Traffic Simulation Research Articles

Enhancing Travel Reservation Benefits Through Incentive and Penalty Mechanisms in Urban Congested Roads

To enhance the refinement of urban traffic demand management, this study explores the impact of introducing incentive and penalty mechanisms in the urban road travel reservation strategy (TRS) on both heterogeneous users and the road network. The existing research on TRS has primarily focused on static evaluations, which have limitations in terms of the one-sidedness of travel service methods and the homogeneity of users’ travel choices. Moreover, these studies overlook the multidimensional decision-making of travelers and the synergistic effects of urban multimodal transportation systems. To overcome these limitations, this paper introduces incentives and penalties for users and develops an agent-based multi-objective optimization model. The model optimizes travel incentive schemes to maximize social benefits, considering the interests of both system managers and travelers. Additionally, an agent-based dynamic traffic simulation model is constructed, incorporating individual travel decisions, real-time traffic conditions, and the balance of road supply and demand. The findings indicate that the introduction of incentive and penalty mechanisms increased the transportation system’s revenue by 17.26% and reduced travel costs by 2.67%. TRS implementation significantly improved traffic performance and reduced congestion across the road network. Specifically, the average speed, road saturation, and network traffic volume increased by 6.7%, 9.3%, and 3.7%, respectively. Moreover, the proportion of users participating in reservation travel increased by 48.5%, with travelers more willing to adjust their travel times. Heterogeneous travelers with different time valuations showed distinct responses to the TRS. In conclusion, TRS offers significant potential in promoting sustainable urban transportation, providing both theoretical insights and practical implications for urban planners and policymakers.

Air Quality-Driven Traffic Management Using High-Resolution Urban Climate Modeling Coupled with a Large Traffic Simulation

This study presents a framework for integrating traffic simulation with high-resolution air pollution modeling to design adaptive traffic management policies aimed at reducing urban air pollution. Building on prior work that establishes the coupling of the MATSim traffic model with the PALM-4U urban climate model, this second part focuses on implementing a feedback loop to inform traffic management decisions based on simulated air pollution concentration levels. The research explores how traffic volumes and atmospheric conditions, such as boundary layer dynamics, influence air quality throughout the day. In an artificial case study of Berlin, a time-based toll is introduced, aimed at mitigating concentration peaks in the morning hours. The toll scheme is tested in two simulation scenarios and evaluated regarding the effectiveness of reducing air pollution levels, particularly NO2 during the morning hours. The case study results serve to illustrate the framework’s capabilities and highlight the potential of integrating traffic and environmental models for adaptive policy design. The presented approach provides a model for responsive urban traffic management, effectively aligning transportation policies with environmental goals to improve air quality in urban settings.

Evaluating spatiotemporal speed metrics for traffic state estimation on complex urban roads

Conducting city-wide tests of intelligent transport systems (ITS) is a costly endeavor that requires extensive funding and planning. Combined traffic simulations can be utilized to alleviate initial costs, and allow easy parameterization to deduce real-world requirements. In this paper, we aim to contribute to two frontiers in the domain of traffic state estimation (TSE) within an urban environment. First, we introduce speed estimation and different sensor technologies, reiterating the foundations of traffic dynamics. Second, we present a simulation-based assessment utilizing the open-source co-simulation framework Eclipse MOSAIC, alongside a newly developed TSE application suite, executed on top of the BeST Berlin traffic scenario. This evaluation highlights the estimation capabilities of different sensor technologies and speed estimates in an urban scenario, addressing their effectiveness in various city areas.

W-GPSR Routing Based on Mobility Prediction for Vehicular Ad-Hoc Network (VANET)

