Traffic operation analysis for underground and ground roads using microscopic traffic simulation

This study examines the calibration and validation of an AIMSUN microscopic traffic simulation model of the Western Brisbane Commuting Corridor. In particular, the study focuses on the two areas of data collection/collation and comparison of model outputs with field data. Collection of travel times for Milton Road and Coronation Drive during morning, evening and off peak conditions was performed using GPS and analyzed with the aid of the Geographic Information System Mapinfo. Achieving the ideal 90% statistical reliability in travel times for peak traffic conditions is a rigorous process, as it requires ongoing data analysis and computation of sample mean and standard deviation. Statistics on vehicle composition and vehicle attributes suitable for model input were gathered from Austroads and Australian Bureau of Statistics. Results of travel times produced from an AIMSUN simulation model of Coronation Drive under morning peak conditions show poor correspondence with recorded travel times. Model error is most pronounced for travel times heading inbound towards the Brisbane CBD, where the simulator produces a mean trip time of less than 4 minutes compared with a observed mean trip time of 13 minutes. This disparity exists because the model considers a 50/50 directional split in traffic volumes, but collected field data indicates a strong imbalance of traffic volumes with a higher portion of commuters traveling inbound under morning peak conditions. Further research into attaining updated statistics regarding traffic volumes and travel demands is therefore recommended. Results also show high error rates in modeling segments of road that are closer to driver destinations, and where lane changes and merges among vehicles are more common. Under congested conditions, parameters that describe driver behavior, or how each vehicle will move and interact in a network, have greater impact. In light of this, key areas of model calibration that will need to be reassessed in order for the traffic simulator to achieve closer correspondence with observed data are driver parameters and dynamic route selection.

Long underground expressways have emerged as an alternative to surface highways in densely urbanized areas; however, their enclosed geometry, extended length, and steep longitudinal gradients introduce traffic-flow dynamics distinct from those of surface roads. This study investigates the combined and interaction effects of traffic volume, heavy-vehicle ratio, longitudinal gradient, lane number, and lane-changing policy on traffic performance in long underground expressways using microscopic traffic simulation. A hypothetical 20 km underground expressway network was evaluated under 72 systematically designed scenarios. Weighted average speed and throughput were analyzed using nonparametric statistics, generalized linear models with interaction terms, and machine learning-based sensitivity analysis. While traffic volume and heavy-vehicle ratio were confirmed as dominant determinants of performance, a key contribution of this study is the identification of the density-dependent role of lane-changing policies. Under moderate traffic density, permissive lane-changing improves efficiency by enabling vehicles to bypass localized disturbances caused by heavy vehicles and longitudinal gradients, thereby enhancing capacity utilization. In contrast, under high-density conditions, permissive lane-changing amplifies lane-change conflicts and shockwave propagation within the confined underground environment, accelerating traffic instability and performance breakdown. These adverse effects are further intensified by steep uphill gradients. The findings demonstrate that lane-changing policies on long underground expressways should be designed in a context-sensitive manner, balancing efficiency and stability across traffic states.

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.

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.

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

Due to urban overcrowding, available land is limited and traffic congestion has increased. Underground roads are being built to mitigate traffic congestion as an alternative. Studies associated with underground roads are needed because these roads are dark and closed and have a high risk of accidents compared to surface roads. In particular, there is limited study on junctions that connect two or more underground roads. In this study, an underground road network including junctions was constructed to analyze the factors behind rear-end collisions at underground road connections. To reflect the driving behavior on underground roads, the scenario analysis was conducted by applying the speed distribution of underground roads in Korea. The results of the analysis showed that variables such as acceleration standard deviation and lateral position standard deviation are crucial for accidents on underground roads. Thus, this study can be used as a basis for traffic management and safety improvement in the operation of underground road junctions in the future.

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.

In Japan, auxiliary lane has been installed with the aim of correcting the lane utilization and increasing traffic capacity as a measure against traffic congestion at sag section of multi-lane motorways. The objective of this paper is to examine an effective method of installing an auxiliary overtaking lane for bottlenecks on motorways. First, a microscopic traffic flow simulation model is calibrated using observation data. Then, the simulation model is applied to study the appropriate installation method (installation location and length, etc.) of auxiliary overtaking lane as a measure against traffic congestion in section where sag is a bottleneck in capacity. As a result, auxiliary overtaking lane should be installed 400m upstream from the beginning of the vertical curve, and should be ended 0.6 to 1.0km from the end of the vertical curve, with a total length of 1.5 to 2.0km.

Traffic Modeling and Safety Analysis on Motorway Ramps

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.

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.

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.

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.

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.