scholarly journals Dynamic Car–Following Model Calibration Using SPSA and ISRES Algorithms

2018 ◽  
Vol 47 (2) ◽  
pp. 146-156 ◽  
Author(s):  
Ioulia Markou ◽  
Vasileia Papathanasopoulou ◽  
Constantinos Antoniou
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Simulation ◽  
Transportation Systems ◽  
Model Parameters ◽  
Time Step ◽  
Car Following ◽  
Calibration Methods ◽  
Wide Range ◽  
Car Following Model ◽  
Parameter Values

Calibration plays a fundamental role in successful applications of traffic simulation and Intelligent Transportation Systems. In this research, the calibration of car–following models is seen as a dynamic problem, which is solved at each individual time–step. The optimization of model parameters is fulfilled using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. The output of the optimization is a distribution of parameter values, capturing a wide range of various traffic conditions. The methodology is demonstrated via a case study, where the proposed framework is implemented for the dynamic calibration of the car–following model used in the TransModeler traffic simulation model and Gipps′ model. This method results to model parameter distributions, which are superior to simply using point parameter values, as they are more realistic, capturing the heterogeneity of driver behavior. Flexibility is thus introduced into the calibration process and restrictions generated by conventional calibration methods are relaxed.

Linear Acceleration Car-Following Model Development and Validation

1998 ◽  
Vol 1644 (1) ◽  
pp. 10-19 ◽  
Author(s):  
M.F. Aycin ◽  
R.F. Benekohal
Keyword(s):  
Intelligent Transportation Systems ◽  
Field Data ◽  
Statistical Tests ◽  
Reaction Times ◽  
Linear Acceleration ◽  
Transportation Systems ◽  
Cruise Control ◽  
Vehicle Simulation ◽  
Car Following ◽  
Car Following Model

A linear acceleration car-following model has been developed for realistic simulation of traffic flow in intelligent transportation systems (ITS) applications. The new model provides continuous acceleration profiles instead of the stepwise profiles that are currently used. The brake reaction times of the drivers are simulated effectively and are independent of the simulation time steps. Chain-reaction times of the drivers are also simulated and perception thresholds are incorporated in the model. The preferred time headways are utilized to determine the simulated drivers’ separation during car-following. The features of the model and the realistic vehicle simulation in car-following and in stop-and-go conditions make this model suitable to ITS, especially to autonomous intelligent cruise-control systems. The car-following algorithm is validated at microscopic and macroscopic levels by using field data. Simulated versus field trajectories and statistical tests show very strong agreement between simulation results and field data.

Methods to Obtain Representative Car-Following Model Parameters from Trajectory-Level Data for Use in Microsimulation

2019 ◽  
Vol 2673 (7) ◽  
pp. 62-73 ◽  
Author(s):  
Rachel M. James ◽  
Britton E. Hammit ◽  
Stephen D. Boyles
Keyword(s):  
Model Parameters ◽  
Average Behavior ◽  
Car Following ◽  
Level Data ◽  
Car Following Model ◽  
Naturalistic Driving Study ◽  
Validation Procedure ◽  
Microsimulation Models ◽  
Parameter Values

Microsimulation models help agencies obtain robust estimates of project benefits and spend their resources effectively. The realism of these models depends on the quality of input data and the realism of the sub-models controlling driver behavior. The availability of trajectory-level driving data provides new opportunities to improve car-following models and their application in practice. Procedures for calibrating car-following models using a single driving trajectory are well documented in the literature. However, methods for identifying a representative parameter set to describe a collection of observed driving behavior must be developed and tested before trajectory-level data can be applied in practice. This paper describes eight methods for obtaining representative sets of calibration parameters to describe a group of drivers or a specific driving condition. The methods are tested using a 100-trip sample from the SHRP2 Naturalistic Driving Study and validated with a 10-fold cross-validation procedure. The method capturing the average behavior while preserving underlying correlations between the calibrated model parameters performed the best across all four models. Methods that adequately captured the average behavior while relaxing the assumption of underlying parameter correlations performed better than all other tested methods. Therefore, simply taking the mean or median of the distribution of observed parameter values offers a practical approach for generating a representative parameter set, significantly outperforming default parameter values.

