Uncertainty quantification is essential for building reliable traffic management and control applications, with current methods broadly classified into distribution-based methods that rely on probability distribution function and nondistribution-based methods that depend on other theoretical instruments. Among nondistribution-based methods, interval Type-II fuzzy inference system-based methods are promising in modeling uncertainty in traffic condition data. However, these methods generally lack a mechanism for parameter adaptation to accommodate evolving traffic patterns. Therefore, in this paper, an interval Type-II fuzzy inference system-based traffic flow uncertainty quantification method is introduced that can dynamically adjust model parameters via the gradient descent algorithm. First, an interval Type-II fuzzy inference system is proposed to model traffic flow uncertainty structure. Subsequently, built upon this structure, an uncertainty quantification method is developed that can produce prediction intervals using streaming traffic flow data. In the empirical study, real-world highway traffic flow data are used to validate the proposed method. Experimental results reveal that the proposed method maintains workable uncertainty prediction performance with respect to different time of day and traffic flow levels. In addition, the proposed method demonstrates a better performance compared with its Type-I counterpart. Discussions are provided on uncertainty quantification, its characteristics, and applications in transportation systems.

Time interval plays a pivotal role for short-term traffic flow prediction since time interval determines traffic data characteristics and, hence, the performances of such prediction methods. In this sense, the effects of time intervals on prediction performance should be investigated systematically when developing short-term traffic flow prediction methods. However, the investigation into time interval effects on short-term traffic flow prediction methods is still limited in the literature, hindering the incorporation of such prediction methods into real-world transportation applications. To this end, this paper proposes an online adaptive fuzzy inference system (FIS)-based short-term traffic flow prediction method with recursive parameter adjustment and designs an experiment to analyze the effect of time intervals on the prediction performance of this proposed method. Using real-world traffic flow data collected from highway systems of the United Kingdom and the United States, the proposed FIS-based method was calibrated through sensitivity analysis, and the effects of time intervals on the proposed FIS-based method were investigated through aggregated performance, group performance, disaggregated prediction performance, and comparative performance. Empirical results showed that the prediction performance of the proposed FIS-based method increases sharply for time intervals from 1- to 10-min, remains stable for time intervals between 10- and 20-min, and decreases slightly for time intervals from 20- to 30-min. This finding delineates the applicable range of time intervals for the proposed FIS-based method, which will be helpful for integrating the proposed prediction method into real-world proactive transportation management systems based on these time intervals. Future studies are recommended to advance the investigation into the effects of time intervals in transportation-related studies.

Uncertainty quantification is important for reliability-oriented transportation operations, and it becomes increasingly evident to complement traffic level forecasting with uncertainty quantification, usually in terms of prediction interval. To this end, the distribution-based approach usually relies on a pre-assumed parametric probability density function, which cannot meet the requirements raised by time-varying or streamlined traffic condition patterns. In addition, the second-order seasonality is pronounced for seasonal traffic condition patterns. Therefore, an online seasonal kernel density estimation approach is proposed by combining the second-order seasonality and nonparametric density estimation in a streamlined fashion. For this approach, four online second-order seasonality adjustment factors are developed, and then four seasonal kernel density estimation models are proposed. Real world data are used to validate the four models, and empirical results show that the proposed models consistently outperform the conventional traffic condition uncertainty quantification models. Future studies are recommended for advancing the investigation of traffic uncertainty quantification.

Traffic flow uncertainty prediction can capture the fluctuations in traffic flow observations and hence serve as an important technology in developing proactive and reliable intelligent transportation applications. The modeling method of traffic flow uncertainty can be roughly categorized into distribution-based approaches and nondistribution-based approaches. Compared with distribution-based approach that uses probability density function to capture traffic flow uncertainty, non-distribution-based approach relies heavily on other theoretical instruments. Considering fuzzy inference system as one of such instruments in uncertainty modeling, this paper proposed an online adaptive method for short-term traffic flow uncertainty prediction based on Takagi-SugenoKang system. The proposed method consists of four steps, i.e., level prediction, time window construction, fuzzy inference system parameter identification, and prediction interval calculation. Empirical study was carried out using real world traffic flow data collected from highway systems in the United Kingdom and the United States. Empirical results show that the proposed method can adaptively fine-tune its structure in real time to meet the requirement of time-varying traffic flow patterns and generate workable prediction intervals. This indicates that the proposed method hold potential to be applied in proactive transportation applications. Further research is recommended to enhance the exploration of predicting traffic flow uncertainty.

