Calibration of nonlinear car-following laws for traffic oscillation prediction

Frequency-domain analysis has been successfully used to (i) predict the amplification of traffic oscillations along a platoon of vehicles with nonlinear car-following laws and (ii) measure traffic oscillation properties (e.g., periodicity, magnitude) from field data. This paper proposes a new method to calibrate nonlinear car-following laws based on real-world vehicle trajectories, such that oscillation prediction (based on the calibrated car-following laws) and measurement from the same data can be compared and validated. This calibration method, for the first time, takes into account not only the driver’s car-following behavior but also the vehicle trajectory’s time-domain (e.g., location, speed) and frequency-domain properties (e.g., peak oscillation amplitude). We use Newell’s car-following model (1961) as an example and calibrate its parameters based on a penalty-based maximum likelihood estimation procedure. A series of experiments using Next Generation Simulation (NGSIM) data are conducted to illustrate the applicability and performance of the proposed approach. Results show that the calibrated car-following models are able to simultaneously reproduce observed driver behavior, time-domain trajectories, and oscillation propagation along the platoon with reasonable accuracy.     

Improving fuel consumption and CO2 emissions calculations in urban areas by coupling a dynamic micro traffic model with an instantaneous emissions model

Τhis study demonstrates the combination of a microscopic traffic simulator (AIMSUN) with an instantaneous emissions model (AVL CRUISE) to investigate the impact of traffic congestion on fuel consumption on an urban arterial road. The micro traffic model was enhanced by an improved car-following law according to Morello et al. (2014) and was calibrated to replicate measured driving patterns over an urban corridor in Turin, Italy, operating under adaptive urban traffic control (UTC). The method was implemented to study the impact of congestion on fuel consumption for the category of Euro 5 diesel <1.4 l passenger cars. Free flow and congested conditions led to respective consumption differences of −25.8% and 20.9% over normal traffic. COPERT 5 rather well predicted the impact of congestion but resulted to a much lower relative reduction in free flow conditions. Start and stop system was estimated to reduce consumption by 6% and 11.9% under normal and congested conditions, respectively. Using the same modelling approach, UTC was found to have a positive impact on CO₂ emissions of 8.1% and 4.5% for normal and congested conditions, respectively, considering the Turin vehicle fleet mix for the year 2013. Overall, the study demonstrates that the combination of detailed and validated micro traffic and emissions models offers a powerful combination to study traffic and powertrain impacts on greenhouse gas and fuel consumption of on road vehicles over a city network.     

A field evaluation case study of the environmental and energy impacts of traffic calming

This study quantifies the energy and environmental impact of a selection of traffic calming measures using a combination of second-by-second floating-car global positioning system data and microscopic energy and emission models. It finds that traffic calming may result in negative impacts on vehicle fuel consumption and emission rates if drivers exert aggressive acceleration levels to speed up to their journeys. Consequently by eliminating sharp acceleration maneuvers significant savings in vehicle fuel consumption and emission rates are achievable through driver education. The study also demonstrates that high emitting vehicles produce CO emissions that are up to 25 times higher than normal vehicle emission levels while low emitting vehicles produce emissions that are 15–35% of normal vehicles. The relative increases in vehicle fuel consumption and emission levels associated with the sample traffic calming measures are consistent and similar for normal, low, and high emitting vehicles.     

An integrated modeling approach for facilitating emission estimations of alternative fueled vehicles

This paper develops an integrated model for reliable estimation of daily vehicle fuel savings and emissions using an integrated traffic emission modeling approach created by incorporating the US Environmental Protection Agency’s vehicle emission model, MOVES, and the PARAMICS microscopic traffic simulation package. A case study is conducted to validate the model using a well-calibrated road network in Greenville, South Carolina. For each transportation fuel considered, both emission and fuel consumption impacts are evaluated based on market shares.     

Evaluating the impacts of urban corridor traffic signal optimization on vehicle emissions and fuel consumption

This study investigates the impacts of traffic signal timing optimization on vehicular fuel consumption and emissions at an urban corridor. The traffic signal optimization approach proposed integrates a TRANSIMS microscopic traffic simulator, the VT-Micro model (a microscopic emission and fuel consumption estimation model), and a genetic algorithm (GA)-based optimizer. An urban corridor consisting of four signalized intersections in Charlottesville, VA, USA, is used for a case study. The result of the case study is then compared with the best traffic signal timing plan generated by Synchro using the TRANSIMS microscopic traffic simulator. The proposed approach achieves much better performance than that of the best Synchro solution in terms of air quality, energy and mobility measures: 20% less network-wide fuel consumption, 8–20% less vehicle emissions, and nearly 27% less vehicle-hours-traveled (VHT).     

