共查询到20条相似文献，搜索用时 15 毫秒
1.
After first extending Newell’s car-following model to incorporate time-dependent parameters, this paper describes the Dynamic Time Warping (DTW) algorithm and its application for calibrating this microscopic simulation model by synthesizing driver trajectory data. Using the unique capabilities of the DTW algorithm, this paper attempts to examine driver heterogeneity in car-following behavior, as well as the driver’s heterogeneous situation-dependent behavior within a trip, based on the calibrated time-varying response times and critical jam spacing. The standard DTW algorithm is enhanced to address a number of estimation challenges in this specific application, and a numerical experiment is presented with vehicle trajectory data extracted from the Next Generation Simulation (NGSIM) project for demonstration purposes. The DTW algorithm is shown to be a reasonable method for processing large vehicle trajectory datasets, but requires significant data reduction to produce reasonable results when working with high resolution vehicle trajectory data. Additionally, singularities present an interesting match solution set to potentially help identify changing driver behavior; however, they must be avoided to reduce analysis complexity. 相似文献
2.
Recent studies have provided that the vehicle trajectories generated by car-following models may not represent the real driving characteristics, thus leading to significant emission estimation errors. In this paper, two of the most widely used car-following models, Wiedemann and Fritzsche models, were selected and analyzed based on the massive field car-following trajectories in Beijing. A numerical simulation method was designed to generate the following car’s trajectories by using the field trajectories as the input. By comparing the simulated and the filed data, the representativeness of the simulated regime fractions and VSP distributions were evaluated. Then, the mechanism of car-following models was investigated from the aspects of regime determination and the acceleration rule in each regime. Further, the regime threshold parameters and acceleration model were optimized for emission estimations. This study found that the “Following” regime threshold of SDX and the maximum acceleration in “Free Driving” regime are critical parameters for Wiedemann model. The differences between the Wiedemann simulated VSP distribution and the field one can be reduced separately by applying the optimized SDX and maximum acceleration model individually. However, a much sharper reduction was observed by optimizing both parameters simultaneously, and the emission estimation errors were further reduced, which were less than 4% in the case studies. Fritzsche model generated more realistic VSP distributions and emissions, while the maximum accelerations could be further optimized for high speed conditions. 相似文献
3.
Janusz Gajda Piotr Piwowar Ryszard Sroka Marek Stencel Tadeusz Zeglen 《Transportation Research Part C: Emerging Technologies》2012,21(1):57-66
This article presents the findings of model and field research into narrow inductive loop used as vehicle wheels detector in normal traffic conditions. The efficiency of the solution was compared with that of strip, polymeric piezoelectric detectors. The findings confirmed that narrow inductive loops can be successfully applied as wheel detectors. 相似文献
4.
This paper does present a comprehensive concept for a robust and reliable truck detection involving solely one single presence sensor (e.g. an inductive loop, but also any other presence sensor) at a signalized traffic junction. Hereby, two operations modes are distinguished: (a) during green traffic light phases, and (b) a much challenging case, during red traffic light phases. First, it is shown how difficult the underlying classification task is, this mainly due to strongly overlapped classes, which cannot be easily separated by simple hyper-planes. Then, a novel soft radial basis cellular neural/nonlinear network (SRB-CNN) based concept is developed, validated and extensively benchmarked with a selection of the best representatives of the current related state-of-the-art classification concepts (namely the following: support vector machines with radial basis function, artificial neural network, naive Bayes, and decision trees). For benchmarking purposes, all selected competing classifiers do use the same features and the superiority of the novel CNN based classifier is thereby underscored, as it strongly outperforms the other ones. This novel SRB-CNN based concept does satisfactorily fulfill the hard industrial requirements regarding robustness, low-cost, high processing speed, low memory consumption, and the capability to be deployed in low cost embedded systems. 相似文献
5.
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. 相似文献
6.
Fuel consumption or pollutant emissions can be assessed by coupling a microscopic traffic flow model with an instantaneous emission model. Traffic models are usually calibrated using goodness of fit indicators related to the traffic behavior. Thus, this paper investigates how such a calibration influences the accuracy of fuel consumption and NOx and PM estimations. Two traffic models are investigated: Newell and Gipps. It appears that the Gipps model provides the closest simulated trajectories when compared to real ones. Interestingly, a reverse ranking is observed for fuel consumption, NOx and PM emissions. For both models, the emissions of single vehicles are very sensitive to the calibration. This is confirmed by a global sensitivity analysis of the Gipps model that shows that non-optimal parameters significantly increase the variance of the outputs. Fortunately, this is no longer the case when emissions are calculated for a group of many vehicles. Indeed, the mean errors for platoons are close to 10% for the Gipps model and always lower than 4% for the Newell model. Another interesting property is that optimal parameters for each vehicle can be replaced by the mean values with no discrepancy for the Newell model and low discrepancies for the Gipps model when calculating the different emission outputs. Finally, this study presents preliminary results that show that multi-objective calibration methods are certainly the best direction for future works on the Gipps model. Indeed, the accuracy of vehicle emissions can be highly improved with negligible counterparts on the traffic model accuracy. 相似文献
7.
