Validation of microscopic traffic simulation models

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 paper presents a cell transmission model with a lane-changing and vehicle-tracking (CTM-LV) framework for simulation of international land ports of entry (POEs). The development of the modeling framework is motivated by the need to simulate lane-specific queuing yet permit lane changing of vehicles between queues–-a behavior that was not accounted for or tested by the earlier cell transmission models and that cannot be easily replicated in microscopic traffic simulation models. The CTM-LV's network representation, traffic flow logic, and lane-changing rules are discussed. A simulation experiment is then presented to illustrate the importance of modeling the lane changing between queues that will affect the estimation of average queue length. The concept of CTM-LV has been applied to simulate the northbound traffic at the Bridge of the Americas POE, for passenger cars entering El Paso, Texas, from Ciudad Juarez, Mexico. Finally, a method is proposed to track selected vehicles within the CTM-LV framework so that an individual vehicle's travel time can be calculated and vehicle trajectories compared against data collected by probe vehicles with Global Positioning System receivers.

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.

Traffic Modeling and Safety Analysis on Motorway Ramps

Traffic congestion is increasingly becoming a severe problem in many large cities around the world. The problem is more complex in developing countries where cities are growing at a much faster rate than those in the developed world. Advanced Traffic Management Systems (ATMS) are one of the Intelligent Transport Systems (ITS) technologies that have been recommended and used as a tool to ease congestion problems in many large cities in the developing world. However, it is unknown how specific local conditions commonly observed in these cities, such as poor lane discipline and complex road user interactions, affect the performance of these systems. GETRAM (Generic Environment for Traffic Analysis and Modeling) was used in this research as a tool to develop microscopic traffic simulation models for the city of Bandung in Indonesia. The field data in this research, comprising throughputs, queue lengths and travel times, were collected during peak and off peak periods from all 90 signalised intersections connected to SCATS (Sydney Co-ordinated Adaptive Traffic System). This field data is believed to comprise one of the largest sets of “real world” data available for the development and validation of microscopic traffic simulation models. Two data sets were collected for this research: the first was used to develop and calibrate the simulation model and the second was used for validation. A number of statistical tests were used to determine the adequacy of the model in replicating traffic conditions. The results of statistical tests clearly showed that all of the calibrated and validated models reproduced field conditions with an acceptable degree of confidence. Therefore, the models were accepted as accurate and valid replications of the “real world”. The validated models were then used to evaluate the performance of SCATS which was implemented in Bandung in June 1997 as a pilot project. The results of comparative evaluation of the models under SCATS and under the Fixed Time control (without SCATS) demonstrated that SCATS did not necessarily always produce better results than the Fixed Time control. Furthermore, the performance of SCATS was strongly influenced by specific local conditions in the city. The multiple regression method was used to investigate the relationship between the traffic performance measures and significant basic variables. Based on this analysis, the main findings were: first, throughput was found to increase at intersections with higher v/c (volume to capacity) ratios. Second, throughput was found to decrease at intersections with higher numbers of phases and movements, longer widths of leg intersections, and farther distances to adjacent intersections. Third, queue length was found to increase at intersections with higher numbers of phases and movements. Based on the above findings, a number of improvements were recommended to enhance the performance of SCATS. This research also used traffic simulation to evaluate the impacts of these recommended improvements in increasing the performance of SCATS. The main findings from this evaluation were: first, restricted number of phases and movements at selected intersections substantially increased the traffic flow (78%) and decreased the queue length (by 55 to 67%) at the intersection. Second, making leg intersections wider—without physically building additional road capacity but by changing the stream with higher road hierarchy and higher v/c ratio from a two-way road into a one-way road—has a great impact on enhancing the performance of SCATS. Traffic flows were found to increase between 7 and 106%, and queue lengths were found to markedly decrease between 77 and 100% at all the suggested intersections. Third, the application of SCATS at intersections which are not closely spaced was not effective. Therefore, it is recommended that intersections which are not closely spaced remain under the Fixed Time control. The results and findings from this study provide road authorities in developing countries with an appreciation and enhanced understanding of the factors that influence the performance of traffic management systems in cities with similar characteristics to those in Bandung. These findings will also assist traffic engineers determine the best practices for the implementation of advanced traffic control systems in their cities.

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.

