Operation Of Traffic System Research Articles

A Multi-Period Charging Service Pricing Game for Public Charging Network Operators Considering the Dynamics of Coupled Traffic-Power Systems

The proliferation of electric vehicles (EVs) couples the operations of traffic systems and power systems, necessitating the interdependent traffic-power modeling to optimize the on-road EV charging decisions. In this paper, the multi-period charging service pricing interactions between multiple charging network operators (CNOs) are discussed considering the dynamics of traffic-power systems. A multi-period user equilibrium model considering mixed vehicle types is formulated to describe the possible vehicle trip transitions between different periods in urban transportation network. For the power distribution network, a convexified multi-period AC optimal power flow model is adopted for local electricity market clearing. With the dynamics of traffic-power systems, the pricing interaction between multiple CNOs is a non-cooperative game. We analyze the existence of Nash Equilibrium with fixed-point theory, and propose an iterative method based on the best response strategy to find <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon$</tex-math> </inline-formula> -Nash Equilibrium. Numerical studies based on an interdependent power-traffic system consisting of two 18-node power distribution networks and one revised Nguyen-Dupuis transportation network demonstrate that with the proposed CNOs’ charging pricing strategy, the CNOs’ service profits can increase by 1%-3% while the PDNs’ operation costs decrease about 1%-2% for the interdependent traffic-power system.

A simulation study of cooperative and autonomous vehicles (CAV) considering courtesy, ethics, and fairness.

Autonomous vehicles (AV) can be programmed to act cooperatively. Previous research on cooperative and autonomous vehicles (CAV) suggests they can substantially improve traffic system operations in terms of mobility and safety. However, these studies do not explicitly take each vehicle's potential gain/loss into consideration and ignore their individual levels of willingness to cooperate. They do not account for ethics and fairness either. In this study, several cooperation/courtesy strategies are proposed to address the above issues. These strategies are grouped into two categories based on non-instrumental and instrumental principles. Non-instrumental strategies make courtesy/cooperation decisions based on some courtesy proxies and a user-specified courtesy level, while instrumental strategies are based only on courtesy proxies related to local traffic performance. Also, a new CAV behavior modeling framework is proposed based on our previous work on cooperative car-following and merging (CCM) control. With such a framework, the proposed courtesy strategies can be easily implemented. The proposed framework and courtesy strategies are coded in SUMO microscopic traffic simulator. They are evaluated considering different levels of traffic demand on a freeway corridor consisting of a work zone and three weaving areas of different types. Interesting findings are drawn from the simulation results, one of which is that the instrumental Local Utilitarianism strategy performs the best in terms of mobility, safety, and fairness. In the future, auction-based strategies can be considered to model how CAV make decisions.

Steady flow control for a novel lattice hydrodynamic model with time delay based on discrete‐time system

This paper aims to investigate the influence of time delay effect and steady flow control strategy on traffic flow. Consider that the time delay in sensing flux information during the operation of traffic system is inevitable. Meanwhile, a new method is applied to model and analyse traffic system. A novel discrete‐time lattice hydrodynamic model considering time delay is introduced in this paper. Based on the proposed model with constant and varying time‐delay, the asymptotic stabilities are investigated and corresponding stable conditions are derived. The traffic flow evolution trend with and without time delay is conducted and compared by numerical simulations. Also, under the influence of different control gain conditions, density wave characteristics for traffic flow system with time delay are intuitively shown. The results indicate that time delay effect in sensing density and flux information can aggravate traffic congestion, and traffic congestion will be effectively relieved by considering steady flow control signal.

Empirical Investigation of Fundamental Diagrams in Mixed Traffic

A thorough understanding of the fundamental relation of traffic flow variables is critical for the efficient operation of traffic systems. However, their relationships in mixed traffic are challenging to model due to the continuously changing vehicle composition. This paper proposes a composition-based approach for estimating the fundamental relationships between traffic flow variables using empirical data. The methodology seeks to eliminate the difficulties in class-specific ss identification by introducing a continuous wavelet transformation with oblique cumulative arrival and oblique occupancy time plots. We used machine learning (ML) algorithms to delineate regimes and showed the fundamental diagrams for a given location that has a composition-invariant free-flow branch but has distinct composition-specific branches in congestion. Also, it was observed that the congested regime (CR) has a wide scatter indicating possible stochastic inter-class interactions for varying vehicular composition. We proposed a distance optimization method to re-cluster the CR data and found that the proposed method improves the fit with the empirical observations. The inter-class interactions result illustrates that the heavy vehicles will dominate the high-speed vehicles with the increase of AO. It is found that beyond a critical level of AO in congestion, all vehicle class travel at the same speed. Finally, it is found that validation with different datasets shows that the proposed methodology is robust in estimating fundamental diagrams under mixed traffic conditions.

