Microscopic Traffic Simulation Research Articles

Modeling Automated Driving in Microscopic Traffic Simulations for Traffic Performance Evaluations: Aspects to Consider and State of the Practice

The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle’s sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations.

Development of a Unity–VISSIM Co-Simulation Platform to Study Interactive Driving Behavior

This paper presents the system development of a co-simulation platform aimed at studying driving behavior with multiple participants. The objective of this study was to create an immersive and interactive environment where different driving scenarios could be simulated and driver behavior could be recorded and analyzed. The platform integrated the Unity game engine with the VISSIM microscopic traffic simulator to create a hybrid simulation environment that combined the advantages of both tools. A virtual reality massive multiplayer online (VRMMO) module was developed to capture the interactions of the participants during the simulation experiments. The external control devices of this co-simulation platform were calibrated using the empirical data of a Controller Area Network (CAN-BUS) from actual driving behaviors. The main contributions of this study are the demonstration of the Unity–VISSIM co-simulation platform in simulating interactive driver behavior and the potential for its use in various research areas, such as intelligent transportation systems, human factors, driving education, and traffic safety analyses. The platform could be a valuable tool for evaluating the effectiveness of collective intelligence countermeasures in improving traffic systems, with relatively lower costs and risks.

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.

From Modeling to Optimizing Sustainable Public Transport: A New Methodological Approach

This paper explores the potential for connected public-transport (PT) mobility as an alternative to motorized private transport (MPT) in medium-sized cities. Despite the high demand for MPT, it occupies a lot of space and contributes to conflicts and reduced livability. The more sustainable mobility solution of PT, however, is often considered slow, unreliable, and uncomfortable. To overcome these issues, the authors investigate the state-of-the-art research of connected PT mobility, including ways to quantify mobility behavior, micro- and macro-simulations of traffic flow, and the potential of not-yet-established modes of transport such as Mobility on Demand (MoD) for last-mile transportation. MoD could reduce the drawbacks of PT and provide sufficient and sustainable mobility to all citizens, including those in rural areas. To achieve this, precise information on individual traffic flows is needed, including origin–destination (OD) relations of all trips per day. The paper outlines a two-step approach involving the expansion of OD relations to include all modes of transport and diurnal variation, followed by microscopic traffic simulations and macroscopic optimization to determine potentials for on-demand offers within inner-city traffic. The paper concludes by calling for critical questioning of the approach to validate and verify its effectiveness.

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.

A simulation study of cooperative and autonomous vehicles (CAV) considering courtesy, ethics, and fairness.

Autonomous vehicles (AV) can be programmed to act cooperatively. Previous research on cooperative and autonomous vehicles (CAV) suggests they can substantially improve traffic system operations in terms of mobility and safety. However, these studies do not explicitly take each vehicle's potential gain/loss into consideration and ignore their individual levels of willingness to cooperate. They do not account for ethics and fairness either. In this study, several cooperation/courtesy strategies are proposed to address the above issues. These strategies are grouped into two categories based on non-instrumental and instrumental principles. Non-instrumental strategies make courtesy/cooperation decisions based on some courtesy proxies and a user-specified courtesy level, while instrumental strategies are based only on courtesy proxies related to local traffic performance. Also, a new CAV behavior modeling framework is proposed based on our previous work on cooperative car-following and merging (CCM) control. With such a framework, the proposed courtesy strategies can be easily implemented. The proposed framework and courtesy strategies are coded in SUMO microscopic traffic simulator. They are evaluated considering different levels of traffic demand on a freeway corridor consisting of a work zone and three weaving areas of different types. Interesting findings are drawn from the simulation results, one of which is that the instrumental Local Utilitarianism strategy performs the best in terms of mobility, safety, and fairness. In the future, auction-based strategies can be considered to model how CAV make decisions.

