Dislocation following total hip arthroplasty (THA) continues to be one of the most common reasons for revision THA. The purpose of this study is to measure the current rate of dislocation following ...THA in the United States. A secondary goal is to identify patients at highest risk of instability after THA.
The Nationwide Readmissions Database was used to identify cases of elective primary THA between 2012 and 2014. All readmissions associated with dislocations were identified. Kaplan-Meier curves were used to assess the time to dislocation in the study population. A multivariate logistic regression was modeled to assess risk factors associated with readmission for dislocation.
A total of 207,285 THAs were identified between 2012 and 2014. Of the total, 2842 dislocation-associated readmissions (1.4%) were identified, at a median of 40 days post-THA. A history of spinal fusion was the strongest independent predictor of dislocation (odds ratio OR, 2.45; 95% confidence interval CI, 1.97-3.04; P < .0001). Parkinson's disease was also significantly associated with dislocation (OR, 1.63; 95% CI, 1.05-2.51; P = .03), as well as dementia (OR, 1.96; 95% CI, 1.13-3.39; P = .02), depression (OR, 1.28; 95% CI, 1.13-1.43; P < .0001), and chronic lung disease (OR, 1.2; 95% CI, 1.07-1.33; P = .001). Inflammatory arthritis and avascular necrosis were independent risk factors for dislocation (OR, 1.56; 95% CI, 1.25-1.97; P < .0001; OR, 1.67; 95% CI, 1.45-1.93; P < .0001).
THA is a highly effective procedure with a low overall rate of instability. A history of spinal fusion was the most significant independent risk factor for dislocation within the first 6 months following THA.
•Revealing differences between a human driver following-AV and following-HV.•Considering both constant speed traffic and dynamic car-following behaviors.•Proposing a driver classification approach to ...separate different driving behaviors.
Although mixed traffic, including both autonomous vehicles (AV) and human-driven vehicles (HV), is expected to prevail in the foreseeable future, our current understanding of the longitudinal characteristics of mixed traffic is limited and, in particular, lacks evidence from field experiments. To bridge this gap, we designed and conducted a set of field experiments to reveal differences in car-following behaviors between a human driver following-AV and following-HV on both constant speed traffic characteristics with discrete speeds ({10,20,…,60}km/h) and dynamic car-following behaviors with continuous speeds (within 0–60 km/h) in both the indifferentiable and differentiable appearance settings of the AV. We recruited 10 drivers for the experiment (14 runs for each driver and collected position and speed data of the tested vehicles along their complete trajectories based on vehicle gaps, headways, and standard deviations of vehicle speed. A K-means clustering algorithm was applied to classify drivers based on their responses in following-AV vs. following-HV with both constant speed and dynamic speed characteristics. The analyses of the differentiable appearance setting show that different drivers exhibit different behaviors in following-AV vs. following-HV: some are AV-believers, some are AV-skeptics, and the others are insensitive. Yet in the indifferentiable appearance setting, there is no significant difference between following a lead AV and following a lead HV. This reveals that drivers’ response to the lead vehicle depends on their subjective trusts on AV technologies rather than the actual driving behavior. The results suggest that, depending on the characteristics and composition of the drivers, classic car-following behavior in pure HV traffic may need to be updated for modeling mixed traffic in the near future.
•Sixty Chinese drivers’ data, with a total mileage of 161,055 km, were collected.•A total of 2100 urban-expressway car-following periods were extracted.•Five car-following models were calibrated, ...validated and cross-compared.•The intelligent driver model performed best among the evaluated models.•Considerable behavioral differences between different drivers were found.•Calibrated model parameters may not be numerically equivalent with observed ones.
Although car-following behavior is the core component of microscopic traffic simulation, intelligent transportation systems, and advanced driver assistance systems, the adequacy of the existing car-following models for Chinese drivers has not been investigated with real-world data yet. To address this gap, five representative car-following models were calibrated and evaluated for Shanghai drivers, using 2100 urban-expressway car-following periods extracted from the 161,055 km of driving data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The models were calibrated for each of the 42 subject drivers, and their capabilities of predicting the drivers’ car-following behavior were evaluated.