The impacts of connected vehicle technology on network-wide traffic operation and fuel consumption under various incident scenarios

ABSTRACT

Incidents are a major source of traffic congestion and can lead to long and unpredictable delays, deteriorating traffic operations and adverse environmental impacts. The emergence of connected vehicles and communication technologies has enabled travelers to use real-time traffic information. The ability to exchange traffic information among vehicles has tremendous potential impacts on network performance especially in the case of non-recurrent congestion. To this end, this paper utilizes a microscopic simulation model of traffic in El Paso, Texas to investigate the impacts of incidents on traffic operation and fuel consumption at different market penetration rates (MPR) of connected vehicles. Several scenarios are implemented and tested to determine the impacts of incidents on network performance in an urban area. The scenarios are defined by changing the duration of incidents and the number of lanes closed. This study also shows how communication technology affects network performance in response to congestion. The results of the study demonstrate the potential effectiveness of connected vehicle technology in improving network performance. For an incident with a duration of 900?s and MPR of 80%, total fuel consumption and total travel time decreased by approximately 20%; 26% was observed in network-wide travel time and fuel consumption at 100% MPR.     

Mobility and environment improvement of signalized networks through Vehicle-to-Infrastructure (V2I) communications

Traffic signals, even though crucial for safe operations of busy intersections, are one of the leading causes of travel delays in urban settings, as well as the reason why billions of gallons of fuel are burned, and tons of toxic pollutants released to the atmosphere each year by idling engines. Recent advances in cellular networks and dedicated short-range communications make Vehicle-to-Infrastructure (V2I) communications a reality, as individual cars and traffic signals can now be equipped with communication and computing devices. In this paper, we first presented an integrated simulator with V2I, a car-following model and an emission model to simulate the behavior of vehicles at signalized intersections and calculate travel delays in queues, vehicle emissions, and fuel consumption. We then present a hierarchical green driving strategy based on feedback control to smooth stop-and-go traffic in signalized networks, where signals can disseminate traffic signal information and loop detector data to connected vehicles through V2I communications. In this strategy, the control variable is an individual advisory speed limit for each equipped vehicle, which is calculated from its location, signal settings, and traffic conditions. Finally, we quantify the mobility and environment improvements of the green driving strategy with respect to market penetration rates of equipped vehicles, traffic conditions, communication characteristics, location accuracy, and the car-following model itself, both in isolated and non-isolated intersections. In particular, we demonstrate savings of around 15% in travel delays and around 8% in fuel consumption and greenhouse gas emissions. Different from many existing ecodriving strategies in signalized road networks, where vehicles’ speed profiles are totally controlled, our strategy is hierarchical, since only the speed limit is provided, and vehicles still have to follow their leaders. Such a strategy is crucial for maintaining safety with mixed vehicles.     

Exploration of correlation between environmental factors and mobility at signalized intersections

Characterizing the relationship between environmental factors and mobility is critical for developing a sustainable traffic signal control system. In this study, the authors investigate the correlation of the environmental impacts of transport and mobility measurements at signalized intersections. A metamodeling-based method involving experimental design, simulations, and regression analysis was developed. The simulations, involving microscopic traffic modeling and emission estimation with an emerging emission estimator, provide the flexibility of generating cases with various intersection types, vehicle types, and other parameters such as driver behavior, fuel types, and meteorological factors. A multivariate multiple linear regression (MMLR) analysis was applied to determine the relationship between environmental and mobility measurements. Given the limitations of using the built-in emissions modules within current traffic simulation and signal optimization tools, the metamodeling-based approach presented in this paper makes a methodological contribution. The findings of this study set up the base for extensive application of simulation optimization to sustainable traffic operations and management. Moreover, the comparison of outputs from an advanced estimator with those from the current tool recommend improving the emissions module for more accurate analysis (e.g., benefit-cost analysis) in practical signal retiming projects.     

Characterization of traffic oscillation propagation under nonlinear car-following laws

Unlike linear car-following models, nonlinear models generally can generate more realistic traffic oscillation phenomenon, but nonlinearity makes analytical quantification of oscillation characteristics (e.g, periodicity and amplitude) significantly more difficult. This paper proposes a novel mathematical framework that accurately quantifies oscillation characteristics for a general class of nonlinear car-following laws. This framework builds on the describing function technique from nonlinear control theory and is comprised of three modules: expression of car-following models in terms of oscillation components, analyses of local and asymptotic stabilities, and quantification of oscillation propagation characteristics. Numerical experiments with a range of well-known nonlinear car-following laws show that the proposed approach is capable of accurately predicting oscillation characteristics under realistic physical constraints and complex driving behaviors. This framework not only helps further understand the root causes of the traffic oscillation phenomenon but also paves a solid foundation for the design and calibration of realistic nonlinear car-following models that can reproduce empirical oscillation characteristics.     