Erroneous High Occupancy Vehicle Lane Data: Detecting Misconfigured Traffic Sensors With Machine Learning

Quality data are vital to the planning and operation of traffic systems. High occupancy vehicle (HOV) lanes, for instance, must comply with federal performance standards. If an agency fails to meet the standards, the facility is considered to be “degraded” and the agency is required to undertake actions that would return the facility to satisfactory operation. This could include removing exempted vehicles (e.g., low-emission vehicles) or increasing toll prices and passenger occupancy limits. Such policy changes may be costly, and may affect related policy goals, such as promoting clean air vehicles. Owing to constant changes in the system (e.g., roadwork, system upgrades), some of the thousands of HOV sensors in California’s transportation system are misconfigured, such as being labeled as general-purpose lanes. In this situation, HOV lane data may be mistakenly aggregated with general-purpose lane data and vice versa, causing a HOV facility to be erroneously reported as degraded and requiring unnecessary policy action. Detecting these misconfigurations is challenging and labor-intensive to accomplish manually. The purpose of this research was to utilize machine learning techniques to detect sensor misconfigurations and to understand the extent to which they affect performance reporting of HOV lanes. The results for Caltrans District 7 (Los Angeles and Ventura counties) showed that about 5% to 8% of Performance Measurement System HOV sensors are misconfigured. Therefore approximately 27 to 44 mi of HOV lanes are erroneously measured, with approximately 10 to 16 mi (38%) of those reporting an erroneously high degradation rating.

An exploration of data-driven microscopic simulation for traffic system and case study of freeway

Traffic simulation systems have been widely used for traffic system analysis and optimization. The traditional simulation system uses analytical models for each kind of driving behaviour, with little regard for the coupling relationship between the models, resulting in limited performance and complicated calibration and validation processes. In this study, a Data-Driven Simulation System (DDSS) framework was introduced to define traffic system operation processes and coordinate submodules. The proposed DDSS includes a General Kernel for running the simulation environment, and a Customized Interface for accessing to various data-driven driving behaviour models. To unify the modelling of multiple driving behaviour and increase prediction accuracy, a data-driven Sim-Hybrid Retraining Constrained LSTM (SHRC-LSTM) model was built. In addition, experiments on two NGSIM freeway testbeds demonstrated that DDSS produced more precise results in terms of efficiency, safety, and emissions than the most widely-used simulation system VISSIM.

A Method of Increasing the Accuracy of Radar Distance Measurement in VTS Systems for Vessels with Very Large Dimensions

The navigation information about a vessel’s position in the waters covered by the vessel traffic system operation is obtained through supervisory services, mainly from coastal navigation radars. Although today it is possible to simultaneously acquire data from many independent radars, the obtained radar image is inconsistent and consists of several echoes next to each other. This makes it difficult to establish which echo represents the monitored unit. Another problem is the method of determining radar distances, which significantly affect the quality of determining the observation position. Errors in radar distance may occur when determining the radar echoes from large vessels, where the position of the unit is not the same as the edge of the radar echo to which the observation is made. In this article, the authors present a method of improving the measured radar distance. The presented proposal was verified in navigation and maneuvering simulation conditions. It could support the process of determining the ship position in vessel traffic service (VTS) systems with increased accuracy.

Car Following and Microscopic Traffic Simulation Under Distracted Driving

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.

Effect of Enhanced ADAS Camera Capability on Traffic State Estimation.

Traffic flow data, such as flow, density and speed, are crucial for transportation planning and traffic system operation. Recently, a novel traffic state estimating method was proposed using the distance to a leading vehicle measured by an advanced driver assistance system (ADAS) camera. This study examined the effect of an ADAS camera with enhanced capabilities on traffic state estimation using image-based vehicle identification technology. Considering the realistic distance error of the ADAS camera from the field experiment, a microscopic simulation model, VISSIM, was employed with multiple underlying parameters such as the number of lanes, traffic demand, the penetration rate of ADAS vehicles and the spatiotemporal range of the estimation area. Although the enhanced functions of the ADAS camera did not affect the accuracy of the traffic state estimates significantly, the ADAS camera can be used for traffic state estimation. Furthermore, the vehicle identification distance of the ADAS camera and traffic conditions with more lanes did not always ensure better accuracy of the estimates. Instead, it is recommended that transportation planners and traffic engineering practitioners carefully select the relevant parameters and their range to ensure a certain level of accuracy for traffic state estimates that suit their purposes.

