Microscopic Traffic Simulation Tool Research Articles

Traffic Performance of Successive Freeway Merge Segments

At freeway interchanges with high entering flows, implementing two successive merges can improve traffic flow and capacity by distributing the merging maneuvers. The German Guidelines for the Design of Motorways define different types of successive (“double”) merges. According to the quality of service assessment procedure for freeway merge segments given in the German Highway Capacity Manual, successive merge segments can be evaluated separately. However, depending on the distance between the merges, the capacity of the second (downstream) merge might be affected by the entering traffic at the first (upstream) merge, which influences the lane-flow distribution. For a more precise evaluation of traffic flow performance and traffic safety depending on the geometric design characteristics, vehicle interactions, lane changes, the resulting capacity of successive merge segments, accident rates, and crash types were analyzed in the study. The research was based on loop detector data as well as trajectory data obtained from video measurements, which provide an insight into the interactions between mainline and entering vehicles. For the analysis of traffic safety, crash data were evaluated over a period of 3 years. Furthermore, successive merge segments with different geometric and traffic characteristics were modeled with the microscopic traffic simulation tool BABSIM to analyze the influence on traffic flow and capacity. As a result, capacity models and recommendations for the geometric design of successive freeway merge segments are provided.

Application of machine learning models to predict driver left turn destination lane choice behavior at urban intersections

When there are multiple lanes to choose from downstream of a turning movement, drivers should choose the innermost lane so that drivers at other approaches of the intersection may make concurrent turning movements in the outermost lane(s). However, human drivers do not always choose the innermost lane, which could lead to crashes with other vehicles. Therefore, predicting human driver behaviors is vital in reducing crashes, as the need to share the roadways with automated vehicles (AVs) continues to grow. In this research, various machine learning models have been used to predict the left turn destination lane choice of human-driven vehicles (HDVs) at urban intersections based on several quantifiable parameters. A total of 174 subject vehicles were extracted and analyzed in Los Angeles, California, and Atlanta, Georgia, using HDV trajectory data from the Next Generation SIMulation (NGSIM) database. Five machine learning techniques, namely binary logistic regression, k nearest neighbors, support vector machines, random forest, and adaptive neuro-fuzzy inference system, were applied to the extracted data to predict the lane choice behavior of drivers. The k nearest neighbors model showed the most promising results for the evaluated data with a correct decision score of over 93% for the unseen test data. This model may be programmed into: (i) AVs, in conjunction with sensors, to predict if an HDV is about to turn into the incorrect destination lane; and (ii) microscopic traffic simulation tools so that modelers can identify potential conflicts when HDVs do not select the appropriate destination lane.

Modified Gipps model: a collision-free car following model

Car following (CF) models are used in microscopic traffic simulation tools to help assess the effects of a new road design or to assess the effect of change in traffic flow. In 1981, Gipps developed a collision avoidance CF model using the Newtonian laws of motion to describe the motion of each vehicle in a stream of traffic. It is one of the most widely used CF models in both research and practice. Although it is claimed that the Gipps model produces collision-free results, the model produces a collision when the intention of the following vehicle is to brake harder than the perceived deceleration of lead vehicle. For the ease of simulations, a traffic simulation tool is expected to not show unrealistic crashes. This study was carried out to make the Gipps model collision-free in all conditions. It first highlights the conditions where the original Gipps model produces a collision. Then the study derives an equation for a collision-free Gipps CF model. This modified Gipps CF model produces collision-free results that always maintain a safe spacing with the lead vehicle.

State of Bicycle Modeling in SUMO

Microscopic traffic simulation tools provide ever-increasing value in the design and implementation of motor vehicle transport systems. Research and development of automated and intelligent technologies have highlighted the usefulness of simulation tools and development efforts have accelerated in recent years. However, the majority of traffic simulation software is developed with a focus on motor vehicle traffic and has limited capabilities in the simulation of bicycles and other micro-mobility modes. Bicycles, e-bikes and cargo bikes represent a non-negligible modal share in many urban areas and their impact on the operation, efficiency and safety of traffic systems must be considered in any comprehensive study. The Differentiation between different types of micro-mobility modes, including microcars, e-kick scooters, different types of bicycles and other personal mobility devices, has not yet attracted enough attention in the development of simulation software which creates difficulties in including these modes in simulation-based studies. On November 25th, 2022, members of the SUMO team at DLR organized a workshop to assess the state of bicycle simulation in SUMO, identify shortcomings and missing capabilities and prioritize the order in which bicycle traffic related features should be modified or implemented in the future. In this paper, different aspects of simulating bicycle traffic in SUMO are examined and an overview of the results of the workshop discussions is given. Some suggestions for the future development of SUMO emerging from this workshop, are presented as a conclusion.