In recent years, Vehicular Ad-Hoc Networks (VANETs) innovation has been regarded as a significant research area. This is owing to the increasing popularity of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications in the area of Intelligent Transportation System (ITS) to improve traffic management, safety, CO2 emission mitigation, and other applications. A variety of routing protocols for VANETs are being recently developed. More specifically, geographic-based routing algorithms such as Greedy Perimeter Stateless Routing (GPSR) have provoked the most interest in VANETs due to their compatibility with a regularly changing network structure and the highly unsteady nature of automobile nodes. This paper proposes an efficient weight based mobility method in VANET to improve the mechanism of the GPSR protocol through optimizing the greedy forwarding strategy; which is so called O-Greedy Mode. Therefore, the key goal is to achieve the optimal data forwarding paths. The next hop is determined by estimating the neighbors' mobility based on each neighbor's Greedy Link Weight Factor (GLWF). The Weighted GPSR (W-GPSR) based on Mobility Prediction is then evaluated using OMNeT++ simulator with Inet, Veins and SUMO traffic simulator. The results demonstrate the efficiency of W-GPSR in contrast with the traditional existing protocols for essential metrics of Packet Delivery Ratio (PDR), throughput, End-to-End Delay (E2ED), Normalized Routing Load (NRL) and Packet Loss Ratio (PLR).

Effects of Anthropomorphic Driving Vehicles on Traffic Flow

Autonomous driving has many positive impacts, such as improving driver and passenger safety, comfort, and traffic efficiency, but all these advantages are based on people’s trust and acceptance of this mode of driving. Anthropomorphic driving can enhance the trust and comfort of drivers and passengers and is seen as a feasible measure to increase people’s acceptance of autonomous driving. This paper reports the microscopic traffic simulation of three scenarios around a frequently congested intersection, using non-automated vehicles, autonomous driving vehicles, and anthropomorphic driving vehicles to explore their impact on traffic efficiency. The result shows that, compared to non-automated vehicles, both autonomous vehicles and anthropomorphic driving vehicles can improve traffic efficiency during congested periods, increase traffic volume per unit time, reduce the total travel time and time loss, and have a higher average speed. Compared to autonomous vehicles, anthropomorphic driving has a shorter total travel time and a similar time loss. In terms of average speed, anthropomorphic driving performed better than autonomous driving in terms of both congested and non-congested times. In summary, compared to non-automated vehicles, autonomous driving vehicles have a positive effect on improving traffic efficiency, while, compared to autonomous driving, anthropomorphic driving has more advantages in increasing traffic efficiency and reducing traffic congestion.

Evaluating urban intersections: a comparative study of roundabouts versus protected U-turns—which is better?

Effective urban traffic management is a significant challenge in rapidly urbanizing areas with increased vehicular usage, impacting both congestion and safety. Busy cities, like many urban centers, face these issues. Roundabouts, known for their ability to reduce severe crashes, represent a traditional approach to intersection design. However, in high-traffic conditions, roundabouts can become less efficient, leading to increased congestion. Alternatively, protected U-turns, which are a relatively modern approach, are designed to handle high traffic volumes more effectively. They potentially reduce waiting times and improve overall traffic flow but may pose different safety and longitudinal challenges. This study aims to explore these complexities by analyzing the effectiveness of roundabouts and protected U-turns in the context of the unique urban landscape of the Hail City in Saudi Arabia. PTV VISSIM was used for dynamic traffic simulation, providing a realistic representation of traffic patterns in Hail City. Parameters such as vehicle queuing lengths, wait times, and overall intersection capacity are comprehensively evaluated. The simulation settings are adjusted to reflect different traffic densities and patterns, thus ensuring a thorough understanding of the performance of each intersection type under varied conditions. Additionally, various statistical analysis techniques were used to better understand the relationship between the independent and dependent variables. Results showed scenarios of high traffic volume, roundabouts led to longer vehicle queues and increased wait times. On the other hand, protected U-turns exhibited a notable capability to manage high-volume traffic, effectively reducing congestion and improving intersection throughput. This research may contribute to urban traffic management literature, offering insightful guidelines for future intersection design and traffic flow optimization in rapidly urbanizing cities.

Investigating of machine learning's capability in enhancing traffic simulation models

Investigating of machine learning's capability in enhancing traffic simulation models

Une étude de modélisation et simulation (M&S) du trafic urbain de la ville Bamako s’appuyant sur l’approche de modélisation multi-perspective et simulation holistique (MPM&HS)

Urban traffic occupies a central place in our daily life of a city and gains complexity due to its increasing interdependence with other socio-economic dimensions (such as climate, demography and environmental policies, to name a few). This work presents a modeling and simulation study based on the Multi-Perspective Modeling and Holistic Simulation (MPM&HS) framework that can address such complexity. It is based on the holistic integration of models that have been independently constructed from the respective perspectives of diverse but interdependent dimensions. The resulting model can answer questions that any of the perspective-specific models cannot answer in isolation. We use a traffic modeling and simulation case study to illustrate the effectiveness of the MPM&HS approach. Keywords: Modeling and Simulation (M&S), Multi-Perspective Modeling and Holistic Simulation (MPM&HS), High-Level Language for System Specification (HiLLS), Transportation system, Urban traffic, Anylogic.

