Automated Vehicles (AVs) hold substantial potential to revolutionize urban transportation, but their safe and reliable operation in mixed-traffic conditions remains a challenge. While computer-based ...simulations can provide cost-effective, safe performance evaluations, they often fail to represent the complex realities of large-scale traffic systems. This paper presents a novel co-simulation approach that integrates high-fidelity AVs with adjustable penetration rates into a comprehensive traffic model. The co-simulation, developed by coupling the automated driving simulator CARLA with the microscopic traffic simulator SUMO, allows for the detailed evaluation of AV performance under diverse traffic conditions. It also aids in the estimation of AVs' impacts on traffic patterns. Despite the computational demands, the co-simulation scenario provides a scalable, dynamic, and robust platform for realistic AV performance assessment.
Fundamental diagrams (FDs) present the relationship between flow, speed, and density, and give some valuable information about traffic features such as capacity, congested and uncongested situations, ...and so forth. On the other hand, high accuracy speed-density models can produce more efficient FDs. Although numerous speed-density models are presented in the literature, there are very few models for connected and autonomous vehicles (CAVs). One of the recent spend-density models that takes into account the penetration rate of CAVs is provided by Lu et al. However, the estimation power of this model has not been tested against other speed-density models, and it has not been applied to high-speed networks such as freeways. Thus, this paper made a comparison between the Lu speed-density model and a well-known speed-density model (Papageorgiou) in freeway and grid networks. Different CAV behaviors (aggressive, normal, and conservative) are evaluated in this comparison. The comparison has been made between two speed-density models using the mean absolute percentage error (MAPE) and a t-test. The MAPE and t-test results show that differences between the two speed-density models are not significant in two case studies and that Lu is a powerful speed-density model to estimate speed compared with a well-known speed-density model. For the sake of comparing the above-mentioned models, this paper investigates the impact of CAVs on capacity based on FDs. The results suggest that the magnitude of the impacts of CAVs on road capacity (capacity increment percentage) which are obtained from two speed-density models are very close to each other. Also, the extent to which CAVs affect road capacity is highly dependent on their behavior.
This paper introduces an intelligent control system for traffic signal applications, called Fuzzy Intelligent Traffic Signal (FITS) control. It provides a convenient and economic approach to improve ...existing traffic light infrastructure. The control system is programmed on an intermediate hardware device capable of receiving messages from signal controller hardware as well as overriding traffic light indications during real-time operations. Signal control and optimization toolboxes are integrated into the embedded software in the FITS hardware device. A fuzzy logic based control has been implemented in FITS. In order to evaluate the effects of FITS system, this study attempts to develop a computational framework to evaluate FITS system using microscopic traffic simulation. A case study is carried out, comparing different commonly used signal control strategies with the FITS control approach. The simulation results show that the control system has the potential to improve traffic mobility, compared to all of the tested signal control strategies, due to its ability in generating flexible phase structures and making intelligent timing decisions. In addition, the effects of detector malfunction are also investigated in this study. The experiment results show that FITS exhibits superior performance than several other controllers when a few detectors are out-of-order due to its self-diagnostics feature.
Unconventional arterial intersection designs have recently gained popularity as an alternative solution to alleviate congestion and mitigate safety problems of at-grade signalized intersections. This ...study investigates the operational and safety performance of the parallel flow intersection (PFI) under different balanced and unbalanced traffic volume conditions in comparison with a conventional counterpart using VISSIM microsimulation and Surrogate Safety Assessment Model software. The operational performance of the two intersections was based on the overall intersection capacity and the average vehicle delay. The safety performance, on the other hand, was based on the number and severity of simulated conflicts. The results showed that the PFI had a higher capacity compared with the conventional intersection under balanced volume conditions. Under unbalanced volume levels, the PFI outperformed the conventional intersection especially with an increase of left-turn volumes. Moreover, the PFI enhanced the safety conditions relative to the conventional intersection by eliminating all the crossing conflicts under all balanced and unbalanced volume levels and reducing the total number and severity of conflicts at all volume conditions. The study provides guidelines for the optimal spacing distance between the main and secondary intersections for the implementation of the PFI based on its safety and operational impacts on the intersection.
A promising traffic management strategy is the application of special signal timing plans on alternative routes during freeway incidents. The development of such plans requires the estimation of the ...route diversion during incident conditions. This study utilizes a data analytic approach to support the estimation of the diversion rate during incidents and to use this information as an input to the development of special signal timing plans during freeway incidents. First, a method is developed to predict the rate of traffic diversion caused by incidents based on the freeway mainline detector data combined with incident data. The diversion prediction method utilizes a combination of cumulative volume analysis, clustering analysis, and predictive data analytics. Three predictive data analytic methods: linear regression, multilayer perceptron, and support vector machine models, are investigated to predict diversion as a function of incident attributes. Next, a methodology is proposed to develop special signal plans to manage the demand surge on the diversion routes without deteriorating the intersection’s overall performance. The evaluation of the developed methodology indicates that it can significantly reduce the delays on the alternative routes.