Capacity Drop at Freeway Ramp Merges with Its Replication in Macroscopic and Microscopic Traffic Simulations: A Tutorial Report

Capacity drop (CD) at overloaded bottlenecks is a puzzling traffic flow phenomenon with some internal and complicated mechanisms at the microscopic level. Capacity drop is not only important for traffic flow theory and modelling, but also significant for traffic control. A traffic model evaluating traffic control measures needs to be able to reproduce capacity drop in order to deliver reliable evaluation results. This paper delivers a comprehensive overview on the subject from the behavioral mechanism perspective, as well as from microscopic and macroscopic simulation points of view. The paper also conducts comparable studies to replicate capacity drop at freeway ramp merges from both macroscopic and microscopic perspectives. Firstly, the subject is studied using the macroscopic traffic flow model METANET with respect to ramp merging scenarios with and without ramp metering. Secondly, one major weakness of commercial microscopic traffic simulation tools in creating capacity drop at ramp merges is identified and a forced lane changing model for ramp-merging vehicles is studied and incorporated into the commercial traffic simulation tool AIMSUN. The extended AIMSUN carefully calibrated against real data is then examined for its capability of reproducing capacity drop in a complicated traffic scenario with merging bottlenecks. The obtained results demonstrate that reproducible capacity drop can be delivered for the targeted bottlenecks using both macroscopic and microscopic simulation tools.

Modelling autonomous vehicle interactions with bicycles in traffic simulation

With the undeniable advance of automated vehicles and their gradual integration in day-today urban traffic, many new technologies have been developed that offer great potential for this emerging field of research. However, testing automated vehicle technologies in real road traffic with vulnerable road users (VRUs) is still a complicated and time consuming procedure. The virtual development and evaluation of automated vehicles using simulation tools offers a good opportunity to test new functions efficiently. However, existing models prove to be insufficient in modeling the interaction between autonomous vehicles and vulnerable road users such as bicyclists and pedestrians. In this paper, an automated vehicle model for microscopic traffic simulation tools is developed with the open-source traffic simulation software Simulation of Urban Mobility (SUMO) and evaluated in common traffic interaction scenarios with bicyclists. A controller model is proposed using different path-finding algorithms from the field of robotic and automated vehicle research, which covers all important control levels of a self-driving vehicle. Finally, its performance is compared to existing car-following and lane-changing models. Results showcase that the autonomous vehicle model achieves either comparable results or has a much steadier and more realistic driving behavior when compared to existing driving models while interacting with bicyclists. The whole source code developed over the course of this work is freely accessible at: https://github.com/FlixFix/Kimarite.

DYNAMIC AND WEB-BASED 4D VISUALIZATION OF STREETSPACE ACTIVITIES DERIVED FROM TRAFFIC SIMULATIONS AND SEMANTIC 3D CITY MODELS

Abstract. Semantic 3D city models can serve as anchor points for different components of urban digital twins. In addition to static 3D models such as buildings, transportation infrastructure, vegetation, or city furniture, this can also include dynamic processes such as traffic movement or changing traffic signals. Integrating these aspects into a dynamic, realistic, and accessible 4D visualization presents a number of requirements and challenges, which are discussed. While the City Geography Markup Language (CityGML) is a well established OGC standard for modeling and exchanging semantic 3D city models, the Cesium Language (CZML) provides capabilities for visualizing time-dependant properties that can be displayed in the Cesium virtual globe. Results of the open-source microscopic traffic simulation tool SUMO include information on locations and orientations of vehicles, bicycles, pedestrians and other traffic members as well as traffic signal information and can be exported as a CSV table. In order to allow a 4D visualization of these simulation results, a scheme for deriving 3D + t CZML documents from data given as 2D + t CSV files is presented. Additionally, dynamically changing traffic lights are integrated. Based on data available for the city of Munich, a CityGMLcompliant streetspace model is generated. This enables the combination of traffic simulation results and semantic 3D city models within a common Cesium based web-visualization and allows a direct and platform independent access to realistic visualizations of streetspace activities. Since this often results in a huge amount of data, a spatio-temporal tiling strategy enabling the visualization of large CZML data is presented.

