Traffic Simulation-Based Sensitivity Analysis of Long Underground Expressways

Traffic Modeling and Safety Analysis on Motorway Ramps

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.

[EN] The quality of traffic on two-lane roads is closely linked to the possibility of making the overtaking maneuver that is especially critical in addition to the level of risk posed to do it. Options to facilitate overtaking are providing a path with geometric conditions for overtaking can be performed on the road with enough opportunities or provide a cross section that facilitates this advancement by auxiliary lanes. In this research, the second option with passing and climbing lanes was discussed.\nTo characterize the passing maneuver on steep grades an experimental study that allowed access to data and important information for calibrating TWOPAS, a microscopic traffic simulation model of two-lane roads, which is main element of the Traffic Analysis Module (TAM) that integrates the Interactive Highway Safety Design Model (IHSDM) was developed. TWOPAS was validated in other two-lane road with very different characteristics to the road considered during calibration thereby evaluating the usefulness of this simulation model was completed.\nConsidering a target of this research the TWOPAS was applied in the analysis of auxiliary lanes in a multitude of scenarios, around 98,000, where traffic volumes, longitudinal gradients, initiation and length of the auxiliary lane and percentage of trucks were varied; the influence of random variation of traffic generated wealth of information for the scope of this paper and considered for further analysis. A computational tool, the PEMAT, which allows automatic execution of TWOPAS (TAM), capturing the results of each simulation and generating outputs in terms of operational indicators was created.\nAmong the results of the analysis of auxiliary lanes are factors to consider the effect of passing and climbing lanes in the Percentage Time Spent Following (PTSF) and the Average Travel Speed (ATS) to adapt the procedure of Highway Capacity Manual (HCM) in 2010 and to estimate the level of service on two-lane road sections with individual auxiliary lanes.\nThe manuals for the calculation of the capacity and level of service on conventional roads does not cover the diversity of geometric and traffic conditions so that a procedure for analyzing the transit operation in any conventional road was proposed to facilitate overtaking by consideration of a number of auxiliary lanes in both directions of traffic simulation model with TWOPAS; thus overtaking can be solved for any configuration of road and transit improvements in methods in manuals or technical guidance.

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

In the widely used HCM and HBS assessment methods of the traffic performance of two-lane highways speed models are an essential element of the analytical methodology. Differences in the approach to analyses, as well as differing numbers of determinants taken into account and criteria used to assess traffic performance may all produce disparate results and reduce the practicality of the application of these methods. The paper compares the HCM and HBS speed models and demonstrates key differences in their approach to the impact of traffic volume, the share of heavy vehicles and longitudinal gradients of the road. The assessment of traffic performance by both methods also demonstrates significant differences in assessments, especially in more adverse road and traffic conditions. The paper will also show assumptions underlying the choice of research sites, methods of traffic research, databases and selected results of analyses as well as the impact of selected factors on speed and speed regression models.

This paper presents a traffic operational comparison of seven new alternative signalized intersection designs with three critical phases: 1) offset thru-cut, 2) thru-cut, 3) seven-phase, 4) redirect left and through from one minor road (redirect L&T), 5) reverse reduced conflict intersection (reverse RCI), and 6) and 7) two new versions of partial median U-turn (MUT) intersection. These designs will also be compared with the four-phase conventional intersection and two alternatives with two-phase traffic signals: full MUT and reduced conflict intersection (RCI) designs under various traffic conditions at near-capacity levels. Overall, 1,800 simulation runs were conducted to estimate traffic operations under different traffic conditions with varying traffic volumes, traffic distributions, and turning traffic ratios. Traffic signals were optimized for all the tests, and the optimized signal data was imported into microsimulation to evaluate the performance of the 10 intersections included. According to the results, four of the three-phase intersection designs (offset thru-cut, thru-cut, MUT #1, and reverse RCI) could result in significantly shorter travel times than the conventional intersection. The traffic operations of these four intersection designs were found to be similar to the two-phase designs and even better than the full MUT in scenarios with high turning traffic ratios. The other three intersection designs (seven-phase, redirect L&T, and MUT #2) also showed potential as possible substitutes for existing four-phase intersections under conditions with higher turning traffic demands.

