Microscopic Traffic Flow Simulation Research Articles

Microscopic simulation of bicycle traffic flow incorporating cyclists’ heterogeneous dynamics and non-lane-based movement strategies

Cycling as a mode of transport is on an upward trend as a low-emission alternative to driving in urbanized areas nowadays. With the increasing number of cyclists, it is of great importance to assess the capacity of cycling infrastructure in practice. Simulation models are useful tools to investigate bicycle flow performance considering cyclists’ distinct moving behaviors. However, existing bicycle simulation models are restricted by either space discretization, lane-based setup, adaptation from models for car traffic, or complicated calibration requirement in a force-based environment. In addition, cyclists’ decision-making ability in the operational-level cycling behavior are not well-captured in these models. This paper proposes a comprehensible microscopic bicycle simulation model which includes a detailed decision-making process and the ability to simulate continuous-space lateral movement. The model consists of three levels, maneuver decision, movement planning, and physical acceleration. It is able to simulate bicycle flow dynamics in undersaturated traffic conditions on an exclusive bike path. As we do not intend to show the empirical validity of the proposed model, the simulation experiment aims at verifying the model and exploring bicycle flow performance in various scenarios by estimating the fundamental diagrams (FDs). The effect of different path widths on bicycle flow capacity is first explored. Other behavioral factors, including desired speed heterogeneity, overtaking incentive, and safety region size perceived by cyclists, which can potentially influence the shape of the FD are also tested. The model can be further extended to simulate relatively complex cycling behavior with cooperative and anticipative strategies and investigate bicycle flow characteristics in congested traffic conditions.

Simulation-Oriented Analysis and Modeling of Distracted Driving

Distracted driving significantly affects the efficiency and safety of traffic flow. Modeling distracted driving behavior in microscopic traffic flow simulation is essential for understanding its critical impacts on traffic flow. However, due to the influence of various external factors and the considerable uncertainties in behavior characteristics, modeling distracted driving behavior remains a challenge. This study proposed a model which incorporates distraction features into the microscopic traffic flow model to simulate distracted driving behavior. Specifically, the study first examines the characteristics of distracted driving, including the intervals and durations of distraction events, as well as the patterns and environments of distraction. It then introduces distraction parameters into the Intelligent Driver Model (IDM), including reaction time delays and perception deviations in both speed difference and following distance. These parameters are quantified by probabilistic distributions to reflect the uncertainty and individual differences in driving behavior. The model is calibrated and validated using 772 distracted following events from the Shanghai Naturalistic Driving Study (SH-NDS) data. Three patterns of distraction (excessive, moderate, mild) are distinguished and modeled separately. The results show that the model’s accuracy surpasses that of the IDM under various road types and traffic volumes, with an average improvement in model accuracy of about 11.30% on expressways with high traffic volume, 4.54% on expressways with low traffic volume, and 4.46% on surface roads. Meanwhile, the model can effectively simulate the variations in reaction times and perceptual deviations in both speed and following distance for different distraction modes at the individual level, maintaining consistency with reality. Finally, the study simulates distracted driving behavior under different road environments and traffic volumes to explore the impact of distracted driving on traffic flow. The simulation results indicate that an increase in the proportion of distraction reduces the efficiency and safety of traffic flow, which is consistent with real-world observations. Since the model considers human distraction factors, it can generate more dangerous driving scenarios in simulations, which holds significant importance for safety-related research. The findings from this study are expected to be helpful for understanding distracted driving behavior and mitigate its negative influence on the efficiency and safety of traffic flow.