Air traffic complexity evaluation with novel complexity features and mRMR-XGBoost

Air traffic complexity evaluation is a critical problem in air traffic system operation, especially for air traffic safety and air traffic controller deployment. Many researches focus on using mathematical modeling or machine learning methods to evaluate air traffic complexity. However, there are still challenges in accurate evaluation, which is affected by lack of effective complexity related features and complicated feature-complexity relationship. Based on the existing complexity features, this paper proposes additional domain features, time-dependent features and data distribution features to construct a novel air traffic complexity feature set. A mRMR method for feature selection is then applied to filter out redundant features. Finally, the filtered feature set and corresponding air traffic complexity level are input into XGBoost model for learning the relationship, so as to achieve high-performance evaluation of air traffic complexity in the face of new air traffic data. The experimental results show that the proposed features are beneficial to the evaluation of air traffic complexity, and the XGBoost model with mRMR method can effectively select the important features and mine the relationships within air traffic complexity data, resulting in an improvement in overall evaluation performance by at least 5% while using less than half of the original number of features.

Formation and reconfiguration of tight multi-lane platoons

Advances in vehicular communication technologies are expected to facilitate cooperative driving in the future. Connected and Automated Vehicles (CAVs) are able to collaboratively plan and execute driving maneuvers by sharing their perceptual knowledge and future plans. In this paper, an architecture for autonomous navigation of tight multi-lane platoons traveling on public roads is presented. Using the proposed approach, CAVs are able to form single or multi-lane platoons of various geometrical configurations. They are able to reshape and adjust their configurations according to changes in the environment. The proposed architecture consists of two main components: an offline motion planner system and an online hierarchical control system. The motion planner uses an optimization-based approach for cooperative formation and reconfiguration in tight spaces. A constrained optimization scheme is used to plan smooth, dynamically feasible and collision-free trajectories for all the vehicles within the platoon. The paper addresses online computation limitations by employing a family of maneuvers precomputed offline and stored on a look-up table on the vehicles. The online hierarchical control system is composed of three levels: a traffic operation system (TOS), a decision-maker, and a path-follower. The TOS determines the desired platoon reconfiguration. The decision-maker checks the feasibility of the reconfiguration plan based on real-time information about the surrounding traffic. The reconfiguration maneuver is executed by a low-level path-following feedback controller in real-time. The effectiveness of the approach is demonstrated through simulations of three case studies: (1) formation reconfiguration (2) obstacle avoidance, and (3) benchmarking against behavior-based planning in which the desired formation is achieved using a sequence of motion primitives. Videos and software can be found online here https://github.com/RoyaFiroozi/Centralized-Planning.

Study on the influence of one-way street optimization design on traffic operation system

Modifying the existing two-way street into a one-way street can alleviate the traffic congestion in a megacity and has the characteristics of economy saving, convenience, and quickness. However, at present, the setting of one-way street in China is mostly determined by human experience and lack of scientific basis. In this context, a one-way street network planning model is established in this paper. According to this model, the optimal method of adding a one-way street on the existing traffic network is found, solved by genetic algorithm. The abstract things are quantified, and some feasible, specific, efficient implementation steps to design a one-way street network are provided for the traffic control department. In addition, taking the traffic in the core area of Tongzhou New City as an example, this paper uses the one-way street planning model to construct the actual street network model and the one-way street network model in the core area of the Tongzhou New City and compares their simulation results. The results show that using the one-way street planning model to modify part of the two-way street into a one-way street in the core area of the Tongzhou New City can indeed improve the traffic operation of the area.