Vehicle-specific noise exposure cost: Noise impact allocation methodology for microscopic traffic simulations

Vehicle-specific noise exposure cost: Noise impact allocation methodology for microscopic traffic simulations

Fuel-Based Nash Bargaining Approach for Adaptive Signal Control in an N-Player Cooperative Game

This paper presents a fuel-based game-theoretic approach for adaptive signal control. Our controller applies Nash bargaining (NB) in an n-player cooperative game to identify optimal phasing splits considering future traffic demands. The fuel-based NB controller utilizes an objective function that combines operational measures (delays and stops) with fuel consumption measures at intersections. The proposed controller was encoded in Python and then implemented and evaluated in a VISSIM microscopic traffic simulation model in an intersection with increasing volumes. The performance of the NB controller was compared to a traditional actuated control as the baseline. The results show that the NB controller was able to achieve superior environmental gains with a 17% saving in fuel consumption and a 17% reduction in CO emissions. In addition, the proposed controller was capable of maintaining acceptable operational conditions as it achieved a 20% reduction in delay, 8% reduction in the number of stops, and 8% reduction in queue lengths compared to the actuated controller. Compared to similar studies that applied NB for adaptive signal control, our fuel-based NB controller stands out as a promising approach to significantly improve fuel consumption at signalized intersections.

Impacts of Connected and Autonomous Vehicles with Level 2 Automation on Traffic Efficiency and Energy Consumption

Evaluating the economic benefits of traffic optimization from connected and autonomous vehicles (CAVs) and relevant traffic organization methods is significant, which will help to put forward suggestions for policymakers to promote the application of CAVs. The impacts and related benefits from CAVs with level 2 automation (L2 CAVs) on traffic efficiency and energy consumption of expressways are analyzed in this paper. Average travel time and actual road capacity are on behalf of traffic efficiency while average electric energy consumption is used to compute traffic energy consumption. The corresponding traffic economic benefits consist of travel-time-saving benefits, road construction benefits, and energy-saving benefits. A benefit evaluation framework is newly proposed and microscopic traffic simulation software is applied as the experiment platform. Different market penetration rates of L2 CAVs and various traffic flow statuses are considered. Besides, dedicated lanes for CAVs are also involved in this research, which are regarded as a traffic organization method expected to promote the realization of CAV’s traffic benefits. It is found that L2 CAVs can save the average travel time and reduce average energy consumption for a single vehicle in most scenes. However, negative impacts on energy consumption are observed in several scenes due to the increase of actual road capacity. Positive economic benefits are obtained as soon as the traffic flow rate is out of saturation, which become increasingly higher as CAV’s market penetration rate turns larger. Additionally, amplification in traffic economic benefits appears only if CAV lanes are provided under proper conditions.

Safe autonomous lane changes and impact on traffic flow in a connected vehicle environment

Despite the recent advancements in autonomous vehicle technologies, performing safe lane changes and merging in dense traffic environments remains an open challenge. One important question is how to find a space to merge into without placing any vehicle in a collision-prone situation. Moreover, what is the traffic flow impact of doing so? To tackle this issue, we start by adopting a safety definition based on a worst-case braking scenario. We then propose a decentralized controller-agnostic cooperative approach for lane changes that relies only on Vehicle-to-Vehicle communications and guarantees safety. In our method, the merging vehicle operates as having two possible leaders, one in its own lane and one in the destination lane, till the lane change maneuver is completed. Furthermore, the future following vehicle in the destination lane cooperates by acting as if the merging vehicle has already changed lanes. The merging vehicle only performs the lane change maneuver when the gaps are safe. Vehicle-level simulations are used to evaluate the approach in detail for a single lane change maneuver. Then, the method’s impact on highway safety and traffic flow is assessed using microscopic traffic simulations based on the commercial software VISSIM. We simulate three types of vehicles: adaptive cruise control equipped vehicles, autonomous vehicles, and connected and autonomous vehicles. The various simulations allow us to identify which improvements are brought by cooperative lane changes. Results indicate that connected and autonomous vehicles can achieve almost zero collision risk in congested and uncongested scenarios while improving traffic flow by 26% in the congested case. However, energy consumption increases significantly in the uncongested scenario. Moreover, our method outperforms an existing cooperative lane-changing method regarding safety and traffic flow. Our simulations with mixed traffic suggest that the proposed method is advantageous only at high penetrations of autonomous vehicles or in congested situations.

