Showing papers on "Traffic simulation published in 2022"

Game-Theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation

Abstract: For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with human-driven vehicles. Their planning and control systems need extensive testing, including early-stage testing in simulations where the interactions among autonomous/human-driven vehicles are represented. Motivated by the need for such simulation tools, we propose a game-theoretic approach to modeling vehicle interactions, in particular, for urban traffic environments with unsignalized intersections. We develop traffic models with heterogeneous (in terms of their driving styles) and interactive vehicles based on our proposed approach, and use them for virtual testing, evaluation, and calibration of AV control systems. For illustration, we consider two AV control approaches, analyze their characteristics and performance based on the simulation results with our developed traffic models, and optimize the parameters of one of them.

Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections

Abstract: Motivated by the need for simulation tools for testing, verification and validation of autonomous driving systems that operate in traffic consisting of both autonomous and human-driven vehicles, we propose a game-theoretic framework for modeling the interactive behavior of vehicles at uncontrolled intersections. The proposed vehicle interaction model is based on a novel formulation of dynamic games with multiple concurrent leader-follower pairs, induced from common traffic rules. Based on simulation results for various intersection scenarios, we show that the model exhibits reasonable behavior expected in traffic, including the capability of reproducing scenarios extracted from real-world traffic data and reasonable performance in resolving traffic conflicts. The model is further validated based on the level-of-service traffic quality rating system and demonstrates manageable computational complexity compared to traditional multi-player game-theoretic models.

Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario

Abstract: There is consensus in industry and academia that Highly Automated Vehicles (HAV) and Connected Automated Vehicles (CAV) will be launched into the market in the near future due to emerging autonomous driving technology. In this paper, a mixed traffic simulation framework that integrates vehicle models with different automated driving systems in the microscopic traffic simulation was proposed. Currently, some of the more mature Automated Driving Systems (ADS) functions (e.g., Adaptive Cruise Control (ACC), Lane Keeping Assistant (LKA), etc.) are already equipped in vehicles, the very next step towards a higher automated driving is represented by Level 3 vehicles and CAV which show great promise in helping to avoid crashes, ease traffic congestion, and improve the environment. Therefore, to better predict and simulate the driving behavior of automated vehicles on the motorway scenario, a virtual test framework is proposed which includes the Highway Chauffeur (HWC) and Vehicle-to-Vehicle (V2V) communication function. These functions are implemented as an external driver model in PTV Vissim. The framework uses a detailed digital twin based on the M86 road network located in southwestern Hungary, which was constructed for autonomous driving tests. With this framework, the effect of the proposed vehicle models is evaluated with the microscopic traffic simulator PTV Vissim. A case study of the different penetration rates of HAV and CAV was performed on the M86 motorway. Preliminary results presented in this paper demonstrated that introducing HAV and CAV to the current network individually will cause negative effects on traffic performance. However, a certain ratio of mixed traffic, 60% CAV and 40% Human Driver Vehicles (HDV), could reduce this negative impact. The simulation results also show that high penetration CAV has fine driving stability and less travel delay.

Use of Universal Simulation Software Tools for Optimization of Signal Plans at Urban Intersections

Abstract: Intersections in cities are important transport hubs, where traffic flows from all roads meet, connect, disconnect or intersect. This research is dedicated to the creation of simulation models of intersections and is based on real observations of two crossroads in a city. The principle is based on observing traffic flows using real traffic counting at peak times. The aim is to reduce traffic congestion by adjusting signal plans on the monitored section using computer simulation and modelling in ExtendSim8 software, which is a universal tool allowing the user to simulate any system or process by creating a logical representation in an easy-to-use format. According to our preliminary literature research, the ExtendSim software has never before been used before to create an intersection simulation to optimize the signal plan. There are several specialized software products for simulating traffic and intersections, but not everyone has access to these, or else they require lengthy user training. Therefore, it is very advantageous to use a universal simulation tool that is not used for traffic simulation at intersections in the city. The article points out that a universal simulation program such as ExtendSim can also be used in the design or assessment of signal plans at intersections.