In urban roundabout management, left-turning vehicles are commonly regulated using a two-stage control strategy, requiring them to stop twice. This method is suitable for scenarios with lower left-turn traffic volumes, but at higher volumes, issues such as increased fuel consumption and limited effectiveness arise. To address these challenges, this paper proposes a hybrid control method to optimize left-turn flow, reduce energy consumption, and enhance adaptability. The roundabout's storage area capacity is determined based on intersection geometry to restrict left-turn entry and prevent deadlock. A counter-flow left-turn phase is introduced to minimize queuing and fuel consumption, allowing left-turning vehicles to bypass circulation and exit directly. Sensitivity analysis shows that the hybrid method significantly reduces fuel consumption when left-turn volumes exceed 160 veh/h, outperforming the traditional two-stage model.

Accurate short-term traffic forecasting is a prerequisite for establishing intelligent transportation systems. In this paper, a new adaptive traffic flow sequence framework is processed. For any given dataset, the framework divides the original sequence into different time periods, generating an adaptive traffic flow sequence. Compared with the sequence using fixed aggregation intervals, adaptive traffic flow data is reduced by 33

Data collection technologies or data sources are critical for highway network management. However, due to the limitations on available management resources, determining the importance of these data sources is necessary to allocate these resources reasonably. This study proposes a complex network based method for evaluating the importance of multiple data sources in highway networks. This method includes mainly three steps. First, the business-data source relation will be identified and formulated for the highway network. Second, a business data source complex network is built from the previously identified business-data relationship. Third, an entropy weight method is used to compute and rank the importance of data source nodes by combining three indexes of degree centrality (DC), closeness centrality (CC) and structural holes (SC) computed based on the complex network. The proposed method is applied and illustrated using the highway network of Xuzhou City, Jiangsu Province, China. The results show that among the data sources, the most important data source is the continuous traffic survey station, followed by an automatic gantry-based station and vehicle detectors-based system. Discussions on the limitations, applications and future studies are provided for the proposed approach.

Uncertainty quantification is important for making reliable transportation decisions. For grey-based uncertainty quantification approaches, the data classification methods for most models cannot yield real-time upper and lower limited data sequence, limiting their application in dynamic transportation systems. Therefore, this paper proposes an adaptive grey prediction interval model to quantify real-time traffic condition uncertainty. To this end, polynomial regression is first used to fit the traffic flow trend function in real time, generating dynamically the upper and lower limited data sequence. Then, upper grey (UG) and lower grey (LG) models are built with parameters optimised using particle swarm optimisation. Finally, based on the real-time upper and lower bounds generated by the UG and LG models, prediction intervals are constructed. Using real-world traffic flow data, the proposed model was shown to be able to generate workable prediction intervals in real time. Future studies are recommended to advance traffic condition uncertainty quantification.

Emerging technologies of connected and automated vehicles (CAVs) applied at intersections have great potential to improve traffic efficiency, driving safety, and fuel economy. This paper proposes a virtual spring strategy for coordinating CAVs to pass through signal-free intersections. A virtual spring coordination system (VSCS) is established to force the movements of conflicting CAVs in terms of spring characteristics. Inspired by the properties of springs with dampers, a distributed control protocol is designed to regulate CAVs to reach a desired state of motion when crossing intersections. For ensuring the convergence of the VSCS, i.e., the total elastic potential energy approaches to zero, sufficient conditions for the internal stability are derived subject to the input saturation. To further improve the anti-disturbance capability of the VSCS, we ensure the upper bound of the disturbance propagation, which is characterized by an $H_{\infty }$ performance index. The proposed strategy is developed in a receding horizon framework and tested under the two scenarios in simulations. The simulation results show the effectiveness and superiority of the proposed strategy.

