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Road geometry estimation using vehicle trails: a linear mixed model approach 使用车辆轨迹的道路几何估计:一种线性混合模型方法
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2023-01-02 DOI: 10.1080/15472450.2021.1974858
Yi-Chen Zhang

In this paper, we propose an estimation algorithm for the shape of the road using trails of leading vehicles via a linear mixed model (LMM) approach. A vehicle trail is essentially the motion trajectory of a vehicle where samples of the historical path are longitudinally collected from the same vehicle at different points in time. Such measurements can be obtained from the fusion system for single or multiple sensor tracking. The aim is to use trails of leading vehicles to depict the road geometry in highway scenarios. The proposed estimation method uses a polynomial-based road model and is built from a LMM, which is one of the most widely used statistical techniques. To avoid the overload of memory usage from trail samples, trail data are first processed by the newly developed compression and chopping mechanisms before being imported into the LMM framework. Moreover, the profile likelihood function is used to alleviate the computational burden and reduce the number of iterations in the Newton-Raphson algorithm in the LMM. Finally, the proposed method is then evaluated by two publicly available next generation simulation (NGSIM) datasets. The large-scale simulation results show that the road shape estimated by the proposed method has the root mean square error (RMSE) less than 0.5 meters in average for all ranges compared with the ground truth road shape. This suggests that our method provides an accurate road shape estimation and captures the shape of the road successfully.

在本文中,我们提出了一种通过线性混合模型(LMM)方法利用领先车辆的轨迹来估计道路形状的算法。车辆轨迹本质上是车辆的运动轨迹,其中历史路径的样本是在不同时间点从同一车辆纵向收集的。这样的测量可以从用于单个或多个传感器跟踪的融合系统获得。其目的是利用领先车辆的轨迹来描绘高速公路场景中的道路几何形状。所提出的估计方法使用基于多项式的道路模型,并由LMM建立,LMM是使用最广泛的统计技术之一。为了避免跟踪样本的内存使用过载,在将跟踪数据导入LMM框架之前,首先通过新开发的压缩和斩波机制对其进行处理。此外,在LMM中的Newton-Raphson算法中,使用轮廓似然函数来减轻计算负担并减少迭代次数。最后,通过两个公开的下一代仿真(NGSIM)数据集对所提出的方法进行了评估。大规模仿真结果表明,与实际道路形状相比,该方法估计的道路形状在所有范围内的均方根误差(RMSE)平均小于0.5米。这表明我们的方法提供了准确的道路形状估计,并成功地捕捉到了道路的形状。
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引用次数: 0
A hybrid autoregressive fractionally integrated moving average and nonlinear autoregressive neural network model for short-term traffic flow prediction 一种混合自回归分数积分移动平均与非线性自回归神经网络的短期交通流预测模型
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2023-01-02 DOI: 10.1080/15472450.2021.1977639
Xuecai Xu , Xiaofei Jin , Daiquan Xiao , Changxi Ma , S.C. Wong

Intelligent traffic control and guidance system is an effective way to solve urban traffic congestion, improve road capacity and guarantee drivers' travel safety, while short-term traffic flow prediction is the core of intelligent traffic control and guidance system. To investigate the long-term memory and the dynamic feature of short-time traffic flow time series, a hybrid model was proposed by integrating autoregressive fractionally integrated moving average (ARFIMA) model and nonlinear autoregressive (NAR) neural network model to predict short-time traffic flow, in which ARFIMA model can address the long-term memory of linear component and NAR neural network can accommodate the dynamic feature of nonlinear residual component. First, the ARFIMA model was employed to predict the linear component of traffic flow, and the results were compared with those of autoregressive integrated moving average (ARIMA) model. Next, the NAR neural network model was adopted to forecast the nonlinear residual components, and the weighted results were considered as the predicted flow of the hybrid model. The proposed hybrid model was validated by using the cross-sectional traffic flow data in California freeways obtained from the open-access PeMS database. The results showed that the ARFIMA model considering the long-term memory can effectively predict the short-term traffic flow, and the prediction accuracy of the hybrid model is better than that of the singular models. The findings provide an alternative for the short-term traffic flow prediction with lower error and higher accuracy.