Estimating the Effects of Freeway Weaving Section Length on Level of Service Based on Microsimulation

At freeway weaving sections, traffic congestion and accidents are frequent because of the disturbance generated by merging or diverging vehicles. To reduce the disturbance and accordingly improve the level of service (LOS), an appropriate length of weaving section should be established. According to the US-HCM, the weaving section density, which is a surrogate for LOS, is determined by weaving length, traffic volume, and weaving volume ratio. However, it is not yet clear whether the US-HCM accurately estimates the density for various traffic conditions. The purpose of this study is to evaluate the effects of weaving length under various traffic conditions through microscopic traffic simulation VISSIM. The GA optimization method is applied to build the car-following and lane-changing models in VISSIM. The results show that weaving length is a critical determinant of LOS and short weaving length significantly increases traffic congestion. Increasing the length of the weaving section is a good countermeasure for alleviating traffic congestion. This study also proposes the appropriate weaving section length, with which the upstream LOS can be maintained in the weaving section. These results would serve as valuable information in the planning or design phase of a weaving section given that the flow demands are specified.

Dynamic Straggler Mitigation for Large-Scale Spatial Simulations

Spatial simulations have been widely used to study real-world environments, such as transportation systems. Applications like prediction and analysis of transportation require the simulation to handle millions of objects while running faster than real time. Running such large-scale simulation requires high computational power, which can be provided through parallel distributed computing. Implementations of parallel distributed spatial simulations usually follow a bulk synchronous parallel (BSP) model to ensure the correctness of simulation. The processing in BSP is divided into iterations of computation and communication, running on multiple workers, followed by a global barrier synchronisation to ensure that all communications are concluded. Unfortunately, the BSP model is plagued by the straggler problem, where a delay in any worker slows down the entire simulation. Stragglers may occur for many reasons, including imbalanced workload distribution or communication and synchronisation delays. The straggler problem can become more severe with increasing parallelism and continuous change of workload distribution among workers. This article proposes methods to dynamically mitigate stragglers and tackle communication delays. The proposed strategies can rebalance the workload distribution during simulation. These methods employ the spatial properties of the simulated environments to combine a flexible synchronisation model with decentralised dynamic load balancing and on-demand resource allocation. All proposed methods are implemented and evaluated using a microscopic traffic simulator as an example of large-scale spatial simulations. We run traffic simulations for Melbourne, Beijing and New York with different straggler scenarios. Our methods significantly improve simulation performance compared to advanced methods such as global dynamic load balancing.

A citywide TD‐learning based intelligent traffic signal control for autonomous vehicles: Performance evaluation using SUMO

AbstractAn autonomous vehicle can sense its environment and operate without human involvement. Its adequate management in an intelligent transportation system could significantly reduce traffic congestion and overall travel time in a network. Adaptive traffic signal controller (ATSC) based on multi‐agent systems using state‐action‐reward‐state‐action (SARSA ()) are well‐known state‐of‐the‐art models to manage autonomous vehicles within urban areas. However, this study found inefficient weights updating mechanisms of the conventional SARSA () models. Therefore, it proposes a Gaussian function to regulate the eligibility trace vector's decay mechanism effectively. On the other hand, an efficient understanding of the state of the traffic environment is crucial for an agent to take optimal actions. The conventional models feed the state values to the agents through the MinMax normalization technique, which sometimes shows less efficiency and robustness. So, this study suggests the MaxAbs scaled state values instead of MinMax to address the problem. Furthermore, the combination of the A‐star routing algorithm and proposed model demonstrated a good increase in performance relatively to the conventional SARSA ()‐based routing algorithms. The proposed model and the baselines were implemented in a microscopic traffic simulation environment using the SUMO package over a complex real‐world‐like ‐intersections network to evaluate their performance. The results showed a reduction of the vehicle's average total waiting time and total stops by a mean value of % and % compared to the considered baselines. Also, the A‐star combined with the proposed controller outperformed the conventional approaches by increasing the vehicle's average trip speed by %.

A microscopic traffic simulation framework to evaluate the performance of bus priority lanes

Bus priority measures are widely considered effective at reducing traffic congestion and bus travel times. Traditionally, the performance of a bus priority measure is evaluated using traffic survey-based methods. However, traffic simulations can also be used as a decision support tool for transport planners to test different bus priority measures in a simulation environment and evaluate the measure’s performance before physical implementation. This research presents the development of a microscopic traffic simulation framework to simulate traffic flows when a bus priority lane (BPL) is in operation and compares its performance against the case where no BPL is in operation. A test-bed along the Galle Road corridor (in Colombo, Sri Lanka) from Cross Junction to Ratmalana was chosen to test the developed simulation framework. The performance of the BPL was evaluated by considering the changes in the vehicle speeds before and after implementing the BPL. Different demand configurations were simulated by varying the general traffic flow and bus flow to understand the BPL's feasible demand configuration(s). The simulation results showed that the BPL was feasible with a relatively high bus flow (240 buses per hour) and a relatively moderate general traffic flow (2,000 / 2,500 vehicles per hour).

