Traffic Microsimulation Research Articles

ОСНОВНІ ПРОБЛЕМИ ІНЖЕНЕРНО-ПЛАНУВАЛЬНИХ РІШЕНЬ ТРАНСПОРТНИХ РОЗВ’ЯЗОК ТИПУ «РОЗІРВАНЕ КІЛЬЦЕ» ТА ШЛЯХИ ЇХ УСУНЕННЯ

Introduction. Interchange designs that provide specific benefits to road users will always be of interest. It is extremely interesting a type of interchange in the same level named in literary sources as a «hamburger roundabout» or «throughabout». Usually, two separate lanes run through the extended roundabout, crossing each other inside the interchange. Such intersections can be regulated by both road signs and traffic lights, depending on their size and the intensity of incoming traffic flows. Problem Statement. The analysis of the designs of the intersection at one level by the type of throughabout is insufficiently presented both in the normative documentation for the design of highways and in modern scientific periodicals. However, this type of intersection refers to alternative junctions used in road construction practice in European countries. Thus, an urgent task is to evaluate the operational efficiency indicators and surrogate traffic safety indicators of the throughabout, which can later serve as a basis for making decisions about the need to construct this type of intersection. Purpose – to identify the main problems of the interchange by the type of throughabout based on the analysis of traffic efficiency and safety indicators, carried out by the method of traffic microsimulation.

CAV Traffic Control to Mitigate the Impact of Congestion from Bottlenecks: A Linear Quadratic Regulator Approach and Microsimulation Study

This work investigates traffic control via controlled connected and automated vehicles (CAVs) using novel controllers derived from the linear-quadratic regulator (LQR) theory. CAV-platoons are modeled as moving bottlenecks impacting the surrounding traffic with their speeds as control inputs. An iterative controller algorithm based on the LQR theory is proposed along with a variant that allows for penalizing abrupt changes in platoon speeds. The controllers use the Lighthill-Whitham-Richards (LWR) model implemented using an extended cell transmission model (CTM) which considers the capacity drop phenomenon for a realistic representation of traffic in congestion. The impact of various parameters of the proposed controller on the control performance is analyzed. The effectiveness of the proposed traffic control algorithms is tested using a traffic control example and compared with existing proportional-integral (PI) and model predictive control (MPC) controllers from the literature. A case study using the TransModeler traffic microsimulation software is conducted to test the usability of the proposed controller as well as existing controllers in a realistic setting and derive qualitative insights. It is observed that the proposed controller works well in both settings to mitigate the impact of the jam caused by a fixed bottleneck. The computation time required by the controller is also small making it suitable for real-time control.

Traffic microsimulation for road safety assessments of vehicle automation scenarios: Model comparison and sensitivity analysis

The possible implementation of Automated Vehicles (AVs) in the traffic flow implies that future safety conditions should be assessed by considering promiscuous traffic scenarios (composed of traditional vehicles and AVs). However, real safety performance data related to AVs are still scarce and unreliable. Thus, road safety assessments of future scenarios should necessarily be based on traffic simulations and surrogate safety measures (i.e., related to traffic conflicts, which are different from observed crashes). In the context of traffic simulation, the first step consists in assessing the currently available traffic models to match their characteristics with the possibility of reliably simulating AVs and their behavior in traffic, according to the specific variables of the investigated site (i.e., road type, road geometric design, number of intersections). After the model selection stage, a sensitivity analysis is necessary to identify the most crucial model parameters and their influence on the simulation output, with particular regard to those affecting safety performances. The sensitivity analysis paves the way for making traffic modelers aware of the effects of possible modifications to the simulation parameters in case of AV scenarios. In this research, this procedure was tested on a two-way two-lane rural road network. Among the analyzed traffic models, the Gipps model was highlighted as the most suitable to account for AVs in this specific context. The sensitivity analysis revealed that the most influencing parameters of the Gipps model in the context of safety assessments are: clearance, safety margin factor and sensitivity factor, which were revealed to be greatly influential on all types of traffic conflicts. Two other control tests with different simulated vehicle types were run to highlight the stability of the results coming from the sensitivity analysis in other scenarios.