智能交通控制与引导系统是解决城市交通拥堵、提高道路通行能力、保障驾驶员出行安全的有效途径,而短时交通流量预测是智能交通控制和引导系统的核心。为了研究短时交通流时间序列的长期记忆和动态特征,将自回归分数积分移动平均(ARFIMA)模型和非线性自回归(NAR)神经网络模型相结合,提出了一种预测短时交通流的混合模型,其中ARFIMA模型可以解决线性分量的长期记忆问题,NAR神经网络可以适应非线性残差分量的动态特征。首先,采用ARFIMA模型对交通流的线性分量进行预测,并与自回归综合移动平均(ARIMA)模型的结果进行了比较。其次,采用NAR神经网络模型对非线性残差分量进行预测,并将加权结果作为混合模型的预测流量。通过使用从开放访问的PeMS数据库中获得的加州高速公路的横断面交通流量数据,验证了所提出的混合模型。结果表明,考虑长期记忆的ARFIMA模型能够有效地预测短期交通流量,混合模型的预测精度优于奇异模型。研究结果为短期交通流量预测提供了一种误差较小、精度较高的方法。
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引用次数: 13
Extending the adaptive time gap car-following model to enhance local and string stability for adaptive cruise control systems 扩展自适应时间间隙汽车跟随模型以提高自适应巡航控制系统的局部和串稳定性
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2023-01-02 DOI: 10.1080/15472450.2021.1983810
Parthib Khound , Peter Will , Antoine Tordeux , Frank Gronwald

In this paper, we extend the nonlinear adaptive time gap car-following model to enhance the local and string stability for adaptive cruise control systems considering a time-lag in the lower level vehicle dynamics and a sensor time-delay. Both over-damped local and string stability analyses are performed mathematically and examined by simulation. The over-damped string stability criterion fulfills all the Lp stability norms, where p[1,]. Here we consider a time-lag operating in the lower level of the longitudinal control system’s architecture, a sensor time-delay, and heterogeneity in the vehicle dynamics of the platoon. The adaptive time gap model without these attributes is intrinsically stable. However, it turns out that the introduction of a lag, a delay, or heterogeneity in the lower vehicular level reduces the performance in terms of stability, yielding unsafe damped oscillating collective behaviors. Henceforth we extend the model to enhance the stability by transforming the model to a homogeneous structure, without changing the fundamental dynamics. The results show that the extended model satisfies over-damped criteria for both local and string stability, considering actuator time-lag, sensor time-delay, and heterogeneity in the lower level vehicle dynamics. Such features are expected for automated driving systems.

在本文中,我们扩展了非线性自适应时隙跟车模型,以增强自适应巡航控制系统的局部稳定性和串稳定性,同时考虑了较低级别车辆动力学中的时滞和传感器时滞。过阻尼局部和管柱稳定性分析均采用数学方法进行,并通过仿真进行检验。过阻尼弦稳定性准则满足所有Lp稳定性范数,其中p∈[1,∞]。在这里,我们考虑了纵向控制系统架构的较低级别中运行的时滞、传感器时滞和车队车辆动力学的异质性。没有这些属性的自适应时间间隔模型本质上是稳定的。然而,事实证明,在较低的车辆水平中引入滞后、延迟或异质性会降低稳定性方面的性能,从而产生不安全的阻尼振荡集体行为。此后,我们在不改变基本动力学的情况下,通过将模型转换为均质结构来扩展模型以增强稳定性。结果表明,考虑到执行器时滞、传感器时滞和低水平车辆动力学的异质性,扩展模型满足局部和串稳定性的过阻尼准则。这样的特征被期望用于自动驾驶系统。
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引用次数: 9
Evaluating connected vehicle-based weather responsive management strategies using weather-sensitive microscopic simulation 使用天气敏感微观模拟评估基于联网车辆的天气响应管理策略
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2023-01-02 DOI: 10.1080/15472450.2021.1990052
Qinhua Jiang , Dong Nian , Yi Guo , Mohamed Ahmed , Guangchuan Yang , Jiaqi Ma

