Automatic generation of route networks for microscopic traffic simulations

Microscopic simulation models have been widely used as tools to investigate the operation of traffic systems and different intelligent transportation systems applications. The fidelity of microscopic simulation tools depends on the driving behavior models that they implement. However, current models commonly do not consider human-related factors, such as distraction. The potential for distraction while driving has increased rapidly with the availability of smartphones and other connected and infotainment devices. Thus, an understanding of the impact of distraction on driving behavior is essential to improve the realism of microscopic traffic tools and support safety and other applications that are sensitive to it. This study focuses on car-following behavior in the context of distracting activities. The parameters of the well-known GM and intelligent driver models are estimated under various distraction scenarios using data collected with an experiment conducted in a driving simulator. The estimation results show that drivers are less sensitive to their leaders while talking on the phone and especially while texting. The estimated models are implemented in a microscopic traffic simulation model. The average speed, coefficient of variation of speed, acceleration noise and acceleration and deceleration time fractions were used as measures of performance indicating traffic flow and safety implications. The simulation results show deterioration of traffic flow with texting and to some extent talking on the phone: average speeds are lower and the coefficient of variation of speeds are higher. Further experimentation with varying fractions of texting drivers showed similar trends.

Traffic simulation is an effective tool for urban planners, traffic engineers, and researchers to study traffic. In particular, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, has demonstrated its importance in analyzing and managing transportation systems. However, integrating data from various sources, generating traffic scenarios, and importing information into traffic simulators to conduct microscopic simulations have always been a challenge. This paper presents a solution to overcome this challenge: RealTwin, a comprehensive tool for automated scenario generation for microscopic traffic simulation. Following a streamlined scenario generation and calibration workflow, RealTwin effectively bridges gaps between traffic data from various sources and traffic simulators, making microscopic traffic simulation more accessible for researchers and engineers across various levels of expertise. Using RealTwin to generate a real-world traffic scenario in Simulation of Urban Mobility (SUMO), VISSIM, and AIMSUN, RealTwin’s ability is demonstrated in the construction of realistic and consistent traffic scenarios in different simulators. Furthermore, this paper introduces and illustrates RealTwin’s capability for technology (e.g., autonomous vehicle) scenario generation. This feature can contribute to more comprehensive microscopic simulations, facilitating the analysis of potential effects of various technological innovations on mobility, energy efficiency, and safety. Finally, RealTwin is used to calibrate a simulation in SUMO. The calibration module enhances RealTwin’s ability to generate consistent simulations across different platforms and more realistic simulations that reflect real-world traffic operations.

This paper presents the conceptual structure of a novel microscopic traffic flow behavior simulation model for motorcycle traffic based on a mobility force structure, called mobility force car-following model. The mobility force is defined as the interactive force of motorcycle by surrounding vehicles and environment of lane layout. The kinetic behavior of the motorcycle will be induced and constrained by the force created due to the spaces surrounding. The concept is to describe the driving behavior using a series of forces of the attraction and repulsion related to the vehicle and spaces surrounding in front of the motorcycle. The model can describe both of the lateral motion and longitudinal motion in views to the motorcycle driving feature. Through a series of calibration for the parameters of the model, the regression statistical significance and the validation of the model output using the field survey data, the results illustrated the enough accuracy of the result of model output. The model is then verified to be suitable to describe the real motorcycle driving behavior preliminarily. Keywords-car-following model; mobility force; force field; microscopic simulation; motorcycle

Common car-following models for microscopic traffic simulation assume a time advancement using fixed-sized time steps. However, a purely time-driven execution is inefficient when the states of some agents are independent of other agents and thus predictable far into the simulated future. We propose a method to accelerate microscopic traffic simulations based on identifying independence among agent state updates. Instead of iteratively updating an agent’s state throughout a sequence of time steps, a computationally inexpensive “fast-forward” function advances the agent’s state to the time of its earliest possible interaction with other agents. We present an algorithm to determine independence intervals in microscopic traffic simulations and derive fast-forward functions for several well-known traffic models. In contrast to existing approaches based on reducing the level of detail, our approach retains the microscopic nature of the simulation. An evaluation is performed for a synthetic scenario and on the road network of Singapore. At low traffic densities, maximum speedup factors of about 2.6 and 1.6 are achieved, while at the highest considered densities, only few opportunities for fast-forwarding exist. We show that the deviation from purely time-driven execution is reduced to a minimum when choosing an adequate numerical integration scheme to execute the time-driven updates. Verification results show that the overall deviation in vehicle travel times is marginal.