ANALYSIS OF THE EFFECT OF U-TURN ON TRAFFIC FLOW PERFORMANCE

This paper aims to explore the impact of U-turn motions on traffic flow and service levels. The research employs the Guidebook (PKJI, 2023) to assess observations related to traffic volume, vehicle speed, and both disturbed and undisturbed flow, which are gathered during the field survey. The data used for analysis was obtained by collecting primary data and secondary data according to the needs of the research. In addition, the data was obtained from a number of reports and documents that had been prepared, as well as the results of other literature studies. The results show that the number of vehicles on a road section is significantly increased, resulting in a significant increase in traffic volume and a decrease in vehicle speed. Moreover, the frequency of the use of the maneuvers affects the level of congestion on the road section, highlighting the need for early and strategic planning in the development of transportation infrastructure, particularly land transportation, which is crucial for facilitating everyday mobility and economic activities. This paper also highlights the importance of incorporating advanced traffic modeling techniques and simulations to enhance prediction of traffic behavior under different scenarios, informing infrastructure improvements and traffic management strategies. Finally, comparing findings with similar studies in other regions could yield valuable benchmarks for assessing traffic efficiency and service quality.

Testing and Validation of the Vehicle Front Camera Verification Method Using External Stimulation.

The perception of the vehicle's environment is crucial for automated vehicles. Therefore, environmental sensors' reliability and correct functioning are becoming increasingly important. Current vehicle inspections and self-diagnostics must be adapted to ensure the correct functioning of environmental sensors throughout the vehicle's lifetime. There are several promising approaches for developing new test methods for vehicle environmental sensors, one of which has already been developed in our previous work. A method for testing vehicle front cameras was developed. In this work, the method is improved and applied again. Various test vehicles, including the Tesla Model 3, Volkswagen ID.3, and Volkswagen T-Cross, are stimulated by simulating driving scenarios. The stimulation is carried out via a tablet positioned before the camera. The high beam assist is used to evaluate the vehicle's reaction. It was observed whether the vehicle switched from high to low beam as expected in response to the stimulation. Although no general statement can be made, the principle of stimulation works. A vehicle reaction can be successfully induced using this method. In further test series, the influence of display brightness is examined for the first time in this work. The results show that the display brightness significantly influences the test procedure. In addition, the method is validated by stimulation with colored images. It is shown that no complex traffic simulation is necessary to trigger a vehicle reaction. In the following validation approach, the CAN data of the Tesla Model 3 is analyzed during the tests. Here, too, the assumption that the vehicle reaction is based solely on the detected brightness instead of identifying road users is confirmed. The final validation approach examines the method's applicability to other vehicles and high beam assist technologies. Although the method could not be used on the Volkswagen T-Cross due to a fault detected by the vehicle's self-diagnosis, it worked well on the Volkswagen ID.3. This vehicle has a dynamic light assist in which individual segments of the high beam are dimmed during stimulation. Although the method developed to stimulate vehicle front cameras is promising, the specific factors that trigger the vehicle responses remain to be seen. This uncertainty suggests that further research is needed better to understand the interaction of stimulation and sensor detection.

Transforming traffic accident investigations: a virtual-real-fusion framework for intelligent 3D traffic accident reconstruction