Exploring Heterogeneity in Car-Following Behaviors Based on Driver Visual Characteristics: Modeling and Calibration

To investigate the heterogeneity of car-following behaviors across different vehicle combinations from the perspective of driver visual characteristics, the NGSIM dataset from I-80 and US-101 highways was selected and distinct car-following segments were extracted for analysis. Firstly, all the effective vehicle trajectories were extracted and categorized into different vehicle types based on their widths, resulting in four combination types of car-following segments. Visual angle and its change rate were introduced as variables representing driver visual characteristics. Additionally, one-way analysis of variance (ANOVA) was used to compare these variables with traditional ones. The driver’s visual characteristic variables were then incorporated to improve the full velocity difference (FVD) model. Genetic algorithms were employed to calibrate the model under different car-following types, revealing pronounced behavioral variations. After implementing the enhanced drivers’ visual angle (DVA) model, substantial reductions in calibration and validation errors were observed, with calibration errors decreasing by 51.93% and 42.22% and validation errors decreasing by 56.61% and 45.26%. This indicates the DVA model’s remarkable adaptability and stability. Lastly, through a sensitivity analysis of errors, the DVA model demonstrated greater robustness toward the improved error evaluation function. By integrating drivers’ visual characteristics, this study provides in-depth insights into heterogeneous car-following behaviors, enhancing our understanding of driver behaviors and micro-traffic simulation systems.

TomTom Data Applications for the Assessment of Tactical Urbanism Interventions: The Case of Bologna

This work aims to evaluate how a temporary school square implemented in the city of Bologna under the principles of the tactical urbanism approach impacted on vehicular patterns through exploiting TomTom Floating Car Data (FCD) from before and after the intervention. Such data, passively collected by vehicles acting as moving sensors on the network, have been used for the analyses instead of data collected through usual methods. After statistical validation of available datasets through two-tailed paired Student’s t-tests, trend analyses have been performed on sample sizes and speed-related values to detect global variations in the first place, and more thoroughly among clusters of road segments based on graph-calculated distance from the intervention site. Results suggest that traffic flows have been relocated from segments directly affected by the intervention, where a decrease has been registered (−23.87%), towards adjacent streets or segments in a buffer area, which have recorded an increase (+3.51% and +3.50%, respectively), so the phenomenon of traffic evaporation did not take place as opposed to more widespread tactical urbanism interventions described in the literature. OD matrices per 15-min time fractions over the three selected peak time slots have been extracted in order to obtain reliable input data for a future development of traffic microsimulation models. The extraction method is based on least squares optimization problems solving systems of linear equations representing OD flows assigned to the observed link, after selecting a set of k¯ shortest paths through a Path Size Logit (PSL) model. Even though the availability of large amounts of data could not overcome typical underdetermination of the problem, due to the key issue of data dependence among traffic counts, the validation of retrieved matrices returned good results in terms of correlation between observed and estimated link flows. In the few cases where the quality of correlation fell, underlying causes have been investigated and the influence of outliers, amplified by the high fragmentation of the provided road graph, might represent the core problem.

A novel approach for brake emission estimation based on traffic microsimulation, vehicle system dynamics, and machine learning modeling

Brake wear is known as the primary source of traffic-related non-exhaust particle generation. Its generation rate is influenced by parameters at different levels: subsystem (type of brakes, pads, materials, etc.), system (vehicles' dynamics, driving style etc.) and suprasystem (road geometries, traffic parameters, etc.). At the subsystem level, we proposed a neural network brake emission modeling, trained and validated through emission data collected from a reduced-scale dynamometer. At the system level, a model of a car dynamics was developed to calculate the wheels’ brake torques and angular velocities. At the suprasystem level, the traffic behavior in a sensitive urban area was characterized experimentally and simulated in a traffic microsimulation software. The vehicle traffic-based records were used to calculate the vehicle dynamic quantities, converted into brake emission through the neural network. To examine the overall traffic impacts on brake emission, the total particle number (PN) and total particle mass were estimated regarding the route choice in the sensitive area and in the whole transportation network. The findings of this study showed significant generation rate of brake emissions (in terms of mass and number) around congested areas (in the order of 10e9 #/s). The brake emission estimation in a real area provides fundamental information to the decision-makers to better insight into the rate of non-exhaust emissions generation.