Vehicle classification is an important traffic parameter for transportation planning and infrastructure management. Length-based vehicle classification from dual loop detectors is among the lowest cost technologies commonly used for collecting these data. Like many vehicle classification technologies, the dual loop approach works well in free flow traffic. Effective vehicle lengths are measured from the quotient of the detector dwell time and vehicle traversal time between the paired loops. This approach implicitly assumes that vehicle acceleration is negligible, but unfortunately at low speeds this assumption is invalid and length-based classification performance degrades in congestion.To addresses this problem, we seek a solution that relies strictly on the measured effective vehicle length and measured speed. We analytically evaluate the feasible range of true effective vehicle lengths that could underlie a given combination of measured effective vehicle length, measured speed, and unobserved acceleration at a dual loop detector. From this analysis we find that there are small uncertainty zones where the measured length class can differ from the true length class, depending on the unobserved acceleration. In other words, a given combination of measured speed and measured effective vehicle length falling in the uncertainty zones could arise from vehicles with different true length classes. Outside of the uncertainty zones, any error in the measured effective vehicle length due to acceleration will not lead to an error in the measured length class. Thus, by mapping these uncertainty zones, most vehicles can be accurately sorted to a single length class, while the few vehicles that fall within the uncertainty zones are assigned to two or more classes. We find that these uncertainty zones remain small down to about 10 mph and then grow exponentially as speeds drop further.Using empirical data from stop-and-go traffic at a well-tuned loop detector station the best conventional approach does surprisingly well; however, our new approach does even better, reducing the classification error rate due to acceleration by at least a factor of four relative to the best conventional method. Meanwhile, our approach still assigns over 98% of the vehicles to a single class. 相似文献
8.
Baibing Li 《Transportation Research Part B: Methodological》2012,46(1):85-99
Vehicle time headway is an important traffic parameter. It affects roadway safety, capacity, and level of service. Single inductive loop detectors are widely deployed in road networks, supplying a wealth of information on the current status of traffic flow. In this paper, we perform Bayesian analysis to online estimate average vehicle time headway using the data collected from a single inductive loop detector. We consider three different scenarios, i.e. light, congested, and disturbed traffic conditions, and have developed a set of unified recursive estimation equations that can be applied to all three scenarios. The computational overhead of updating the estimate is kept to a minimum. The developed recursive method provides an efficient way for the online monitoring of roadway safety and level of service. The method is illustrated using a simulation study and real traffic data. 相似文献
9.
Recent developments of information and communication technologies (ICT) have enabled vehicles to timely communicate with each other through wireless technologies, which will form future (intelligent) traffic systems (ITS) consisting of so-called connected vehicles. Cooperative driving with the connected vehicles is regarded as a promising driving pattern to significantly improve transportation efficiency and traffic safety. Nevertheless, unreliable vehicular communications also introduce packet loss and transmission delay when vehicular kinetic information or control commands are disseminated among vehicles, which brings more challenges in the system modeling and optimization. Currently, no data has been yet available for the calibration and validation of a model for ITS, and most research has been only conducted for a theoretical point of view. Along this line, this paper focuses on the (theoretical) development of a more general (microscopic) traffic model which enables the cooperative driving behavior via a so-called inter-vehicle communication (IVC). To this end, we design a consensus-based controller for the cooperative driving system (CDS) considering (intelligent) traffic flow that consists of many platoons moving together. More specifically, the IEEE 802.11p, the de facto vehicular networking standard required to support ITS applications, is selected as the IVC protocols of the CDS, in order to investigate how the vehicular communications affect the features of intelligent traffic flow. This study essentially explores the relationship between IVC and cooperative driving, which can be exploited as the reference for the CDS optimization and design. 相似文献
10.
