Journal of Advanced Transportation最新文献

{"title":"Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network","authors":"Zhiqi Shen,&nbsp;Kaiquan Cai,&nbsp;Quan Fang,&nbsp;Xiaoyan Luo","doi":"10.1155/2024/4349402","DOIUrl":"10.1155/2024/4349402","url":null,"abstract":"<p>Accurate air traffic flow prediction assists controllers formulate control strategies in advance and alleviate air traffic congestion, which is important to flight safety. While existing works have made significant efforts in exploring the high dynamics and heterogeneous interactions of historical air traffic flow, two key challenges still remain. (1) The transfer patterns of air traffic are intricate, subject to numerous constraints and limitations such as controllers, flight regulations, and other regulatory factors. Relying solely on mining historical traffic evolution patterns makes it difficult to accurately predict the constrained air traffic flow. (2) Weather conditions exert a substantial influence on air traffic, making it exceptionally difficult to simulate the impact of external factors (such as thunderstorms) on the evolution of air traffic flow patterns. To address these two challenges, we propose a Spatiotemporal Knowledge Distillation Network (ST-KDN) for air traffic flow prediction. Firstly, recognizing the inherent future insights embedded within flight plans, we develop a “teacher-student” distillation model. This model leverages the prior knowledge of upstream-downstream migration patterns and future air traffic trends inherent in flight plans. Subsequently, to model the influence of external factors and predict air traffic flow disturbed by thunderstorm weather, we propose a student network based on the “parallel-fusion” structure. Finally, employing a feature-based knowledge distillation approach to integrate prior knowledge from flight plans and extract meteorological features, our method can accurately capture complex and constrained spatiotemporal dependencies in air traffic and explicitly model the impact of weather on air traffic flow. Experimental results on real-world flight data demonstrate that our method can achieve better prediction performance than other state-of-the-art comparison methods, and the advantages of the proposed method are particularly prominent in modeling the complicated transfer pattern of air traffic and inferring nonrecurrent flow patterns.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Optimization Strategy for Truncating Ultralong Bus Lines Integrated with Metro Networks","authors":"Cuiying Song,&nbsp;Yujun Chen,&nbsp;Jihui Ma","doi":"10.1155/2024/6446235","DOIUrl":"10.1155/2024/6446235","url":null,"abstract":"<p>For most ultralong bus lines, low punctuality rates and vehicle bunching are two main challenges. One potential strategy to solve these two problems is to split an ultralong line into two or three shorter lines according to historical passenger flow patterns as well as other presetting objectives and constraints. Thus, in this paper, a multiobjective optimization model is first established in consideration of both passenger flow distribution and coline metro networks. Then, the optimal truncated stop is determined by estimating the model results generated by enumerating each potential bus stop derived from related metro stops. The nondominated sorting genetic algorithm II (NSGA-II) is applied to solve the model efficiently. A case study conducted on a real bus line associated with metro networks in Beijing, China, demonstrates the efficiency of the optimization strategy adoption in terms of operating cost and passenger travel time, even with a slight increase in passenger cost. Besides, the multivehicle type is also referred in the experiments to verify economic efficiency to reduce operational costs.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140933957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accounting for BEV Users’ Risk Attitudes and Charging Inertia in En Route Charging Choice Behavior","authors":"Zhicheng Jin,&nbsp;Hao Li,&nbsp;Di Chen,&nbsp;Lu Yu,&nbsp;Huizhao Tu","doi":"10.1155/2024/9926334","DOIUrl":"10.1155/2024/9926334","url":null,"abstract":"<p>This paper innovatively explores BEV (battery electric vehicle) users’ risk attitudes and charging inertia, examining their effects on en route charging and charging route choice behavior. An attitudinal survey was conducted to explore the two latent variables of risk attitudes and charging inertia in relation to socioeconomic and travel-related characteristics. ICLV (Integrated choice and latent variable) models are adopted to estimate the latent variables and the charging choice behavior simultaneously. Specifically, uncertainty in energy consumption is first considered in the ICLV model, which is represented by the available range (AR) uncertainty. Multinomial logit (MNL) models directly incorporating socioeconomic attributes are employed as a reference for comparison with ICLV models. Results illustrate that risk attitudes and charging inertia both play significant roles in modeling en route charging choice behavior. Risk-averse users and users having charging inertia value AR uncertainty more. Battery range, charging frequency, and income emerge as the most crucial factors influencing users’ intention to charge en route. The results show significant heterogeneity of BEV users in attitudes and charging choice behavior, underscoring the importance of accounting for the heterogeneity in en route charging demand estimation and deployment optimization of public charging stations, particularly for medium-to long-distance trips.