Assessing bicycle-vehicle conflicts at urban intersections utilizing a VR integrated simulation approach

Animosity between drivers and cyclists has existed on urban road networks for many years. Conflicts between these two groups of road users are exceptionally high in the shared right-of-way environments. Benchmarking methods of conflict assessments are mostly based on statistical analysis with limited data sources. The actual crash data would be valuable to understand the features of bike-car collisions, however the available data are spatially and temporally sparse. To this end, this paper proposes a simulation-based bicycle-vehicle conflict data generation and assessment approach. The proposed approach uses a three-dimensional visualization and virtual reality platform, integrating traffic microsimulation to reproduce a naturalistic driving/cycling-enabled experimental environment. The simulation platform is validated to reflect the human-resembled driving/cycling behaviors under different infrastructure designs. Comparative experiments are carried out on bicycle-vehicle interactions under different conditions, with data collected from a total of 960 scenarios. Based on the results of the surrogate safety assessment model (SSAM), the obtained key insights include: (1) scenarios of a high conflict probability do not lead to actual crashes, which suggests that the classic SSM-based measurements such as TTC or PET values may not sufficiently reflect real cyclist-driver interactions; (2) the major cause of conflicts is variation in vehicle acceleration, which suggests that drivers are considered to be the main party responsible for bicycle-vehicle conflict/crash occurrence; (3) the proposed approach is able to generate near-miss events and reproduce interaction patterns between cyclists and drivers, facilitating experiments and data collections which would be typically unavailable for this type of study.

Traffic Microsimulation of the Main Junction Connecting the Urban Road Network with the Sea-Port Container Terminal

Efficient transportation connectivity between ports and the hinterland is essential for the functioning of the port, especially for container terminals that predominantly rely on road transport. The integration of port and urban traffic flows, particularly in terminals located near cities, has a double impact on the level of service of urban traffic and the efficiency of the port. This paper examines the effects of a newly built container terminal, with a capacity of less than 1 million TEU per year, on urban traffic in Rijeka, Croatia. A microscopic-level simulation method is used together with traffic network modeling design. The research identifies potential bottlenecks or critical elements in the network with lower performance for the current traffic demand and the future growth scenarios of the container traffic. The study’s findings contribute to understanding the dynamic characteristics of port and urban traffic flow correlation, crucial for efficient transportation management and sustainable urban development in port cities.

Optimizing of Traffic-Signal Timing Based on the FCIC-PI—A Surrogate Measure for Fuel Consumption

Optimizing signal timing improves sustainability metrics (e.g., fuel consumption or “FC”). Historically, traffic agencies have retimed signal timing to improve mobility measures (e.g., delays). However, optimizing signals to reduce delays does not necessarily mitigate sustainability measures. Hence, this study introduces an approach that integrates a newly derived surrogate measure for FC, traffic microsimulation software, and a stochastic genetic algorithm. This approach optimizes signal timing to reduce the surrogate measure and reduce sustainability metrics. This study also evaluated the impact of heavy vehicles’ presence in a fleet on signal timing and FC savings. A 13-intersection arterial on Washington Street in the Chicago metro area served as a case study. Optimized signal timing delivered solutions that balanced both sustainability and mobility. The estimated excess FC savings ranged between 8 and 12% under moderate operating conditions, with no heavy vehicles, compared to the initial signal timing. The savings reached up to ~14% when many heavy vehicles existed on the side streets. Most of the improvements came without worsening traffic-mobility efficiency, which shows the possibility of a fair tradeoff between mobility and sustainability. All optimization scenarios showed that a slightly longer cycle length than the one implemented in the field is required to reduce FC.

