Journal of Intelligent Transportation Systems最新文献_第10页

Experience of drivers of all age groups in accepting autonomous vehicle technology 各年龄段驾驶员接受自动驾驶汽车技术的经验

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2023-03-28 DOI: 10.1080/15472450.2023.2197115

{"title":"Experience of drivers of all age groups in accepting autonomous vehicle technology","authors":"","doi":"10.1080/15472450.2023.2197115","DOIUrl":"10.1080/15472450.2023.2197115","url":null,"abstract":"<div><p>Autonomous vehicles (AVs) may benefit the health and safety of drivers across the driving lifespan, but perceptions of drivers are not known. Lived experiences of drivers exposed to AVs in combination with surveys, can more accurately reveal their perceptions. We quantified facilitators and barriers from data collected in older (N = 104) and younger drivers (N = 106). Perceptions were assessed via Autonomous Vehicle User Perception Survey (AVUPS) subscales (i.e., <em>intention to use</em>, <em>barriers</em>, <em>well-being</em>, and <em>acceptance</em>) pertaining to group exposure (simulator first [SF] or autonomous shuttle first [ASF]). We quantified the effects of group, time, and group × time interaction. Multiple linear regressions identified predictors (e.g., <em>optimism</em>, <em>ease of use</em>, <em>life space</em>, <em>driving exposure</em>, and <em>driving difficulty, age, gender, race)</em> of the AVUPS subscales. The regression analyses indicated that <em>optimism</em> and <em>ease of use</em> positively predicted <em>intention to use</em>, <em>barriers</em>, <em>well-being</em>, and the <em>total acceptance</em> score. <em>Driving difficulty</em> significantly predicted <em>barriers</em>, whereas <em>miles driven</em> negatively predicted <em>well-being.</em> The regression results indicated that predictors of user <em>acceptance</em> of AV technology included <em>age, race, optimism</em>, <em>ease of use,</em> with 33.6% of the variance in <em>acceptance</em> explained. The findings reveal foundational information about driver <em>acceptance</em>, <em>intention to use</em>, <em>barriers</em>, and <em>well-being</em> related to AVs. New knowledge pertains to how <em>demographics</em>, <em>optimism</em>, <em>ease of use</em>, <em>life space</em>, <em>driving exposure</em>, and <em>driving difficulty</em> inform AV acceptance. We provided strategies to inform city planners and other stakeholders on improving upon deployment practices of AVs.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 5","pages":"Pages 651-667"},"PeriodicalIF":2.8,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135239047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel pedestrian road crossing simulator for dynamic traffic light scheduling systems 用于动态交通灯调度系统的新型行人过马路模拟器

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2023-03-06 DOI: 10.1080/15472450.2023.2186229

{"title":"A novel pedestrian road crossing simulator for dynamic traffic light scheduling systems","authors":"","doi":"10.1080/15472450.2023.2186229","DOIUrl":"10.1080/15472450.2023.2186229","url":null,"abstract":"<div><p>The major advances in intelligent transportation systems are pushing societal services toward autonomy where road management is to be more agile in order to cope with changes and continue to yield optimal performance. However, the pedestrian experience is not sufficiently considered. Particularly, signalized intersections are expected to be popular if not dominant in urban settings where pedestrian density is high. This paper presents the design of a novel environment for simulating human motion on signalized crosswalks at a fine-grained level. Such a simulation not only captures typical behavior, but also handles cases where large pedestrian groups cross from both directions. The proposed simulator is instrumental for optimized road configuration management where the pedestrians’ quality of experience, for example, waiting time, is factored in. The validation results using field data show that an accuracy of 98.37% can be obtained for the estimated crossing time. Other results using synthetic data show that our simulator enables optimized traffic light scheduling that diminishes pedestrians’ waiting time without sacrificing vehicular throughput.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 5","pages":"Pages 636-650"},"PeriodicalIF":2.8,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89101367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of local traffic conditions using Wi-Fi sensor technology 利用 Wi-Fi 传感器技术估算当地交通状况

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2023-02-06 DOI: 10.1080/15472450.2023.2177103