The daily occurrence of traffic accidents has led to the development of 3D reconstruction as a key tool for reconstruction, investigation, and insurance claims. This study proposes a novel virtual-real-fusion simulation framework that integrates traffic accident generation, unmanned aerial vehicle (UAV)-based image collection, and a 3D traffic accident reconstruction pipeline with advanced computer vision techniques and unsupervised 3D point cloud clustering algorithms. Specifically, a micro-traffic simulator and an autonomous driving simulator are co-simulated to generate high-fidelity traffic accidents. Subsequently, a deep learning-based reconstruction method, i.e., 3D Gaussian splatting (3D-GS), is utilized to construct 3D digitized traffic accident scenes from UAV-based image datasets collected in the traffic simulation environment. While visual rendering by 3D-GS struggles under adverse conditions like nighttime or rain, a clustering parameter stochastic optimization model and mixed-integer programming Bayesian optimization (MIPBO) algorithm are proposed to enhance the segmentation of large-scale 3D point clouds. In the numerical experiments, 3D-GS produces high-quality, seamless, and real-time rendered traffic accident scenes achieve a structural similarity index measure of up to 0.90 across different towns. Furthermore, the proposed MIPDBO algorithm exhibits a remarkably fast convergence rate, requiring only 3–5 iterations to identify well-performing parameters and achieve a high R2 value of 0.8 on a benchmark cluster problem. Finally, the Gaussian Mixture Model assisted by MIPBO accurately separates various traffic elements in the accident scenes, demonstrating higher effectiveness compared to other classical clustering algorithms.

Long Short Term Memory Based Traffic Prediction Using Multi-Source Data

AbstractTraffic prediction is a task where the goal is to determine the number and type of vehicles, or some other traffic related metric, at certain time point. In addition to predicting the short-term evolution of traffic, prediction can be done for estimating traffic for distant future based on the trends found in historical traffic data, which is a critical component of traffic simulators being able to spawn realistic number of vehicles under prevailing situation. Such prediction system needs to be dependent on the characteristics of the situation and not the preceding traffic flow. This work presents a deep learning based prediction pipeline that uses a Long Short Term Memory (LSTM) network to map temporal, weather and traffic accident data accurately into traffic flow to predict traffic flow over multiple timesteps from various non-traffic inputs. Traffic data can then be produced based on independent data like weather forecasts and be used for other applications. As far as we know, no previous traffic predictor combines so many input variables to predict traffic flow with vehicle type information. To make the event based traffic accident dataset compatible with time series data, a novel preprocessing step based on power law decay phenomenon is added. Quantitative experiments show that the proposed preprocessing step and optimized hyperparameters improve the accuracy of the predictor on multiple metrics compared to a model without accident information. In two established statistical evaluation metrics, Mean Absolute Error and Mean Squared Error, the improvement was over $$20 \%$$ 20 % for certain vehicle types.

A Maneuver Coordination Analysis Using Artery V2X Simulation Framework

This paper examines the impact of Vehicle-to-Everything (V2X) communications on vehicle cooperation, focusing on increasing the robustness and feasibility of Cooperative, Connected, and Automated Vehicles (CCAVs). V2X communications enable CCAVs to obtain a holistic environmental perception, facilitating informed decision making regarding their trajectory. This technological innovation is essential to mitigate accidents resulting from inadequate or absent communication on the roads. As the importance of vehicle cooperation grows, the European Telecommunications Standards Institute (ETSI) has been standardizing messages and services for V2X communications, in order to improve the synchronization of CCAVs actions. In this context, this preliminary work explores the use of Maneuver Coordination Messages (MCMs), under standardization by ETSI, for cooperative path planning. This work presents a novel approach by implementing these messages as well as the associated Maneuver Coordination Service (MCS) with a Cooperative Driving System to process maneuver coordination. Additionally, a trajectory approach is introduced along with a message generation mechanism and a process to dynamically handle collisions. This was implemented in an Artery V2X simulation framework combining both network communications and SUMO traffic simulations. The obtained results demonstrate the effectiveness of using V2X communications to ensure the safety and efficiency of Cooperative Intelligent Transportation Systems (C-ITS).

Study of Distribution of Free Flow Speeds on Urban Road Sections Depending on Their Functional Purpose and One-Way Traffic—Evidence from Kharkiv (Ukraine)

Data on the distribution of the free flow speed (FFS) of cars are used to solve a wide range of tasks in the field of road transport, starting from road design and ending with the development of traffic modeling and simulation programs. The purpose of this study was to obtain the distribution of vehicle speeds on typical sections of the city road network, characterized by the presence of one-way traffic. The data were obtained by field observations using a portable radar. As a result, statistical characteristics and speed distribution laws for four sections of streets in the city of Kharkiv were analyzed. It was shown that the characteristics of FFS distributions differ depending on the functional class of the streets. Average FFS values on main street segments were on average 19 km/h higher. The one-way traffic has less impact on the FFS distribution, especially for arterial streets. The characteristics of FFS distributions differ depending on the type and functional class of streets; they can be described with sufficient accuracy by typical distribution laws, such as Normal, Log-normal, Gamma, and Chi-square. The results of this study can be useful for traffic modeling problems.