Evaluation of the Operational Effects of Autonomous and Connected Vehicles through Microsimulation

Abstract: Proper evaluation of traffic operations integrating connected and autonomous vehicles (CAVs) requires accurate representation of these emerging technologies in microscopic simulation. This paper evaluates the ability of microscopic simulator PTV-VISSIM (Version 10.0) to simulate CAVs, and presents a comprehensive CAV model extension. In addition, emissions modeling is integrated with VISSIM to calculate real-time energy and emission estimates. The evaluation of VISSIM revealed that its internal CAV modeling has several limitations, such as modeling connectivity and complex vehicle behavior. For external modeling, there are two available VISSIM interfaces. The Component Object Model (COM) Application Programming Interface (API) is the superior approach for fetching data and modeling connectivity, whereas the External Driver Model (EDM) is a better tool for lateral and longitudinal control. The simulation extension developed leveraged both interfaces. An isolated signalized intersection was simulated to demonstrate the impact of connected vehicle (CV), autonomous vehicle (AV), and CAV traffic on speed, delay, and travel time. In addition, trajectory data, combined with the Motor Vehicle Emission Simulator (MOVES) method, were utilized to obtain energy, fuel consumption, and greenhouse gas emissions. The results show that CAVs result in net improvement in travel time and speed. However, emissions did not follow the same trend. While increasing AV penetration rates resulted in emissions reductions, increasing CV and CAV penetration rates resulted in higher emissions. While the CV logic chosen for testing seeks to maximize the likelihood of vehicle arrival-on-green, the algorithm likely results in increased variations in second-by-second accelerations, leading to overall higher emissions.

Impacts of Different Types of Automated Vehicles on Traffic Flow Characteristics and Emissions: A Microscopic Traffic Simulation of Different Freeway Segments

Abstract: Different types of automated vehicles (AVs) have emerged promptly in recent years, each of which might have different potential impacts on traffic flow and emissions. In this paper, the impacts of autonomous automated vehicles (AAVs) and cooperative automated vehicles (CAVs) on capacity, average traffic speed, average travel time per vehicle, and average delay per vehicle, as well as traffic emissions such as carbon dioxide (CO2), nitrogen oxides (NOx), and particulate matter (PM10) have been investigated through a microsimulation study in VISSIM. Moreover, the moderating effects of different AV market penetration, and different freeway segments on AV’s impacts have been studied. The simulation results show that CAVs have a higher impact on capacity improvement regardless of the type of freeway segment. Compared to other scenarios, CAVs at 100% market penetration in basic freeway segments have a greater capacity improvement than AAVs. Furthermore, merging, diverging, and weaving segments showed a moderating effect on capacity improvements, particularly on CAVs’ impact, with merging and weaving having the highest moderating effect on CAVs’ capacity improvement potential. Taking average delay per vehicle, average traffic speed, and average travel time per vehicle into account, simulation results were diverse across the investigated scenarios. The emission estimation results show that 100% AAV scenarios had the best performance in emission reductions in basic freeway and merging sections, while other scenarios increased emissions in diverging and weaving sections.

A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior

Abstract: The significant advancements in intelligent transportation systems (ITS) have contributed to the increased development in traffic modeling. These advancements include prediction and simulation models that are used to simulate and predict traffic behaviors on highway roads and urban networks. These models are capable of precise modeling of the current traffic status and accurate predictions of the future status based on varying traffic conditions. However, selecting the appropriate traffic model for a specific environmental setting is challenging and expensive due to the different requirements that need to be considered, such as accuracy, performance, and efficiency. In this research, we present a comprehensive literature review of the research related to traffic prediction and simulation models. We start by highlighting the challenges in the long-term and short-term prediction of traffic modeling. Then, we review the most common nonparametric prediction models. Lastly, we look into the existing literature on traffic simulation tools and traffic simulation algorithms. We summarize the available traffic models, define the required parameters, and discuss the limitations of each model. We hope that this survey serves as a useful resource for traffic management engineers, researchers, and practitioners in this domain.