The purpose of this study is to perform analysis, modeling, and simulation (AMS) to investigate the effectiveness of connected vehicle (CV)-based Weather Responsive Management Strategies (WRMS) to address safety concerns on freeway corridors under adverse weather conditions. This study investigates three CV-based WRMS applications: Forward Collision Warning (FCW), Early Lane Change (ELC) advisory, and Variable Speed Limit (VSL), designs operational alternatives for WRMS using CV data, and develops an AMS tool using a weather-sensitive microscopic traffic simulator to understand the effectiveness of the three WRMS under different scenarios. Various CV market penetration rates (MPR), weather conditions, and WRMS algorithm settings are tested in this study. The case study is based on a real-world freeway corridor, a segment of the I–80 Connected Vehicle Testbed in Wyoming. The simulation results show the effectiveness of selected WRMS applications and provide operational insights that state and local transportation agencies may use in future strategic planning and operations of their weather-responsive programs.

本研究的目的是进行分析、建模和仿真(AMS),以研究基于联网车辆(CV)的天气响应管理策略(WRMS)在恶劣天气条件下解决高速公路走廊安全问题的有效性。本研究调查了三种基于CV的WRMS应用:前向碰撞警告(FCW)、早期换道(ELC)咨询和可变限速(VSL),使用CV数据设计了WRMS的操作替代方案,并使用天气敏感的微观交通模拟器开发了AMS工具,以了解三种WRMS在不同场景下的有效性。本研究测试了各种CV市场渗透率(MPR)、天气条件和WRMS算法设置。该案例研究基于怀俄明州I-80联网车辆试验台的一段真实世界的高速公路走廊。模拟结果显示了选定的WRMS应用程序的有效性,并提供了运营见解,供州和地方交通机构在其天气响应计划的未来战略规划和运营中使用。
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引用次数: 2
Modeling vehicle collision instincts over road midblock using deep learning 利用深度学习对道路中间路段的车辆碰撞本能进行建模
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2023-01-01 DOI: 10.1080/15472450.2021.2014833
Shubham Patil , Narayana Raju , Shriniwas S. Arkatkar , Said Easa

The present research aims to understand the safety over the midblock road sections and proposes a safety framework using the conventional Time to Collision (TTC) measure. In the present work, the safety framework underlines a supporting structure connecting the actions of the surrounding vehicles and assesses the collisions changes for a given subject vehicle. The Framework principally checks the likelihood of lateral overlap and the time gap between the subject vehicle and its surrounding vehicles. Later, for the trajectory data development, an automated trajectory data development tool is built with the help of image processing for generating the trajectory data from the study sections. In supporting the developed safety framework, the lateral movement of the vehicles is modeled precisely with the help of deep learning. Further, the conceptualized safety framework is tested with the developed trajectory data sets over the study sections. From the results, it is observed that, in mixed traffic, the collision points are over the entire geometry of the study section. In the case of homogeneous traffic, the collision instincts are clustered toward the median lanes. With the advancement of technology, trajectory data development can be a real-time exercise, and the safety framework can be implemented. By applying the study methodology, the critical spots over the road network can be flagged for better treatment and improve safety over the sections.

本研究旨在了解中间路段的安全性,并使用传统的碰撞时间(TTC)措施提出了一个安全框架。在目前的工作中,安全框架强调了连接周围车辆动作的支撑结构,并评估了给定主题车辆的碰撞变化。该框架主要检查横向重叠的可能性以及主题车辆与其周围车辆之间的时间间隔。随后,对于轨迹数据开发,在图像处理的帮助下构建了一个自动轨迹数据开发工具,用于从研究部分生成轨迹数据。在支持开发的安全框架时,借助深度学习对车辆的横向运动进行了精确建模。此外,概念化的安全框架在研究部分用开发的轨迹数据集进行了测试。从结果中可以观察到,在混合交通中,碰撞点位于研究路段的整个几何形状上。在同质交通的情况下,碰撞本能集中在中央分隔带车道上。随着技术的进步,轨迹数据开发可以是一种实时的练习,并且可以实现安全框架。通过应用研究方法,可以标记道路网络上的关键点,以便更好地处理并提高路段的安全性。
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引用次数: 6
A human-centric machine learning based personalized route choice prediction in navigation systems 导航系统中基于以人为中心机器学习的个性化路线选择预测
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2023-01-01 DOI: 10.1080/15472450.2022.2069499
Bingrong Sun , Lin Gong , Jisup Shim , Kitae Jang , B. Brian Park , Hongning Wang , Jia Hu