scholarly journals Experimental Analysis of Car-Following Dynamics and Traffic Stability

2005 ◽  
Vol 1934 (1) ◽  
pp. 22-32 ◽  
Author(s):  
Prakash Ranjitkar ◽  
Takashi Nakatsuji ◽  
Akira Kawamura
Keyword(s):  
Intelligent Transportation Systems ◽  
Optimization Technique ◽  
Model Performance ◽  
Transportation Systems ◽  
Model Parameters ◽  
Cruise Control ◽  
Position Measurement ◽  
Car Following ◽  
The Past ◽  
Safety Research

The study of car-following dynamics is useful for capacity analysis, safety research, and traffic simulation. There is also growing interest in its applications in intelligent transportation systems, such as advanced vehicle control and safety systems and autonomous cruise control systems. A large number of car-following models have been developed in the past five decades. Some of them were investigated and validated against experimental data; nevertheless, the results were not that consistent for some models, e.g., those for the General Motors (GM) model. As a part of the problem, the data acquisition and calibration techniques were not advanced then. The past few decades have seen remarkable advancements in these techniques, e.g., the use of the differential Global Positioning System (GPS) for position measurement, the use of Doppler's principle for speed measurements, and the use of genetic algorithms for optimization. It might be useful to reassess some outstanding issues in car-following dynamics in light of the latest technological advancements. This paper attempts to investigate car-following dynamics on the basis of the real-time kinematic GPS data collected from test track experiments. The GM model was evaluated along with some well-known simulation models, including the Gipps model and the Leutzbach and Wiedemann model. A genetic algorithm-based optimization technique was adapted for calibration. The sensitivities of drivers to their speeds and spacings from the vehicle ahead were found to vary among drivers. The interpersonal variations in model performance were significant. The GM model parameters were identified with improved reliability. The stability of traffic flow was analyzed experimentally.

scholarly journals A Space-Based Car-Following Model: Development and Application for Managed Motorway System Safety Evaluation

2021 ◽  
Vol 1 (3) ◽  
pp. 443-465
Author(s):  
Kaveh Bevrani ◽  
Edward Chung ◽  
Pauline Teo
Keyword(s):  
Traffic Safety ◽  
Intelligent Transportation Systems ◽  
Simulation Models ◽  
Transportation Systems ◽  
Speed Limit ◽  
Variable Speed ◽  
Driver Behaviour ◽  
Car Following ◽  
Micro Simulation ◽  
Car Following Model

Traffic safety studies need more than what the current micro-simulation models can provide, as they presume that all drivers exhibit safe behaviors. Therefore, existing micro-simulation models are inadequate to evaluate the safety impacts of managed motorway systems such as Variable Speed Limits. All microscopic traffic simulation packages include a core car-following model. This paper highlights the limitations of the existing car-following models to emulate driver behaviour for safety study purposes. It also compares the capabilities of the mainstream car-following models, modelling driver behaviour with precise parameters such as headways and time-to-collisions. The comparison evaluates the robustness of each car-following model for safety metric reproductions. A new car-following model, based on the personal space concept and fish school model is proposed to simulate more accurate traffic metrics. This new model is capable of reflecting changes in the headway distribution after imposing the speed limit from variable speed limit (VSL) systems. This model can also emulate different traffic states and can be easily calibrated. These research findings facilitate assessing and predicting intelligent transportation systems effects on motorways, using microscopic simulation.

scholarly journals An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment

2020 ◽  
Vol 12 (4) ◽  
pp. 1552 ◽  
Author(s):  
Shuaiyang Jiao ◽  
Shengrui Zhang ◽  
Bei Zhou ◽  
Zixuan Zhang ◽  
Liyuan Xue
Keyword(s):  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Traffic Information ◽  
Trajectory Data ◽  
Optimal Velocity ◽  
Car Following ◽  
V2v Communication ◽  
Communication Environment ◽  
Car Following Model ◽  
Vehicle Trajectory