Validating a Traffic Conflict Prediction Technique for Motorways Using a Simulation Approach

Abstract: With the ever-increasing advancements in the technology of driver assistant systems, there is a need for a comprehensive way to identify traffic conflicts to avoid collisions. Although significant research efforts have been devoted to traffic conflict techniques applied for junctions, there is dearth of research on these methods for motorways. This paper presents the validation of a traffic conflict prediction algorithm applied to a motorway scenario in a simulated environment. An automatic video analysis system was developed to identify lane change and rear-end conflicts as ground truth. Using these conflicts, the prediction ability of the traffic conflict technique was validated in an integrated simulation framework. This framework consisted of a sub-microscopic simulator, which provided an appropriate testbed to accurately simulate the components of an intelligent vehicle, and a microscopic traffic simulator able to generate the surrounding traffic. Results from this framework show that for a 10% false alarm rate, approximately 80% and 73% of rear-end and lane change conflicts were accurately predicted, respectively. Despite the fact that the algorithm was not trained using the virtual data, the sensitivity was high. This highlights the transferability of the algorithm to similar road networks, providing a benchmark for the identification of traffic conflict and a relevant step for developing safety management strategies for autonomous vehicles.

Safety assessment of coordinated signalized intersections in a connected vehicle environment: a microsimulation approach

Abstract: Abstract In this study, the safety impact of the coupled implementation of signal coordination and connected vehicles (CVs) is examined in a microsimulation environment created in VISSIM. The Surrogate Safety Assessment Model (SSAM) was implemented to generate results of surrogate safety measures. The findings provided evidence that CVs can improve the safety performance at all market penetration rates (MPRs) of CVs in terms of all performance metrics. In addition, further safety improvements were achieved at higher CV MPRs. It was observed that coordinated signals had lower likelihoods of experiencing collisions compared to uncoordinated signals. Specifically, coordinated signals showed significantly higher time-to-collision (TTC) and post-encroachment time (PET) values when compared to uncoordinated signals at the 100% CV MPR only. Moreover, the impact of CV technologies on reducing the total number of conflicts (TNC) would be stronger than that of traffic signal coordination alone while both would lead to reductions in the TNC.

A Systematic Solution of Human Driving Behavior Modeling and Simulation for Automated Vehicle Studies

Abstract: Though automated vehicles (AVs) are believed to play a crucial role in future transport, human driving vehicles will share the road with automated vehicles for a relatively long period. So, we need to enable automated vehicles to run along with human drivers especially when they may have conflicts in the right of way. One key problem is how to appropriately model human driving behaviors and quickly simulate their actions when training/testing automated vehicles. Many existing models were originally built for traffic flow studies and may not be suitable for automated vehicles studies. In this paper, we propose a set of new principles of human driving behaviors modeling and simulations. Then, we propose a Data-Driven Simulator (D2Sim) model for human behavior learning, description, and vehicle interaction simulation. In contrast to conventional microscopic traffic flow models, the D2Sim is a trajectory generation model that accepts rich driving environment information (e.g., lane geometry, crosswalks, traffic signals, surrounding vehicles, etc.). Different from many empirical trajectory records replay models, we can arbitrarily set the long-term intentions of the simulated vehicles and intentionally design the corner cases that had not been observed in practice. In addition, the D2Sim adopts adversarial learning to comprehend complex yet stochastic human driving behaviors from empirical data. Testing results show that the proposed model can quickly generate high-resolution trajectory data for training and testing.

Differentiable Hybrid Traffic Simulation

Abstract: We introduce a novel differentiable hybrid traffic simulator, which simulates traffic using a hybrid model of both macroscopic and microscopic models and can be directly integrated into a neural network for traffic control and flow optimization. This is the first differentiable traffic simulator for macroscopic and hybrid models that can compute gradients for traffic states across time steps and inhomogeneous lanes. To compute the gradient flow between two types of traffic models in a hybrid framework, we present a novel intermediate conversion component that bridges the lanes in a differentiable manner as well. We also show that we can use analytical gradients to accelerate the overall process and enhance scalability. Thanks to these gradients, our simulator can provide more efficient and scalable solutions for complex learning and control problems posed in traffic engineering than other existing algorithms. Refer to https://sites.google.com/umd.edu/diff-hybrid-traffic-sim for our project.