Online transportation network cyber-attack detection based on stationary sensor data

Penetration of connected vehicles and crowdsourced mapping applications give rise to security vulnerabilities in transportation networks. Accurate detection of cyber-attacks on transportation networks is critical to minimize impacts on transportation systems. This task is particularly challenging because the impacts of regional cyber-attacks can be invisible on aggregated traffic data, especially when only sensor data is accessible to transportation agencies. We propose an analytical framework that leverages real-time road link sensory data to conduct online data-driven transportation network anomaly detection using non-parametric long short-term memory (LSTM) and parametric Gaussian process model. The online anomaly detection models can continuously update model coefficients as real-time sensory data arrives. We utilize a city-scale microscopic traffic simulation to validate our cyber-attack detecting framework. The cyber-attack detection model achieves a F1 score, which is a harmonic mean of the precision and recall of classifiers, between 84% to 96% considering different initial training data sizes. We compare with major offline models to demonstrate the effectiveness and robustness of online models. In addition, we devised a meta-heuristic method to solve the multi-objective sensor location problem to simultaneously enhance anomaly detection efficiency and maximize traffic information gain. This study demonstrates a systematic approach to address the emerging concerns of cyber-security in transportation networks with minimum requirements for infrastructure upgrades. Our results can help transportation security authorities identify potential cyber-attacks and protect transportation infrastructure from malicious cyber-hackers.

Trajectory Simulation of Emergency Vehicles and Interactions with Surrounding Traffic

Emergency services play an important role in the life of a city and are subject to constant public scrutiny. The efficient dispatch of emergency vehicles (EMVs) requires realistic shortest-path algorithms involving the movement of EMVs within an urban network under emergency conditions. Trip-time estimates used in shortest-path algorithms would be much more precise if it were possible to model more realistically the interactions between EMVs and surrounding traffic, as well as the reactions of other vehicles in the presence of an EMV. Therefore, EMV trajectories should be studied at the microscopic level to accurately model the impact of EMV travel along a path shared with other vehicles. In this research, we develop three models to incorporate specific non-EMV reactions associated with changing lanes, mounting the sidewalk, and approaching an intersection, plus two algorithms to actuate traffic lights at signalized intersections. These models and algorithms were coded in commercial microscopic traffic simulation software through the implementation of an application programming interface (API) designed to overcome the limitations of the software to realistically simulate disturbed traffic conditions and anomalous nonemergency vehicle driver behaviour observed in the presence of an EMV. Basic information about these real-world effects was gleaned from video footage recorded in Santiago, Chile, by traffic cameras, fire truck-mounted cameras, and truck-originated GPS pulses. To validate the design, a real EMV trip captured by the footage was simulated by the API. The simulation considerably reduced the degree of error in delineating the path followed by the EMV compared to the default simulations generated by most commercially available software, thereby demonstrating that the API can provide highly accurate estimates of EMV trip times in an emergency context.

A lane-changing behaviour algorithm for connected and autonomous vehicles to facilitate emergency vehicle movements on urban arterials in microsimulation

ABSTRACT Reducing the response time of emergency vehicle(EV) is accompanied with many health and traffic-related benefits. New technologies can be utilised in creating a cooperative lane-changing behaviour for surrounding traffic to reduce the response time. In this research, the influence of lane-changing behaviour on the delay of EV and surrounding vehicles was studied in microscopic traffic simulation. A driving behaviour and a pre-emption algorithm were developed to prioritise EVs on a signalised corridor. Two areas in front of EV were defined including stopping and lane-changing areas with different behaviours in a connected and autonomous traffic environment. It was concluded that using stopping and lane-changing areas together is more favourable regarding the delays of both EV and surrounding traffic. Also, the results verified the presence of an optimum lane-changing threshold after which the EV delay became constant.