Traffic flow uncertainty quantification is important for making reliable decisions in transportation operations. Compared with well-studied level prediction or point prediction models, the study of uncertainty quantification that can capture the second-order fluctuations of traffic observations is still in its infancy. Current traffic flow uncertainty quantification approaches can be classified in general into distribution- or nondistribution-based. For the former, generalized autoregressive conditional heteroscedasticity (GARCH) model and stochastic volatility (SV) have been widely applied to quantify traffic flow uncertainty in terms of prediction interval, usually under a parametric Gaussian distribution assumption. However, a parametric model relies on a prespecified model structure and cannot meet the requirement raised by the time-varying traffic condition patterns. Therefore, this paper proposed a real-time traffic condition uncertainty quantification approach based on a nonparametric probability density function (PDF) estimation. For this approach, the real-time nonparametric kernel density estimation method is applied to capture the time-varying probability density of traffic flow data based on which prediction intervals are constructed in real time using the quantiles computed from the estimated time-varying nonparametric PDF. Real-world traffic flow data are applied to validate the proposed approach. The results show that the proposed approach outperforms the comparative models of an online GARCH filter and three lower and upper bound estimation (LUBE) models based on multilayer perceptron (MLP), spiking neural network (SNN), and long short-term memory networks (LSTM). The findings indicate that the quantification of traffic condition uncertainty is complementary to the conventional traffic condition level modeling, and combined, traffic level modeling and traffic uncertainty quantification can support the development of proactive and reliable transportation applications.

Identifying the importance of metro stations is crucial for enhancing the efficiency and safety of the urban rail transit system. This study proposes an integrated approach to evaluate the importance of metro stations based on jointly the static metro network topology and the dynamic real-time passenger flows. The static characteristics of metro stations are reflected through node degree and clustering coefficient of metro network topology based on the complex network theory. The dynamic characteristics of metro stations are assessed using the passenger flow time series, which were modeled using the autoregressive integrated moving average plus autoregressive conditional heteroscedasticity model (i.e., ARIMA+GARCH model). By combining the static and dynamic characteristics of metro stations, a comprehensive index is proposed to quantify the importance of metro stations. The integrated approach is tested using metro network and passenger flow data collected from Suzhou, China. The results indicate that compared with the topology analysis, the passenger flows could strengthen the importance of transfer stations and their neighboring stations. Especially for the loop lines that formed by the intersection of two or more lines within the network, the importance index of metro stations within these loop lines is inversely proportional to the number of stations in the loop. It indicates that as a metro network continues to expand with the addition of new lines and stations, this is a high likelihood of increased concentration of importance with the network. The concentration of importance in the metro network can be attributed to the emergence of highly connected loop lines that facilitate efficient movement of passengers.

Uncertainty quantification is important for making reliable decisions in transportation planning and operations. In the field of short-term traffic condition forecasting, uncertainty quantification methods include primarily distribution-based approaches and nondistribution-based approaches. For the former, the generalized autoregressive conditional heteroscedasticity (GARCH) model has been widely applied to model and quantify traffic condition uncertainty in terms of prediction interval under normality assumption. However, this normality assumption has not been systematically investigated yet. Therefore, this paper attempts to investigate this normality assumption and thereby quantify traffic condition uncertainty, using a method with steps of residual calculation and investigation, normality investigation, distribution estimation, uncertainty quantification, and performance measurement. Using real-world traffic flow data, the distributions of the selected samples are shown to be nonnormal using the Kolmogorov-Smirnov test and normal probability plot. Distribution estimation using non-normal models shows that the t location-scale distribution and generalized error distribution (GED) can be used to model traffic condition uncertainty. Uncertainty quantification using GARCH under these nonnormal distributions further show that nonnormal models outperform the normal model, with the GARCH model under t location-scale distribution yielding the best performance. Future studies are recommended to promote the investigation into traffic condition uncertainty quantification and application. (C) 2022 American Society of Civil Engineers.

Abstract This paper constructs and settles a charging facility location problem with the link capacity constraint over a mixed traffic network. The reason for studying this problem is that link capacity constraint is mostly insufficient or missing in the studies of traditional user equilibrium models, thereby resulting in the ambiguous of the definition of road traffic network status. Adding capacity constraints to the road network is a compromise to enhance the reality of the traditional equilibrium model. In this paper, we provide a two-layer model for evaluating the efficiency of the charging facilities under the condition of considering the link capacity constraint. The upper level model in the proposed bi-level model is a nonlinear integer programming formulation, which aims to maximize the captured link flows of the battery electric vehicles. Moreover, the lower level model is a typical traffic equilibrium assignment model except that it contains the link capacity constraint and driving distance constraint of the electric vehicles over the mixed road network. Based on the Frank-Wolfe algorithm, a modified algorithm framework is adopted for solving the constructed problem, and finally, a numerical example is presented to verify the proposed model and solution algorithm.

掌握公路网的车型占比对提升公路网运行管理效率具有重要意义.文章基于交通调查数据,提出一种计算公路网车型占比的二次加权平均法,即首先根据实时交通调查数据计算路段分车型年平均日交通量,然后以路段长度为权值,一次加权计算路线分车型年平均日交通量,最后以路线长度为权值,二次加权计算公路网分车型年平均日交通量,并计算车型占比.文章选取徐州市普通国省道示例路网展示了公路网车型占比的计算过程.