Examining Traffic Operations at Multi-Legged Intersection Operating under Heterogeneous Traffic: A Case Study in India

Abstract: An intersection is a critical component of any road network, where traffic from different approaches merges and diverges. These merging and diverging traffic movements affect traffic operations and safety significantly. Different traffic management measures and control strategies and necessary geometric improvements are suggested to optimize traffic operations at a given intersection. It is also more prudent to assess the effectiveness of potential strategies towards improvement using simulation at first before certain appropriate strategies/alternatives are finally selected for on-field implementation. Kamrej intersection, a multi-legged junction in Surat, located in the western part of India, is taken as a case study. Traffic data such as traffic volume counts and spot speeds are used to generate and calibrate the present field condition (base model) using PTV-VISSIM software. The calibrated model is then validated using vehicle-class-wise average travel time for each traffic movement (straight and turning) and traffic flow during peak hours. Average delay is used as a measure to assess the effectiveness of selected potential traffic management alternatives. When different demand management scenarios are assessed, it is found that delays for straight and right movements can be reduced substantially, contributing towards improvement in efficiency and traffic operations at selected intersections. The current approach holds promise, thereby contributes to improving traffic operations at selected multi-legged intersections.

Agent-Based Simulation Approach to Determine Safety Impacts of Demand-Responsive Transport (DRT) in Wayne County, Michigan

Abstract: Road safety is one of the major concerns in transportation system management. Safety performance analyses usually assess crash frequencies and the impacts of countermeasures on the number of crashes. However, the advent of new mobility solutions makes safety evaluation more challenging; data tend to be sparse, and the impacts of such services on demand and the performance of the broader network is not completely understood. This paper attempts to fill this gap by creating a novel method to estimate the safety impacts of Demand-Responsive Transport (DRT) services. Using an agent-based mesoscopic model in the Multi-Agent Transport Simulation (MATSim) Toolkit, we simulate DRT as a new mobility solution in Wayne County, Michigan, and obtain spatial and temporal distributions of traffic volume and operating speed to predict the frequency and severity of crashes. We tested for different DRT service designs, such as fleet size, detour tolerance, and initial placement of the fleet. Our findings indicate that introducing a DRT service on its own increases vehicle kilometers traveled (VKT) by 22% and consequently the number of crashes by 17%. However, this impact could be ameliorated by increasing the detour tolerance, which can significantly increase the number of shared rides and decreases the crash frequency (10.8%) and crash severity as a result of a lower VKT (15%). The output of the proposed framework serves as a starting point of a safety performance evaluation using an agent-based simulation approach to achieve a safe and successful implementation of shared mobility services in large-scale urbanized areas.

Simulation-Based Analysis of “What-If” Scenarios with Connected and Automated Vehicles Navigating Roundabouts

Abstract: Despite the potential of connected and automated vehicles (CAVs), there are still many open questions on how road capacity can be influenced and what methods can be used to assess its expected benefits in the progressive transition towards fully cooperative driving. This paper contributes to a better understanding of the benefits of CAV technologies by investigating mobility-related issues of automated vehicles operating with a cooperative adaptive cruise control system on roundabout efficiency using microscopic traffic simulation. The availability of the adjustment factors for CAVs provided by the 2022 Highway Capacity Manual allowed to adjust the entry capacity equations to reflect the presence of CAVs on roundabouts. Two mechanisms of entry maneuver based on the entry lane type were examined to compare the capacity target values with the simulated capacities. The microscopic traffic simulator Aimsun Next has been of great help in building the “what-if” traffic scenarios that we analysed to endorse hypothesis on the model parameters which affect the CAVs’ capabilities to increase roundabouts’ throughput. The results highlighted that the increasing penetration rates of CAVs have greater impacts on the operational performances of roundabouts, and provided a synthetic insight to assess the potential benefits of CAVs from an efficiency perspective.