Microscopic traffic simulation models offer an effective way to analyze and assess different transportation systems thanks to their efficiency and reliability. As traffic management issues become more prevalent, notably in urban areas, simulation tools enable a significant opportunity to replicate real-world conditions before implementation. Therefore, the calibration of traffic simulation models plays a substantial role in obtaining accurate and confidential results. Nowadays, urban regions are facing the challenge of restricted space for developing traffic solutions. As a consequence of environmental restrictions, the use of mini-roundabouts rather than larger roundabouts is increasing. Based on the given literature review, it is seen that not much attention was given to the complex modeling and calibration of microsimulation models of mini-roundabouts and unsignalized intersections. The objective of this study is to offer the calibration of microscopic traffic simulation of urban road network, including closely located mini-roundabouts and unsignalized intersection. To this end, an open-source tool called SUMO (Simulation of Urban Mobility) was utilized as a simulation environment in this study. The necessary data for developing a microsimulation model in SUMO was gathered using a videography technique. The traffic count data and speed were considered performance measures between field observations and simulation outputs. The routeSampler tool of SUMO, which has recently emerged in the literature, was used to match traffic count data and the corresponding time interval for traffic volume data calibration. The calibration of car-following model parameters using a trial-and-error approach was employed based on mean absolute percent error (MAPE) between simulated speeds and field-measured speeds. According to the findings of the study, the simulation model fulfilled the calibration aims of the FHWA guideline and is suitable for further research.

Microscopic traffic simulation models have been playing an important role in the evaluation of transportation engineering and planning practices for the past few decades, particularly in cases in which field implementation is difficult or expensive to conduct. To achieve high fidelity and credibility for a traffic simulation model, model calibration and validation are of utmost importance. Most calibration efforts reported in the literature have focused on the informal practice, and they have seldom proposed a systematic procedure or guideline for the calibration and validation of simulation models. This paper proposes a procedure for microscopic simulation model calibration. The validity of the proposed procedure was demonstrated by use of a case study of an actuated signalized intersection by using a widely used microscopic traffic simulation model, Verkehr in Staedten Simulation (VISSIM). The simulation results were compared with multiple days of field data to determine the performance of the calibrate...

This research involves a study of the existing carsharing services in Singapore. In this paper, a novel method to forecast and relocate vehicle resources for carsharing services was proposed. The proposed model consists of three main components, i.e., microscopic traffic simulation, focus forecasting and inventory replenishing. An actual suburban network of the Clementi area of Singapore and including several carsharing ports was used for the microscopic simulations conducted in this study. Based on the experimental results, the proposed method has the potential to improve the carsharing service currently operating in Singapore.

Investigation of Automated Vehicle Effects on Driver's Behavior and Traffic Performance

MITSIMLab (MIcroscopic Traffic SIMulation Laboratory) is a microscopic traffic simulation model that evaluates the impacts of alternative traffic management system designs at the operational level and assists in their subsequent refinement. MITSIMLab models the travel and driving behavior of individual vehicles, the detailed movement of transit vehicles, and the various control and information provision strategies through a generic controller. A calibration methodology for important parameters and inputs was also developed. The model has been extended to address the special driving behavior evidenced in urban networks and has been used as a test bed for the evaluation of advanced traveler information systems (ATIS). Calibration and validation results from networks in the United States and Europe are discussed.KeywordsRoute ChoiceLane ChangeLink Travel TimeSimultaneous Perturbation Stochastic ApproximationLead VehicleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Beijing, a world famous historic metropolitan city, is facing serious traffic congestion in its central business district due to the ever increasing traffic demands induced by high speed economic development. To improve the local traffic situation, how to plan the road network in this area and protect the cultural relics simultaneously has become a difficult problem that concerns the government decision-makers and urban planners. The purpose of this paper is to present a case study of microscopic traffic simulation in the road network planning of Chaoyangmen-Fuchengment Street in Beijing city. A high performance microscopic traffic simulation tool, PARAMICS (PARAllel MICroscopic Simulator), is selected to model, visualize, evaluate and compare two types of schemes, i.e. public transportation dedicated lane schemes and one-way traffic schemes. This study indicates that microscopic traffic simulation is able to provide quantitative and effective decision support for the road network planning.