Validation of the Rakha-Pasumarthy-Adjerid car-following model for vehicle fuel consumption and emission estimation applications

The Rakha-Pasumarthy-Adjerid (RPA) car-following model has been demonstrated to successfully replicate empirical driver car-following behavior. However, the validity of this model for fuel consumption and emission (FC/EM) estimation has yet to be studied. This paper attempts to address this research need by analyzing the applicability of the model for FC/EM estimation and comparing its performance to other state-of-practice car-following models; namely, the Gipps, Fritzsche and Wiedemann models. Naturalistic empirical data are employed to generate ground truth car-following events. The model-generated second-by-second Vehicle Specific Power (VSP) distributions for each car-following event are then compared to the empirical distributions. The study demonstrates that the generation of realistic VSP distributions is critical in producing accurate FC/EM estimates and that the RPA model outperforms the other three models in producing realistic vehicle trajectory VSP distributions and robust FC/EM estimates. This study also reveals that the acceleration behavior within a car-following model is one of the major contributors to producing realistic VSP distributions. The study further demonstrates that the use of trip-aggregated results may produce erroneous conclusions given that second-by-second errors may cancel each other out, and that lower VSP distribution errors occasionally result in greater bias in FC/EM estimates given the large deviation of the distribution at high VSP levels. Finally, the results of the study demonstrate the validity of the INTEGRATION micro-simulator, given that it employs the RPA car-following model, in generating realistic VSP distributions, and thus in estimating fuel consumption and emission levels.     

Comparison of emerging ground propulsion systems for electrified aircraft taxi operations

Aviation is a mode with high fuel consumption per passenger mile and has significant environmental impacts. It is important to seek ways to reduce fuel consumption by the aviation sector, but it is difficult to improve fuel efficiency during the en-route cruise phase of flight because of technology barriers, safety requirements, and the mode of operations of air transportation. Recent efforts have emphasized the development of innovative Aircraft Ground Propulsion Systems (AGPS) for electrified aircraft taxi operations. These new technologies are expected to significantly reduce aircraft ground-movement-related fuel burn and emissions. This study compares various emerging AGPS systems and presents a comprehensive review on the merits and demerits of each system, followed with the local environmental impacts assessment of these systems. Using operational data for the 10 busiest U.S. airports, a comparison of environmental impacts is performed for four kinds of AGPS: conventional, single engine-on, external, and on-board systems. The results show that there are tradeoffs in fuel and emissions among these emerging technologies. On-board system shows the best performance in the emission reduction, while external system shows the least fuel burn. Compared to single-engine scenario, external AGPS shows the reduction of HC and CO emissions but the increase of NO_x emission. When a general indicator is considered, on-board AGPS shows the best potential of reducing local environmental impacts. The benefit-cost analysis shows that both external and on-board systems are worth being implemented and the on-board system appeals to be more beneficial.     

Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic management strategies

This paper presents a computationally efficient and theoretically rigorous dynamic traffic assignment (DTA) model and its solution algorithm for a number of emerging emissions and fuel consumption related applications that require both effective microscopic and macroscopic traffic stream representations. The proposed model embeds a consistent cross-resolution traffic state representation based on Newell’s simplified kinematic wave and linear car following models. Tightly coupled with a computationally efficient emission estimation package MOVES Lite, a mesoscopic simulation-based dynamic network loading framework DTALite is adapted to evaluate traffic dynamics and vehicle emission/fuel consumption impact of different traffic management strategies.     

A trajectory smoothing method at signalized intersection based on individualized variable speed limits with location optimization

