Under Section III-B in[1], the first paragraph is rectified as below to include a missed citation:

The way in which autonomous transport will be adopted is likely to determine the net social benefits delivered by the technology and the sustainability of the transport system. Autonomous vehicles (AVs) will change travel behavior due to reduction in the effort needed for humans to drive a vehicle, the need for them to find a parking space, and the costs related to vehicle operation. The AVs’ benefits are likely to increase their adoption compared to conventional human-driven vehicles, possibly leading to more vehicle kilometers travelled (VKT) and consequently weakening their benefits in large cities particularly if they are used in competition with public transport (PT). This paper evaluates the interplay between AVs and PT, and how road network pricing can be used to influence behavioral changes when personal autonomous vehicles (PAVs) are highly available. An agent-based demand model framework is proposed to estimate the mode share of PAVs and PT based on their perceived travelling costs on a real transport network in Melbourne, Australia. The modelling results suggest that convenient and affordable PAVs could compete with traditional PT and reduce overall PT patronage by up to 10%. However, through considering road network pricing schemes, the role of PAVs could be shifted from competing with PT to a complementary first-and-last-mile service that increases PT share by almost 17%. The results also show that road pricing policies can be used as effective interventions to manage PAV operations by reducing empty vehicle trips by 20%.

Physics-informed neural networks (PINN s) are a newly emerging research frontier in machine learning, which incorporate certain physical laws that govern a given data set, e.g., those described by partial differential equations (PDEs), into the training of the neural network (NN) based on such a data set. In PINN s, the NN acts as the solution approximator for the PDE while the PDE acts as the prior knowledge to guide the NN training, leading to the desired generalization performance of the NN when facing the limited availability of training data. However, training PINNs is a non-trivial task largely due to the complexity of the loss composed of both NN and physical law parts. In this work, we propose a new PINN training framework based on the multi-task optimization (MTO) paradigm. Under this framework, multiple auxiliary tasks are created and solved together with the given (main) task, where the useful knowledge from solving one task is transferred in an adaptive mode to assist in solving some other tasks, aiming to uplift the performance of solving the main task. We implement the proposed framework and apply it to train the PINN for addressing the traffic density prediction problem. Experimental results demonstrate that our proposed training framework leads to significant performance improvement in comparison to the traditional way of training the PINN.

Connected and automated vehicles (CAVs), in the context of cooperative intelligent transportation systems (C-ITS), are capable of exchanging information with each other and the infrastructure. They can also form platoons that can drive with shorter time headways resulting in potential higher capacity, smoother flow, less fuel consumption, and reduction in collisions. However, the literature assessing their impacts is still fragmented and does not provide a solid evidence base about their potential contributions, particularly that they are not widely deployed yet. This study aims to establish this evidence using a systematic literature review (SLR) of the relevant body of knowledge. In this paper, 347 journal publications from 2000 to 2021, are analyzed and critiqued. Comprehensive bibliographic and co-citation analyses are conducted to uncover the key findings and bring out a valid and balanced perspective of CAVs and their impacts on traffic operations. A co-citation analysis of the body of knowledge on this topic resulted in the formation of four main clusters of research focus that included safety, efficiency, communication, and technology. This SLR presents an unbiased and objective summary of scientific findings on the topic and serves as a starting point for future investigations on CAVs by identifying research gaps, limitations and challenges, and potential areas of research to overcome these challenges.

The IEEE Transactions on Intelligent Transport Systems was founded in 2000 to enhance the sharing of international research on theoretical and practical technology developments in the ITS field. Since then, it has become a leading journal in the field and has attracted a high caliber of multi-disciplinary authors and publications. In recognition of twenty plus years of contributions to the field, this paper analyses the evolution of the journal over its lifetime for the period 2000–2021. A bibliometric analysis is conducted on 3,428 peer-reviewed publications (articles and reviews) using data collected from Core Collection Database of Web of Science. The paper identifies the most influential and cited articles and their impacts on the development of research in the ITS field. The analysis also includes detailed information on top leading authors, their organizations and countries where the research was funded and developed. The analysis shows how the growing interest and diversity of transport technology topics has led to an increase in the number and quality of publications in journal over the past twenty plus years. A visualization of bibliographic coupling, co-authorship and keywords analysis is also presented using the VOSviewer software leading to insightful findings regarding the journal’s impact and standing in this field of research.

Autonomous Mobility-on-Demand (AMoD) systems hold potential promise for addressing urban mobility challenges. Their key principle is to utilize fleets of shared self-driving vehicles to respond to customer demand on flexible routes in real-time. This research investigates station-based AMoD car-sharing systems and uses scenario analyses to identify plausible future paths for their deployment. A traffic simulation model which implements real-time rebalancing of idle vehicles is developed to evaluate their performance under uncertain travel demands. Unlike other literature which assumed homogeneous demand and resulted in low increases in vehicle kilometers travelled (VKT), this study relied on realistic heterogeneous demand and showed a significant increase in VKT. A case study for Melbourne demonstrated the impacts and showed that while AMoD can meet the demand for travel using only 16% of the current vehicle fleet, they would produce 77% increase in VKT. This would significantly increase congestion in any real-world scenario and goes against the hype of AMoD being the answer to congestion problems.

This paper presents a car-following model that was developed using a neural network approach for mapping perceptions to actions. The model has a similar formulation to the desired spacing models that do not consider reaction time or attempt to explain the behavioral aspects of car following. The model's performance was evaluated based on field data and compared to a number of existing car-following models. The results showed that neural network models outperformed the Gipps and psychophysical family of car-following models. A qualitative drift behavior analysis also confirmed the findings. The model was validated at the microscopic and macroscopic levels, and the results showed very close agreement between field data and model outputs. Local and asymptotic stability analysis results also demonstrated the robustness of the model under mild and severe traffic disturbances

Microscopic traffic-simulation tools are increasingly being applied to evaluate the impacts of a wide variety of intelligent transport systems (ITS) applications and other dynamic problems that are difficult to solve using traditional analytical models. The accuracy of a traffic-simulation system depends highly on the quality of the traffic-flow model at its core, with the two main critical components being the car-following and lane-changing models. This paper presents findings from a comparative evaluation of car-following behavior in a number of traffic simulators [advanced interactive microscopic simulator for urban and nonurban networks (AIMSUN), parallel microscopic simulation (PARAMICS), and Verkehr in Stadten-simulation (VISSIM)]. The car-following algorithms used in these simulators have been developed from a variety of theoretical backgrounds and are reported to have been calibrated on a number of different data sets. Very few independent studies have attempted to evaluate the performance of the underlying algorithms based on the same data set. The results reported in this study are based on a car-following experiment that used instrumented vehicles to record the speed and relative distance between follower and leader vehicles on a one-lane road. The experiment was replicated in each tool and the simulated car-following behavior was compared to the field data using a number of error tests. The results showed lower error values for the Gipps-based models implemented in AIMSUN and similar error values for the psychophysical spacing models used in VISSIM and PARAMICS. A qualitative "drift and goal-seeking behavior" test, which essentially shows how the distance headway between leader and follower vehicles should oscillate around a stable distance, also confirmed the findings.