Real-world route navigation data indicate that nontrivial portion of drivers do not prefer the system-recommended best routes. Current navigation systems have simplified assumptions about drivers’ route choice preferences and do not adequately accommodate drivers’ heterogeneous route choice preferences, mainly because of: (i) difficulty in acquiring exogenous criteria (e.g., sociodemographic information) that are typically used to differentiate drivers’ preferences in behavioral modeling; and (ii) difficulty in capturing preference of individuals due to limited preference data at the individual level. To address these, this paper introduced a human-centric machine learning technique named Multi-Task Linear Classification Model Adaption (MT-LinAdapt). It can capture drivers’ common aspects of route choice preferences and yet adapts to each driver’s own preference. In addition, any evolvement of individual drivers’ preferences can be simultaneously integrated to update the common preference for further individual drivers’ preference adaptation. This paper evaluated MT-LinAdapt against two state-of-the-art route recommendation strategies including an aggregate-level and an individual-level data-based strategies, which are categorized based on the data used for modeling. With a real-world dataset containing 30,837 drivers’ navigation usage data in Daegu City, South Korea, MT-LinAdapt was compared to existing strategies for its performance at different levels of data availability, and showed at least the same performance with existing strategies when minimum preference data is available and achieves up to 7% higher prediction accuracy as more data becomes available. Higher prediction accuracies are expected to bring better user satisfaction and compliance rates which can further help with transportation system control and management strategies.

真实世界的路线导航数据表明,相当一部分驾驶员不喜欢系统推荐的最佳路线。当前的导航系统简化了对驾驶员路线选择偏好的假设,并且不能充分适应驾驶员的异质路线选择偏好,主要是因为:(i)难以获得通常用于在行为建模中区分驾驶员偏好的外生标准(例如,社会人口统计信息);以及(ii)由于个人层面的偏好数据有限,难以捕捉个人的偏好。为了解决这些问题,本文引入了一种以人为中心的机器学习技术,称为多任务线性分类模型自适应(MT-LinAdapt)。它可以捕捉驾驶员的路线选择偏好的共同方面,并适应每个驾驶员自己的偏好。此外,可以同时整合个体驾驶员偏好的任何演变,以更新共同偏好,从而进一步适应个体驾驶员的偏好。本文针对两种最先进的路线推荐策略对MT-LinAdapt进行了评估,这两种策略包括基于聚合级别和基于单个级别数据的策略,这两个策略是根据用于建模的数据进行分类的。使用包含韩国大邱市30837名驾驶员导航使用数据的真实世界数据集,将MT LinAdapt与现有策略在不同数据可用性水平下的性能进行了比较,并且当最小偏好数据可用时显示出与现有策略至少相同的性能,并且随着更多数据可用,实现了高达7%的预测精度。更高的预测精度有望带来更好的用户满意度和合规率,这将进一步有助于交通系统的控制和管理策略。
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引用次数: 0
Massively parallelizable approach for evaluating signalized arterial performance using probe-based data 利用基于探针的数据评估动脉信号表现的大规模并行方法
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2023-01-01 DOI: 10.1080/15472450.2022.2069497
Subhadipto Poddar , Pranamesh Chakraborty , Anuj Sharma , Skylar Knickerbocker , Neal Hawkins

Effective performance of arterial corridors is essential to community safety and vitality. Considering the dynamic nature of traffic demand, efficient management of these corridors require frequent updating of the traffic signal timings through various strategies. Agency resources for these activities are commonly scarce and are primarily by public complaints.

This study provides a workflow using probe-based data to measure and compare different segments on arterial corridors in terms of the traffic signal performance measures that can capture travel time dynamics across signalized intersections. The proposed methodology identifies a group of dynamic days followed by evaluation of travel rate based upon remaining non-dynamic days. Dynamic days represent the variability of traffic on a segment. Consequently, a corridor having high number of dynamic segments along with poor performance during normal days would be a candidate for adaptive control. Further, to handle the large-scale data source collected from city-wide or statewide traffic signals, the study adopts parallel computation-based strategy using MapReduce technique. A case study was conducted on 11 corridors within Des Moines, Iowa, to demonstrate the efficacy of the proposed approach, which identified two arterial corridors, Merle Hay Road and University Avenue, to be suitable for adaptive traffic signal control.