A New Method for the Quality of Service Assessment of Highly Frequented Bicycle Facilities in Germany

Owing to increasing bicycle traffic volumes in Germany, more and more bicycle facilities are being designed with widths beyond the standard values given in the existing design guidelines. However, the level of service assessment method for bicycle facilities in the current edition of the German Highway Capacity Manual, HBS, is based on empirical data from facilities with mostly low traffic demand and small widths. Therefore, a new quality of service assessment method for highly frequented bicycle facilities was developed. For this, the cycling behavior of cyclists at high traffic volumes was analyzed, considering different bicycle types. Bicycle traffic flow quality, which is mainly characterized by the ability for cyclists to pass, was examined on segments of bicycle facilities with different geometric parameters, including facility type, gradient, and width. The speed and lateral distance behavior of cyclists were analyzed by using radar and video measurements. It was found that a large variance in the bicycle speed distribution mainly resulted from an inhomogeneous composition of bicycle types. The bicycle facilities were modeled with the microscopic traffic simulation tool PTV Vissim and calibrated using the empirical data. The models were used to analyze the impact of different geometric parameters and high proportions of specific bicycle types. As expected, the simulations revealed that the quality of service was mainly influenced by the lane width. Based on the results, a method to assess the quality of service of bicycle facilities, in which traffic density is used as the measure of effectiveness, was developed.

Investigation of car-following models in disordered traffic using trajectory data obtained from unmanned aerial vehicles

ABSTRACT Car-following models are the cornerstones of microscopic traffic simulation tools and intelligent transportation systems, but the applicability of car-following models to disordered traffic have not been investigated in detail with longer trajectory dataset. To address this gap, two car-following models namely, Intelligent Driver Model (IDM) and Full Velocity Difference Model (FVDM) are calibrated using trajectory data collected on an urban arterial road in Chennai, India using Unmanned Aerial Vehicles. The raw data are smoothed for noise removal and the car-following pairs are identified based on the lateral overlap and following duration. The models are calibrated by minimizing the deviations between the observed and simulated longitudinal gaps between leader and follower using genetic algorithm. The obtained errors are between 2.5% and 19.5%, which are comparable with standard ranges of error reported in literature. The optimal parameter values represent the distinct characteristics of disordered traffic in comparison with the homogeneous traffic.

Investigation of the effect of autonomous vehicles (AV) on the capacity of an urban transport network

In this paper, we assess the effects of different shares of autonomous vehicles (AVs) on the traffic flow and, in particular, on the maximum possible capacity at signal-controlled intersections. For this purpose, all signal-controlled nodes in the traffic network of the Düsseldorf metropolitan area were systematically simulated and evaluated using the microscopic traffic simulation tool SUMO.The analysis shows that defensively parameterized AVs – as envisaged in the umbrella project of this research – may decrease the maximum possible traffic at signal-controlled intersections. Moreover, the simulation runs indicate that capacity at these intersections decreases almost linearly with a growing share of AV. In a second part of this analysis, a freeway section was simulated with the same varying shares of CV and AV to investigate free-flow traffic. In this case, the simulation results of the maximum traffic flow can be approximated by a third-order polynomial fit. The minimum capacity is found for the uniform share of both vehicle types (i.e. 50 % AV and 50 % CV).The overall intent of this project is to provide an approach to determine system-wide and long-term effects of AVs from local microscopic observations. To this end, the SUMO microscopic traffic simulation will be utilized to derive realistic flow capacities for signal-controlled intersections. In a next step, these capacities will be transferred to a mesoscopic traffic simulation. Subsequently, flow capacities can be systematically adjusted in this network-wide mobility simulation to parameterize the influence of future infrastructure and vehicle technologies.

Pre-study and insights to a sequential MATSim-SUMO tool-coupling to deduce 24h driving profiles for SAEVs

New mobility concepts such as shared, autonomous, electric vehicle (SAEV) fleets raise questions to the vehicles’ technical design. Compared to privately owned human driven cars, SAEVs are expected to exhibit different load profiles that entail the need for newly dimensioned powertrain and battery components. Since vehicle architecture is very sensitive to operating characteristics, detailed SAEV driving cycles are crucial for requirement engineering. As real world measurements reach their limit with new mobility concepts, this contribution seeks to evaluate three different traffic simulation approaches in their ability to model detailed SAEV driving profiles. (i) The mesoscopic traffic simulation framework MATSim is analyzed as it is predestined for large-scale fleet simulation and allows the tracking of individual vehicles. (ii) To improve driving dynamics, MATSim’s simplified velocity profiles are enhanced with real-world driving cycles. (iii) A sequential tool-coupling of MATSim with the microscopic traffic simulation tool SUMO is pursued. All three approaches are compared and evaluated by means of a comprehensive test case study. The simulation results are compared in terms of driving dynamics and energy related key performance indicators (KPI) and then benchmarked against real driving cycles. The sequential tool-coupling approach shows the greatest potential to generate reliable SAEV driving profiles.