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Driver Compliance and Aggressiveness in Connected Vehicles on Mixed Traffic Flow Efficiency: A Simulation Study","authors":"Chenhao Qian,&nbsp;Taojun Feng,&nbsp;Zhiyuan Li,&nbsp;Yanjun Ye,&nbsp;Shengwen Yang","doi":"10.1155/2024/3414116","DOIUrl":"10.1155/2024/3414116","url":null,"abstract":"<p>Connected vehicles (CVs) are becoming increasingly prevalent in today’s transportation systems, and understanding their behavior in mixed traffic flow is crucial for enhancing traffic efficiency and safety. This paper presents a comprehensive study investigating the impact of CV drivers’ compliance and aggressiveness on mixed traffic flow through simulation experiments. The unique contribution of this research lies in the adoption of a clustering method to classify CV drivers’ compliance and aggressiveness based on trajectory data captured by Unmanned Aerial Vehicles (UAVs). This approach allows for the accurate calibration of car-following and lane-changing models, surpassing previous methodologies. The study outlines two primary methods: the intelligent driver model (IDM) with driver compliance (CVs-IDM) and the lane-change 2013 model with drivers’ style. These methods are applied to simulate various scenarios of mixed traffic flow, considering different CV penetration rates and driver types. The pivotal findings reveal that higher CV penetration rates lead to reduced traffic flow disturbance, improved safety, and enhanced efficiency. Specifically, CV drivers exhibiting high compliance and normal aggressiveness demonstrate optimal performance in terms of disturbance reduction, safety, and overall efficiency. This research offers valuable insights for policymakers and practitioners. It recommends increasing the CV penetration rate in mixed traffic flow to enhance overall efficiency. Moreover, selecting the appropriate CV driver type based on the penetration rate can further optimize traffic flow, positively impacting transportation systems and promoting safer and more efficient mixed traffic environments.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metro Train Stopping Scheme Decision Based on Multisource Data in Express-Local Train Mode","authors":"Jin Li,&nbsp;Yaqiu Wang,&nbsp;Shiyin Zhang,&nbsp;Huasheng Liu","doi":"10.1155/2024/7311720","DOIUrl":"10.1155/2024/7311720","url":null,"abstract":"<p>The urban rail transit network has gradually realized grid operation with the increase in the coverage rate. Therefore, the stopping schemes in accordance with the trend of the passenger flow are more conducive to improving the attractiveness of the rail transit and improving the sharing rate of the urban public transit. Traditional data from a single source may not be sufficient to describe the overall trend of the passenger flow in a period of time, and the error is possible in the case of insufficient data. Based on the multisource data, the spatial weight function is introduced to fuse the point of interest data and real estate information data, from which one obtains the residential index and office index, and the cluster analysis is conducted to obtain the potential stop scheme. Then, the optimization model of the train operation plan is established, aiming at minimizing the passenger travel time and the generalized system cost, and is constrained by a series of driving conditions. Compared with the single data source, multisource data can better reflect passenger flow trends and land use characteristics. Compared with the traditional all-station stopping scheme, a reasonable setting of crossing stations and running express-local trains can better satisfy the demands of the passenger flow. Finally, the optimization of Changchun rail Transit Line 1 shows that the model can reduce the travel time of passengers and the operating cost of the rail transit company and improve the quality of service, so as to achieve a win-win situation between passengers and the rail transit company.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Travel Time Reliability Estimation in Urban Road Networks: Utilization of Statistics Distribution and Tensor Decomposition","authors":"Linzhi Zou,&nbsp;Jiawen Wang,&nbsp;Minqian Cheng,&nbsp;Jiayu Hang","doi":"10.1155/2024/4912642","DOIUrl":"10.1155/2024/4912642","url":null,"abstract":"<p>The travel time reliability (TTR) is crucial for evaluating the reliability of road networks, but real traffic data is often incomplete and sparse. This study validates that road network TTR conforms to a normal distribution and devises a quantification approach for road network TTR. Two reliability estimation methods are tailored for two data sources: section detectors and mobile detectors. Simulation experiments have confirmed the effectiveness of these methods. The study emphasizes that the TTR estimation method using traffic section data (S-TTR), which is based on the verified normal distribution assumption, maintains average absolute errors below 10%. On the other hand, the TTR estimation method that utilizes sparse trajectory data (T-TTR), which relies on tensor decomposition, proficiently fills in all missing data with an average error of 0.0059.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of Expressway Construction Area Information on Drivers’ Route Choice Behaviours","authors":"Yuexiang Li,&nbsp;Bao Guo,&nbsp;Wei Zhao,&nbsp;Mengqi Lv,&nbsp;Peng Lu,&nbsp;Chengcheng Wang,&nbsp;Zhonggang Ji,&nbsp;Qiuchen Xu","doi":"10.1155/2024/9966775","DOIUrl":"10.1155/2024/9966775","url":null,"abstract":"<p>Expressway traffic information is important for guiding driving routes and alleviating traffic congestion. However, the current research on expressway guidance information focuses on existing expressways. In this study, strategies for providing expressway guidance information under reconstruction and expansion scenarios are investigated. Multiple factors of expressway reconstruction and expansion, such as the length of construction areas and the number of lanes occupied by construction areas, are extracted. A panel latent class logit model considering individual heterogeneity is established to fit the survey data collected by 825 respondents. The results show that the proposed panel latent class logit model fits the data best, and the studied drivers could be categorized into three classes, i.e., the information provision time-sensitive class, the information promotion detour class, and the information suppression detour class. The research results can support expressway operators in designing appropriate traffic information provision strategies, providing personalized guidance to drivers, and ensuring the safe operation of expressways in construction areas.