Influence of information flow topology and maximum platoon size on mixed traffic stability

Beyond platooning with pure connected and automated vehicles (CAVs), mixed platooning with both CAVs and human-driven vehicles (HDVs) emerges as a practical formation pattern in mixed traffic flow. This paper investigates how the two types of information flow topology (IFT) as “looking ahead” or “looking behind”, alongside the maximum size of mixed platoons, influence the stability of the entire traffic flow. Precisely, the mixed traffic system is partitioned into three subsystems: independent HDVs, independent CAVs, and mixed platoons, and a dynamical modeling framework is established under two types of specific IFT: Multi-Predecessor Following (MPF) for “looking ahead” and Multi-Successor Leading (MSL) for “looking behind”. Then, a unified string stability evaluation method is proposed, which captures the role of IFT and maximum platoon size, the latter being associated with the emergence probability of different subsystems and CAV penetration rates. Practical considerations, such as the heterogeneity of vehicle dynamics and the absence of HDV communication capabilities, are also incorporated. Extensive numerical analysis and nonlinear traffic simulations reveal that “looking behind” shows superior performance in mitigating traffic perturbations, especially at low penetration rates, compared with the prevailing choice of “looking ahead”. In addition, increasing the platoon size could further enhance traffic flow stability. These results provide practical insights into the design of IFT and platoon size for CAV cooperation in future mixed traffic environments.

Complexity Evaluation of Test Scenarios for Autonomous Vehicle Safety Validation Using Information Theory

The validation of autonomous vehicles remains a vexing challenge for the automotive industry’s goal of fully autonomous driving. The systematic hierarchization of the test scenarios would provide valuable insights for the development, testing, and verification of autonomous vehicles, enabling nuanced performance evaluations based on scenario complexity. In this paper, an information entropy-based quantification method is proposed to evaluate the complexity of autonomous vehicle validation scenarios. The proposed method addresses the dynamic uncertainties within driving scenarios in a comprehensive way which includes the unpredictability of dynamic agents such as autonomous vehicles, human-driven vehicles, and pedestrians. The numerical complexity calculation of the approach and the ranking of the scenarios are presented through sample scenarios. To automate processes and assist with the calculations, a novel software tool with a user-friendly interface is developed. The performance of the approach is also evaluated through six example driving scenarios, then through extensive simulation using an open-source microscopic traffic simulator. The performance evaluation results confirm the numerical classification and demonstrate the method’s adaptability to diverse scenarios with a comparison of complexity calculation ranking to the ratio of collision, near collision, and normal operation tests observed during simulation testing. The proposed quantification method contributes to the improvement of autonomous vehicle validation procedures by addressing the multifaceted nature of scenario complexities. Beyond advancing the field of validation, the approach also aligns with the broad and active drive of the industry for the widespread deployment of fully autonomous driving.

Fatigue Evaluation of Steel Truss Arch Bridge Based on Traffic Load Simulation Using Weigh-in-Motion Data: Case Study of Rumpiang Bridge

The aim of this study was to evaluate the fatigue life of Rumpiang Bridge in Barito Kuala Regency, South Kalimantan, which features a 200-meter-long steel truss arch structure. Fatigue evaluation was performed by referencing the AASHTO Manual for Bridge Evaluation 2018 and the provisions in RSNI T-03-2005 and using the cumulative fatigue damage method. Actual traffic loads based on weigh-in-motion (WIM) data obtained from Prof. Dr. Ir. Sutami Road in Lebak Regency, Banten over a seven-day period were simulated using structural modeling in MIDAS Civil 2022. The results indicated that Rumpiang Bridge is susceptible to fatigue failure, due to a cross-girder element experiencing a maximum stress range of 171.30 MPa and an effective stress range of 75.31 MPa. The current accumulated fatigue damage is 0.179, with an estimated remaining fatigue life of 16 years. This relatively short remaining life is primarily due to 35.02% of vehicles recorded in the WIM data being overloaded. Implementation of traffic restrictions can extend the fatigue life of the bridge. After conducting various weight restriction scenarios, it was found that a restriction with a maximum vehicle load of 26 tons would result in a fatigue life that exceeds the design life of the bridge.