In intelligent transportation systems, vehicles can obtain more information, and the interactivity between vehicles can be improved. Therefore, it is necessary to study car-following behavior during the introduction of intelligent traffic information technology. To study the impacts of drivers’ characteristics on the dynamic characteristics of car-following behavior in a vehicle-to-vehicle (V2V) communication environment, we first analyzed the relationship between drivers’ characteristics and the following car’s optimal velocity using vehicle trajectory data via the grey relational analysis method and then presented a new optimal velocity function (OVF). The boundary conditions of the new OVF were analyzed theoretically, and the results showed that the new OVF can better describe drivers’ characteristics than the traditional OVF. Subsequently, we proposed an extended car-following model by combining V2V communication based on the new OVF and previous car-following models. Finally, numerical simulations were carried out to explore the effect of drivers’ characteristics on car-following behavior and fuel economy of vehicles, and the results indicated that the proposed model can improve vehicles’ mobility, safety, fuel consumption, and emissions in different traffic scenarios. In conclusion, the performance of traffic flow was improved by taking drivers’ characteristics into account under the V2V communication situation for car-following theory.

scholarly journals Optimization and Evaluation of Platooning Car-Following Models in a Connected Vehicle Environment

2021 ◽  
Vol 13 (6) ◽  
pp. 3474
Author(s):  
Guang Yu ◽  
Shuo Liu ◽  
Qiangqiang Shangguan
Keyword(s):  
Communication Technology ◽  
Intelligent Transportation Systems ◽  
Rapid Development ◽  
Safety Margin ◽  
Practical Method ◽  
Transportation Systems ◽  
Systematic Evaluation ◽  
Connected Vehicle ◽  
Car Following ◽  
Cooperative Driving

With the rapid development of information and communication technology, future intelligent transportation systems will exhibit a trend of cooperative driving of connected vehicles. Platooning is an important application technique for cooperative driving. Herein, optimized car-following models for platoon control based on intervehicle communication technology are proposed. On the basis of existing indicators, a series of evaluation methods for platoon safety, stability, and energy consumption is constructed. Numerical simulations are used to compare the effects of three traditional models and their optimized counterparts on the car-following process. Moreover, the influence of homogenous and heterogeneous attributes on the platoon is analyzed. The optimized model proposed in this paper can improve the stability and safety of vehicle following and reduce the total fuel consumption. The simulation results show that a homogenous platoon can enhance the overall stability of the platoon and that the desired safety margin (DSM) model is better suited for heterogeneous platoon control than the other two models. This paper provides a practical method for the design and systematic evaluation of a platoon control strategy, which is one of the key focuses in the connected and autonomous vehicle industry.

Driver Reactions to Uphill Grades: Inference from a Stochastic Car-Following Model

2020 ◽  
Vol 2674 (11) ◽  
pp. 343-351 ◽  
Author(s):  
Tu Xu ◽  
Jorge Laval
Keyword(s):  
Maximum Likelihood ◽  
Likelihood Estimation ◽  
Truck Drivers ◽  
Model Parameters ◽  
Gravitational Effects ◽  
Car Following ◽  
Car Following Model ◽  
Operational Impacts ◽  
The Impact ◽  
Engine Power

This paper analyzes the impact of uphill grades on the acceleration drivers choose to impose on their vehicles. Statistical inference is made based on the maximum likelihood estimation of a two-regime stochastic car-following model using Next Generation SIMulation (NGSIM) data. Previous models assume that the loss in acceleration on uphill grades is given by the effects of gravity. We find evidence that this is not the case for car drivers, who tend to overcome half of the gravitational effects by using more engine power. Truck drivers only compensate for 5% of the loss, possibly because of limited engine power. This indicates not only that current models are severely overestimating the operational impacts that uphill grades have on regular vehicles, but also underestimating their environmental impacts. We also find that car-following model parameters are significantly different among shoulder, median and middle lanes but more data is needed to understand clearly why this happens.

scholarly journals How Can Smart Mobility Innovations Alleviate Transportation Disadvantage? Assembling a Conceptual Framework through a Systematic Review