{"title":"Estimation of local traffic conditions using Wi-Fi sensor technology","authors":"","doi":"10.1080/15472450.2023.2177103","DOIUrl":"10.1080/15472450.2023.2177103","url":null,"abstract":"<div><p>Real-time traffic data is fundamental for active traffic monitoring and control. Traditionally, traffic data are collected using location-based sensors and spatial sensors. However, both sensors have well-known limitations due to installation, operations, maintenance costs, and environmental factors. This study develops a methodology to use Wi-Fi sensors for traffic state characterization on urban roads to overcome these limitations. We examine the received signal strength indicator (RSSI) patterns and identify three distinct RSSI signature patterns. These patterns are used to develop methodologies to estimate (a) Whether the position of the end of the queue is upstream or downstream of the detector, (b) Whether the traffic conditions in the vicinity of the detector are uniformly uncongested or uniformly congested, and (c) The maximum queue length and the time is taken for the queue to grow to the maximum extent. The estimates from the methodology are validated with empirical data that showed good concurrence with field conditions, and the methods proposed in this article have the potential to estimate the traffic conditions using sparse data from Wi-Fi sensors.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 5","pages":"Pages 618-635"},"PeriodicalIF":2.8,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78334026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Road crack avoidance: a convolutional neural network-based smart transportation system for intelligent vehicles 道路裂缝规避：基于卷积神经网络的智能车辆智能交通系统

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2023-02-04 DOI: 10.1080/15472450.2023.2175613

{"title":"Road crack avoidance: a convolutional neural network-based smart transportation system for intelligent vehicles","authors":"","doi":"10.1080/15472450.2023.2175613","DOIUrl":"10.1080/15472450.2023.2175613","url":null,"abstract":"<div><p>Prediction using computer vision is getting prevalent nowadays because of satisfying results. The vision of Internet of Vehicles (IoV) expedites Vehicle to everything (V2X) communications by implementing heterogeneous global networks. Road crack is one of the major factors that causes road mishaps and damage to vehicles. To ensure smooth and safe driving, avoiding road crack in transportation planning and navigation is significant. To address this issue, we proposed a novel convolutional neural network (CNN)-based smart transportation system. We showed how to quantify the severity of the cracks. We proposed a post-processing algorithm to provide option to the driver to select the safest road toward the destination. The communication system for the proposed smart transportation system has also been introduced. The performance comparison of a few popular CNN architectures has been investigated. Simulation results showed that Resnet50 algorithm provides significantly high accuracy compared with SqueezeNet and InceptionV3 algorithm in order to detect road cracks for the proposed transportation system. We demonstrated high accuracy of measuring the crack severity via numerical analysis. The integration of the proposed system in next generation smart vehicles can ensure accurate detection of road cracks earlier enough providing the option to select alternate safe route toward a destination as advanced driver assistance service. Moreover, the proposed system can also play a key role in order to reduce road mishaps notably by warning the driver about the updated road surface conditions.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 5","pages":"Pages 605-617"},"PeriodicalIF":2.8,"publicationDate":"2023-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78613891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bayesian regression analysis of truck drivers’ use of cooperative adaptive cruise control (CACC) for platooning on California highways 基于贝叶斯回归分析的卡车驾驶员协同自适应巡航控制(CACC)在加州高速公路上的应用

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2023-01-02 DOI: 10.1080/15472450.2021.1990051

Shiyan Yang , Steven E. Shladover , Xiao-Yun Lu , Hani Ramezani , Aravind Kailas , Osman D. Altan

