Microscopic Simulation Platform Research Articles

The Feasibility and Operational Performance of Implementation Median U-turn Intersections: A CRITIC Method

Currently, a growing number of cities are adopting the Median-U-Turn (MUT) intersection design to enhance road capacity and traffic efficiency. The critical question in selecting the right intersection design is how significantly the implementation of MUT can enhance intersection performance, focusing on three key aspects: intersection efficiency, capacity, and the environmental impact of the design. To address this question, an evaluation of operational performance under various prevailing conditions (roadway and control) was conducted using VISSIM, a microscopic simulation platform. This evaluation involved five scenarios: conventional intersections (with increased cycle length, grade separation with a signalized at-grade intersection, grade separation with a roundabout), MUT, and signalized crossover MUT at a dense urban arterial intersection in Amman, Jordan's capital. The performance was compared using several metrics: average control delay, number of stops, average travel speed and time, average stopped delay, Carbon Monoxide (CO) emissions, fuel consumption, and vehicle safety. The Criteria Importance Through Intercriteria Correlation (CRITIC) technique was subsequently used to select the optimal design. The findings indicate that the existing intersection configuration is the least effective, while the MUT with signals at the crossing U-turn points is the most efficient solution.

Can turbo-roundabouts and restricted crossing U-Turn be effective solutions for urban three-leg intersections?

This research explored the potential of replacing conventional single-lane roundabouts by turbo-roundabouts and yield- and stop-controlled restricted crossing U-Turn (RCUT) at intersections located in urban corridors. A simulation approach based on a well-integrated assessment of traffic performance, emissions and driving volatility indicators of alternative intersection designs (AIDs) is therefore a major contribution of the study. The paper also addressed the impacts that several locations of the U-Turn crossovers have on travel and idling times, as well as on carbon dioxide and nitrogen oxides emissions. Traffic, pedestrian, and cyclist flow data were collected from two urban three-leg single-lane roundabouts. A microscopic simulation platform of traffic and emissions (respectively, VISSIM and vehicle specific power) was used to evaluate intersection-specific design and operations. The results indicated that turbo-roundabout and yield-controlled RCUT outperformed the existing single-lane roundabout at the two sites. Turbo-roundabout generally yielded the lowest travel times and emissions compared to the other intersections. The Yield-RCUT also performed better than the single-lane roundabout for U-Turn crossover located 100–170 m from the main intersection. Our findings bring a solid basis for academic research and transportation planners to promptly consider the implementation of AIDs and contribute to sustainable mobility in cities.

Developing a Variable Speed Limit Control Strategy for Mixed Traffic Flow Based on Car-Following Collision Avoidance Theory

Variable speed limit (VSL) control is an effective technology to improve safety near freeway bottlenecks. This study aims to develop a control strategy for mixed traffic flow consisting of both human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) based on collision avoidance theory. A microscopic simulation platform is first established, and four vehicle longitudinal dynamic models including Cruising model, Intelligent Driver Model (IDM), Adaptive Cruise Control model (ACC), Cooperative Cruise Control model (CACC) and one vehicle lateral dynamic model Minimizing Overall Braking Induced by Lane Changes model (MOBIL) are incorporated into the simulation platform. Then, a new VSL control strategy derived from collision avoidance theory is proposed for mixed traffic flow at the initial stage of CAVs’ popularization. Extensive simulation experiments are conducted, and surrogate safety measures and total travel time indicators are utilized to evaluate the safety and efficiency performances of the proposed VSL control. Results indicate that the proposed VSL control strategy can effectively improve the safety performance near freeway bottlenecks with an acceptable efficiency level.