Simulation-Oriented Analysis and Modeling of Distracted Driving

Distracted driving significantly affects the efficiency and safety of traffic flow. Modeling distracted driving behavior in microscopic traffic flow simulation is essential for understanding its critical impacts on traffic flow. However, due to the influence of various external factors and the considerable uncertainties in behavior characteristics, modeling distracted driving behavior remains a challenge. This study proposed a model which incorporates distraction features into the microscopic traffic flow model to simulate distracted driving behavior. Specifically, the study first examines the characteristics of distracted driving, including the intervals and durations of distraction events, as well as the patterns and environments of distraction. It then introduces distraction parameters into the Intelligent Driver Model (IDM), including reaction time delays and perception deviations in both speed difference and following distance. These parameters are quantified by probabilistic distributions to reflect the uncertainty and individual differences in driving behavior. The model is calibrated and validated using 772 distracted following events from the Shanghai Naturalistic Driving Study (SH-NDS) data. Three patterns of distraction (excessive, moderate, mild) are distinguished and modeled separately. The results show that the model’s accuracy surpasses that of the IDM under various road types and traffic volumes, with an average improvement in model accuracy of about 11.30% on expressways with high traffic volume, 4.54% on expressways with low traffic volume, and 4.46% on surface roads. Meanwhile, the model can effectively simulate the variations in reaction times and perceptual deviations in both speed and following distance for different distraction modes at the individual level, maintaining consistency with reality. Finally, the study simulates distracted driving behavior under different road environments and traffic volumes to explore the impact of distracted driving on traffic flow. The simulation results indicate that an increase in the proportion of distraction reduces the efficiency and safety of traffic flow, which is consistent with real-world observations. Since the model considers human distraction factors, it can generate more dangerous driving scenarios in simulations, which holds significant importance for safety-related research. The findings from this study are expected to be helpful for understanding distracted driving behavior and mitigate its negative influence on the efficiency and safety of traffic flow.

Simulation-based quantitative methods for vehicle emissions and a CO2 charging policy

With the worsening vehicle emissions, carbon emission charges are becoming an increasingly popular policy to reduce emissions. This paper proposes a policy to charge for the additional CO2 emissions due to the increasing traffic flow for each vehicle, thereby extending traditional travel time-based traffic assignment to emissions-charged traffic assignment. Considering that vehicle movements in the network are affected by signal timings and the car-following together with lanechanging interactions of different types of vehicles, microscopic traffic flow simulation is combined with a CO2 emission model to formulate the relations between link flow and emissions of different types of vehicles. Accordingly, the additional CO2 emissions due to increasing traffic flow are quantified and charged for each vehicle, leading to multi-vehicle-type and multi-criteria traffic assignment. Through an example network, the proposed flow-emissions model is verified and the impacts of different CO2 charging prices in the morning peak hour are investigated. Analysis of the results shows monotone increasing relations between price and reduced total emissions as well as increased revenue within the price range. In addition, the charging policy also leads to traffic assignment that achieves a system-optimal assignment since the CO2 pricing aligns with the general concept of pricing externality, thereby reducing the total travel time.

Generating and Calibrating a Microscopic Traffic Flow Simulation Network of Kyoto

Microscopic traffic flow simulations as tools for enabling detailed insights on traffic efficiency and safety gained numerous popularity among transportation researchers, planners and engineers in the first to decades of the 21st century. By implementing a test bed for simulation scenarios of complex urban transportation infrastructure it is possible to inspect specific effects of introducing small infrastructural changes related to the built environment and to the introduction of advanced traffic control strategies. The possibility of reproducing present problems or the transportation services, such as the ones of public bus services is a key motivation of this work. In this research, we reproduce the road network of the city of Kyoto for observing specific travel patterns of public buses such as the bus bunching phenomena. Therefore, a selection of currently available data sets is used for calibrating a cutout of the Kyoto road network of a relatively large extent. After introducing a method for geodata extraction and conversion, we approach the calibration by introducing virtual detectors representing present inductive loops and make use of historical traffic count records. Additionally, we introduce bus routes partially contributed by volunteer mappers (OSM project). First simulation outcomes show numerous familiar (local knowledge) flow patterns.

Evaluation of a Dynamic Lane Assignment on Urban Freeways by Using a Microscopic Traffic Flow Simulation