{"title":"A Bayesian regression analysis of truck drivers’ use of cooperative adaptive cruise control (CACC) for platooning on California highways","authors":"Shiyan Yang , Steven E. Shladover , Xiao-Yun Lu , Hani Ramezani , Aravind Kailas , Osman D. Altan","doi":"10.1080/15472450.2021.1990051","DOIUrl":"https://doi.org/10.1080/15472450.2021.1990051","url":null,"abstract":"<div><p>Cooperative Adaptive Cruise Control (CACC), as an advanced version of adaptive cruise control (ACC), automates brake and engine controls based on the information received from wireless V2V communications and remote sensors, enabling smaller vehicle-following time gaps. It can improve the safety of vehicle platooning and increase fuel savings. As an extension of our previous investigation of truck drivers’ acceptance of CACC, this case study investigates factors affecting the use of CACC for truck platooning. Nine commercial fleet drivers were recruited to operate two following trucks in a CACC-enabled string on freeways in Northern California. We analyzed the usage of CACC time gaps and its correlation with truck drivers’ stated preferences for these time gaps, and we found that the highest preferred Gap 3 (1.2 s) was used the most. Moreover, a Bayesian regression model was built to show that truck drivers are more likely to disengage CACC when driving in low-speed traffic or on downgrades where this CACC could not provide sufficient braking. In high-speed traffic or on upgrades, truck drivers are more likely to engage CACC, particularly at Gap 3. Truck position, however, does not affect truck drivers’ time gap selection. The findings encourage the adoption of CACC in the trucking industry through implementing driver-preferred time gaps and responsive braking systems, and operating on routes with minimal interference to truck speeds.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"27 1","pages":"Pages 80-91"},"PeriodicalIF":3.6,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49744996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Stabilizing mixed cooperative adaptive cruise control traffic flow to balance capacity using car-following model 基于车辆跟随模型的稳定混合合作自适应巡航控制交通流容量平衡

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2023-01-02 DOI: 10.1080/15472450.2021.1985490

Yanyan Qin , Hao Wang

{"title":"Stabilizing mixed cooperative adaptive cruise control traffic flow to balance capacity using car-following model","authors":"Yanyan Qin , Hao Wang","doi":"10.1080/15472450.2021.1985490","DOIUrl":"https://doi.org/10.1080/15472450.2021.1985490","url":null,"abstract":"<div><p>String stability is important for understanding traffic flow dynamics, while its analytical study for mixed traffic is deficient. We focus on an analytical framework on string stability of mixed traffic consisting of cooperative adaptive cruise control (CACC), adaptive cruise control (ACC), and human vehicles. The analytical framework was conducted based on generalized car-following model formulations of the three vehicular types. Then string stability criterions of one-class traffic flow, mixed traffic flow, and local CACC/ACC platoons were derived. Taking into account the mixed flow with partial degenerations of CACC and three concrete car-following models, an example application was studied. The example application analyzed the string stability from the perspectives of the homogeneous flow, the mixed traffic flow, and the local CACC/ACC platoon, respectively. The example application also studied balance between stability and capacity for the mixed traffic with CACC vehicles. Results show the usefulness of the proposed analytical framework, in term of not only analyzing string stability but also providing suggestions for dynamic regulations of CACC/ACC management strategy to balance string stability and traffic capacity for the mixed CACC-human flow.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"27 1","pages":"Pages 57-79"},"PeriodicalIF":3.6,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49745038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Weather impact on macroscopic traffic stream variables prediction using recurrent learning approach 用循环学习方法预测天气对宏观交通流变量的影响

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2023-01-02 DOI: 10.1080/15472450.2021.1983809

