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The impacts of autonomous vehicles, coupled with greater inter-vehicle and system connectivity, may be far-reaching on several levels. They entail changes to (1) the demand and behavior side, (2) the supply of mobility services, and (3) network and facility operational performance. We focus here on their impact on traffic flow and operations, especially in mixed traffic situations in which autonomous vehicles share the road with regular, human-driven vehicles, along with connected vehicles that may also have some automated functions. These mixed traffic situations correspond to likely deployment scenarios of the technologies, especially in the long transition towards 100% deployment. We explain using elementary traffic science concepts how autonomous vehicles and connected vehicles are expected to increase the throughput of highway facilities, as well as improve the stability of the traffic stream. A microsimulation framework featuring varying behavioral mechanisms for the three classes of vehicles is introduced. The framework is used to examine the throughput and stability questions through a series of experiments under varying market penetration rates of autonomous and/or connected vehicles; at low market shares, the impacts are relatively minor on either throughput or stability. However, as market shares increase, autonomous vehicles exert a greater influence on both dimensions compared to the same shares of connected vehicles. Applications of the framework to examine the effectiveness of selected traffic management approaches are discussed, including dedicated lanes for autonomous vehicles (good only if its use is optional and when the market share of autonomous vehicles is greater than the percentage of nominal capacity represented by that lane), and speed harmonization.

A simple yet efficient traffic flow model, in particular one that describes vehicle longitudinal operational control and further characterizes a traffic flow fundamental diagram, is always desirable. Though many models have been proposed in the past with each having its own merits, research in this area is far from conclusive. This paper contributes a new model, i.e., the longitudinal control model, to the arsenal with a unique set of properties. The model is suited for a variety of transportation applications, among which a concrete example is provided in this paper.

Mixed non-motorized traffic is largely unaffected by motor vehicle congestion, offering high accessibility and convenience, and thus serving as a primary mode of “last-mile” transportation in urban areas. To advance stochastic capacity estimation methods and provide reliable assessments of non-motorized roadway capacity, this study proposes a stochastic capacity estimation model based on power spectral analysis. The model treats discrete traffic flow data as a time-series signal and employs a stochastic signal parameter model to fit stochastic traffic flow patterns. Initially, UAVs and video cameras are used to capture videos of mixed non-motorized traffic flow. The video data were processed with an image detection algorithm based on the YOLO convolutional neural network and a video tracking algorithm using the DeepSORT multi-target tracking model, extracting data on traffic flow, density, speed, and rider characteristics. Then, the autocorrelation and partial autocorrelation functions of the signal are employed to distinguish among four classical stochastic signal parameter models. The model parameters are optimized by minimizing the AIC information criterion to identify the model with optimal fit. The fitted parametric models are analyzed by transforming them from the time domain to the frequency domain, and the power spectrum estimation model is then calculated. The experimental results show that the stochastic capacity model yields a pure EV capacity of 2060–3297 bikes/(h·m) and a pure bicycle capacity of 1538–2460 bikes/(h·m). The density–flow model calculates a pure EV capacity of 2349–2897 bikes/(h·m) and a pure bicycle capacity of 1753–2173 bikes/(h·m). The minimal difference between these estimates validates the effectiveness of the proposed model. These findings hold practical significance in addressing urban road congestion.

To explore the potential relationship between the leading vehicle and the following vehicle during car-following, we proposed a novel car-following model combining a convolutional neural network (CNN) with a long short-term memory (LSTM) network. Firstly, 400 car-following periods were extracted from the natural driving database and the OpenACC car-following experiment database. Then, we developed a CNN-LSTM car-following model, and the CNN is employed to analyze the potential relationship between the vehicle’s dynamic parameters and to extract the features of car-following behavior to generate the feature vector. The LSTM network is adopted to save the feature vector and predict the speed of the following vehicle. Finally, the CNN-LSTM model is trained and tested with the extracted car-following trajectories data and compared with the classical car-following models (LSTM model, intelligent driver model). The results show that the accuracy and the ability to learn the heterogeneity of the proposed model are better than the other two. Furthermore, the CNN-LSTM model can accurately reproduce the hysteresis phenomenon of congested traffic flow and apply to heterogeneous traffic flow mixed with adaptive cruise control vehicles on the freeway, which indicates that it has strong generalization ability.

Car-following behavior is the result of the interaction of various elements in the specific driver-vehicle-environment aggregation. Under the intelligent and connected condition, the information perception ability of vehicles has been significantly enhanced, and abundant information about the driver-vehicle-environment factors can be obtained and utilized to study car-following behavior. Therefore, it is necessary to comprehensively take into account the driver-vehicle-environment factors when modeling car-following behavior under intelligent and connected conditions. While there are a considerable number of achievements in research on car-following behavior, a car-following model with comprehensive consideration of driver-vehicle-environment factors is still absent. To address this gap, the literature with a focus on car-following behavior research with consideration of the driver, vehicle, or environment were reviewed, the contributions and limitations of the previous studies were analyzed, and the future exploration needs and prospects were discussed in this paper. The results can help understand car-following behavior and the traffic flow characteristics affected by various factors and provide a reference for the development of traffic flow theory towards smart transportation systems and intelligent and connected driving.