Calibration and Validation Methodology for Simulation Models of Intelligent Work Zones

Abstract: Intelligent work zone (IWZ) systems use innovative technologies in real time to mitigate the negative impacts of work zones on the traveling public. The automatic queue detection (AQD) system is one type of IWZ being promoted by the Federal Highway Administration. Several field studies found that AQD systems were successful in reducing average travel speeds which led to a reduction in the number of rear-end crashes and in user costs. It has been shown that well-calibrated microsimulation models are appropriate analysis tools for traditional work zone traffic. To date, there are no tools in the literature that have been developed for AQD operations that have been calibrated and validated with empirical data. This paper aims to fill this gap by providing a comprehensive guideline for calibrating and validating a microsimulation model that can emulate traffic conditions and their impacts on freeway IWZ equipped with AQD systems. Specifically, the methodology includes (1) developing the base work zone model, (2) replicating the AQD warning messages, (3) calibrating the key simulation parameters using three types of data, and (4) validating the resulting simulation model using segment travel times. This comprehensive approach was demonstrated on a Nebraska work zone that was equipped with an AQD system. VISSIM simulation model was used for the underlying tool in the demonstration. The results presented in this paper are useful to both practitioners and scholars who wish to study the impact of the IWZ accurately through microsimulation.

Multi-lane-merging strategy for connected automated vehicles on freeway ramps

Abstract: Most studies assume the ramp to be a single lane regardless of the possibility of multiple lanes on a freeway ramp. This assumption limits the application of previous ramp metering strategies in the real world. Therefore, this paper proposes a strategy for a centralized controller to efficiently merge connected automated vehicles from a multiple-lane ramp. The proposed strategy aims to allow vehicles from different lanes to pass through the conflict point with the objective of minimum delay and fuel consumption. Numerical experiments are carried out to compare the proposed strategy with first-in-first-out (FIFO) and Vissim built-in strategies. Simulation results indicate that the proposed strategy can reduce more delay than FIFO and Vissim-based strategies. Furthermore, the proposed strategy is also found to be the most reliable in various scenarios with different traffic demand splits, safe headways, and numbers of lanes.

Research on integrated simulation platform for urban traffic control connecting simulation and practice

Abstract: Though effective in theoretical simulation, the established traffic control models and optimization algorithms will result in model mismatch or even control strategy failure in actual application. However, they are commonly adopted in traffic signal control research, resulting in the unavailability of many exceptional control algorithms in practice. Simulation should function as a bridge between theoretical research and actual application, allowing the gap between the two to be communicated and made up for. However, an effective connection between the two has yet to be established to enable simulation methods in existing traffic control research. To this end, we designed and developed a simulation platform for "Online Application-HILS (Hardware-in-the-Loop Simulation)-Practice" integration over traffic signal control. In this paper, the architecture and characteristics of the integrated simulation platform were described. Besides, the function of each module of the platform was detailed, followed by listing simulation examples for six complex scenarios, with the active control scenario being selected for simulation comparison analysis. The findings demonstrated extensive road network simulation with the integrated simulation platform, multidimensional control variables, control strategies with support, as well as stable and reliable operation. It can be used to verify several sorts of traffic control simulation with variable dimensions.