Evaluation of Fuel Economy Benefits of Radar-Based Driver Assistance in Randomized Traffic

<div>Certain advanced driver assistance systems (ADAS) have the potential to boost energy efficiency in real-world scenarios. This article details a radar-based driver assistance scheme designed to minimize fuel consumption for a commercial vehicle by predictively optimizing braking and driving torque inputs while accommodating the driver’s demand. The workings of the proposed scheme are then assessed with a novel integration of the driver assistance functionality in randomized traffic microsimulation. Although standardized test procedures are intended to mimic urban and highway speed profiles for the purposes of evaluating fuel economy and emissions, they do not explicitly consider the interactions present in real-world driving between the ego vehicle equipped with ADAS and other vehicles in traffic. This article presents one approach to address the drawback of standardized test procedures for evaluating the fuel economy benefits of ADAS technologies. This approach is demonstrated by using a microsimulation of a traffic network into which the ego vehicle with the proposed driver assistance scheme is embedded for continuous interaction with the traffic. The analysis and results from stochastic simulations consider variations in the behavioral style of the driver of the ego vehicle and the traffic density. Strong variations, up to 10% in the fuel economy benefits, are observed between both variations presented in this study and what is obtained in typical deterministic evaluations mirroring standard test procedures.</div>

Exploring the effects of measures of performance and calibration strategies on calibrating traffic microsimulation model: A quantitative analysis approach

AbstractIn the subject of traffic microsimulation model (TMM) calibration, measure of performance (MoP) plays an essential role. However, due to the diversity of MoP types, choosing a MoP or MoP combination (usually used for multi‐criteria calibration strategy) that can represent the characteristics of field traffic operation has become the key to the calibration problem. This paper proposed a quantitative analysis approach (suitable, in general, for any TMM) with three aspects. Through this approach, more detailed and representative MoPs can be studied. At the same time, the effect of different calibration strategies with various MoP combinations on TMM calibration can also be compared comprehensively. The methodology is tested on a specific case study (a signalized link with a cyclic interrupted flow) by VISSIM, where various MoPs and calibration strategies (single‐criteria, the a priori‐based multi‐criteria, and the a posteriori‐based multi‐criteria calibration strategy) are implemented for comprehensive inspection and comparison. The results show that the TMM performance is clearly dependent on the MoPs and calibration strategies. Moreover, the a posteriori‐based multi‐criteria calibration strategy is more stable than the other two strategies for better performance TMM. The findings of this study provide new insights into the effects of MoPs and calibration strategies on TMM calibration.

Development of a Signal-Free Intersection Control System for CAVs and Corridor Level Impact Assessment

Assuming a full market penetration rate of connected and autonomous vehicles (CAVs) would provide an opportunity to remove costly and inefficient traffic lights from intersections, this paper presents a signal-free intersection control system relying on CAVs’ communicability. This method deploys a deep reinforcement learning algorithm and pixel reservation logic to avoid potential collisions and minimize the overall delay at the intersection. To facilitate a traffic-oriented assessment of the model, the proposed model’s application is coupled with VISSIM traffic microsimulation software, and its performance is compared with other intersection control systems, including fixed traffic lights, actuated traffic lights, and the Longest Queue First (LQF) control system. The simulation result revealed that the proposed model reduces delay by 50%, 29%, and 23% in moderate, high, and extreme volume regimes, respectively, compared to another signal-free control system. Noticeable improvements are also gained in travel time, fuel consumption, emission, and Surrogate Safety Measures.

The Role of Intersection Geometry in Urban Air Pollution Management

Air pollution represents one of the most complex problems of humanity. Traffic contributes significantly to this by emitting large amounts of harmful gases. This problem is particularly pronounced at urban intersections due to frequent changes in vehicle movement dynamics. This paper primarily presents the influence of intersection geometry on pollutant emissions levels. In addition, the influence of various traffic policies promoting greater use of public transport and zero-emission vehicles is also examined. The research combines the field part of recording existing intersections in Sarajevo, Bosnia and Herzegovina with traffic microsimulation. Detailed data on vehicles’ movements were obtained by advanced video processing using the DataFromSky tool, while the PTV Vissim 2022 and Bosch ESTM (2022) software were used to simulate traffic and estimate emissions at geometrically different intersections. The results showed that, in saturated traffic conditions, signalized intersections cause up to 50% lower emissions compared with two-lane and turbo roundabouts and that the impact of the geometric change is more significant than the impact of zero-emission vehicles. In unsaturated conditions, the differences in emissions at different intersections are negligible, with the highest reductions in pollution achieved by using zero-emission vehicles.