Optimizing vehicle dynamics co-simulation performance by introducing mesoscopic traffic simulation

Microscopic traffic simulation is often used in conjunction with vehicle dynamics simulation to test cooperative or perception-based driver assistance functions. On the other hand, visualization and the interaction of a large number of swarm vehicles is computationally burdensome, limiting the size of scenarios. To remedy this problem, this paper introduces a mesoscopic traffic model, namely the extension of the shockwave profile model to road networks, that handles traffic as continuous flows of vehicles. Adopting the idea of level of detail from computer graphics, the modeling of swarm vehicles is carried out in less detail farther away from the EGO vehicle. These levels are defined using the classical (macroscopic, mesoscopic, and microscopic) categorization of traffic modeling. The macroscopic traffic model is responsible for the traffic demand and traffic assignment. The proposed mesoscopic model is capable of capturing the fluctuating nature of traffic on lane level. Closer to the EGO vehicle microscopic traffic simulation is employed while the EGO vehicle is modeled in full-detail including vehicle dynamics too. The 3D rendering of the simulation is performed by the vehicle dynamics simulator. The challenge in the proposed methodology is transitioning between the mesoscopic and the microscopic models, i.e., selecting the boundary and spawning/destroying vehicle agents. The paper addresses this challenge with a linear (w.r.t. the vehicle number) time complexity algorithm. Practically, a dynamic downscaling of the microscopic simulation to mesoscopic level is realized outside the vicinity of the EGO vehicle. The proposed methodology is generic, and can be adapted to most existing vehicle dynamics and microscopic traffic simulator software. The solution is tested with SUMO as microsimulation and Carla as vehicle dynamics simulation through a simple path following test case in two scenarios: a congested residential area and a complex scenario with both a highway section and an urban area. Simulation results suggest that the simulation performance could be improved by 200–500% while retaining modeling accuracy, compared to the case when only microscopic simulation is used.

Multiparadigm Modeling Framework to Evaluate the Impacts of Travel Patterns on Electric Vehicle Battery Lifespan

The widespread adoption of electric vehicles (EVs) can help attain economic and environmental sustainability by reducing oil dependency and greenhouse gas emissions. However, several issues need to be addressed before EVs can become a popular vehicle choice among the general public. A key issue is the perpetual reduction in EV battery capacity caused by battery degradation over time with usage. This can lead to a reduced driving range and cause “range anxiety” for EV drivers. This becomes even more critical in developing countries where consumers are highly sensitive to battery replacement costs. Thus, to promote EVs in developing economies, policymakers and vehicle manufacturers need to develop attractive incentive schemes and warranty strategies preceded by a thorough assessment of the useable EV battery lifespan for a wide range of users. This paper develops a multiparadigm modeling framework to compute battery degradation for a large population of EVs by capturing the effects of travel patterns, traffic conditions, and ambient temperature. The proposed framework consists of four different building blocks: (i) a microscopic traffic simulation model to generate speed profiles, (ii) an EV power consumption model, (iii) a battery equivalent circuit model, and (iv) a semiempirical battery degradation model. The proposed framework can also be used to assess the battery life-cycle of electric-powered automated vehicles by adjusting their travel patterns accordingly. A case study is presented using travel diary data of around 700 households from the U.S. National Household Travel Survey of 2009 to simulate household travel patterns and corresponding battery lifespan distribution.

How Do Vehicles Make Decisions during Implementation Period of Discretionary Lane Change? A Data-Driven Research

To investigate and compare the lane changing behavior of passenger cars and heavy vehicles during the implementation period (defined as the interval from the start time to the end time of a lane change maneuver), this study applies the gradient boosting decision tree (GBDT) method to model the lane changing behavior of heavy vehicles and passenger cars, respectively. Results show that the lane change models vary with the vehicle types and lane change directions. Different factors are considered by the drivers of passenger cars and heavy vehicles when implementing lane changes to different directions. Partial dependence plots of GBDT models reveal that the influence of independent variables on lane changing behavior is nonlinear and complicated, which means that the same variable leads to various effects on the lane change decision across different vehicle types and lane change directions. In contrast with other state-of-the-art methods, the proposed method can obtain more accurate results. The findings indicate that it is necessary to build specific lane change models based on vehicle types and lane change directions for microscopic traffic simulators and autonomous vehicles.