2020 ◽  
Vol 10 (18) ◽  
pp. 6306 ◽  
Author(s):  
Luke Butler ◽  
Tan Yigitcanlar ◽  
Alexander Paz
Keyword(s):  
Conceptual Framework ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Autonomous Vehicles ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Technological Innovations ◽  
Potential Solution ◽  
Wide Range ◽  
Smart Mobility

Transportation disadvantage is about the difficulty accessing mobility services required to complete activities associated with employment, shopping, business, essential needs, and recreation. Technological innovations in the field of smart mobility have been identified as a potential solution to help individuals overcome issues associated with transportation disadvantage. This paper aims to provide a consolidated understanding on how smart mobility innovations can contribute to alleviate transportation disadvantage. A systematic literature review is completed, and a conceptual framework is developed to provide the required information to address transportation disadvantage. The results are categorized under the physical, economic, spatial, temporal, psychological, information, and institutional dimensions of transportation disadvantage. The study findings reveal that: (a) Primary smart mobility innovations identified in the literature are demand responsive transportation, shared transportation, intelligent transportation systems, electric mobility, autonomous vehicles, and Mobility-as-a-Services. (b) Smart mobility innovations could benefit urban areas by improving accessibility, efficiency, coverage, flexibility, safety, and the overall integration of the transportation system. (c) Smart mobility innovations have the potential to contribute to the alleviation of transportation disadvantage. (d) Mobility-as-a-Service has high potential to alleviate transportation disadvantage primarily due to its ability to integrate a wide-range of services.

scholarly journals Predicting Future Locations of Moving Objects by Recurrent Mixture Density Network

2020 ◽  
Vol 9 (2) ◽  
pp. 116
Author(s):  
Rui Chen ◽  
Mingjian Chen ◽  
Wanli Li ◽  
Naikun Guo
Keyword(s):  
Prediction Model ◽  
Intelligent Transportation Systems ◽  
Moving Objects ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Performance Comparison ◽  
Parameter Analysis ◽  
Location Prediction ◽  
Time Step ◽  
Mixture Density

Accurate and timely location prediction of moving objects is crucial for intelligent transportation systems and traffic management. In recent years, ubiquitous location acquisition technologies have provided the opportunity for mining knowledge from trajectories, making location prediction and real-time decisions more feasible. Previous location prediction methods have mostly developed on the basis of shallow models whereas shallow models are not competent for some tricky challenges such as multi-time-step location coordinates prediction. Motivated by the current study status, we are dedicated to a deep-learning-based approach to predict the coordinates of several future locations of moving objects based on recent trajectory records. The method of this work consists of three successive parts: trajectory preprocessing, prediction model construction, and post-processing. In this work, a prediction model named the bidirectional recurrent mixture density network (BiRMDN) was constructed by integrating the long short-term memory (LSTM) and mixture density network (MDN) together. This model has the ability to learn long-term contextual information from recent trajectory and model real-valued location coordinates. We employed a vessel trajectory dataset for the implementation of this approach and determined the optimal model configuration after several parameter analysis experiments. Experimental results involving a performance comparison with other widely used methods demonstrate the superiority of the BiRMDN model.

Simulation of an Arterial Incident Environment with Probe Reporting Capability

1998 ◽  
Vol 1644 (1) ◽  
pp. 116-123 ◽  
Author(s):  
Natacha Thomas ◽  
Bader Hafeez
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Simulation ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Incident Detection ◽  
Travel Times ◽  
Control Center ◽  
Microscopic Traffic Simulation ◽  
Arterial Streets ◽  
Detection Systems

Intelligent transportation systems have created new traffic monitoring approaches and fueled new interests in automated incident detection systems. One new monitoring approach utilizes actual travel times experienced by vehicles, called probes, equipped to transmit this information in real time to a control center. The database needed to design and calibrate arterial incident detection systems based on probe travel times is nonexistent. A microscopic traffic simulation package, Integrated Traffic Simulation, was selected and enhanced to generate vehicle travel times for the incident and incident-free conditions on an arterial. We evaluated the enhanced model. Significant variations in probe travel times were observed in the event of incidents. Average travel time, contrary to average occupancy, may increase, decrease, or remain constant on arterial streets downstream of an incident.

Sign in / Sign up

Export Citation Format

Share Document