A general framework for combining traffic flow models and Bayesian network for traffic parameters estimation

Abstract: This work focuses on traffic parameters estimation based on trajectory data in an arterial corridor with multiple signalized intersections. We develop a general framework that can combine various traffic flow models with Bayesian Network (BN) for estimating the overall traffic parameters using partially observed vehicle trajectory data (with unknown penetration rate). The BN is formulated to establish the probabilistic relationship between the traffic arrival process, traffic states, traffic flow model parameters and observed vehicle trajectories. More specifically, given traffic arrival information (e.g., traffic arrival volume) and fundamental diagram parameters (e.g., capacity, jam density, and free flow speed), vehicle trajectories are derived or simulated based on traffic flow modelling (e.g., shockwave analysis, Cell Transmission Model (CTM), or microscopic traffic simulation model VISSIM). Here, the extracted entry time of an observed vehicle at a pre-defined location upstream of the signal and its travel time are used to establish the probabilistic relationship. On the other hand, they are also the input parameters of the model for the estimation. Then, by combining a dynamic traffic flow model with Bayesian inference, we develop a framework to establish the learning process for traffic parameters estimation, such as traffic volume and traffic flow model parameters. The proposed framework is evaluated with different traffic flow models using the NGSIM dataset of an arterial corridor with three signalized intersections. For the CTM-BN model, the mean absolute percentage error (MAPE) of the estimation is generally below 5% when the penetration rate is above 12%. Regarding the VISSIM-BN model, the MAPE of the estimation is generally below 5% when the penetration rate is above 6%. These results demonstrate the applicability of the framework even under a relatively low penetration rate, and that the fidelity of the dynamic traffic model used does influence the estimation performance.

A Novel Dynamic Lane-Changing Trajectory Planning Model for Automated Vehicles Based on Reinforcement Learning

Abstract: Lane changing behavior has a significant impact on traffic efficiency and may lead to traffic delays or even accidents. It is important to plan a safe and efficient lane-changing trajectory that coordinates with the surrounding environment. Most conventional lane-changing models need to establish and solve constrained optimization models during the whole process, while reinforcement learning can just take the current state as input and directly output actions to vehicles. This study develops a lane-changing model using the deep deterministic policy gradient method, which can simultaneously control the lateral and longitudinal motions of the vehicle. To optimize its performance, a reward function is properly designed by combining safety, efficiency, gap, headway, and comfort features. To avoid collisions, a safety modification model is developed to check and correct acceleration at every time step. The driving trajectory data of 1169 lane-changing scenarios extracted from the Next Generation Simulation (NGSIM) dataset are used to train and test the model. The proposed model can quickly converge in training phase. Testing results show it can complete safe and efficient lane changing in different lane-changing scenarios with both shorter time headway and lane-changing duration than human drivers. Compared with the conventional dynamic lane-changing trajectory planning model, our model can reduce collision risk. It is also evaluated in automated and nonautomated mixed traffic in SUMO. Simulation results show that the proposed model also has a positive effect on the average speed of overall traffic flow.

How to Model the Effect of Gradient on Bicycle Traffic in Microscopic Traffic Simulation

Abstract: Microscopic traffic simulation is a useful tool for the planning of motorized traffic, yet bicycle traffic still lacks this type of modeling support. Nonetheless, certain microscopic traffic simulators, such as Vissim, model bicycle traffic by applying models originally designed for car traffic. The gradient of a bicycle path has a significant impact on the speed of cyclists; therefore, this impact should be captured in microscopic traffic simulation. We investigate two calibration approaches to reproduce the effect of gradient on the speed of cyclists using the default driver behavioral model in Vissim. The first approach is to modify the simulated gradient to represent different values of the gradient-acceleration parameter: a fixed value that represents a decrease in the maximum acceleration that cyclists can apply on an uphill. The second approach is to adjust the maximum-acceleration function. We evaluate both approaches by applying a Vissim model of a bidirectional bicycle path with a 3% gradient in Stockholm. The results show that the current default implementation in the Vissim model underestimates the effect of gradient on speed. Moreover, the gradient-acceleration parameter does not directly reduce the maximum acceleration of all cyclists, but only of those cyclists riding above a certain speed. We conclude that by using a higher gradient-acceleration value than the default, we accurately estimate the observed mean speed on the uphill. However, neither of the investigated calibration approaches provides accurate estimates of the speed distributions. We emphasize the need for developing more accurate behavioral models designed for cyclists.