Traffic signals on urban highways force vehicles to stop frequently and thus causes excessive travel delay, extra fuel consumption and emissions, and increased safety hazards. To address these issues, this paper proposes a trajectory smoothing method based on Individual Variable Speed Limits with Location Optimization (IVSL-LC) in coordination with pre-fixed traffic signals. This method dynamically imposes speed limits on some identified Target Controlled Vehicles (TCVs) with Vehicle to Infrastructures (V2I) communication ability at two IVSL points along an approaching lane. According to real-time traffic demand and signal timing information, the trajectories of each approaching vehicle are made to run smoothly without any full stop. Essentially, only TCVs’ trajectories need to be controlled and the other vehicles just follow TCVs with Gipps’ car-following model. The Dividing RECTangles (DIRECT) algorithm is used to optimize the locations of the IVSLs. Numerical simulation is conducted to compare the benchmark case without vehicle control, the individual advisory speed limits (IASL) and the proposed IVSL-LC. The result shows that compared with the benchmark, the IVSL-LC method can greatly increase traffic efficiency and reduce fuel consumption. Compared with IASL, IVSL-LC has better performance across all traffic demand levels, and the improvements are the most under high traffic demand. Finally, the results of compliance analysis show that the effect of IVSL-LC improves as the compliance rate increases.     

Spatiotemporal intersection control in a connected and automated vehicle environment

Current research on traffic control has focused on the optimization of either traffic signals or vehicle trajectories. With the rapid development of connected and automated vehicle (CAV) technologies, vehicles equipped with dedicated short-range communications (DSRC) can communicate not only with other CAVs but also with infrastructure. Joint control of vehicle trajectories and traffic signals becomes feasible and may achieve greater benefits regarding system efficiency and environmental sustainability. Traffic control framework is expected to be extended from one dimension (either spatial or temporal) to two dimensions (spatiotemporal). This paper investigates a joint control framework for isolated intersections. The control framework is modeled as a two-stage optimization problem with signal optimization at the first stage and vehicle trajectory control at the second stage. The signal optimization is modeled as a dynamic programming (DP) problem with the objective to minimize vehicle delay. Optimal control theory is applied to the vehicle trajectory control problem with the objective to minimize fuel consumption and emissions. A simplified objective function is adopted to get analytical solutions to the optimal control problem so that the two-stage model is solved efficiently. Simulation results show that the proposed joint control framework is able to reduce both vehicle delay and emissions under a variety of demand levels compared to fixed-time and adaptive signal control when vehicle trajectories are not optimized. The reduced vehicle delay and CO₂ emissions can be as much as 24.0% and 13.8%, respectively for a simple two-phase intersection. Sensitivity analysis suggests that maximum acceleration and deceleration rates have a significant impact on the performance regarding both vehicle delay and emission reduction. Further extension to a full eight-phase intersection shows a similar pattern of delay and emission reduction by the joint control framework.     

Influences of the driver’s bounded rationality on micro driving behavior,fuel consumption and emissions

In this paper, we propose an extended car-following model to study the influences of the driver’s bounded rationality on his/her micro driving behavior, and the fuel consumption, CO, HC and NO_X of each vehicle under two typical cases, where Case I is the starting process and Case II is the evolution process of a small perturbation. The numerical results indicate that considering the driver’s bounded rationality will reduce his/her speed during the starting process and improve the stability of the traffic flow during the evolution of the small perturbation, and reduce the total fuel consumption, CO, HC and NO_X of each vehicle under the above two cases.     

Multi-criteria analysis of optimal signal plans using microscopic traffic models

Increasing concerns on environment and natural resources, coupled with increasing demand for transport, put lots of pressure for improved efficiency and performance on transport systems worldwide. New technology nowadays enables fast innovation in transport, but it is the policy for deployment and operation with a systems perspective that often determines success. Smart traffic management has played important roles for continuous development of traffic systems especially in urban areas. There is, however, still lack of effort in current traffic management and planning practice prioritizing policy goals in environment and energy. This paper presents an application of a model-based framework to quantify environmental impacts and fuel efficiency of road traffic, and to evaluate optimal signal plans with respect not only to traffic mobility performance but also other important measures for sustainability. Microscopic traffic simulator is integrated with micro-scale emission model for estimation of emissions and fuel consumption at high resolution. A stochastic optimization engine is implemented to facilitate optimal signal planning for different policy goals, including delay, stop-and-goes, fuel economy etc. In order to enhance the validity of the modeling framework, both traffic and emission models are fine-tuned using data collected in a Chinese city. In addition, two microscopic traffic models are applied, and lead to consistent results for signal optimization. Two control schemes, fixed time and vehicle actuated, are optimized while multiple performance indexes are analyzed and compared for corresponding objectives. Solutions, representing compromise between different policies, are also obtained in the case study by optimizing an integrated performance index.     

Do cooperative systems make drivers’ car-following behavior safer?