At a merging section of freeways, the mainline traffic flow conflicts with the traffic flow from an onramp, which often causes traffic congestion. To mitigate this conflict, it is important to dynamically manage the lane utilization on the mainline at bottleneck points. Recently, connected and automated vehicles (CAVs) are expected to be widely used on freeways. Guiding CAVs to change lane to avoid conflicts with merging vehicles may alleviate traffic congestion. In this paper, we propose a novel dynamic lane guidance system that can dynamically manage the lane changes in the merging sections with urban freeway ramps. In the proposed system, CAVs are guided to change lane at the lanes upstream of bottlenecks according to traffic conditions, which are detected by the following two methods: 1) cross-sectional vehicle speed information obtained from traffic detectors and 2) vehicle speed information obtained from driving trajectories based on probe data. Then, the effectiveness of the proposed dynamic lane guidance system is evaluated by a microscopic traffic simulator, Vissim, which is calibrated to reproduce the observed traffic states based on full vehicle trajectory data collected at the section near the Tsukamoto merge area on the Hanshin Expressway Route 11 Ikeda Line in Japan. As a result, the obtained results showed that dynamic lane guidance is effective in reducing overall travel time, thus demonstrating the usefulness of dynamic lane guidance. It was also found that traffic control based on partial vehicle information, such as probe data, is sufficiently effective.

Bidirectional Green Waves for Road Arterials: Optimization and Simulation

Consider a bidirectional arterial road along which several junctions are controlled by fixed-time traffic signals. The driving times between the junctions are far from showing the ideal condition of integer multiples of half the cycle time. At the junctions, separate left-turn phases have to be scheduled. In particular, the two opposite directions of the arterial can but do not need to be scheduled at the same time. This paper provides an integrated mathematical optimization model that maximizes the widths and lengths of the green bands of both directions of the arterial. Within this, the model selects among three predefined signal programs with different phase sequences, and computes optimum offsets for the selected signal programs at each junction. Arranging the left-turn phases before or after the arterial’s through-traffic in a dedicated way provides much better results in the optimization model than always scheduling them at the same time. To validate the optimization result, we implemented a microscopic traffic flow simulation model (using the SUMO simulation package) inspired by certain arterials in the city of Berlin, Germany. With 11 crossings, the result was an average of less than 1.7 stops per car—already including one possible stop at the two entry points of the coordinated arterial. This compares favorably with the average number of at least 3.4 stops per car when the straight traffic in both directions is always scheduled at the same time, that is, in parallel.

3-step calibration process of a microscopic traffic flow simulation platform for mixed traffic scenarios

Microscopic traffic flow simulation is used in multiple fields nowadays, resulting in varying objectives and requirements regarding model calibration. Traffic engineers analyse the impact on traffic flow using simulation tools and in automobile industry simulation is used for developing and analysing automated driving functions and their functionalities. We developed a simulation platform with pre-defined applications, allowing operators to test the impact of automated driving on traffic flow by varying mixed traffic scenarios. As calibration of the base case for different domains is a time-consuming process, we present in this paper a 3-step calibration process, which allows efficient model calibration by focusing on macroscopic traffic flow dynamics as well as microscopic driving behaviour.

TraEDITS: Diversity and Irregularity-Aware Traffic Trajectory Editing

We present TraEDITS, a novel traffic trajectory editing framework for autonomous vehicle testing, which can generate new traffic behaviors by controlling each vehicle interactively to increase the diversity or irregularity of traffic testing data. Given a traffic flow with its original trajectories, user’s edits, environmental constraints, and physical constraints as input, our framework is able to generate an edited traffic flow through a global path planning module and a data-driven microscopic traffic flow simulation module. With the way-points from the user, our global planning module generates lane-level navigation by heuristic-based path planning in discrete space. By taking internal properties of the vehicle, velocity continuity, reference path, and collision avoidance into consideration, our simulation module generates vehicles’ motions based on energy optimization driven by real traffic data. Given edits of the desired speed, the lateral deviation, or the reference path, our approach can generate a new trajectory of the selected vehicle, and adjust the surrounding vehicles’ trajectories accordingly. As a result, our framework is able to simulate irregular or even rare events existing in real traffic. Meanwhile, our framework can enhance the diversity and irregularity of traffic behaviors and interactions by creating challenging scenarios like swerve, nudge, and U-turn. We validate the usability and plausibility of our framework through extensive experiments and user studies.