In the field of Intelligent Transportation Systems (ITS), one of the most promising sub-functions is that of Advanced Driver Assistance Systems (ADAS). Development of an effective ADAS, and one that is able to gain drivers’ acceptance, hinges on the development of a human-like car-following model, and this is particularly important in order to ensure the driver is always ‘in the (vehicle control) loop’ and is able to recover control safely in any situation where the ADAS may release control. One of the most commonly used models of car-following is that of the Action Point (AP) (psychophysical) paradigm. However, while this is widely used in both micro-simulation models and behavioural research, the approach is not without its weaknesses. One of these, the potential redundancy of some of the identified APs, is examined in this paper and its basic structure validated using microscopic driving behaviour collected on thirteen subjects in Italy. Another weakness in practical application of the Action Point theory is the identification of appropriate thresholds, accounting for the perception, reaction and adjustment of relative speed (or spacing) from the leading vehicle. This article shows that this identification is problematic if the Action Point paradigm is analysed in a traditional way (car-following spirals), while it is easier if the phenomenon is analysed in terms of car-following ‘waves’, related to Time To Collision (TTC) or the inverse of TTC. Within this new interpretative framework, the observed action points can be observed to follow a characteristically linear pattern. The identification of the most significant variables to be taken into account, and their characterisation by means of a simple linear pattern, allows for the formulation of more efficient real-time applications, thereby contributing to the development and diffusion of emerging on-board technologies in the field of vehicle control and driver’s assistance. 相似文献
11.
Research on using high-resolution event-based data for traffic modeling and control is still at early stage. In this paper, we provide a comprehensive overview on what has been achieved and also think ahead on what can be achieved in the future. It is our opinion that using high-resolution event data, instead of conventional aggregate data, could bring significant improvements to current research and practices in traffic engineering. Event data records the times when a vehicle arrives at and departs from a vehicle detector. From that, individual vehicle’s on-detector-time and time gap between two consecutive vehicles can be derived. Such detailed information is of great importance for traffic modeling and control. As reviewed in this paper, current research has demonstrated that event data are extremely helpful in the fields of detector error diagnosis, vehicle classification, freeway travel time estimation, arterial performance measure, signal control optimization, traffic safety, traffic flow theory, and environmental studies. In addition, the cost of event data collection is low compared to other data collection techniques since event data can be directly collected from existing controller cabinet without any changes on the infrastructure, and can be continuously collected in 24/7 mode. This brings many research opportunities as suggested in the paper. 相似文献
12.
Because of many advantages, loop detectors are the most common practice for obtaining data to control intersections. However, they have some drawbacks, including the fact that multiple detectors are usually required to monitor a location. The current practice in many cities is to install four consecutive loop detectors per lane, or two at the stop bar and one as an advanced detector. In some cities, there are also departure detectors. All these configurations have some practical problems and do not produce accurate counts especially in shared lanes. In this paper, a new placement configuration for departure detectors is proposed and named the mid‐intersection detector (MID). In this configuration, departure detectors are moved back to the middle of the intersection in such a way that they can be activated by more than one movement at different times. In some cases, departure detectors lack equations for calculating turning movements, a problem solved by MIDs because each movement passes more detectors along its path (without increasing the number of loops), and therefore they can produce more accurate and reliable data for obtaining turning movement counts. Copyright © 2017 John Wiley & Sons, Ltd. 相似文献
13.
To connect microscopic driving behaviors with the macro-correspondence (i.e., the fundamental diagram), this study proposes a flexible traffic stream model, which is derived from a novel car-following model under steady-state conditions. Its four driving behavior-related parameters, i.e., reaction time, calmness parameter, speed- and spacing-related sensitivities, have an apparent effect in shaping the fundamental diagram. Its boundary conditions and homogenous case are also analyzed in detail and compared with other two models (i.e., Longitudinal Control Model and Intelligent Driver Model). Especially, these model formulations and properties under Lagrangian coordinates provide a new perspective to revisit the traffic flow and complement with those under Eulerian coordinate. One calibration methodology that incorporates the monkey algorithm with dynamic adaptation is employed to calibrate this model, based on real-field data from a wide range of locations. Results show that this model exhibits the well flexibility to fit these traffic data and performs better than other nine models. Finally, a concrete example of transportation application is designed, in which the impact of three critical parameters on vehicle trajectories and shock waves with three representations (i.e., respectively defined in x-t, n-t and x-n coordinates) is tested, and macro- and micro-solutions on shock waves well agree with each other. In summary, this traffic stream model with the advantages of flexibility and efficiency has the good potential in level of service analysis and transportation planning. 相似文献
14.
The categorization of the type of vehicles on a road network is typically achieved using external sensors, like weight sensors, or from images captured by surveillance cameras. In this paper, we leverage the nowadays widespread adoption of Global Positioning System (GPS) trackers and investigate the use of sequences of GPS points to recognize the type of vehicle producing them (namely, small-duty, medium-duty and heavy-duty vehicles). The few works which already exploited GPS data for vehicle classification rely on hand-crafted features and traditional machine learning algorithms like Support Vector Machines. In this work, we study how performance can be improved by deploying deep learning methods, which are recently achieving state of the art results in the classification of signals from various domains. In particular, we propose an approach based on Long Short-Term Memory (LSTM) recurrent neural networks that are able to learn effective hierarchical and stateful representations for temporal sequences. We provide several insights on what the network learns when trained with GPS data and contextual information, and report experiments on a very large dataset of GPS tracks, where we show how the proposed model significantly improves upon state-of-the-art results. 相似文献
15.