The results show that the intelligent driver model (IDM) has good transferability to model traffic situations not presented in calibration, and it performs best among the evaluated models. Compared to the Wiedemann 99 model used by VISSIM®, the IDM is easier to calibrate and demonstrates a better and more stable performance. These advantages justify its suitability for microscopic traffic simulation tools in Shanghai and likely in other regions of China. Additionally, considerable behavioral differences among different drivers were found, which demonstrates a need for archetypes of a variety of drivers to build a traffic mix in simulation. By comparing calibrated and observed values of the IDM parameters, this study found that (1) interpretable calibrated model parameters are linked with corresponding observable parameters in real world, but they are not necessarily numerically equivalent; and (2) parameters that can be measured in reality also need to be calibrated if better trajectory reproducing capability are to be achieved.
•We propose a recurrent neural network based microscopic car following model.•The model has a stronger performance in predict future traffic oscillations.•The model has a much stronger performance in ...capture oscillations caused by aggressive drivers.
This paper proposes a recurrent neural network based microscopic car following model that is able to accurately capture and predict traffic oscillation. Neural network models have gained increasing popularity in many fields and have been applied in modelling microscopic traffic flow dynamics due to their parameter-free and data-driven nature. We investigate the existing neural network based microscopic car following models, and find out that they are generally accurate in predicting traffic flow dynamics under normal traffic operational conditions. However, they do not maintain accuracy under conditions of traffic oscillation. To bridge this research gap, we first propose four neural network based models and evaluate their applicability to predict traffic oscillation. It is found that, with an appropriate structure and objective function, the recurrent neural network based model has the capability of perfectly re-establishing traffic oscillations and distinguish drivers characteristics. We further compare the proposed model with a classical car following model (Intelligent Driver Model). Based on our case study, the proposed model outperforms the classical car following model in predicting traffic oscillations with different driver characteristics.
•A vast literature review unveiled confusion and ambiguities in car-following calibration.•A methodology based on Pareto-efficiency is proposed to compare calibration settings.•Calibration settings ...which are to be avoided are outlined.•The sum of NRMSE or Theil’s U on spacing, speed and acceleration is recommended.•To promote and enable transparent and reproducible research codes and data are shared.
A comprehensive literature review reveals that there exist lots of ambiguities, confusion and even contradictions in setting a car-following calibration problem. In particular, confusion arises in the selection of measure of performances and goodness-of-fit functions. In this study, a methodology to compare and rank objective functions is thus proposed, which is based on Pareto-efficiency and on indifference curves. The methodology has been applied to all objective functions used in the field literature so far (and to new ones), in a vast set of calibration experiments. The experiments involved two car-following models and two adaptive cruise control (ACC) algorithms, and four different datasets, including both automated and human-driven vehicles trajectories. Since results are consistent among all the calibration experiments, a sound and robust guideline to calibrate car-following dynamics has been proposed. It includes recommendation about what calibration settings should be avoided and what are to be adopted. On the one hand, a general agreement on a sound calibration setting for car-following models is deemed necessary for comparing results from different studies which use different models and datasets. On the other hand, any new car-following model or objective function being developed in the future shall be compared with existing ones in a fair and impartial manner. For these reasons, and to promote and enable transparent and reproducible research, codes and data from this study are shared with the community.
•Adaptive Cruise Control (ACC) is an increasingly widespread technology in modern vehicles.•ACC can be considered a precursor of automated vehicles behavior on motorways.•There is still limited ...understanding of commercial ACC control logic.•Actual impact of ACC on motorway traffic dynamics is still widely unknown.•openACC aims to allow the scientific community to better understand ACC and its implications.
