies critical factors necessary for the successful integration of AVs. Visioning for future driverless cities includes high-quality urban areas, active mobility, and innovative developments. Policies and Packaging suggested a focus on restricting vehicular access, transit-oriented development, and encouraging public transportation. Appraisal reveals skepticism about the positive impacts of AVs, urging policies that limit access to urban areas and promote sustainable modes of transportation. The main contribution of this study lies in its comprehensive application of backcasting to AV implementation, offering a structured approach to envisioning future urban scenarios, formulating supportive policies, and evaluating their impact. This analysis provides a solid foundation for future research, urging us to explore the intersection between AVs, citizen participation, and environmental sustainability to achieve more efficient and sustainable cities.

Transitioning to a driverless city: Evaluating a hybrid system for autonomous and non-autonomous vehicles

This study proposes a joint model for transport mode choice and parking strategy in dense urban environments, incorporating emerging scenarios derived from the introduction of autonomous vehicles, both private and shared. Based on a stated preference survey and a hybrid approach with latent variables, the factors that influence user decisions are analysed, considering traditional observable attributes (cost, travel time, parking search time) and attitudinal constructs (technological acceptance, perceived sustainability). The results show that cost and time remain key determinants, but new relevant variables emerge, such as recovery time and preferences for unconventional parking strategies. In particular, the model reveals that users with greater acceptance of AVs are more likely to prefer strategies such as returning the vehicle to the home or dropping off without parking. These findings provide valuable evidence for urban mobility planning and policy design that promotes efficient use of space and the transition to more sustainable forms of transport. The model developed allows for the anticipation of technology adoption scenarios and the targeting of differentiated strategies according to user profiles • Joint hybrid model integrating mode and parking choices under AV scenarios. • Inclusion of recovery time as a behavioural component of the trip chain. • Latent variable captures predisposition toward autonomous driving technologies. • Findings reveal differentiated user responses based on attitudes and technology affinity. • Policy insights for efficient and sustainable urban parking management.

The introduction of autonomous vehicles (AVs) and shared autonomous vehicles (SAVs) is projected to enhance network performance and accessibility. The future share distribution of AV and SAV is not yet apparent, nor is which of these two future transport modes will become dominant. Therefore, this research deploys a simulation-based dynamic traffic assignment using Visum software to investigate the impact of varying the share distribution of AVs and SAVs on Budapest’s network performance and consumer surplus in three projected future traffic scenarios for the years 2030 and 2050 compared to the Base scenario for 2020. The three future scenarios are presented and characterized by different penetration rates of AVs and SAVs to reflect the uncertainty in the market share of these future cars as follows: Mix-Traffic scenario for 2030, and AV-Focused and SAV-Focused scenarios for 2050. The results revealed that the emergence of AVs and SAVs would improve the overall network performance, and better performance was observed with increasing the share distribution of SAVs. Similarly, the consumer surplus increased in all future scenarios, especially with increasing the share distribution of AVs. Consequently, the advent of AVs and SAVs will improve traffic performance and increase consumer surplus, benefiting road users and authorities.