Archana Nigam , Sanjay Srivastava

{"title":"Weather impact on macroscopic traffic stream variables prediction using recurrent learning approach","authors":"Archana Nigam , Sanjay Srivastava","doi":"10.1080/15472450.2021.1983809","DOIUrl":"https://doi.org/10.1080/15472450.2021.1983809","url":null,"abstract":"<div><p>Accurate prediction of the macroscopic traffic stream variables is essential for traffic operation and management in an intelligent transportation system. Adverse weather conditions like fog, rainfall, and snowfall affect the driver’s visibility, vehicle mobility, and road capacity. The rainfall effect on traffic is not directly proportional to the distance between the weather station and the road because of terrain feature constraints. The prolonged rainfall weakens the drainage system, affects soil absorption capability, which causes waterlogging. The weather event has a spatiotemporal correlation with traffic stream variables, as waterlogging on the road due to rainfall affects the traffic on adjacent roads. The spatiotemporal and prolonged impact of rainfall is not studied in the literature. In this research, we examine whether the inclusion of the rainfall variable improves the traffic stream variables prediction of a deep learning model or not. We use the RNN and LSTM models to capture the spatiotemporal correlation between traffic and rainfall data using past and current traffic and weather information. To capture the prolonged impact of rainfall more extended past sequence of rainfall data than traffic data is used in this study. The roads prone to waterlogging are more affected due to rainfall compared to freeways. Thus we examine the effect of rain on traffic stream variables prediction for different types of roads. The test experiments show that the inclusion of weather data improves the prediction accuracy of the model. The LSTM outperforms other models to capture the spatiotemporal relationship between the rainfall and traffic stream variables.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"27 1","pages":"Pages 19-35"},"PeriodicalIF":3.6,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49757954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Congestion-aware heterogeneous connected automated vehicles cooperative scheduling problems at intersections 基于拥塞感知的交叉口异构互联自动化车辆协同调度问题

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2023-01-02 DOI: 10.1080/15472450.2021.1990053

Farzana R. Chowdhury , Peirong (Slade) Wang , Pengfei (Taylor) Li

{"title":"Congestion-aware heterogeneous connected automated vehicles cooperative scheduling problems at intersections","authors":"Farzana R. Chowdhury , Peirong (Slade) Wang , Pengfei (Taylor) Li","doi":"10.1080/15472450.2021.1990053","DOIUrl":"https://doi.org/10.1080/15472450.2021.1990053","url":null,"abstract":"<div><p>More and more vehicles are connected today via emerging connected and automated vehicle (CAV) technologies. An intriguing application of CAVs is to cross intersections without stops through cooperative scheduling by traffic control infrastructure. Nonetheless, with the increase of CAVs’ requests for green, two problems will surface: (I) accommodating too many CAVs’ green requests will generate severe interruptions to general traffic; (II) simple scheduling policies like first-come-first serve is inappropriate due to heterogeneous importance of CAVs. To overcome these challenges, we present a mixed-integer linear programming (MILP) formulation for congestion-aware heterogeneous CAV scheduling problems at intersections in this paper. The objective is to ensure that intensive and heterogeneous green requests by CAVs can be scheduled at intersections while the mobility of background traffic is still maintained. The MILP formulation is developed in the context of discrete space-time and phase-time networks whose variables are space-time arc choice variables with respect to individual vehicles and phase-time arc choice variables. We also build an efficient search algorithm based on the “A-D curves” for real-time applications. Three experiments are conducted to validate the proposed MILP formulation and search algorithm. The simulation-based performance evaluation for the congestion-aware CAV scheduling reveal promising results for real-world applications in the future.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"27 1","pages":"Pages 111-126"},"PeriodicalIF":3.6,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49764233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

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

Journal of Intelligent Transportation Systems Pub Date : 2023-01-02 DOI: 10.1080/15472450.2021.1974858

Yi-Chen Zhang

{"title":"Road geometry estimation using vehicle trails: a linear mixed model approach","authors":"Yi-Chen Zhang","doi":"10.1080/15472450.2021.1974858","DOIUrl":"https://doi.org/10.1080/15472450.2021.1974858","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"27 1","pages":"Pages 127-144"},"PeriodicalIF":3.6,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49745306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Journal of Intelligent Transportation Systems Pub Date : 2023-01-02 DOI: 10.1080/15472450.2021.1977639

Xuecai Xu , Xiaofei Jin , Daiquan Xiao , Changxi Ma , S.C. Wong

{"title":"A hybrid autoregressive fractionally integrated moving average and nonlinear autoregressive neural network model for short-term traffic flow prediction","authors":"Xuecai Xu , Xiaofei Jin , Daiquan Xiao , Changxi Ma , S.C. Wong","doi":"10.1080/15472450.2021.1977639","DOIUrl":"https://doi.org/10.1080/15472450.2021.1977639","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"27 1","pages":"Pages 1-18"},"PeriodicalIF":3.6,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49745099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13