干线走廊的有效运行对社区的安全和活力至关重要。考虑到交通需求的动态性质,这些走廊的有效管理需要通过各种策略频繁更新交通信号时间。用于这些活动的机构资源通常很少,主要是通过公众投诉。本研究提供了一个使用基于探针的数据来测量和比较干线走廊上不同路段的交通信号性能指标的工作流程,该指标可以捕捉信号交叉口的行程时间动态。拟议方法确定了一组动态天数,然后根据剩余的非动态天数评估旅行费率。动态天数表示路段上交通量的可变性。因此,在正常日子里,具有大量动态路段以及较差性能的走廊将是自适应控制的候选者。此外,为了处理从全市或全州交通信号采集的大规模数据源,本研究采用了基于MapReduce技术的并行计算策略。对爱荷华州得梅因市的11条走廊进行了案例研究,以证明所提出方法的有效性,该方法确定了两条干线走廊,Merle Hay路和大学大道,适用于自适应交通信号控制。
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引用次数: 4
Ad-hoc platoon formation and dissolution strategies for multi-lane highways 多车道高速公路的自组织组队与解散策略
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2023-01-01 DOI: 10.1080/15472450.2021.1993212
Santa Maiti , Stephan Winter , Lars Kulik , Sudeshna Sarkar

Vehicle platooning, a coordinated and controlled vehicle-following strategy, addresses the issue of high fuel consumption of heavy-duty vehicles. This research considers platoons that are formed on the fly in an ad-hoc manner. We investigate two types of ad-hoc platoon formation and corresponding platoon dissolution strategies. The first approach forms a platoon greedily without considering the order of destinations of the platoon members. This approach enables a quick formation but imposes an overhead of platoon rebuilding, and consequently, additional fuel cost when platoon members leave. An alternative approach forms a platoon in the order of the destinations of its platoon members. This ordered approach incurs a comparatively higher formation time due to vehicles’ reorganization but does not lead to further overhead of platoon rebuilding. We investigate whether these ad-hoc formation and dissolution strategies can preserve the original fuel benefit of platooning, and which of the two ad-hoc formation strategies are more fuel-efficient. The experimental results show that the greedy formation of the platoon is more fuel-efficient for a multi-lane highway. The proposed prediction model provides 90.4% prediction accuracy for the greedy approach and 82.2% prediction accuracy for the ordered approach on average, for platoon sizes from two to six vehicles.

车辆排队是一种协调和控制的车辆跟随策略,解决了重型车辆的高油耗问题。这项研究考虑了以特定方式在飞行中形成的排。我们研究了两种类型的特设排的形成和相应的排解散策略。第一种方法贪婪地形成一个排,而不考虑排成员的目的地顺序。这种方法实现了快速编队,但增加了排重建的开销,从而在排成员离开时增加了燃料成本。另一种方法是按照排成员的目的地顺序形成一个排。由于车辆的重组,这种有序的方法产生了相对较高的编队时间,但不会导致车队重建的进一步开销。我们研究了这些特别编队和解散策略是否可以保留排成队的原始燃料效益,以及两种特别编队策略中哪一种更省油。实验结果表明,对于多车道公路,贪婪排的形式更省油。对于2至6辆车的车队规模,所提出的预测模型为贪婪方法提供了90.4%的预测精度,为有序方法提供了82.2%的预测精度。
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引用次数: 8
Arterial corridor travel time prediction under non-recurring conditions 非循环条件下动脉通道通行时间预测
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2023-01-01 DOI: 10.1080/15472450.2021.2023017
Sajjad Shafiei , Eileen Wang , Hanna Grzybowska , Chen Cai