Investigation of Vehicular Pollutant Emissions at 4-Arm Intersections for the Improvement of Integrated Actions in the Sustainable Urban Mobility Plans (SUMPs)

Sustainable urban mobility planning is a strategic and integrated approach that aims to effectively address the complexities of urban transportation. Additionally, vehicle emissions are still a significant problem found in cities. Its greatest concentration involves intersections, as they have the highest number of stop-and-go operations, resulting in the highest engine load. Although electrification of vehicles is underway, the coming years and the energy crisis may cause the full transformation and fulfillment of the European Green Deal to be postponed. This state of affairs means that much effort should still go into possibly modifying the current infrastructure to make it more environmentally friendly. The article addresses the use of vertical road markings such as “stop”, “give way”, and also signal controllers signs, at four-arm X intersections. The modeling of intersection variants was carried out in the traffic microsimulation software VISSIM. The created model was calibrated according to real world data. The actual part of the work concerns the assumption of specific traffic flow scenarios, for which measurements of delay and emissions of harmful exhaust components such as NOx and PM10 were made. The results obtained can have practical application in proposals for creating unequal intersections. Based on the results, it can be concluded that below the traffic volume value of 1200 vehicles/h, an intersection can be considered with a yield sign and stop sign for two directions of traffic. However, for traffic volumes from 1200 vehicles/h to 2000 vehicles/h, an intersection with stop signs can be used for all traffic directions. The results may also provide some information on the location of the crosswalks and the improvement of strategies to be introduced into the SUMPs.

Illegal Maneuver Effect on Traffic Operations at Signalized Intersections: An Observational Simulation-Based Before-After Study Using Response Surface Methodology

This study investigates three illegal maneuvers at signalized intersections: pedestrians jaywalking at signalized crosswalks (JSC), vehicles stopping near the intersections (SNI), and vehicles occupying the through lane instead of the left-turn lane to make a left turn (OTL). Traffic microsimulation models of four intersections were developed using Aimsun, and data were collected by a drone over a 3-hour period. The car-following model (Gibbs model) implemented in Aimsun was calibrated for each of the intersections and validated at the 95% confidence level. The validated Aimsun models were used to perform 13 experiments designed to investigate the interaction effects of decreasing two or more illegal maneuvers on travel time and fuel consumption. These 13 experiments were identified using the design of experiments D-optimality criterion. To investigate the main and interaction effects of decreasing two or more illegal maneuvers on travel time and fuel consumption, the response surface methodology (RSM) was used. Using RSM, the statistical model that was found to best fit the simulation results was a quadratic form. The results showed that the dependent variables “travel time ratio” and “fuel consumption ratio” are affected not only by a decrease in violations ratio but also by the volume of traffic as the exogenous variable. It was found that decreasing two of the violations, namely, JSC and SNI, improves travel time and fuel consumption but decreasing OTL has the opposite effect, resulting from the inadequate design of left-turning lane length/capacity and/or inadequate signal timing to accommodate left-turning volume.

A Resilient Intelligent Traffic Signal Control Scheme for Accident Scenario at Intersections via Deep Reinforcement Learning

Deep reinforcement learning methods have shown promising results in the development of adaptive traffic signal controllers. Accidents, weather conditions, or special events all have the potential to abruptly alter the traffic flow in real life. The traffic light must take immediate and appropriate action based on a reasonable understanding of the environment. In this way, traffic congestion would be prevented. In this paper, we develop a reliable controller for such a highly dynamic environment and investigate the resilience of these controllers to a variety of environmental disruptions, such as accidents. In this method, the agent is provided with a complete understanding of the environment by discretizing the intersection and modifying the state space. The proposed algorithm is independent of the location and time of accidents. If the location of the accident changes, the agent does not need to be retrained. The agent is trained using deep Q-learning and experience replay. The model is evaluated in the traffic microsimulator SUMO. The simulation results demonstrate that the proposed method is effective at shortening queues when there is disruption.