Identifying Factors Affecting the Time Taken for Drivers to Complete Freeway Merging Maneuvers under Varying Weather, Traffic, and Geometric Conditions

AbstractInadequate understanding of driver heterogeneity and characteristics creates challenges building models in microscopic traffic simulation tools that accurately represent merging behavior in...

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.

Evaluating the Effects of One-Way Traffic Management on Different Vehicle Exhaust Emissions Using an Integrated Approach

One-way traffic management is a recognized traffic organization to improve traffic efficiency and safety, but its effects on different traffic emissions remains unclear. This paper aims to investigate the impacts of one-way traffic management on three typical vehicle exhaust emissions including Carbonic Oxide (CO), Hydrocarbon Compounds (HC), and Nitrogen Oxides (NOx) in a traffic system using an integrated approach. Field experiment was conducted to collect the vehicular emission data under different traffic conditions using the onboard portable emission measurement system. An instantaneous emission model (i.e., Vehicle Specific Power) is calibrated using the collected field emission data and is incorporated into the microscopic traffic simulation tool VISSIM for quantifying the emissions before and after one-way traffic management through simulation. Two scenarios based on real networks and traffic demands of peak hours in part areas of Shanghai are developed for simulation and evaluation. The results show that in the intersections, the emission rates of COHC, NOx after one-way traffic management is significantly reduced by 20.46%, 21.29% and 21.06%, respectively. In the road sections, the emission rates of CO, HC, NOx in the road sections decrease by 23.38% and 26.29%. The overall CO, HC, NOx emissions in the studied network reduce by 21.34%, 22.29% and 23.77% separately due to one-way traffic management. The results provide insights into the derivative effects of one-way traffic management on traffic emissions in the intersections, road sections and network levels, and thus support scientific traffic management for promoting the sustainability of transport system.

Car following model for urban signalised intersection to estimate speed based vehicle exhaust emissions

Transportation sector is the major contributor to air pollution through vehicle exhaust emissions caused by rapid growth of urbanization in India. It is essential to estimate vehicle exhaust emissions precisely to implement effective control strategy beneficial for pollution reduction. Traffic simulation is a useful approach for analyzing dynamic traffic conditions associated with heterogeneity. VISSIM is a microscopic traffic simulation tool widely used to model real traffic conditions and to estimate speed based vehicle emissions. VISSIM model follows the car following model for simulating driving behavior parameters of an individual vehicle class. VISSIM's add on module EnViVer is used for estimating emissions through microscopic approach. This paper presents traffic simulation and emission modelling approach for a signalised intersection of Vadodara city in India. Traffic volume and speed profile of test vehicles have been collected for the selected route of the city. Calibration and Validation of model are achieved through standard performance measures. To observe the effect of vehicle compositions and signal green interval time on emissions, different models have been generated and their respective emissions are estimated. The result indicates that by changing vehicle composition and signal green interval time, emissions reduce significantly as 25.54% CO2, 6.69% NOx and 25.59% PM10.

Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study

Although car-following behavior is the core component of microscopic traffic simulation, intelligent transportation systems, and advanced driver assistance systems, the adequacy of the existing car-following models for Chinese drivers has not been investigated with real-world data yet. To address this gap, five representative car-following models were calibrated and evaluated for Shanghai drivers, using 2100 urban-expressway car-following periods extracted from the 161,055 km of driving data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The models were calibrated for each of the 42 subject drivers, and their capabilities of predicting the drivers’ car-following behavior were evaluated.The results show that the intelligent driver model (IDM) has good transferability to model traffic situations not presented in calibration, and it performs best among the evaluated models. Compared to the Wiedemann 99 model used by VISSIM®, the IDM is easier to calibrate and demonstrates a better and more stable performance. These advantages justify its suitability for microscopic traffic simulation tools in Shanghai and likely in other regions of China. Additionally, considerable behavioral differences among different drivers were found, which demonstrates a need for archetypes of a variety of drivers to build a traffic mix in simulation. By comparing calibrated and observed values of the IDM parameters, this study found that (1) interpretable calibrated model parameters are linked with corresponding observable parameters in real world, but they are not necessarily numerically equivalent; and (2) parameters that can be measured in reality also need to be calibrated if better trajectory reproducing capability are to be achieved.