SCVAN- BKM: Swarm clustering for vehicular ad hoc network with a secure blockchain-based key management scheme

The advancements in telematics and communication technology for automobiles have contributed to the development of Vehicular ad Hoc Networks (VANETs). These networks serve as the foundation for Intelligent Transportation Systems (ITS). Communication across various VANET entities is challenging due to the open communication environment and the highly dynamic nature of VANETs. One of the major challenges in VANETs is ensuring stable and secure transmission. To address these factors, several studies have been conducted. The article discusses a clustering algorithm for VANETs that leverages SCVAN-DPSO to create stable clusters and provide secure communication for mitigating malicious nodes. Additionally, Hyperelliptic Curve Cryptography (HECC) with signcryption in blockchain is proposed. This algorithm is designed to identify and mitigate malicious nodes, ensuring robust and secure communication within the network. To verify vehicles in VANETs, this study also suggests a safe and reliable key management method that generates and stores cryptographic keys using blockchain’s distributed ledger technology. By doing so, the risk of rogue nodes is reduced, and a safe, stable connection between vehicles is guaranteed. This work has been implemented using NS 3.34 simulation with the SUMO real-time traffic mobility simulator, and its performance has been evaluated. The proposed algorithm SCVAN-BKM provides a 99.6 % packet delivery ratio for 20 nodes, 0.4 % packet loss, end-to-end delay of 32.65 ms, throughput of 9.522 Mbps, and a cluster lifetime of 105 s. As the number of nodes increases in the communication zone, stable communication is maintained due to the clustering algorithm used in the proposed approach, as cluster formation supports stability in the high-mobility nature of vehicles, while secure communication is ensured using blockchain technology. The findings demonstrate that our approach is more effective and yields better outcomes than existing systems.

Evaluation of automated driving safety in urban mixed traffic environments

AbstractConflicting driving behaviours between automated vehicles and manually driven vehicles may compromise driving safety. The aim of this study is to analyse the safety of mixed traffic on urban roads. The driving simulation tests were conducted using a multi‐agent driving simulator, which allows real‐time synchronization of multiple simulators. These data were further processed to derive the driving behaviour parameters of manually driven vehicles in VISSIM traffic simulations. Driving safety evaluation indicators included conflict‐related indicators, as well as individual safety indicators. The safety evaluation indicators were normalized through min–max normalization, and the risk scores were summed to evaluate the urban roads. The analysis revealed that driving safety was poor at unsignalized intersections with a market penetration rate of 10% and 50% and at signalized intersections with traffic islands and a market penetration rate of 100%, where conflicts arise from the deceleration of leading vehicles and lane changes. This finding is about the driving behaviour of automated vehicles, which maintain a greater distance from the leading vehicle than manually driven vehicles, resulting in poorer driving safety due to lane changes rather than deceleration. Using the findings of this study, criteria for assessing the safety of mixed traffic situations in existing road infrastructures can be established.

Learning to search for parking like a human: A deep inverse reinforcement learning approach

The prevalence of on-street parking searches in urban downtown areas has led to significant externalities such as congestion, pollution, and collisions. Understanding the intricacies of parking search behavior is crucial for developing effective management strategies to mitigate these issues. Parking search is inherently a complex, sequential decision-making process, influenced by diverse driver preferences and dynamic urban environments. This study introduces a deep inverse reinforcement learning (DIRL) approach to model drivers’ parking search behavior. First, we constructed a high-fidelity parking simulation platform using Unity3D to replicate an urban road network, enabling the collection of 987 valid trajectories. We modeled the parking search process as a Markov decision process (MDP), with meticulously designed state-action pairs for accurate representation. Then, a maximum entropy-based DIRL model was developed to learn the reward function and search-for-parking policies of drivers. The experimental results demonstrate that the maximum entropy DIRL model significantly outperforms the traditional maximum entropy inverse reinforcement learning model, achieving a 19.0% improvement in accurately capturing final parking states and a 13.5% enhancement in characterizing overall trajectory distributions. Finally, we integrated these trained models into traditional traffic simulation systems to effectively observe the traffic state evolution with different parking search behaviors, providing valuable insights for optimizing urban traffic management strategies.