The application of vehicle-to-everything (V2X) technology has resulted in the traffic environment being different from how it was in the past. In the V2X environment, the information perception ability of the driver–vehicle unit is greatly enhanced. With V2X technology, the driver–vehicle unit can obtain a massive amount of traffic information and is able to form a connection and interaction relationship between multiple vehicles and themselves. In the traditional car-following models, only the dual-vehicle interaction relationship between the object vehicle and its preceding vehicle was considered, making these models unable to be employed to describe the car-following behavior in the V2X environment. As one of the core components of traffic flow theory, research on car-following behavior needs to be further developed. First, the development process of the traditional car-following models is briefly reviewed. Second, previous research on the impacts of V2X technology, car-following models in the V2X environment, and the applications of these models, such as the calibration of the model parameters, the analysis of traffic flow characteristics, and the methods that are used to estimate a vehicle’s energy consumption and emissions, are comprehensively reviewed. Finally, the achievements and shortcomings of these studies along with trends that require further exploration are discussed. The results that were determined here can provide a reference for the further development of traffic flow theory, personalized advanced driving assistance systems, and anthropopathic autonomous-driving vehicles.

In the past several decades, the car-following model has attracted many experts and scholars from the fields of traffic engineering, computational physics, psychology and so on, these experts and scholars have carried on the thorough research and the exploration to it, causes the model analysis essential factor from simple to rich, from simple to complex. With the continuous improvement of the model, the improved car-following model is more and more in line with the actual traffic conditions. With the development of traffic technology, driver’s car-following behavior will change greatly, which is realized by developing and establishing ITS, driver information guidance system and vehicle automatic intelligent cruise system. Therefore, a lot of car-following models for specific problems have emerged, which is of great practical significance to the further study of car-following theory. In this paper, the research background and significance of car-following model are deeply discussed, the current research status and conclusions are comprehensively analyzed, and the future research direction in this field is prospected.

Researchers are increasingly turning to roadway traffic flow theory to propose effective solutions for challenges such as traffic congestion and low efficiency in waterway transportation. However, since roadway traffic flow theory was originally developed for highway transportation, its direct application to waterways raises questions due to the inherent differences between the two modes of transportation. Meanwhile, research results and methodologies from other transportation modes can provide valuable insights for studying waterway traffic flow theory. Addressing these questions is essential for advancing research in this field. This research conducts a comparative analysis to explore the similarities and differences between typical transportation modes and waterway transportation, examining how these distinctions affect the application of existing traffic flow theories. It also categorizes recent research outcomes related to traffic flow theories from various transportation modes based on their relevance to waterway traffic flow theory. The discussion includes the applicability of these models and methods in the context of waterway transportation, considering the unique characteristics of waterway traffic. Finally, this study highlights current challenges in applying traffic flow theories to waterways and offers suggestions for future research.

Computer simulations of traffic and driving provide essential solutions to reduce risk and cost in traffic-related studies and research. Through nearly 90 years of simulation development, many research projects have attempted to improve the various aspects of realism through the use of traffic theory, cameras, eye-tracking devices, sensors, etc. However, the previous studies still present limitations, such as not being able to simulate mixed and chaotic traffic flows, as well as limited integration/interoperability with 3D driving simulators. Thus, instead of reusing previous traffic simulators, in this paper, we define relevant concepts and describe the development and testing of a novel traffic generator. First, we introduce realistic aspects to improve traffic generation, including interactive physics (i.e., interactions based on physics among the vehicles, infrastructure, and weather) and natural traffic behaviors (e.g., road user behaviors and traffic rules), allowing the self-driving vehicle behaviors to mimic human behaviors under stochastic factors such as random vehicles and speed. Second, we gain experiences from the technical deficiencies of existing systems. Third, we propose methods for traffic generation based on the action point angle of sight (APAS) formula, which adheres to these constraints and is interoperable with modern driving simulators. We also conducted quantitative evaluations in two experiments (comprising 250 trials), in order to prove that the proposed solution can effectively simulate mixed traffic flows. Moreover, the approaches presented in this study can help self-driving cars to find their way at an intersection/T-junction, as well as allowing them to steer automatically after an accident occurs. The results indicate that traffic generation algorithms based on these new traffic theories can be effectively implemented and used in modern driving simulators and multi-driving simulators, outperforming previous traffic generators based on repurposed technologies.

Short-term traffic flow forecasting is an essential part of intelligent transportation systems. However, it is challenging to model traffic flow accurately due to its rapid changes over time. The Kolmogorov–Arnold Network (KAN) has shown parameter efficiency with lower memory and computational overhead via spline-parametrized functions to handle high-dimensional temporal data. In this paper, we propose to unlock the potential of the Kolmogorov–Arnold network for traffic flow forecasting by optimizing its parameters with a heuristic algorithm. The gravitational search algorithm learns to understand optimized KANs for different traffic scenarios. We conduct extensive experiments on four real-world benchmark datasets from Amsterdam, the Netherlands. The RMSE of GSA-KAN is reduced by 3.95%, 6.96%, 2.71%, and 2.29%, and the MAPE of GSA-KAN is reduced by 6.66%, 5.88%, 6.41%, and 4.87% on the A1, A2, A4, and A8 datasets, respectively. The experimental results demonstrate that GSA-KAN performs advanced parametric and nonparametric models.