Comparisons of mandatory and discretionary lane changing behavior on freeways

This research performs comparative analyses on drivers’ behavior during mandatory and discretionary lane changes. We do this by examining the statistical properties of four lane changing decision variables that describe the gaps between the subject vehicle and the surrounding vehicles. Mandatory and discretionary lane changes in NGSIM’s I-80 Freeway and U.S. Highway 101 data collection sites were identified. First, for each variable at the same site, descriptive statistics for the two types of lane changes were compared, and hypothesis tests on the difference between two means were conducted. Then, for each decision variable at the same site, the observed cumulative distributions between the mandatory and discretionary lane changes were compared by means of the Kolmogorov–Smirnov test. This test was repeated for the fitted distributions of the same decision variable at the same site. The results show that, for the three decision variables associated with gaps in the target lane, the means and distributions between the two types of lane changes are not significantly different. The only variable found to have significant differences in means and distributions is the gap between the subject vehicle and the preceding vehicle in the original lane. This may be because this variable is not an important input in mandatory lane change decisions. This finding provides statistical justification for researchers to develop models with different inputs for mandatory and discretionary lane changes in driver assist systems, in autonomous vehicles, and in microscopic traffic simulation tools.

Exploring environmentally sustainable traffic signal warrant for planning application

ABSTRACTWith the concerns over the greenhouse gas (GHG) emissions associated with the surface transportation, the environmentally sustainable traffic signal warrant is essential to provide guidelines for making environmentally conscious decisions about control types at intersections. This research conducted a pilot study on developing CO2 emissions-oriented traffic signal warrants. Intersection control types that were analyzed for this research include two-way stop, four-way stop, traffic signal, and roundabout. Two environmentally sustainable traffic signal warrants were proposed: Type I (without roundabout) and Type II (with roundabout). The proposed traffic signal warrants were compared with the existing mobility-oriented traffic signal warrant of the Manual on Uniform Traffic Control Devices (MUTCD) in terms of CO2 emissions and total delay. The measurements were collected and estimated using a microscopic traffic simulation tool, Verkehr In Stadten—SIMulationsmodell (VISSIM). The environmentally sustainable traffic signal warrants show that the annual CO2 emissions of the ground transportations in the USA were reduced by 7.2% with Type I warrant and 13.9% with Type II warrant.

Maximum Green Time Settings for Traffic-Actuated Signal Control at Isolated Intersections Using Fuzzy Logic

Traffic Actuated Control (TAC) is widely used for signal control in isolated intersections. TAC is a responsible method for online traffic data. In TAC, green durations are dynamically determined based on online traffic data. These determinations are subject to predefined static parameters which do not respond to sudden traffic flow changes. One type of these parameters in TAC is maximum green time. In this paper, maximum green times are dynamically determined by means of fuzzy control. At first, it monitors the whole intersection traffic conditions and then dynamically adjusts maximum green times. The main advantage of the proposed approach is its capability to adjust maximum green times which are responsive to real time traffic condition in isolated intersections. The efficiency of the proposed method has been evaluated by AIMSUN 7 microscopic traffic simulation tool. The evaluation results show the efficiency and the robustness of the proposed method compared to maximum green times provided by SYNCHRO8.

Modelling acceleration decisions in traffic streams with weak lane discipline: A latent leader approach

Acceleration is an important driving manoeuvre that has been modelled for decades as a critical element of the microscopic traffic simulation tools. The state-of-the art acceleration models have however primarily focused on lane based traffic. In lane based traffic, every driver has a single distinct lead vehicle in the front and the acceleration of the driver is typically modelled as a function of the relative speed, position and/or type of the corresponding leader. On the contrary, in a traffic stream with weak lane discipline, the subject driver may have multiple vehicles in the front. The subject driver is therefore subjected to multiple sources of stimulus for acceleration and reacts to the stimulus from the governing leader. However, only the applied accelerations are observed in the trajectory data, and the governing leader is unobserved or latent. The state-of-the-art models therefore cannot be directly applied to traffic streams with weak lane discipline.This prompts the current research where we present a latent leader acceleration model. The model has two components: a random utility based dynamic class membership model (latent leader component) and a class-specific acceleration model (acceleration component). The parameters of the model have been calibrated using detailed trajectory data collected from Dhaka, Bangladesh. Results indicate that the probability of a given front vehicle of being the governing leader can depend on the type of the lead vehicle and the extent of lateral overlap with the subject driver. The estimation results are compared against a simpler acceleration model (where the leader is determined deterministically) and a significant improvement in the goodness-of-fit is observed. The proposed models, when implemented in microscopic traffic simulation tools, are expected to result more realistic representation of traffic streams with weak lane discipline.