Testing Connected Vehicle-Based Accident Mitigation for Red-Light Violation Using Simulation Strategies

Abstract: Simulation is often suggested as a way to analyze the safety improvements of geometric changes and operational strategies. But the results from simulations are mixed. This paper presents new ideas about how to do such assessments, especially in the context of testing the value of vehicle to vehicle (V2V), vehicle to infrastructure (V2I), and vehicle to pedestrian (V2P) communications in preventing crashes because of red-light violation at signalized intersections. Algorithms are created that watch for impending collisions through sensing and then issue speed and trajectory changes to avoid accidents. Red-light violation is a primary focus because it increases the likelihood that collisions will occur. VISSIM is used to test these ideas, including new communication and control algorithms that link to vehicles, pedestrians, and signal controllers through the communication interface. The algorithms predict unsafe conditions, determine an appropriate crash remedial decision, and communicate those controls with the appropriate vehicles and pedestrians. The impacts of these algorithms are explored under various demand patterns, connected vehicle market penetration rates, and red-light violation rates in a hypothetical simulated environment. The simulation analysis suggests that the number of near-crash events can be reduced significantly if V2V and V2P communications are implemented. Moreover, adding V2I communication on top of these may further reduce the number of near-crash events. These results suggest that not only could such control strategies have significant impacts, but also those impacts can be assessed through simulation.

Extensible Co-Simulation Framework for Supporting Cooperative Driving Automation Research

Abstract: Autonomous vehicles (AVs) and cooperative automated vehicles (CAVs) are expected to largely reshape our mobility systems. The limited deployment of AVs and CAVs on roads makes it difficult to fully assess their impact and interactions with other road users. Advanced simulations are often sought for conducting accelerated tests of AVs and CAVs in a virtual environment. However, existing off-the-shelf simulators are typically focused on conventional traffic simulation and human-driving simulation. Advanced simulators that enable core functionalities (e.g., sensing and communication) of AVs and CAVs have been underexploited. In this paper, the authors aim to develop a realistic co-simulation framework for testing autonomous driving and cooperative driving automation (CDA). The proposed co-simulation framework utilizes the open-source concept to support the AV and CAV community in developing and deploying AV and CAV technologies. This framework integrates multiple open-source platforms, including Eclipse MOSAIC™ simulation framework, Eclipse Simulation of Urban Mobility (SUMO™) traffic simulator, and CARLA AV driving simulator. The framework enables AV and CAV simulation in mixed traffic environments. The developed co-simulation models have been tested with different scales of networks and traffic flow. The assessment of the co-simulation framework shows that it can support faster-than-real-time simulation for use in accelerated tests with more realistic scenarios. In addition, the developed co-simulation framework is proven to be extensible with the inclusion of other network simulators for supporting vehicle-to-everything (V2X) communication among vehicles.

An Integrated Traffic and Vehicle Co-Simulation Testing Framework for Connected and Autonomous Vehicles

Abstract: Simulation testing is critical for the development and optimization of connected and autonomous vehicle (CAV) driving systems. Existing simulation tools for autonomous driving testing generally focus on single-vehicle-based perception, decision, and control. Nevertheless, cooperative maneuvering among CAV and human-driven vehicles is of utmost importance in many high-value scenarios, e.g., intersections, platooning, and bottlenecks. To this end, this article aims to establish a comprehensive and realistic co-simulation framework that combines both vehicle and traffic simulation. For the sake of presentation and without loss of generality, CARLA is employed for vehicle simulation as it features high-fidelity vehicle dynamics models, while Simulation of Urban Mobility is employed for traffic simulation as it provides advanced traffic models. Further, vehicle trajectory extraction technology is applied to extract vehicle trajectories from videos and use them as an input of the co-simulation framework. Moreover, three different scenarios comprising the presence of obstacles on the highway, congested city intersections, and complete CAV testing are described to verify the rationality of the framework. The real data-driven, full-chain co-simulation testing method proposed in this article can provide a realistic virtual environment for testing decision- and motion-planning level vehicular functions of CAVs.