Parameters Influencing Lane Flow Distribution on Multilane Freeways in PTV Vissim

Abstract In a parameter study, we systematically varied parameter values, and quantified the resulting traffic flow in each individual lane. We modeled two-, three-, and four-lane freeway sections with the microscopic traffic flow simulation tool PTV Vissim. We compared the results with findings from literature. Simulations using car following model Wiedemann 99 fit better to empirical studies than those using Wiedemann 74. Empirically determinable parameters, that have a relevant influence on lane flow distribution are desired speed distributions (mean for heavy-duty vehicles and standard deviation for cars), heavy-duty vehicle share, and the gradient of the section. Additionally, the driving behavior parameters CC1 (headway time), CC3 (threshold for entering following), and safety distance reduction factor have an influence. As CC1 is one of the most relevant parameters for calibrating capacity, CC3 and the safety distance reduction factor remain for lane flow adjustment.

Comparative Safety Assessment of Automated Driving Strategies at Highway Merges in Mixed Traffic

We present a simulation-based approach to assess the safety impacts of vehicles equipped with Automated Driving Systems (ADS) in mixed traffic with Human-driven Vehicles (HV). Specifically, we compare two generic longitudinal strategies of ADS to handle a cut-in: Reactive ADS acting only when the cut-in vehicle crosses the target lane boundary, and Predictive ADS acting at the onset of the cut-in manoeuvre. We identify their distinctive effects on the traffic safety under cut-in maneuvers of adjacent human-driven vehicles at highway merges. We employ a microscopic traffic flow simulator that describes the lane changing process with high detail, accounting for the vehicle interaction and consequent trajectory updates. These high-resolution trajectories are post-processed to estimate a set of relevant surrogate measures of safety. By analyzing these measures, we find that the predictive ADS significantly outperforms the reactive ADS in aspects such as temporal proximity to crash, expected crash severity and the driving risk (combining the two aspects), and the number of aborted lane changes by HV. The negative safety impact of reactive ADS becomes prominent at penetration rate >10%. The major difference between the two ADS approaches appears in the dynamics of risk during the lane changing. When a vehicle cuts in ahead of Reactive ADS, the risk peaks approximately halfway through the maneuver; whereas with Predictive ADS the risk remains marginal throughout. This work demonstrates the potential of simulation-based safety assessment to differentiate the safety impacts of automation functionalities at an early stage of product development.

Calibration of Microscopic Traffic Flow Simulation Models considering Subsets of Links and Parameters

This study proposes a methodology for the calibration of microscopic traffic flow simulation models by enabling simultaneous selection of traffic links and associated parameters. The analyst selects any number and combination of links and model parameters for calibration. Most calibration methods consider the entire network and use ad hoc approaches without enabling a specific selection of location and associated parameters. In practice, only a subset of links and parameters is used for calibration based on several factors such as expert knowledge of the system or constraints imposed by local governance. In this study, the calibration problem for the simultaneous selection of links and parameters was formulated using a mathematical programming approach. The proposed methodology is capable of calibrating model parameters considering multiple time periods and performance measures simultaneously. Traffic volume and speed are the performance measures used in this study, and the methodology is developed without considering the characteristics of a specific traffic flow model. A genetic algorithm was implemented to find a solution to the proposed mathematical program. In the experiments, two traffic models were calibrated: the first set of experiments included selection of links only, while all associated parameters were considered for calibration. The second set of experiments considered simultaneous selection of links and parameters. The implications of these experiments indicate that the models were calibrated successfully subject to selection of a minimum number of links. As expected, the more links and parameters that are used for calibration, the more time it takes to find a solution, but the overall results are better. All parameter values were reasonable and within constraints after successful calibration.

Criticality Measures to Evaluate the Triggering Decision of Collision Avoidance Functions at Intersections

In order to significantly reduce the amount of testing required to validate the behavior of vehicle safety functions with real vehicles, the scenarios in the simulation must reproduce the traffic as realistically as possible. For this reason, a microscopic traffic flow simulation is used. Since the road users show a perfect behavior by default, driving errors have to be induced. The necessary adjustments in the existing driver models are presented schematically in this paper. These changes in the driving behavior ensure that some road users cause situations that are critical for others. Such scenarios are required as input data for testing vehicle safety functions in a vehicle dynamics simulation. A decisive factor for the quality with which a critical situation can be successfully managed by a vehicle safety function is the trigger time. For this purpose, approaches are presented for estimating the period in which a collision can still be avoided by braking or evasive maneuver. Three intersection scenarios illustrate the functioning of these criteria