Accurate travel time prediction for major freeways and corridors is crucial but challenging when road incidents happen. Data-driven models require a large set of historical data to estimate the spatial and temporal correlations between road incidents and traffic dynamics. More often than not, the amount of historical data under non-recurring conditions is limited when it comes to training the models. This paper investigates the application of data-driven models on an enriched database with simulated travel times. A well-calibrated traffic simulation is used to capture the artificial incident’s impact on a major urban corridor in Sydney, Australia. This procedure is repeated for multiple created incidents, resulting in a synthetic dataset validated by the available actual historical data. Several machine learning models, such as Regression Tree, Support Vector Regression, Extreme Gradient Boosting, and Recurrent Neural Networks are trained and tested based on the simulated travel time and incident information. As a baseline model for comparison, the measured travel time at the prediction time is considered equal to multi-step ahead travel time. Based on the results, the data-driven models developed with the simulated data outperformed the baseline, indicating that our approach can be effectively employed in the travel time prediction.

当道路事故发生时,准确预测主要高速公路和走廊的行驶时间至关重要,但具有挑战性。数据驱动的模型需要大量的历史数据来估计道路事故和交通动态之间的空间和时间相关性。通常情况下,在训练模型时,非重复性条件下的历史数据量是有限的。本文研究了数据驱动模型在具有模拟旅行时间的丰富数据库中的应用。一个经过良好校准的交通模拟被用来捕捉人工事件对澳大利亚悉尼一条主要城市走廊的影响。对多个创建的事件重复此过程,生成由可用实际历史数据验证的合成数据集。基于模拟的旅行时间和事件信息,训练和测试了几种机器学习模型,如回归树、支持向量回归、极限梯度提升和递归神经网络。作为比较的基线模型,预测时间的测量行程时间被认为等于多步前进行程时间。基于这些结果,用模拟数据开发的数据驱动模型优于基线,表明我们的方法可以有效地用于旅行时间预测。
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引用次数: 0
New fuel consumption model considering vehicular speed, acceleration, and jerk 新的燃油消耗模型考虑车辆的速度,加速度,和抽搐
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2023-01-01 DOI: 10.1080/15472450.2021.2000406
Licheng Zhang , Kun Peng , Xiangmo Zhao , Asad J. Khattak

A novel computational model for the volatile state was developed to improve eco-driving in intelligent transportation systems (ITS). First, the volatile state was divided into eight types using vehicle acceleration and jerk as delineating criteria. Data analysis showed that each jerk type had a different proportion and contribution level to fuel consumption. Next, the model was created by considering eight instantaneous driving decisions as represented by vehicle speed, acceleration, and jerk. The model input included vehicle speed multiplied by acceleration, with jerk as a classifier. The model was calibrated using quadratic polynomial fitting, and validated using another portion of the data. Finally, predictions were compared with the widely used Vehicle Specific Power (VSP) model and the Virginia Tech Microscopic (VT-Micro) model to evaluate model performance. The new model thoroughly captured the measured fuel consumption and provided more accurate predictions in new routes than the above-mentioned models. The mean absolute percentage error value of the new model was ∼4.9% and 3.2% lower than those of the VSP and VT-Micro models, respectively. The determinant coefficient value was up to 95.8%, which was ∼4.6% and 8.5% higher than those of the VSP and VT-Micro models, respectively.

为了改善智能交通系统(ITS)中的生态驾驶,开发了一种新的挥发性计算模型。首先,使用车辆加速度和急动度作为描述标准,将挥发性状态分为八种类型。数据分析表明,每种颠簸类型对燃油消耗的比例和贡献程度不同。接下来,通过考虑八个瞬时驾驶决策来创建该模型,这些决策由车辆速度、加速度和急动表示。模型输入包括车辆速度乘以加速度,并将急动作为分类器。该模型使用二次多项式拟合进行校准,并使用另一部分数据进行验证。最后,将预测与广泛使用的车辆比功率(VSP)模型和弗吉尼亚理工大学微观(VT-Micro)模型进行比较,以评估模型性能。与上述模型相比,新模型完全捕捉到了测量到的油耗,并在新路线上提供了更准确的预测。新模型的平均绝对百分比误差值分别比VSP和VT-Micro模型低约4.9%和3.2%。决定性系数值高达95.8%,分别比VSP和VT-Micro模型高出约4.6%和8.5%。
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引用次数: 8
期刊
Journal of Intelligent Transportation Systems
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