Severe weather events can have a significant impact on transportation networks. Many previous studies tried to analyze and explore the tremendous impact of extreme weather events on traffic behavior, speed, travel time and capacity. The purpose of this research was to analyze and discuss the impact of precipitation, temperature, visibility and wind speed on hourly weekday traffic flow volume in Atlanta, Georgia. This study focused on investigating which weather variables affect traffic volume, developing a machine learning based predictive technique to derive weather-traffic volume decision rules, and building a decision support tool. The correlation between extreme weather events and traffic volume was investigated by comparing traffic volume between a base case scenario and an extreme weather scenario. This research used 2 main data sources: hourly traffic data as recorded by 50 Automatic Traffic Recorder (ATR) sites around Atlanta and hourly precipitation data from 4 climate stations retrieved from the Integrated Surface Hourly (ISH) weather data archives of the National Centers of Environmental Information (NCEI). The statistical analysis and spatiotemporal relationships between traffic volume and weather variables were analyzed individually and evaluated using statistical tests. A machine learning technique was also used to simultaneously examines weather variables and hours of days to predict leading variables that greatly contributed towards a reduction in traffic volume. According to the results of this analysis, there were significant impacts of visibility, precipitation and temperature on traffic volumes especially during certain hours of the day. Extensive statistical analysis proved that during certain hours of the day, individual weather elements such as precipitation, minimum temperature, visibility and wind can have statistically significant individual impact in reducing traffic volume. Machine learning techniques helped derive models that can be used to predict conditions resulting in a decrease in traffic volume. A decision support tool was also developed to visualize traffic volume and weather interactions.

With the continuous improvement of national economic strength, the transportation field has also been developed at an unprecedented speed, and the transportation system simulation is an important technology and method in the field of transportation engineering. Establishing rele-vant models through traffic system simulation can not only improve the efficiency of urban traf-fic utilization in urban planning, but also optimize some existing traffic problems and promote the sustainable development of the traffic field. This paper first describes the specific concept of the traffic system simulation model, and discusses the development status of the traffic system simulation model from the microscopic traffic simulation model, the mesoscopic traffic simula-tion model, and the macroscopic traffic simulation model, as well as the characteristics and dif-ferences of the three types of models. Finally, it selects examples of each type of simulation model for specific analysis. The overview of the traffic system simulation model can improve the public's awareness of the traffic system simulation and help the specific development of urban traffic.

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.

The Transportation System be it in any country plays an important role in forming a sustainable development with reference to economic and social Stature of a country. Modelling the vehicular traffic in an efficient way especially in India have been a major concern due to its heterogeneous traffic condition. Microscopic traffic simulation models have got the better hand in modelling the heterogeneous traffic condition. VISSIM is been extensively utilized in the field of traffic operation, transportation management, designing and evaluating the performance before implementing the improved conditions in the realistic field condition. In this model decisions upon controlling and designing are controlled by their reliability and accuracy. This paper evaluates the driving performance parameters by selecting un - signalized which are been modeled by using VISSIM. Prior to modelling controllable and un - controllable input parameters have been defined. Simulation is been done for these input parameters by using the default parameters that VISSIM possess. These results concluded that the simulation which runs with default value was incapable in replicating the realistic field condition. In addition, these values resulted in larger queue lengths and travel time than observed in the test junctions. Thus calculable evaluation is performed by virtue of Iteration Analysis to discover the impact of the selected parameters. Further numbers of simulation runs were performed by changing the default values within the ranges specified by acknowledge the importance of balancing the parameters so that the INTERSECTIONS can be enhanced. Therefore, outcome received from the standardize models were comparable to those observed from the field data surveys. The simulation results using default parameters conveyed only 15 - 20% similarity to the field condition, whereas the results obtained by iteration analysis matched nearly 70 - 80% of the field observed data. Finally these analysis proved that standardize driving performance parameters.

Assessing spatial and temporal effects due to a crash on a freeway through traffic simulation

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.

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

Current lane-based microscopic traffic simulators combine car-following and lane changing logic to describe the (often discrete) lateral vehicle motion on multi-lane road segments. However, the simulated lateral trajectories are physically unplausible and inside-lane behavior such as lane-keeping and curve negotiation cannot be modelled. In this work, we integrate lateral vehicle dynamics and yaw motion into a traffic simulation framework, aiming to describe lateral motion and vehicle interactions with more precision. The resulting framework consists of two coupled layers, an upper tactical level that plans maneuvers such as lane-changing; and a lower operational layer with a control module (steering and acceleration control) that operates in a closed loop with the bicycle model of vehicle dynamics. The feedback mechanism between the layers allows for dynamic trajectory re-planning. Unlike the microscopic traffic models, the proposed framework accounts for lateral vehicle dynamics and yaw motion; provides additional variables such as vehicle heading and front wheel steering angle; and is hence termed as submicroscopic. Case study results demonstrate the power of the framework to include lateral maneuvers such as curve negotiation, corrective steering, lane change abortion and fragmented lane changing. The framework was operationalized to model multi-lane traffic flow consisting of human-driven vehicles. At the macroscopic level, the traffic flow simulation can reproduce phenomena such as capacity drop. Thus the framework preserves the properties of the component models and at the same time describe the continuous 2-D planar movement of vehicles.

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.