Car-following models are always of great interest of traffic engineers and researchers. In the age of mass data, this paper proposes a nonparametric car-following model driven by field data. Different from most of the existing car-following models, neither driver’s behaviour parameters nor fundamental diagrams are assumed in the data-driven model. The model is proposed based on the simple k-nearest neighbour, which outputs the average of the most similar cases, i.e., the most likely driving behaviour under the current circumstance. The inputs and outputs are selected, and the determination of the only parameter k is introduced. Three simulation scenarios are conducted to test the model. The first scenario is to simulate platoons following real leaders, where traffic waves with constant speed and the detailed trajectories are observed to be consistent with the empirical data. Driver’s rubbernecking behaviour and driving errors are simulated in the second and third scenarios, respectively. The time–space diagrams of the simulated trajectories are presented and explicitly analysed. It is demonstrated that the model is able to well replicate periodic traffic oscillations from the precursor stage to the decay stage. Without making any assumption, the fundamental diagrams for the simulated scenario coincide with the empirical fundamental diagrams. These all validate that the model can well reproduce the traffic characteristics contained by the field data. The nonparametric car-following model exhibits traffic dynamics in a simple and parsimonious manner. 相似文献
16.
Truck flow patterns are known to vary by season and time-of-day, and to have important implications for freight modeling, highway infrastructure design and operation, and energy and environmental impacts. However, such variations cannot be captured by current truck data sources such as surveys or point detectors. To facilitate development of detailed truck flow pattern data, this paper describes a new truck tracking algorithm that was developed to estimate path flows of trucks by adopting a linear data fusion method utilizing weigh-in-motion (WIM) and inductive loop point detectors. A Selective Weighted Bayesian Model (SWBM) was developed to match individual vehicles between two detector locations using truck physical attributes and inductive waveform signatures. Key feature variables were identified and weighted via Bayesian modeling to improve vehicle matching performance. Data for model development were collected from two WIM sites spanning 26 miles in California where only 11 percent of trucks observed at the downstream site traversed the whole corridor. The tracking model showed 81 percent of correct matching rate to the trucks declared as through trucks from the algorithm. This high accuracy showed that the tracking model is capable of not only correctly matching through vehicles but also successfully filtering out non-through vehicles on this relatively long distance corridor. In addition, the results showed that a Bayesian approach with full integration of two complementary detector data types could successfully track trucks over long distances by minimizing the impacts of measurement variations or errors from the detection systems employed in the tracking process. In a separate case study, the algorithm was implemented over an even longer 65-mile freeway section and demonstrated that the proposed algorithm is capable of providing valuable insights into truck travel patterns and industrial affiliation to yield a comprehensive truck activity data source. 相似文献
17.
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 NOX 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 NOX of each vehicle under the above two cases. 相似文献
18.
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. 相似文献
19.
This paper derives a five-parameter social force car-following model that converges to the kinematic wave model with triangular fundamental diagram. Analytical solutions for vehicle trajectories are found for the lead-vehicle problem, which exhibit clockwise and counter-clockwise hysteresis depending on the model’s parameters and the lead vehicle trajectory. When coupled with a stochastic vehicle dynamics module, the model is able to reproduce periods and amplitudes of stop-and-go waves, as reported in the field. The model’s stability conditions are analysed and its trajectories are compared to real data. 相似文献
20.
Vehicle longitudinal control systems such as (commercially available) autonomous Adaptive Cruise Control (ACC) and its more sophisticated variant Cooperative ACC (CACC) could potentially have significant impacts on traffic flow. Accurate models of the dynamic responses of both of these systems are needed to produce realistic predictions of their effects on highway capacity and traffic flow dynamics. This paper describes the development of models of both ACC and CACC control systems that are based on real experimental data. To this end, four production vehicles were equipped with a commercial ACC system and a newly developed CACC controller. The Intelligent Driver Model (IDM) that has been widely used for ACC car-following modeling was also implemented on the production vehicles. These controllers were tested in different traffic situations in order to measure the actual responses of the vehicles. Test results indicate that: (1) the IDM controller when implemented in our experimental test vehicles does not perceptibly follow the speed changes of the preceding vehicle; (2) strings of consecutive ACC vehicles are unstable, amplifying the speed variations of preceding vehicles; and (3) strings of consecutive CACC vehicles overcome these limitations, providing smooth and stable car following responses. Simple but accurate models of the ACC and CACC vehicle following dynamics were derived from the actual measured responses of the vehicles and applied to simulations of some simple multi-vehicle car following scenarios. 相似文献