The main goal of in-vehicle technologies and co-operative services is to reduce congestion and increase traffic safety. This is achieved by alerting drivers on risky traffic conditions ahead of them and by exchanging traffic and safety related information for the particular road segment with nearby vehicles. Road capacity, level of service, safety, and air pollution are impacted to a large extent by car-following behavior of drivers. Car-following behavior is an essential component of micro-simulation models. This paper investigates the impact of an infrastructure-to-vehicle (I2V) co-operative system on drivers’ car-following behavior. Test drivers in this experiment drove an instrumented vehicle with and without the system. Collected trajectory data of the subject vehicle and the vehicle in front, as well as socio-demographic characteristics of the test drivers were used to estimate car-following models capturing their driving behavior with and without the I2V system. The results show that the co-operative system harmonized the behavior of drivers and reduced the range of acceleration and deceleration differences among them. The observed impact of the system was largest on the older group of drivers.     

Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale

Coupling a traffic microsimulation with an emission model is a means of assessing fuel consumptions and pollutant emissions at the urban scale. Dealing with congested states requires the efficient capture of traffic dynamics and their conditioning for the emission model. Two emission models are investigated here: COPERT IV and PHEM v11. Emission calculations were performed at road segments over 6 min periods for an area of Paris covering 3 km². The resulting network fuel consumption (FC) and nitrogen oxide (NOx) emissions are then compared. This article investigates: (i) the sensitivity of COPERT to the mean speed definition, and (ii) how COPERT emission functions can be adapted to cope with vehicle dynamics related to congestion. In addition, emissions are evaluated using detailed traffic output (vehicle trajectories) paired with the instantaneous emission model, PHEM.COPERT emissions are very sensitive to mean speed definition. Using a degraded speed definition leads to an underestimation ranging from −13% to −25% for fuel consumption during congested periods (from −17% to −36% respectively for NOx emissions). Including speed distribution with COPERT leads to higher emissions, especially under congested conditions (+13% for FC and +16% for NOx). Finally, both these implementations are compared to the instantaneous modeling chain results. Performance indicators are introduced to quantify the sensitivity of the coupling to traffic dynamics. Using speed distributions, performance indicators are more or less doubled compared to traditional implementation, but remain lower than when relying on trajectories paired with the PHEM emission model.     

Feedback control speed harmonization algorithm: Methodology and preliminary testing

The capacity drop phenomenon, which reduces the maximum bottleneck discharge rate following the onset of congestion, is a critical restriction in transportation networks that produces additional traffic congestion. Consequently, preventing or reducing the occurrence of the capacity drop not only mitigates traffic congestion, but can also produce environmental and traffic safety benefits. In addressing this problem, the paper develops a novel bang-bang feedback control speed harmonization (SH) or Variable Speed Limit (VSL) algorithm, that attempts to prevent or delay the breakdown of a bottleneck and thus reduce traffic congestion. The novelty of the system lies in the fact that it is both proactive and reactive in responding to the dynamic stochastic nature of traffic. The system is proactive because it uses a calibrated fundamental diagram to initially identify the optimum throughput to maintain within the SH zone. Furthermore, the system is reactive (dynamic) because it monitors the traffic stream directly upstream of the bottleneck to adjustment the metering rate to capture the dynamic and stochastic nature of traffic. The steady-state traffic states in the vicinity of a lane-drop bottleneck before and after applying the SH algorithm is analyzed to demonstrate the effectiveness of the algorithm in alleviating the capacity drop. We demonstrate theoretically that the SH algorithm is effective in enhancing the bottleneck discharge rate. A microscopic simulation of the network using the INTEGRATION software further demonstrates the benefits of the algorithm in increasing the bottleneck discharge rate, decreasing vehicle delay, and reducing vehicle fuel consumption and CO₂ emission levels. Specifically, compared with the base case without the SH algorithm, the advisory speed limit increases the bottleneck discharge rate by approximately 7%, reduces the overall system delay by approximately 20%, and reduces the system-wide fuel consumption and CO₂ emission levels by 5%.     

Optimal vehicle speed trajectory on a signalized arterial with consideration of queue

Vehicle speed trajectory significantly impacts fuel consumption and greenhouse gas emissions, especially for trips on signalized arterials. Although a large amount of research has been conducted aiming at providing optimal speed advisory to drivers, impacts from queues at intersections are not considered. Ignoring the constraints induced by queues could result in suboptimal or infeasible solutions. In this study, a multi-stage optimal control formulation is proposed to obtain the optimal vehicle trajectory on signalized arterials, where both vehicle queue and traffic light status are considered. To facilitate the real-time update of the optimal speed trajectory, a constrained optimization model is proposed as an approximation approach, which can be solved much quicker. Numerical examples demonstrate the effectiveness of the proposed optimal control model and the solution efficiency of the proposed approach.