Optimizing the Traffic Operation System of 14th-Ramadhan Signalized Intersection in Baghdad City using HCS Technique

The purposes of the study are assessing the current level of service (LOS) at the 14th-Ramadhan signalized intersection in CBD area of Al-Mansoor district in Baghdad city and increasing the improvement of the traffic operation system by investigating appropriate proposals to increase the traffic capacity of this facility. Data of traffic flow collected using Tally mark method by many observers. Evaluation of the current intersection is to be taken in consideration using HCS Technique (2010), also existing and proposal layouts are displayed using AutoCAD (2017). The results showed that the selected traffic facility are currently suffered from serious lowering in service level causing forced conditions (LOS “F”). Thus, most important considerations should be considered for developing in the LOS by proposing many solutions. In summary, the study is proposed to execute the overpass structure (Grade Separation Bridge) along 14th-Ramadhan street from southbound to northbound directions and vice versa (two lanes in each direction) considering the third proposal (P3). By using HCS2010, the LOS will be changed from F to B assuming that it is expected to continue at higher traffic performance for approximately at least 15 year later

Experiments simulation and design to set traffic lights’ operation rules

This empirical study aims to minimize the travel time of motor vehicles on one of the most important avenues in Celaya City, Guanajuato, Mexico, by means of the optimal synchronization of existing traffic lights. In the optimization process, the following three factors are considered with 3 evaluation levels each: traffic light cycle times; the synchrony defined as staggered, parallel and actual; and the speed limit. The response variables to consider were the average time in the system, the fuel consumption and the greenhouse effect gas (CO2) emissions. Different experiments were performed using the simulation model developed in the PTV-VISSIM software, which represents the vehicle traffic system. The obtained results for the different proposed scenarios allow proper levels to be determined for vehicle traffic system operation to improve mobility, reduce contamination rates and decrease fuel consumption for different motor vehicles using the avenue. As a result, it was possible to establish a methodology that combines microsimulation and design of experiments to program traffic lights and define the operating conditions of a complex vehicular flow system.

Traffic system operation optimization incorporating buffer size

This paper presents a general framework for resolving resource utilizations conflicts of air traffic system, expressed in the form of a max-plus linear model. The general air traffic system optimization model is presented taking into account buffer size in view of different purposes. The proposed air traffic system model is very flexible and it is extensible focusing on distinct circumstances. The dynamics of air traffic systems is characterized through the occurrence of discrete events such as aircrafts entering or leaving sub-segments. We focus on input control of air traffic system modeled by max-plus algebra by controlling the inflow of aircraft into the system. The constraints between input variables, state variables and output variables were obtained. The proposed method aims to minimize system total delay to meet demand specifications, allowing the air traffic controller to obtain the best control policy, which delays the occurrence of input events or varying input rate of system resources. Through simulations we verify the performance of the proposed max-plus linear model control scheme.

Non-recurrent congestion analysis using data-driven spatiotemporal approach for information construction

A systematic approach to quantify Incident-Induced Delay (IID) is proposed in this study. The paper complements existing literature by developing a data-driven method to dynamically determine the spatiotemporal extent of individual incidents. The information construction process can be further used to uncover a variety of features that are associated with any specific incidents for optimal freeway management. Additionally, this study contributes two particular highlights: secondary incident identification and K-Nearest Neighbor (KNN) pattern matching. Secondary incident identification, as a pre-processing for IID estimation, disentangles the convoluted influences of subsequent incidents. The proposed method uses KNN pattern matching, an essentially heuristic search process to separate the delay solely induced by incidents from the recurrent congestion. The proposed algorithm on IID quantification was implemented on Interstate 15 in the state of Utah using data obtained from 2013. Results and implications are presented. Hot spot analysis is conducted that can be potentially used for incident mitigation and to inform investment decisions. The proposed methodology is easily transferable to any traffic operation system that has access to sensor data at a corridor level.

Travel time prediction based on historical trajectory data

Travel time prediction could be applied to various fields and purposes. For traffic managers, travel time prediction is a fundamental part of traffic system operation. Its results may assist traffic management department in adjusting traffic flow through time-dependent rules. From the travellers' viewpoints, travel time prediction saves travel time and improves reliability through the selection of travel routes pre-trip and en route to optimize travel plans. A large number of research efforts on travel time prediction have been conducted, but trip travel time prediction is relatively limited, compared with link travel time. Travellers are more interested in specific trip travel time than average link travel time. The advances in positioning technologies, such as Global Positioning System (GPS), make it possible to collect a large number of vehicle trajectories which cover the whole road network as long as data are enough and is growing as an alternative data set for travel time prediction as well as other traffic studies. In view of these, we extend the conventional methodology of link travel time prediction to trip using historical trajectory data from taxis in urban road network. This article basically consists of the following several parts, extracting origins and destinations, searching for matched trips, testing for normal distribution, detecting and removing outliers, predicting travel time in a statistic way, and evaluating the reliability of prediction results. Experiments based on taxi data in Shenzhen are conducted and the results are evaluated.