Criticality Measures to Evaluate the Triggering Decision of Collision Avoidance Functions at Intersections

In order to significantly reduce the amount of testing required to validate the behavior of vehicle safety functions with real vehicles, the scenarios in the simulation must reproduce the traffic as realistically as possible. For this reason, a microscopic traffic flow simulation is used. Since the road users show a perfect behavior by default, driving errors have to be induced. The necessary adjustments in the existing driver models are presented schematically in this paper. These changes in the driving behavior ensure that some road users cause situations that are critical for others. Such scenarios are required as input data for testing vehicle safety functions in a vehicle dynamics simulation. A decisive factor for the quality with which a critical situation can be successfully managed by a vehicle safety function is the trigger time. For this purpose, approaches are presented for estimating the period in which a collision can still be avoided by braking or evasive maneuver. Three intersection scenarios illustrate the functioning of these criteria.

Stress Testing Method for Scenario-Based Testing of Automated Driving Systems

Classical approaches and procedures for testing of automated vehicles of SAE levels 1 and 2 were based on defined scenarios with specific maneuvers, depending on the function under test. For automated driving systems (ADS) of SAE level 3+, the scenario space is infinite and calling for virtual testing and verification. However, even in simulation, the generation of safety-relevant scenarios for ADS is expensive and time-consuming. This leads to a demand for stochastic and realistic traffic simulation. Therefore, microscopic traffic flow simulation models (TFSM) are becoming a crucial part of scenario-based testing of ADS. In this paper, a co-simulation between the multi-body simulation software IPG CarMaker and the microscopic traffic flow simulation software (TFSS) PTV Vissim is used. Although the TFSS could provide realistic and stochastic behavior of the traffic participants, safety-critical scenarios (SCS) occur rarely. In order to avoid this, a novel Stress Testing Method (STM) is introduced. With this method, traffic participants are manipulated via external driver DLL interface from PTV Vissim in the vicinity of the vehicle under test in order to provoke defined critical maneuvers derived from statistical accident data on highways in Austria. These external driver models imitate human driving errors, resulting in an increase of safety-critical scenarios. As a result, the presented STM method contributes to an increase of safety-relevant scenarios for verification, testing and assessment of ADS.

A Multi-Objective Approach for the Calibration of Microscopic Traffic Flow Simulation Models

The calibration of traffic-flow simulation models continues to be a significant problem without a generalized, comprehensive, and low-cost solution. Existing calibration approaches either have not explicitly addressed the multi-objective characteristics of the problem or determining their hyperparameters requires significant effort. In addition, statistical evaluation of alternative solution algorithms is not performed to ensure dominance and stability. This study proposes an adaptation and advanced implementation of the Multi-Objective Global-Best Harmony Search (MOGBHS) algorithm for calibrating microscopic traffic-flow simulation models. The adapted MOGBHS provides five key capabilities for solving the proposed problem including 1) consideration of multiple objectives, 2) easily extendable to memetic versions, 3) simultaneous consideration of continuous and discrete variables, 4) efficient ordering of no dominated solutions, 5) relatively easy tuning of hyperparameters, and 6) easily parallelization to maximize exploration and exploitation without increasing computing time. Three traffic flow models of different dimensionality and complexity were used to test the performance of seventeen metaheuristics for solving the calibration problem. The efficiency and effectiveness of the algorithms were tested based on convergence, minimization of errors, calibration criterion, and two statistical nonparametric tests. The proposed approach dominated all alternative algorithms in all cases and provided the most stable and diverse solutions.