The Yos Sudarso Road section in Wolio District, Baubau City, is a major road with a strategic function, serving as the main connector between important centers such as the commercial area, Kamali Beach, and Murhum Port. The diverse land-use functions along this road generate high daily activity variations, which directly impact its traffic performance. Currently, Jalan Yos Sudarso is experiencing a decline in performance due to increased traffic volume and significant side friction from commercial activities. This research aims to analyze the traffic flow conditions and the Level of Service (LOS) of the road. The analysis method utilizes the Pedoman Kapasitas Jalan Indonesia (PKJI) 2023, based on primary data from surveys on road geometry, traffic volume, and side friction during peak hours. The traffic flow conditions reveal a very high level of side friction caused by on-street parking and vehicle maneuvers into/out of commercial establishments. This activity effectively reduces the road's effective width, consequently degrading its performance. During the peak hour (09:00–10:00), the number of side friction incidents reached 327 incidents per hour, with a concurrent traffic volume of 918.5 pcu/hour (passenger car units per hour). The analysis results show that the road section's actual capacity is 1,116.9 pcu/hour with a Degree of Saturation (DS) value of 0.82. This indicates congested flow conditions and a Level of Service (LOS) category D. This condition suggests that the road can still function, but it no longer provides an optimal level of service.

Passenger Car Equivalents (PCEs in the following) are used to transform a mixed fleet of vehicles into a fleet of equivalent passenger cars and to analyze capacity and level-of-service of roads and intersections. Most roundabouts guidelines propose constant values for PCEs but a single PCE value can result improper under heterogeneous traffic conditions. PCEs should be vary with traffic and road conditions and consequently PCEs applied to undersaturated traffic conditions can overestimate the heavy vehicle effect or be not sensitive to the traffic level or characteristics of heavy vehicles. Compared to other at-grade intersections, the interaction between the operational performances of the heavy vehicles and the geometric features at roundabouts can produce significant impacts on the heavy vehicle paths and traffic operations due to the curvilinear nature of the roundabout design. Literature presents various methods of estimation to obtain PCE values for heavy vehicles. The focus of this paper is to review statistical methods and traffic simulation studies based on microscopic approaches used to calculate PCEs for heavy vehicles driving roundabouts. Effects on capacity and estimates of PCEs based on models currently employed in roundabout analysis are also compared. The results obtained in this study aim at providing an overview of the existing knowledge concerning the estimation of PCEs at roundabouts and can represent a guideline for transportation engineers in planning, design and capacity analysis of roundabouts that operate under conditions of mixed traffic.

SUMMARYIn the past, two‐way left‐turn lane (TWLTL) median treatments have been frequently used in Florida to inexpensively improve traffic and safety performances. In order to identify factors that may have significant impacts on safety operations in TWLTL sections and to identify TWLTL locations that present existing and future safety concerns, a research project was carried out and results are summarized in the paper. In the research, a three‐year crash history database with crashes and section characteristics from a total of 1688 TWLTL sections all over Florida was developed and used. A negative binomial regression model was developed to determine the statistical relationship between the number of crashes per mile per year and several variables such as traffic volume, access density, posted speed, and number of lanes. In regard to the methodology, in order to identify locations with safety concerns, several steps are needed: development of real crash data distribution, determination of statistical distribution models that better represent the actual crash data, determination of percentile values for the average number of crashes, estimation of crash rates for sections with the same characteristics, estimation of critical values for the variables corresponding to the percentile values for average number of crashes, calculation of tables of critical average annual daily traffic values, and generation of a list of TWLTL locations with critical safety concerns. Results presented in the paper have been used in real applications. Copyright © 2012 John Wiley & Sons, Ltd.

Traffic congestion in urban areas is posing many challenges, and a traffic flow model that accurately predicts traffic conditions can be useful in responding to them. With the limitation of infrastructure and the difficulty for real world experiment, traffic simulation is a good tool for the validation and future potential applications of the traffic flow model. This research presents a stochastic traffic flow model which uses a stochastic partial differential equation to describe the evolution of traffic flow on the highway. The stochastic model is calibrated and validated by real traffic data on a highway and is proved to have better predictive power than the deterministic model. Also, a microscopic traffic simulation is built and calibrated by the same highway data, and the validation result shows that the simulation is capable of representing the real traffic. By using traffic simulation to further explore the capability of the stochastic model, the validation demonstrated that the prediction works well at most locations, and the interpolation error may be improved by considering the influence of ramps and the change in the number of lanes.