Considering the range anxiety issue caused by the limited driving range and the scarcity of battery charging stations,the conventional multinomial logit(MNL)model with the overlapping path issue was used in route choice modeling to describe the route choice behavior of travelers effectively.Furthermore,the generalized nested logit-based stochastic user equilibrium(GNL-SUE)model with the constraints of multiple user classes and distance limits was proposed.A mathematical model was developed and solved by the method of successive averages.The mathematical model was proven to be analytically equivalent to the modified GNL-SUE model,and the uniqueness of the solution was also confirmed.The proposed mathematical model was tested and compared with the GNL-SUE model without a distance limit and the MNL-SUE model with a distance limit.Results show that the proposed mathematical model can effectively handle the range anxiety and overlapping path challenges.

提高交通数据资源可用性是提升公路数据资源价值的重要手段,可用的交通数据资源能让道路管理者和使用者进行更好的管理与决策.文章通过元数据分析法,建立普通国省道数据资源可用性框架,由物理层、服务层、元数据层和工具层4部分组成,连接业务与数据,以提高交通数据资源可用性,并以路段平均速度为例,展示交通数据资源可用性框架.

Targeting the needs for dynamic evaluation of the road network traffic operational state, a dynamic framework for urban road network operational state evaluation is proposed based on heuristics weight optimization. In this framework, first, seven indexes are selected for intersections and sections, including saturation, delay, queue length, and equilibrium for intersections, and travel time, number of stops, and travel time dispersion for sections. Second, based on the seven indexes, a simple weighted average method is applied to evaluate the road network state following the intersection-section-network structure. Third, and most importantly, an optimization model is proposed and solved using particle swarm optimization algorithm to find dynamically the optimal weights with respect to expert evaluation during the optimization time interval. Finally, the optimized weights are then applied to evaluate the road network state for application time interval. The proposed framework is validated using VISSIM simulation for two road networks selected from Lianyungang City. Results show that the proposed framework can dynamically fine-tune the weights so that the road network operational state can be evaluated dynamically. Future studies are recommended to incorporate more indexes and apply the framework in real world applications.

Traffic assignment has been recognized as one of the key technologies in supporting transportation planning and operations. To better address the perfectly rational issue of the expected utility theory (EUT) and the overlapping path issue of the multinomial logit (MNL) model that are involved in the traffic assignment process, this paper proposes a cumulative prospect value (CPV)-based generalized nested logit (GNL) stochastic user equilibrium (SUE) model. The proposed model uses CPV to replace the utility value as the path performance within the GNL model framework. An equivalent mathematical model is provided for the proposed CPV-based GNL SUE model, which is solved by the method of successive averages (MSA). The existence and equivalence of the solution are also proved for the equivalent model. To demonstrate the performance of the proposed CPV-based GNL SUE model, three road networks are selected in the empirical test. The results show that the proposed model can jointly deal with the perfectly rational issue and the overlapping path issue, and additionally, the proposed model is shown to be applicable for large road networks.

短时交通流预测是提高普通国省道交通运行效率和安全的关键技术之一.普通国省道具有分布地域广、情况复杂的特点,要求短时交通流预测方法具有良好的适应性,然而,针对短时交通流预测算法适应性及其机制的系统性研究尚不多见.选取1种自适应卡尔曼滤波算法,系统分析其适应性和适应机制.获取江苏省徐州市普通国省道路网中8个交通调查站所采集的实际交通流数据开展实例分析,结果表明:在不同的交通流量水平下,所选算法均值预测的平均绝对百分比误差在10.98％～15.92％之间,区间预测的无效覆盖率在5.21％～6.15％之间,表明所选的自适应卡尔曼滤波算法在不同交通流水平下都具有良好的预测性能;对所选算法的参数进行分析发现,算法参数能够随交通流水平的变化而自动调整,具有良好的自适应机制;所选算法能够在预测初期实现有效的性能调整和收敛.

公路网交通运行性能指标汇聚方法是计算公路网性能指标、提高公路网交通运行效率与管理水平、改善路网交通运行状况的重要手段.文章结合我国公路网交通运行性能指标发展现状,给出了4类指标汇聚方法,包括算术平均法、加权平均法、直接累加法和累加相除法,并采用实际公路网数据对各种汇聚方法进行实例计算.