Transport Sector Impacts of a Border between Ireland and Northern Ireland after a Hard Brexit

More than half of British voters chose to leave the European Union (EU) leading to a series of negotiations between the United Kingdom and the EU. The withdrawal of the UK from the EU is widely referred to as Brexit. As the only country that shares a land border with the UK, the impact of Brexit on Ireland is expected to be greater than on any other European country. The objective of the research is to evaluate the potential impact of Brexit on the transport sector in Ireland at a micro level by focusing on cross‐border commuters and by also assessing the impact on road freight transport. Potential crossing scenarios are examined at six crossing locations. Assuming a hard border is implemented, each crossing is modelled in VISSIM, a microscopic traffic flow simulation software, using traffic data from Transport Infrastructure Ireland (TII) and dwell time estimated based on the US–Canada border crossings. Six scenarios are considered to determine the impact on cross‐border traffic at different flow conditions and with varying levels of technology used in border infrastructure leading to short versus long processing times. The paper evaluates travel measures including delays, queue lengths and emissions. The worst‐case scenario has a vehicle delay of 18.4 min and the highest delay‐associated costs across all locations modelled are estimated at €60.7 million per year. Estimated emissions generated at the border crossings raise concerns about environmental impacts of a hard Brexit. Interviews with stakeholders emphasized the critical role of technology in reducing the impact of a hard Brexit on cross‐border commuters and on the freight sector. A key finding is the importance of using technology tools to facilitate controls and reduce processing times. The results indicate that technology use leads to significant time and cost savings as well as reduced environmental impacts.

Deep reinforcement‐learning‐based driving policy for autonomous road vehicles

In this work the problem of path planning for an autonomous vehicle that moves on a freeway is considered. The most common approaches that are used to address this problem are based on optimal control methods, which make assumptions about the model of the environment and the system dynamics. On the contrary, this work proposes the development of a driving policy based on reinforcement learning. In this way, the proposed driving policy makes minimal or no assumptions about the environment, since a priori knowledge about the system dynamics is not required. Driving scenarios where the road is occupied both by autonomous and manual driving vehicles are considered. To the best of our knowledge, this is one of the first approaches that propose a reinforcement learning driving policy for mixed driving environments. The derived reinforcement learning policy, firstly, is compared against an optimal policy derived via dynamic programming, and, secondly, its efficiency is evaluated under realistic scenarios generated by the established SUMO microscopic traffic flow simulator. Finally, some initial results regarding the effect of autonomous vehicles' behavior on the overall traffic flow are presented.

Can Automated Vehicles Improve Cyclist Safety in Urban Areas?

Automated vehicles (AVs) are expected to assist in decreasing road traffic fatalities, particularly among passenger cars. However, until now limited research has been conducted on how they will impact the safety of vulnerable road users (VRUs) (i.e., cyclists and pedestrians). Therefore, there is a clear need to start taking into account the interactions between AVs and VRUs as an integrated element of the transport network, especially in urban areas where they are dominant. The objective of this study is to verify whether the anticipated implementation of AVs can actually improve cyclists’ safety. For this purpose, the microscopic traffic flow simulation software PTV Vissim combined with the surrogate safety assessment model (SSAM) were utilized. The road network used for this analysis was generated based on a real study case in a medium-sized city in Belgium, where narrow streets in the city center are shared on many occasions between vehicles and cyclists. The findings of the analysis show a notable reduction in the total number of conflicts between cars, but also between cars and cyclists, compared to the current situation, assuming a 100% market penetration scenario for AVs. Moreover, the severity level of conflicts also decreased as a result of the lack of human-driven vehicles in the traffic streams.

A Novel Method to Integrate Speed and Emissions Characteristics in Pavement Marking Design

Pavement marking is a popular method to reduce freeway off-ramp speed. The objective of this study is to develop a methodology for selecting the optimal pavement marking design that will improve traffic safety and reduce emission levels in the freeway off-ramp. Six pavement marking pattern scenarios on a freeway off-ramp were examined through the following sub-tasks. The first step focused on the testing of six selected scenarios for pavement marking design using a VR driving simulator to obtain driving behavior patterns. Next, the driving behavior pattern for each scenario was implemented in VISSIM, a microscopic traffic flow simulation, to extract the speed reduction for each scenario considering vehicle interactions. Then, the vehicle emission level of each scenario was evaluated by a Motor Vehicle Emission Simulator (MOVES). Through a multiple-criteria decision making model, the best pavement marking design scenario that was both eco-friendly and effective in reducing vehicle speed was selected. Study results showed that transverse line type is the optimal design of pavement marking that improves traffic safety while decreasing emission at a freeway off-ramp. The proposed methodology in this study would quantitatively support the selection of optimal pavement marking design that will promote both traffic safety and traffic environment.