Lane-changing maneuvers of heavy vehicles have significant influence on surrounding traffic characteristics because of the physical effects that the heavy vehicles impose on surrounding vehicles. These effects are the result of heavy vehicles’ length, size, weight, and limitations in their maneuverability. Because of the importance of heavy vehicle lane-changing maneuvers, this article presents an exclusive fuzzy logic lane-changing decision model with 2 and 3 fuzzy sets for heavy vehicle drivers on freeways. To examine the accuracy of the heavy vehicle drivers’ lane-changing decision model, the authors compared the number of estimated lane-changing maneuvers of heavy vehicles with the observed number of heavy vehicle lane-changing maneuvers. In addition, the authors compared estimated traffic flow measurements (e.g., traffic flow, average speed) with the observed traffic flow measurements through microscopic traffic simulations. Results show that the fuzzy logic heavy vehicle lane-changing model can better replicate the observed lane-changing decisions of heavy vehicle drivers than can the current lane-changing decision models. Furthermore, using an exclusive heavy vehicle lane-changing decision model can increase the accuracy of the microscopic traffic simulation software in estimating the macroscopic traffic flow measurements.

An Impact study of highway design on casualty and non-casualty traffic accidents

This paper presents a model specifically developed to explore the mobility impacts of connected and automated driving and shared mobility. It is an explorative iterative model that uses an elasticity model for destination choice, a multinomial logit model for mode choice and a network fundamental diagram to assess traffic impacts. To the best of the authors’ knowledge, it is the first model that combines a network fundamental diagram with choice models. A second contribution is the inclusion of automated vehicles, automated (shared) taxis, automated shared vans and new parking concepts in the model as well as the way in which they affect mobility choices and traffic conditions. The insights into the direct mobility impacts are the third contribution. The short computation time of the model enables exploration of large numbers of scenarios, sensitivity analyses and assessments of the impacts of interventions. The model was applied in a case study of the Dutch Province of North-Holland, in which the potential impacts of automated and shared vehicles and mitigating interventions were explored. In this case study, four extreme scenarios were explored, in which 100% of the vehicles have SAE-level 3/4 or 5 and people have a low or high willingness to share. The extremes were chosen to get insights into maximum effects. The results show that if automated vehicles and sharing are accepted, it is likely that there will be considerable changes in mobility patterns and traffic performance, with both positive and problematic effects.

Accurate traffic data are essential for supporting a multitude of transportation related decisions which affect transportation system operations, management, and planning. However, the lack of accurate and timely data is often cited as a common problem for both transportation practitioners and policy makers. This paper discusses the temporary use of advanced sensor technologies for collecting and presenting traffic data for supporting transportation system operations. It illustrates the use of video-based and radar-based traffic detection technologies. The range of applications included in this paper comprises specific traffic assessment, traffic signal operations, freeway performance evaluation, and highway work zones. In addition, the paper illustrates the significant value of video data in supporting transportation decisions among policy makers and the public. The paper covers two classes of advanced traffic detection sensors; the Autoscope Vide Vehicle Detection system and the Wavetronix SmartSensor Traffic Detector based on Digital Wave Radar. It discusses the potential applications, typical set up requirements, Types of data generated by these systems, and the performance of these technologies under different traffic condition environments. The information contained in this paper is based on various traffic operations projects conducted at the Advanced Traffic Analysis Center at North Dakota State University. These projects range from analysis of school traffic safety to evaluating traffic operations during a major multi-year construction project along Interstate Highway 29 in Fargo.

In the future, mixed traffic flow will include two types of vehicles: connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs). CAVs emerge as new solutions to disrupt the traditional transportation system. This new solution shares real-time data with each other and the roadside units (RSU) for network management. Reinforcement learning (RL) is a promising approach for traffic signal management in complex urban areas by leveraging information gathered from CAVs. In particular, coordinating signal management at many intersections is a critical challenge in multi-agent reinforcement learning (MARL). According to this vision, we propose an approach that combines an actor–critic network–based multi-agent deep deterministic policy gradient (MADDPG) model and a rerouting technique (RT) to increase traffic performance in vehicular networks. This algorithm overcomes the inherent non-stationary of Q-learning and the high variance of policy gradient (PG) algorithms. Based on centralized learning with decentralized execution, the MADDPG model employs one actor and one critic for each agent. The actor network uses local information to execute actions, while the critic network is trained with extra information, including the states and actions of other agents. Through a centralized learning process, agents can coordinate with each other, diminishing the influence of an unstable environment. Unlike previous studies, we not only manage traffic light systems but also consider the effect of platooning vehicles on increasing throughput. Experimental results show that our model outperforms other models in terms of traffic performance in different scenarios.