Adaptive Cruise Control (ACC) systems are becoming increasingly available as a standard equipment in modern commercial vehicles. Their penetration rate in the fleet is constantly increasing, as well as their use, especially under freeway conditions. At the same time, limited information is openly available on how these systems actually operate and their differences depending on the vehicle manufacturer or model. This represents an important gap because as the number of ACC vehicles on the road increases, traffic dynamics on freeways may change accordingly, and new collective phenomena, which are only marginally known at present, could emerge. Yet, as ACC systems are introduced as comfort options and their operation is entirely under the responsibility of the driver, vehicle manufacturers do not have explicit requirements to fulfill nor they have to provide any evidence about their performances. As a result, any safety implication connected to their interactions with other road users escapes any monitoring and opportunity of improvement.
This work presents a set of experimental car-following campaigns, providing an overview of the behavior of commercial ACC systems under different driving conditions. Furthermore, the suggestion of a unified data structure across the different tests facilitates comparison between the different campaigns, vehicles, systems and specifications. The complete data is published as an open-access database (OpenACC), available to the research community. As more test campaigns will be carried out, OpenACC will evolve accordingly.
The activity is performed in the framework of the openData policy of the European Commission Joint Research Centre with the objective to engage the whole scientific community towards a better understanding of the properties of ACC vehicles in view of anticipating their possible impacts on traffic flow and prevent possible problems connected to their widespread introduction. In this light, OpenACC, over time, also aims at becoming a reference point to study if and how the parameters of such systems need to be regulated, how homogeneously they behave, how new ACC car-following models should be designed for traffic microsimulation purposes and what are the key differences between ACC systems and human drivers.
•A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient ...algorithm was used to optimize the model.•The model outperformed traditional and recent data-driven car-following models.•The model demonstrated good capability of generalization.
This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.
•A car-following model based on sequence to sequence (seq2seq) learning is proposed.•The model makes multi-step predictions with the consideration of reaction delay.•The model outperforms other ...models in simulating trajectory and capturing heterogeneous driving behaviors.•The model well reproduces different levels of hysteresis phenomenon.•The model extended with spatial anticipation improves platoon simulation accuracy and traffic flow stability.
Car-following behavior modeling is of great importance for traffic simulation and analysis. Considering the multi-steps decision-making process in human driving, we propose a sequence to sequence (seq2seq) learning based car-following model incorporating not only memory effect but also reaction delay. Since the seq2seq architecture has the advantage of handling variable lengths of input and output sequences, in this paper, it is applied to car-following behavior modeling to memorize historical information and make multi-step predictions. We further compare the seq2seq model with a classical car-following model (IDM) and a deep learning car-following model (LSTM). The evaluation results indicate that the proposed model outperforms others for reproducing trajectory and capturing heterogeneous driving behaviors. Moreover, the platoon simulation demonstrates that the proposed model can well reproduce different levels of hysteresis phenomenon. The proposed model is further extended with spatial anticipation, which improves platoon simulation accuracy and traffic flow stability.
•A long short-term memory (LSTM) neural networks based car-following model is proposed.•Three characteristics of the asymmetric driving behavior are investigated using LSTM.•The LSTM model ...outperforms other models in capturing the asymmetric driving behavior.
Asymmetric driving behavior is a critical characteristic of human driving behaviors and has a significant impact on traffic flow. In consideration of the asymmetric driving behavior, this paper proposes a long short-term memory (LSTM) neural networks (NN) based car-following (CF) model to capture realistic traffic flow characteristics by incorporating the driving memory. The NGSIM data are used to calibrate and validate the proposed CF model. Meanwhile, three characteristics closely related to the asymmetric driving behavior are investigated: hysteresis, discrete driving, and intensity difference. The simulation results show the good performance of the proposed CF model on reproducing realistic traffic flow features. Moreover, to further demonstrate the superiority of the proposed CF model, two other CF models including recurrent neural network based CF model and asymmetric full velocity difference model, are compared with LSTM-NN model. The results reveal that LSTM-NN model can capture the asymmetric driving behavior well and outperforms other models.
PDF