Aiming at the problems of heavy workload of basic data collection, complicated manual parameters calibration and inaccurate calibration in the traditional microscopic traffic simulation model parameter calibration, an adaptive microscopic traffic simulation model parameter calibration method based on floating car data is proposed. First, the basic data of the simulation road network were obtained by using the floating car technology. Secondly, the parameters calibration process of the microscopic traffic simulation model was constructed, and the self- adaptive orthogonal genetic was used to achieve the model parameters calibration. Finally, using the actual data of the South Ring Road main line, District Changping, Beijing to simulate and verify. The results show that the proposed method in this paper can not only reduce the workload of manual calibration, but also the model parameter calibration is more accurate, which proves the feasibility and effectiveness of the method.

This study analyzed traffic congestion scenarios in underground roads diverging and merging sections and underground sections that run parallel to ground roads using a microscopic traffic simulation model. The appropriate ratio of the ramp inflow or outflow of traffic volume to the entry traffic volume at the starting section of the underground road network was analyzed. Furthermore, the effect of traffic operation such as adequate throughput, average traffic speed, occupancy, and volume versus capacity (V/C) ratios before the occurrence of traffic congestion in all three road networks was properly produced. To maintain a minimum level of service of all underground sections at the D–E level, the underground inflow traffic volume should be equal to approximately 70% of the 4000 pcph inflow traffic volume from an open road to the underground. The results of this study are a useful reference for establishing traffic control application standards to minimize underground traffic congestion. They also provide useful insights for underground road design.

Recently, increasing attention has been put on the use of automated vehicle control systems to reduce traffic crashes due to driver errors. Proposed systems rely on either onboard range sensors or vehicle-to-vehicle wireless communications to obtain pertinent information about surrounding vehicles and to determine appropriate acceleration or deceleration commands. Although numerous studies have used microscopic traffic simulation to study the effects that these systems might have on traffic flows, generally these studies did not consider all factors that may affect their operation. A particular element that has been ignored is delay in data acquisition. Using a microscopic traffic simulation model that was developed to simulate intelligent transportation system applications relying on the use of onboard vehicle sensors or wireless transmissions, this paper attempts to quantify the safety effects created by the use of delayed information in automated vehicle control systems. The study clearly indicates that information delay affects the operation of vehicle control systems, particularly when sensor or transmission delays result in the use of information older than a certain threshold, found in this case to be 0.5 s.

Recently, increasing attention has been put on the use of automated vehicle control systems to reduce traffic crashes due to driver errors. Proposed systems rely on either onboard range sensors or vehicle-to-vehicle wireless communications to obtain pertinent information about surrounding vehicles and to determine appropriate acceleration or deceleration commands. Although numerous studies have used microscopic traffic simulation to study the effects that these systems might have on traffic flows, generally these studies did not consider all factors that may affect their operation. A particular element that has been ignored is delay in data acquisition. Using a microscopic traffic simulation model that was developed to simulate intelligent transportation system applications relying on the use of onboard vehicle sensors or wireless transmissions, this paper attempts to quantify the safety effects created by the use of delayed information in automated vehicle control systems. The study clearly indicates that information delay affects the operation of vehicle control systems, particularly when sensor or transmission delays result in the use of information older than a certain threshold, found in this case to be 0.5 s.

Traffic simulators can capture the complex dynamics of road networks thus are widely used to understand the behavior of the traffic system and predict the effects of transportation technologies, events and new policy changes. To build a traffic simulation model that can accurately capture the real traffic conditions, the Origin-Destination (OD) matrix is needed to be fed into the simulator as an input. In general, it is difficult to acquire information about the real OD matrix of a network. In this paper, we propose a neural network model for estimating the OD matrix that excites the microscopic simulation model for complex traffic networks to reproduce real world traffic flows using only the flow rate information of a small subset of links in the traffic network, which is relatively easy to collect. An optimization method is used to generate the training dataset of the neural network. Combining the OD matrix estimation model, the training dataset generation method, and the microscopic traffic simulator, an integrated traffic simulation-prediction system is developed to predict the behavior of the transportation system. We test the proposed system on the road network of the central terminal area of the Los Angeles International Airport and demonstrate that the integrated traffic simulation-prediction system can reproduce the real world traffic flow with low relative root mean square errors especially for high volume links and can be used to simulate the behavior of real world traffic scenarios.