This paper aims to evaluate the sensitivity of the proposed cooperative dynamic bus lane system with microscopic traffic simulation models. The system creates a flexible bus priority lane that is ...only activated on demand at an appropriate time with advanced information and communication technologies, which can maximize the use of road space. A decentralized multi-lane cooperative algorithm is developed and implemented in a microscopic simulation environment to coordinate lane changing, gap acceptance, and car-following driving behavior for the connected vehicles (CVs) on the bus lane and the adjacent lanes. The key parameters for the sensitivity study include the penetration rate and communication range of CVs, considering the transition period and gradual uptake of CVs. Multiple scenarios are developed and compared to analyze the impact of key parameters on the system’s performance, such as total saved travel time of all passengers and travel time variation among buses and private vehicles. The microscopic simulation models showed that the cooperative dynamic bus lane system is significantly sensitive to the variations of the penetration rate and the communication range in a congested traffic state. With a CV system and a communication range of 150 m, buses obtain maximum benefits with minimal impacts on private vehicles in the study simulation. The safety concerns induced by cooperative driving behavior are also discussed in this paper.
Agent-based modeling has become increasingly prevalent in transportation systems simulation as the scenarios around new technologies and policies become increasingly complex. This increased ...complexity, in terms of traveler behavior, traffic flow, modal operations, system management, and so on, requires increasingly sensitive and detailed representation of the core components of the simulation, especially as it relates to traffic flow. Many future mobility solutions rely on connectivity, communication, advanced sensing, and detailed information flows, while the simulation also needs to remain computationally tractable. In this paper, we propose a new Lagrangian Coordinate within the POLARIS Agent-Based-Modeling Framework (LC-POLARIS) of traffic flow, which combines computational efficiency while adding microscopic features. This model allows multi-class traffic flow by setting different speed-spacing relationships. In this modeling, automated vehicles have lower reaction time, while light-duty trucks have lower free-flow speed and higher jam spacing. The model captures the reduction in capacity and higher queue spillback because of higher jam spacing of trucks. Also, the model yields higher capacity for both passenger and light duty automated vehicles on expressways. The LC-ABM traffic flow model is validated in Bloomington, IL, USA, in a scenario with different rates of four vehicle types.
•An in-depth overview was made on the application of traditional vehicle detector and machine vision.•Various kinds of simulation models for traffics on road bridges were summarised and compared.•The ...potentials of intelligent cognition in supporting design, operation, and maintenance were discussed.•Comparison of various frontier load cognition methods offers reference to academia and professionals.
Traffic load is a crucial but complicated factor in determining the in-service performance and deterioration behavior of bridges. A better understanding of traffic loads in different traffic densities has become increasingly important in structure health monitoring. As a result, for the traffic load measurement, the relevant technologies had great progress in the past decades. Therefore, we focus on introducing the state-of-the-art approaches most relevant to the traffic load cognition on road bridges, including in-site measurement and data-driven simulation. General principles of the traffic load cognition are firstly presented by reviewing different statistical analysis techniques for determining the spatial-temporal factors of vehicles. Then, this paper reviews various measurement methods carried out for the essential data of traffic loads. The methods are roughly grouped into mechanical, optical and microwave sensor-based methods. Within each category, technical descriptions of the sensor types, properties and applications are discussed in terms of theoretical formulas and feasible scenarios. This paper also implements qualitative and comprehensive comparisons between multiple measurement sensors to show the efficiency of each method or technique. Base on in-site measurement, several kinds of simulation models can be established for traffic loads on road bridges, including the modelling of single vehicles and the overall traffic flow. Considering the significant contribution of statistics-based deterministic, direct probabilistic methods, and artificial intelligence to traffic load cognition, we carried out the investigation on them in vehicle modelling. For on-bridge traffic flow simulation, three representative microscopic models are reviewed, involving the car-following, hydrodynamic, and cellular automatic models. Overall, this study highlights the application of intelligent cognition methods in identifying and simulating traffic loads on road bridges, potentially providing support for digitalised design, operation, and maintenance.
Routing algorithms typically suggest the fastest path or slight variation to reach a user's desired destination. Although this suggestion at the individual level is undoubtedly advantageous for the ...user, from a collective point of view, the aggregation of all single suggested paths may result in an increasing impact (e.g., in terms of emissions).In this study, we use SUMO to simulate the effects of incorporating randomness into routing algorithms on emissions, their distribution, and travel time in the urban area of Milan (Italy). Our results reveal that, given the common practice of routing towards the fastest path, a certain level of randomness in routes reduces emissions and travel time. In other words, the stronger the random component in the routes, the more pronounced the benefits upon a certain threshold. Our research provides insight into the potential advantages of considering collective outcomes in routing decisions and highlights the need to explore further the relationship between route randomization and sustainability in urban transportation.
•Formulate pedestrians’ decision-making behavior at signalized crosswalk.•Represent pedestrian evasion behavior and collision avoidance with vehicles.•Calibrate model parameters by separating into ...measurable and non-measurable groups.•Visually reproduce pedestrian crossing behavior in microscopic simulation.
Limited pedestrian behavior models shed light on the case at signalized crosswalk, where pedestrian behavior is characterized by group or individual evasion with surrounding pedestrians, collision avoidance with conflicting vehicles, and response to signal control and crosswalk boundary. This study fills this gap by developing a microscopic simulation model for pedestrian behavior analysis at signalized intersection. The social force theory has been employed and adjusted for this purpose. The parameters, including measurable and non-measurable ones, are either directly estimated based on observed dataset or indirectly derived by maximum likelihood estimation. Last, the model performance was confirmed in light of individual trajectory comparison between estimation and observation, passing position distribution at several cross-sections, collision avoidance behavior with conflicting vehicles, and lane-formation phenomenon. The simulation results also concluded that the model enables to visually represent pedestrian crossing behavior as in the real world.
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