Safety Effects of Connected and Automated Vehicle-Based Variable Speed Limit Control near Freeway Bottlenecks considering Driver’s Heterogeneity

A control strategy using variable speed limit (VSL) is a proven solution to reduce freeway collision risks and improve safety. However, the heterogeneity of human drivers restricts the effectiveness of traditional VSL controls, which may be made up by recent advanced technologies of connected and automated vehicles (CAVs). This study aims to propose a CAV-based VSL control system to address the limitations caused by human drivers on VSL control’s effectiveness. First, the heterogeneity of human drivers is analyzed, and its impact on the safety performance of VSL is examined. Specifically, a microscopic simulation platform is established, and two vehicle dynamic models developed for CAVs and human-driven vehicles (HDVs) are incorporated into the simulation platform. Based on a widely utilized surrogate safety measurement, time-to-collision, its derivative metrics are applied to evaluate collision risks, and the total travel time is used to assess operational efficiency. Extensive simulations are conducted to examine the performance of the proposed CAV-VSL system. The results indicate the following: (1) the heterogeneity of human drivers negatively affects the performance of the VSL; (2) the performance of the proposed control system in a mixed flow can be improved by advanced wireless communication technology; (3) CAVs are able to implement the VSL control strategy effectively resulting in the proactive reduction of the heterogeneity.

Applying Bayesian Optimization for Calibration of Transportation Simulation Models

The parameters of a transportation simulation model need to pass through a careful calibration process to ensure that the model’s output is as close as possible to the actual system. Owing to the computationally expensive and black-box nature of a simulation model, there is a need for robust and efficient calibration algorithms. This paper proposes a Bayesian optimization framework for the high-dimensional calibration problem of transportation simulation models. Bayesian optimization uses acquisition functions to determine more promising values for future evaluation, instead of relying on local gradient approximations. It guarantees convergence to the global optimum with a reduced number of evaluations, therefore is very computationally efficient. The proposed algorithm is applied to the calibration of a simulation network coded in simulation of urban mobility (SUMO), an open-source microscopic transportation simulation platform, and compared with a well-known method named simultaneous perturbation stochastic approximation (SPSA). To assess the calibration accuracy, speed distributions obtained from the two models calibrated using these two different methods are compared with the observation. For both the Bayesian optimization and SPSA results, the simulated and observed distributions are validated to be from the same distribution at a 95% confidence level for multiple sensor locations. Thus, the calibration accuracy of the two approaches are both acceptable for a stochastic transportation simulation model. However, Bayesian optimization shows a better convergence and a higher computational efficiency than SPSA. In addition, the comparative results of multiple implementations validate its robustness for a noisy objective function, unlike SPSA which may sometimes get stuck in a local optimum and fail to converge in a global solution.

Adaptive traffic signal control algorithms based on probe vehicle data

In this paper, two new adaptive traffic signal control algorithms are proposed based on data from probe vehicles to realize the coordinated signal control of arterial roads. One is an iterative signal control algorithm, and the other is an optimized signal control algorithm. The proportion of vehicles in the nonstop group is selected as the indicator of the traffic state. The value for this indicator can be accurately estimated by data from probe vehicles. Our goal is to ease traffic congestion and enhance the capacities of traffic networks. Compared with the Webster fixed-time signal control algorithm, these two new adaptive signal control algorithms are evaluated on a microscopic simulation platform. Simulation results show that the average travel time is reduced by 32% under the iterative signal control algorithm and by 23% under the optimized signal control algorithm, and the average delay times are reduced by 36% and 35%, respectively. In the meantime, the average number of stops under the iterative signal control algorithm is reduced by 43%, and under the optimized signal control algorithm, by 67%. They indicate that the two new adaptive signal control algorithms are effective for easing traffic congestion and achieve the adaptive signal control objectives using real-time traffic information.