Open-sourced real-time visualization platform for traffic simulation

To solve the city's traffic congestion problem, micro-traffic simulation is widely used to analyze the current situation and implement a plan to solve the problem. With the nature of the simulation, it is possible to design multiple scenarios and predict future traffic conditions. The state of the network is usually represented visually through 3D graphics and numbers such as speed, delay time, etc. However, analysis results are generally not intuitive, making it difficult for non-experts to judge traffic indicators. This study presents the possibility of utilizing an open-sourced real-time web-based platform linking simulation results to dashboards. Here, the micro traffic simulation, VISSIM is used. Traffic flow at intersections and location information of individual vehicles are stored in the cloud from VISSIM at analysis intervals. In real-time, the saved results are loaded into Elasticsearch, an open-source visualization tool. Data are visualized according to temporal and spatial characteristics and constitute the dashboard layout. Users intuitively analyze and predict traffic conditions through this visualization platform. In addition, traffic conditions can be easily communicated to general users who use web-based information.

Centralized and decentralized algorithms for two-to-one matching problem in ridehailing systems

In the context of on-demand ridehailing, we propose a heuristic matching algorithm where a passenger can share their ride with one more passenger while experiencing a high-quality service with a minimal increase in travel time. To evaluate the performance, we implemented the algorithm in a traffic microsimulator and compared it with a ride-matching algorithm developed by Simonetto et al. (2019) at IBM. Moreover, to enhance efficiency and reduce computational time, we proposed a decentralized version that is based on vehicle-to-infrastructure (V2I) and infrastructure-to-infrastructure (I2I) communication. Application on the downtown Toronto road network demonstrated that the service rate in the centralized version improved by 24%, compared to the IBM algorithm. The decentralized version demonstrated a 25.53 times speedup and the service rate improved by 19%, compared to the IBM algorithm. Furthermore, a sensitivity analysis was conducted over both centralized and decentralized versions to address how different variables and parameters can affect the system’s performance.

Electric Vehicle Charging Pricing Design for Agent-Based Traffic Microsimulation

Over the past few years, electric vehicles (EVs) have become a popular mode of individual transportation due to their significant benefits over internal combustion engine vehicles, resulting in significant growth in their penetration rate. However, the current flat electricity charging pricing and rapid diffusion of EVs may impose challenges to both transportation systems and power grids. It is well-accepted that the implementation of charging pricing schemes is a promising solution for changing and controlling EV users’ charging behavior. Nevertheless, in most research studies that have examined pricing schemes, the charging logic is defined in a way that agents will charge their vehicles while performing their daily activities. This will make the application of pricing schemes unfeasible, as agents will not have the freedom to unplug their EVs whenever they want, to reduce their billing costs. Accordingly, this paper provides an agent and activity-based framework for charging pricing schemes by decoupling activity and charging start and end times. Following this, three different charging pricing schemes have been introduced, including time of use (TOU), non-linear and zonal pricing and tested on the Montreal scenario.

Impacts of on-street parking regulations on cooperative, connected, and automated mobility - a traffic microsimulation study

This study aims to investigate the mobility impacts of on-street parking regulations for Connected and Automated Vehicles (CAVs) under mixed traffic fleets. A calibrated and validated network model of the city of Leicester in the UK was selected to test the implementation under various deployment scenarios. The modelling results indicated that replacing on-street parking with driving lanes, cycle lanes and public spaces can potentially lead to better traffic performance (27% to 30% reduction in travel time, 43% to 47% reduction in delays) compared to the other tested measures. The less significant impact of replacement with pick-up/drop-off points is due to increased stop-and-go events while vehicles pick-up and drop-off passengers, consequently leading to more interruptions in the flow and increased delays. The paper provides examples of interventions that can be implemented for on-street parking during the implementation of CAVs for regional decision-makers and local authorities.