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140626080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Risky Driving Behavior of Young Truck Drivers: Improved Theory of Planned Behavior Based on Risk Perception Factor","authors":"Zijun Liang,&nbsp;Xuejuan Zhan,&nbsp;Ran Deng,&nbsp;Xin Fu","doi":"10.1155/2024/9966501","DOIUrl":"10.1155/2024/9966501","url":null,"abstract":"<p>In response to the issue of young truck drivers’ weaker perception of potential risks, which makes them more prone to engaging in risky driving behaviors, the direct influence of risk perception on behavior was innovatively considered. An improved theory of planned behavior (TPB) model was developed and a study on risky driving behavior among young truck drivers was conducted. Valid questionnaire data from 330 young truck drivers in China were collected, and the improved TPB model was validated and analyzed through structural equation modeling. The results indicate that the improved TPB model can effectively explain the risky driving behavior among young truck drivers. Specifically, attitudes toward behavior, subjective norms, and perceived behavioral control have significant positive effects on behavioral intention, while behavioral intention and perceived behavioral control have significant positive effects on behavior. In addition, risk perception has a significant negative effect on behavioral intention and behavior. Furthermore, a comparison with the traditional TPB model reveals that the improved TPB model performs better in terms of fit and explanatory power. Fit indices CMIN/DF, RMSEA, and AGFI were optimized by 16%, 18%, and 1.5%, respectively, and there was a 5% increase in explanatory power for behavior variance, validating the rationality and effectiveness of the improved TPB model. This provides decision support for the development of intervention measures for risky driving behavior among young truck drivers in the future.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140609665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-Delay following Model for Connected and Automated Vehicles with Collision Conflicts and Forced Deceleration","authors":"Wenbo Wang,&nbsp;Fei Hui,&nbsp;Kaiwang Zhang,&nbsp;Xiangmo Zhao,&nbsp;Asad J. Khattak","doi":"10.1155/2024/6632473","DOIUrl":"10.1155/2024/6632473","url":null,"abstract":"<p>The connected and automated car-following model can provide a model reference for the queue control algorithm of connected and automated driving and has become a hot research topic in the field of connected vehicles and intelligent transportation. A queue of fast-moving vehicles on urban roads can cause traffic congestion when forced to slow down and, in serious cases, can cause rear-impact accidents. Therefore, this paper introduces information on the time delay of information reception and processing, a collision risk quantification factor reflecting the speed characteristics of the front vehicle, and the speed limit and proposes an improved intelligent driver collision quantification model that considers drastic changes in the speed of the front vehicle. Additionally, the model parameters are calibrated using real vehicle data from urban roads combined with an improved salp swarm algorithm. Finally, the evolution rule of disturbance in the traffic flow under different states is analyzed using a time-space diagram, and the DIDM-CSCL model is compared with the classical IDM. The results show that the improved IDM can better describe the following behavior at the microscopic level, which provides a basis for research related to connected and automated driving.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140577072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Precrash Scenario Analysis Comparing Safety Performance across Autonomous Vehicle Driving Modes","authors":"Tao Wang,&nbsp;Juncong Chen,&nbsp;Wenyong Li,&nbsp;Jun Chen,&nbsp;Xiaofei Ye","doi":"10.1155/2024/4780586","DOIUrl":"10.1155/2024/4780586","url":null,"abstract":"<p>Precrash scenario analysis for autonomous vehicles (AVs) is critical for improving the safety of autonomous driving, yet the scenario differences between different driving modes are unexplored. Using the precrash scenario typology of the USDOT, this study classified 484 AV crash reports from the California DMV from 2018 to 2022, revealing the differences in the scenario proportions of the three modes of autonomous driving, driving takeover, and conventional driving in 34 types of scenarios. The results showed that there were significant differences in the proportion of six scenarios such as “Lead AV stopped” and “Lead AV decelerating” among different driving modes (<i>p</i> &lt; 0.05). To analyze the relative risk of different driving modes in specific scenarios, an evaluation model of the risk level of AV precrash scenarios was established using the analytic hierarchy process (AHP). The findings indicated that ​ autonomous driving has the highest risk rating and poses the greatest danger in Scenario 1, while conventional driving is associated with Scenario 2b, and driving takeover corresponds to Scenario 3, respectively. In-depth analysis of the crash characteristics and causes of these three typical scenarios was conducted, and suggestions were made from the perspectives of autonomous driving system (ADS) and drivers to reduce the severity of crashes. This study compared precrash scenarios of AV by different driving modes, providing references for the optimization of ADS and the safety of human-machine codriving.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140577071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}