Statistical Model for Estimating Carbon Dioxide Emissions from a Light-Duty Gasoline Vehicle

The objective of this research was development of a statistical model for estimating vehicle tailpipe emissions of carbon dioxide (CO2). Forty hours of second-by-second emissions data (144,000 data points) were collected using an On-Board emissions measurement System (Horiba OBS-1300) installed in a 2007 Dodge Charger car. Data were collected for two roadway types, arterial and highway, around Arlington, Texas, and two different time periods, off peak and peak (both a.m. and p.m.). Multiple linear regression and SAS software were used to build emission models from the data, using predictor variables of velocity, acceleration and an interaction term. The arterial model explained 61% of the variability in the emissions; the highway model explained 27%. The arterial model in particular represents a reasonably good compromise between accuracy and ease of use. The arterial model could be coupled with velocity and acceleration profiles obtained from a micro-scale traffic simulation model, such as CORSIM, or from field data from an instrumented vehicle, to estimate percent emission reductions associated with local changes in traffic system operation or management.

Simulation-Based, Scenario-Driven Integrated Corridor Management Strategy Analysis

The U.S. Department of Transportation affirmed the integrated corridor management (ICM) initiative to mitigate traffic congestion on urban corridor networks by systematically leveraging and diverting traffic to better utilize the available capacities of parallel arterials. ICM strategies would be of utmost importance when traffic incidents that may cause significant delays occur on freeways. By effectively diverting upcoming traffic to the adjacent arterials via variable message signs and real-time traveler information systems (e.g., mobile devices), the impact of incidents can be alleviated. To understand fully the effectiveness of ICM strategies, a framework needs to be developed to model, simulate, and analyze ICM operations under different scenarios. This study developed and calibrated a VISSIM simulation model based on field-collected video and sensor data for the South of Downtown area of Seattle, Washington. The analysis aimed to quantify networkwide ICM performance by empirically diverting traffic to adjacent arterials in response to incident management for freeway operations. Multiple scenarios were built into the simulation model to account for different diversion rates as well as recurrent and nonrecurrent congestion situations. When more traffic is diverted into an arterial, the arterial's performance degrades while improvements in travel time and delay for the freeway become marginal. Through quantitative analysis of the delay, throughput, and travel time of freeway segments and the arterial network, the trade-off in the overall system performance was carefully studied and understood. The research findings set up a solid foundation for ICM strategy development and optimization of traffic system operations.

Drivers’ Workloads through the Driving Vehicle Test at Intersections

Abstract Different from general roads, intersections are the points where roads having different geometric structure and traffic operation system are met, and thereby they have complicated road structur e and environmental factors. Various changes in driving pattern s such as collision between vehicles approaching from roads adjac ent to intersections, sudden stop of vehicles upon stop sign, q uick start upon green lights kept increasing traffic accidents. It i s known that traffic accidents are mainly derived from human fa ctors. This study, in order to find out factors affecting drivers' behaviors within intersections, measured physiological responses such as brain wave, sight, driving speed, and so on by using state-o f-the-art measuring device. As to concentration brain wave at individual intersections, it was found out that brain wave of t estes was higher at main Arterial and accident-prone intersecti ons compared with that of subsidiary Arterial. In addition, it was detected that drivers' visual activity was widely distributed a t accident-prone intersections, meaning that it enhanced cautious driving from nearby vehicles. As to major factors causing drivers' workloads, factors from nearby vehicles such as deceleration, acceleration, lane change of nearby vehicles appeared as direct factors causing drivers' workloads, clarifying that these facto rs were closely related to causes of traffic accidents at intersections. Results of this study are expected to be used as basic data for evaluation of safety at intersections in consideration of physiological response of drivers.Key words : driving vehicle test, concentration brain wave indicator, ey e-gaze distribution, drivers' workloads, safety evaluation at i ntersections * 주저자 : 한국도로공사 도로교통연구원 박사후연수자 ** 공저자 : 한국도로공사 도로교통연구원 책임연구원 *** 공저자 : 서남대학교 토목공학과 교수**** 공저자 및 교신저자 : 원광대학교 토목환경공학과 교수†논문접수일 : 2012년 5월 3일†논문심사일 : 2012년 6월 19일†게재확정일 : 2012년 6월 19일