Modeling and simulation of overtaking events by heterogeneous non-motorized vehicles on shared roadway segments

Overtaking events performed by non-motorized vehicles have a profound effect on the level of service and safety of roads shared by motorized and non-motorized vehicles. With the proliferation of e-bikes, heterogeneity within non-motorized vehicles has grown, which results in the speed of these vehicles spreading out. As a result, the frequency of overtaking has risen, causing the decision-making process and action execution of non-motorized vehicle overtaking to become more complicated to depict. To address this, this research identifies three types of typical delayed overtaking events based on empirical data. For each type of overtaking event, a utility-based multinomial logit sub-lane choice model and conditional probability-based overtaking motivation model are proposed to emulate the overtaking behavior of non-motorized vehicles. The model is calibrated and verified using 490 delayed overtaking trajectories that were captured during peak hours on two typical shared road segments in Shanghai, China. The proposed model is then integrated into a microscopic simulation platform and the simulation results ascertain that the proposed model performs well in estimating travel time, overtaking frequency, and the lateral location of heterogeneous non-motorized vehicles.

Group-based hierarchical adaptive traffic-signal control Part II: Implementation

In part I of this study (Lee et al., 2017), the formulation of a theoretical framework for a group-based adaptive traffic-control method for isolated signalized junctions is presented, which includes tactical and local levels of signal timing optimization. The global level control aims to determine the time-varying cycle structure, with a resolution of cycles, and the real-time adjustment of the green phase, with a resolution of seconds, based on longer-term traffic information observed by traffic detectors. Overall, the purpose of the study is to actualize a multi-resolution strategy for a group-based adaptive signal-control method and establish a microscopic simulation platform to implement the proposed methodology and test its effectiveness. To actualize the global proactive-optimization scheme, in this paper, a rolling-horizon approach to the temporal and spatial variables, signal structures for four-arm intersections, and discrete directional search methods is applied using the developed mathematical framework. The formulation of the group-based max-pressure policy is realized using the logical form of the local reactive-control policy at a typical directional three-lane, four-arm approach to an isolated intersection. The integrated group-based adaptive traffic-signal control is actualized using VISSIM, Fortran, and VBA based on the developed tactical and local levels of signal timing optimization. The results of the computer simulations and the case study presented in this paper show that the integrated group-based adaptive traffic-signal-control logic outperforms the other methods over a wide range of traffic conditions, from free-flowing traffic to extreme congestion. Moreover, the proposed models perform much better than the existing fixed-signal plan and the actuated signal-control in asymmetric traffic conditions.

Multi-objective optimization for short distance trips in an urban area: choosing between motor vehicle or cycling mobility for a safe, smooth and less polluted route

Abstract Mobility in urban areas is highly complex because of the variety of possible facilities and routes, the multitude of origins and destinations, the increase of population density and traffic. Furthermore, people are willing to use more environmentally friendly transportation modes, such as cycling, to do short-distance trips in urban areas. This paper develops a multi-objective model for passengers in urban transportation network for short trips using bicycle or motor vehicle. The main objective of this paper is to improve the urban network mobility in order to decrease traffic congestion, road conflicts between road users and pollution. Furthermore, optimization objectives could comprehensively reflect expectations of passengers from the dimension of traffic and emissions as criteria and use a motor vehicle or a bicycle as an alternative. The methodology of this study was applied based on the real world case study in the city of Aveiro, Portugal. The present work uses a microscopic simulation platform of traffic (VISSIM), road safety (SSAM) and emissions (Vehicle Specific Power – VSP) to analyze traffic operations, road conflicts and to estimate carbon dioxide (CO2) and nitrogen oxide (NOX) emissions. Three-dimensional Pareto Fronts, which were expressed through traffic performance, road conflicts between motor vehicles and bicycles and emissions, were optimized using the fast Non-Dominated Genetic Algorithm (NSGA-II).