An Integrated Agent-Based Microsimulation Model for Hurricane Evacuation in New Orleans

Abstract: Mass evacuation of urban areas due to hurricanes is a critical problem that requires extensive basic and applied research. Knowing the accurate evacuation time needed for the entire region in advance such that the evacuation order can be issued on a timely basis is crucial for the officials. Microsimulation modeling, which focuses on the characteristics of individual motorists and travel behavior, has been used widely in traffic simulation as it can lead to the most accurate result. However, because detailed driver response modeling and path processing must be incorporated, vehicle-based microscopic models have always been used only to simulate small to medium sized urban areas. Few studies have attempted to address problems associated with mass evacuations using vehicle-based microsimulation at a regional scale. This study develops an integrated two-level approach by separating the entire road network of the study area into two components, highways (i.e., interstate highways and causeways) and local roads. A vehicle-based microsimulation model was used to simulate the highway part of the road traffic, whereas the local part of the road traffic simulation utilized an agent-based model. The integrated microsimulation model was used to simulate hurricane evacuation in New Orleans. Validation results confirm that the proposed model performs well in terms of high model accuracy (i.e., close agreement between the real and simulated traffic patterns) and short model running time. Sufficient evacuation time is a premise to protect people’s life safety when an area is threatened by a deadly disaster. To decrease the network clearance time, this study also examined the effectiveness of three evacuation strategies for disaster evacuation, including a) simultaneous evacuation strategy, b) staged evacuation strategy based on spatial vulnerabilities, and c) staged evacuation strategy based on social vulnerabilities. The simulation results showed that both staged evacuation strategies can decrease the network clearance time over the simultaneous evacuation strategy. Specifically, the spatial vulnerability-based staged evacuation strategy can decrease the overall network clearance time by about four hours, while the social vulnerability-based staged evacuation strategy can decrease the network clearance time by about 2.5 hours.

Research on Using the Tecnomatix Plant Simulation for Simulation and Visualization of Traffic Processes at the Traffic Node

Abstract: Simulation software Tecnomatix Plant Simulation was originally created for a modelling and subsequent simulation of production and logistics processes. Its variability, however, opens its use also in other areas such as transport in urban agglomerations. Based on that, research was implemented to verify the program’s application in urban transport, specifically to visualize and simulate traffic processes at the traffic node. The paper describes a methodology which made it possible to create a simulation program for traffic light intersections, and presents examples of the simulation model application. The proposed methodology will enable the application of Tecnomatix Plant Simulation to create a complex simulation model of the logistics process. It will mainly enable to simulate the field of production logistics and city logistics within one simulation model.

Route Choice Behavior Modeling for Emergency Evacuation and Efficiency Analysis Based on Type-II Fuzzy Theory

Abstract: For route choice during an outside emergency evacuation, evacuees make different route choice decisions due to their preference diversity. Furthermore, uncertainties of human behavior are inevitable, leading to a high complexity of macroscopic traffic flows and great impact on the evacuation efficiency. In this work, a simulation-based evacuation traffic planning is presented for homogenizing the network traffic flows. In the simulation, the evacuees are divided into two types: panicky and rational ones, and their route choice behavior is captured by two distinct models, respectively. The proportion of panicky evacuees is dynamically changed based on the traffic information level (TIL) and traffic states of downstream links at a decision point. Type-II fuzzy logic system (T2FLS) is used to model the uncertainties of evacuees’ subjective perception on route costs. Comprehensive numerical experiments are done to analyze the impact of TIL, behavior diversity, and the heterogeneity of macroscopic traffic flows on evacuation efficiency. Results show that the proposed evacuation traffic planning is beneficial for improving the evacuation efficiency, alleviating the proportion of panicky evacuees, and homogenizing traffic flows in a large-scale network.

Empirical Verification of Car-Following Parameters Using Naturalistic Driving Data on Freeway Segments

Abstract: Microscopic traffic simulation is a well-established tool for the analysis of transportation systems, with a wide variety of applications in operations, safety, and planning. An essential c...