Operational Evaluation of Two-Lane Roundabouts at Freeway Ramp Terminals: Comparison Between Roundabout and Signalized Interchanges

In the United States, roundabouts have recently been constructed to replace signalized intersections at freeway ramp terminals as both a safety and an operational treatment. In practice, this treatment is in need of guidelines specifying conditions when the roundabout or signalized intersection is more appropriate to assist practitioners in deciding which alternative to choose. In particular, research providing a comprehensive operational comparison between roundabouts and signalized interchanges is lacking. The current research—though a strictly calibrated microscopic simulation platform—analyzes and models the control delay at double-lane roundabouts and signalized interchanges. Both roundabouts and signalized interchanges were modeled in a Vissim simulation platform. Capacity at each roundabout entrance was calibrated and validated separately for passenger cars and heavy vehicles, since both vehicle types have different critical and follow-up headways. The design of the simulation experiments covered 2,880 different scenarios for roundabouts and signalized interchanges with varying ramp and arterial volumes, ramp spacing, and heavy-vehicle percentages. From the simulation results, control delay and level of service of the off-ramp and arterial approaches of roundabouts and signalized diamond interchanges were modeled and compared. Ultimately, guidelines for the selection between double-lane roundabouts and signalized interchanges were developed and presented in the form of look-up tables. These tables provide an easy-to-use tool for practitioners to determine the appropriate double-lane interchange to install under specific combinations of traffic demand, heavy-vehicle percentage, and ramp spacing conditions.

Signal coordination scheme based on traffic emission

This study presents a new optimal signal coordination scheme with an emphasis on emission reduction as a result of minimised passenger delay. By analysing the limitation of the algebraic method, a new algorithm using arrival–departure curves was proposed. A microscopic emission simulation platform based on VISSIM (a microscopic multi-modal traffic flow simulation model) and comprehensive modal emission model (CMEM) was established to estimate traffic emission reduction. A multi-objective genetic algorithm was used to find Pareto solutions that simultaneously reduce both passenger delay and vehicle emissions. Using the real-world traffic data collected in Changzhou city, an example of the signal coordination scheme was developed and its impact on emissions was simulated in the VISSIM-CMEM platform. The simulation results show that the proposed scheme achieved an average emission reduction of 28.8% on public transport vehicles and 18.7% on all vehicles over existing signal schemes.

Genetic Algorithm–Based Approach for Optimal Deployment of Roadside Units in Connected and Automated Vehicle Environments

Traffic management strategies play a critical role in connected and automated vehicle (CV/AV) environments in which vehicles will be the primary source of traffic information. In CV/AV environments, traffic data are collected from vehicles, and decisions are made at the transportation management centers (TMCs) and communicated back to vehicles via deployed roadside units (RSUs). This process poses a challenge at the early deployment stages of the technology because of the anticipated low market penetration. Under such conditions, RSUs must be optimally located throughout the network to provide continuity and expand the coverage of vehicle-to-vehicle communication systems. This study presents a genetic algorithm–based approach for determining the optimal locations of RSUs by maximizing the connectivity robustness measure, by taking into consideration vehicle clustering (groups of vehicles in transmission range of each other), network size, and other factors. Traffic simulation data were generated from a microscopic simulation platform (VISSIM) and used to test the proposed approach for different penetration rates. The results show that the proposed approach identified locations where more vehicles can communicate. The optimized RSU locations enabled communication between more vehicles in the network, which was identified by the increased robustness of connectivity. This aspect can maximize the amount of exchanged information between vehicles and the RSUs. Consequently, better traffic monitoring can be achieved by collecting more representative data of the traffic conditions in the network. Thus, optimal decisions, such as vehicle rerouting, are made at the TMCs and disseminated to as many vehicles as possible, which helps in achieving better management.

Simulation Framework for Rebalancing of Autonomous Mobility on Demand Systems

We are observing a disruption in the urban transportation worldwide. The number of cities offering shared-use on-demand mobility services is increasing rapidly. They promise sustainable and affordable personal mobility without a burden of owning a vehicle. Despite growing popularity, on-demand services, such as carsharing, remain niche products due to small scale and rebalancing issues. We are proposing an extension to the traditional carsharing, which is Autonomous Mobility on Demand (AMOD). AMOD provides a one-way carsharing with self- driving electric vehicles. Autonomous vehicles can make the carsharing more attractive to customers as they (i) reduce the operating cost, which is incurred when a manually driven system is unbalanced, and (ii) release people from the burden of driving.This study is built upon our previous work on Autonomous Mobility on Demand (AMOD) systems. Our methodology is simulation-based and we make use of SimMobility, an agent-based microscopic simulation platform. In the current work we focus on the framework for testing different rebalancing policies for the AMOD systems. We compare three different rebalancing methods: (i) no rebalancing, (ii) offline rebalancing, and (iii) online rebalancing. Simulation results indicate that rebalancing reduces the required fleet size and shortens the customers’ wait time.

Analysis of Traffic Conditions in Urban Region Based on Data from Fixed Detectors

To evaluate traffic conditions in an urban region, the average travel speed in the street network and the average traffic flow per lane are selected as indexes. The results for identifying traffic conditions are obtained using macroscopic fundamental diagram. Because of the difficulty of collecting the traffic parameter of travel speed from widely distributed fixed detectors directly, in this paper, the relationship between average travel speed and queue lengths at signalized intersections within the urban area is established based on an equivalent assumption. Finally, the average travel speed estimation model proposed in this paper is tested with microscopic simulation platform, and the results showed that the average travel speed estimation model performed well with satisfactory accuracy. In addition, the best performance of the network efficiency can be identified based on the reflections of macroscopic fundamental diagram, and the evaluation system is simple enough for application in engineering. Therefore, the result is instructive both for generating traffic control strategies and for making route choices.

Design of Reinforcement Learning Parameters for Seamless Application of Adaptive Traffic Signal Control

Adaptive traffic signal control (ATSC) is a promising technique to alleviate traffic congestion. This article focuses on the development of an adaptive traffic signal control system using Reinforcement Learning (RL) as one of the efficient approaches to solve such stochastic closed loop optimal control problem. A generic RL control engine is developed and applied to a multi-phase traffic signal at an isolated intersection in Downtown Toronto in a simulation environment. Paramics, a microscopic simulation platform, is used to train and evaluate the adaptive traffic control system. This article investigates the following dimensions of the control problem: 1) RL learning methods, 2) traffic state representations, 3) action selection methods, 4) traffic signal phasing schemes, 5) reward definitions, and 6) variability of flow arrivals to the intersection. The system was tested on three networks (i.e., small, medium, large-scale) to ensure seamless transferability of the system design and results. The RL controller is benchmarked against optimized pretimed control and actuated control. The RL-based controller saves 48% average vehicle delay when compared to optimized pretimed controller and fully-actuated controller. In addition, the effect of the best design of RL-based ATSC system is tested on a large-scale application of 59 intersections in downtown Toronto and the results are compared versus the base case scenario of signal control systems in the field which are mix of pretimed and actuated controllers. The RL-based ATSC results in the following savings: average delay (27%), queue length (28%), and l CO2 emission factors (28%).

Modeling Reservation-Based Autonomous Intersection Control in VISSIM

The use of autonomous vehicles is attracting more and more attention as a promising approach to improving both highway safety and efficiency. Most previous studies on autonomous intersection management relied heavily on custom-built simulation tools to implement and evaluate their control algorithms, but the use of nonstandard simulation platforms makes the comparison of systems almost impossible. Furthermore, without support from standard simulation platforms, reliable and trustworthy simulation results are hard to obtain. In this context, this paper explores a way to model autonomous intersections through the use of VISSIM, a standard microscopic simulation platform. A reservation-based intersection control system named autonomous control of urban traffic (ACUTA) was introduced and implemented in VISSIM through the use of VISSIM's external driver model. The operational and safety performance characteristics of ACUTA were evaluated with VISSIM's easy-to-use evaluation tools. In comparison with the results obtained with optimized signalized control, significantly reduced delays, along with a higher intersection capacity and lower volume-to-capacity ratios under various traffic demand conditions, resulted from the use of ACUTA. The safety performance of ACUTA was evaluated by use of the surrogate safety measure model, and few conflicts between vehicles within the intersection were detected. Moreover, the key steps and elements for implementation of ACUTA in VISSIM were introduced. These steps and elements can be useful for other researchers and practitioners implementing their autonomous intersection control algorithms in a standard simulation platform. By use of a standard simulation platform, the performance characteristics of autonomous intersection control algorithms can eventually be compared.

Assessing Effect of Traffic Signal Control Strategies on Vehicle Emissions

In response to the increased traffic congestion and worsened air pollution, using the portable emission measurement system (PEMS), this paper collects and compares the real world emissions under signal coordination and non-coordination in Beijing, and analyzes the emission levels and distribution characteristics under these two control strategies. Furthermore, by integrating the microscopic traffic simulation model of VISSIM and the VSP-based approach of emission modeling, the paper develops an integrated microscopic simulation platform of traffic emissions, which is finally used to evaluate the impact of two traffic control strategies, alternative signal timing and traffic flow on emissions.

System architecture of a decision support system for freeway incident management in Republic of Korea

Traffic Management Centers play a vital role in efficient functioning of freeway network in the post-incident scenario. As per present practice in Korea, the traffic managers use a heuristic approach for incident analysis based on their experience of similar scenarios. However, this approach induces uncertainty thereby reducing the overall effectiveness of the subsequent incident management and rescue operations. This paper proposes a decision support system to account for these shortcomings. We name our system as ‘FIAS’ – Freeway Incident Analysis System. The novel idea presented in this paper is the use of historical, real-time and spatial data simultaneously to forecast post-incident traffic flows on a microscopic simulation platform, Cellular Automata. FIAS incorporate two additional rules in the conventional model to depict more realistic incident flow characteristics. This paper focuses on the system architecture of the model and tests its performance by comparing its predicted values with a real incident data. The evaluation results confirm the validity of FIAS as it can model the time dependent microstructure of traffic flows with significant accuracy.

Microscopic Traffic-Emission Simulation and Case Study for Evaluation of Traffic Control Strategies

Many studies have shown that the quality of vehicle emissions is typically affected by vehicles' instantaneous speeds and acceleration rates. In order to develop effective strategies for vehicle emission controls, it is necessary to develop a microscopic simulation platform for estimating vehicle emissions that can capture the vehicles' instantaneous modal activities. This article is intended to develop an integrated microscopic traffic-emission simulation platform by using the microscopic traffic simulation model VISSIM and the modal emission model CMEM. A sub-network selected from the Haidian district of Beijing is used to build a traffic simulation network, whose traffic and emission conditions are subsequently evaluated. This article first analyzes the relationship between the instantaneous emission/fuel consumption rate and the instantaneous speed/acceleration. Then, it analyzes and calculates the emissions for a variety of vehicle types in the network. Finally, it evaluates the impact of two alternative traffic control and management strategies using the developed platform.

Microsimulation of Flexible Transit System Designs in Realistic Urban Networks

The use of microsimulation is not prevalent in modeling transit systems. The software available is rarely capable of modeling transit systems that route vehicles in real time or systems that adjust to the level of demand. It is not straightforward to model a transit system with the available simulation packages, especially when the transit scheme is not “standard.” Even the simulation of basic schemes such as a streetcar system or a paratransit service is not an option in existing simulation software. A cursory study would reveal that simulation of any vehicle class other than personal automobiles is always developed as an afterthought in the existing large-scale microsimulation packages. In most cases, simulation developers have done only a superficial addition of transit simulation on top of detailed simulation of automobiles and control mechanisms on freeways and arterials. A microsimulation framework has been developed for a general transit system. Concrete applications of such a framework have been developed and are presented in this paper. Two different transit systems are modeled using the same general framework to show the flexibility of the simulation scheme: a bus rapid transit system and a large-scale real-time routed transit design. The intent is to describe the flexibility that can be brought into a properly designed microscopic simulation platform to model innovative transit system designs, especially in larger urban networks. A simulation scenario is presented in which both transit systems function. The details of both simulations are described along with the problems encountered, and ways to solve them are exposed.