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Journal of advanced transportation
Institute for Transportation Inc.
Journal of advanced transportation

Institute for Transportation Inc.

不定期

0197-6729

Journal of advanced transportation/Journal Journal of advanced transportationSCI
正式出版
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    Mixed Integer Linear Programming Based Speed Profile Optimization for Heavy-Haul Trains

    Huazhen YuYihui WangAndrea D’ArianoAnzheng Lai...
    1.1-1.15页
    查看更多>>摘要：Automatic heavy-haul train (HHT) operation technology has recently received considerable attention in the field of rail transportation. In this paper, a discrete-time-based mathematical formulation is proposed to address the speed profile optimization problem in order to ensure the safe, efficient, and economical operation of heavy-haul trains (HHTs). Due to the presence of long and steep downgrades (LSDs) on some heavy-haul lines, the brake forces of the HHT are typically jointly determined by air braking and electric braking. The time characteristics of the air braking, such as the command delay and the change process caused by the air pressure, are taken into account, and then formulas are presented to calculate the air brake force. In addition, the influence of the neutral section on the control of the electric braking is considered via space-based constraints. The resulting problem is a nonlinear optimal control problem. To achieve linearization, auxiliary 0-1 binary variables and the big-M approach are introduced to transform the nonlinear constraints regarding slope, curve, neutral section, air brake force, and air-filled time into linear constraints. Moreover, piecewise affine (PWA) functions are used to approximate the basic resistance of the HHT. Finally, a mixed integer linear programming (MILP) model is developed, which can be solved by CPLEX. The experiments are carried out using data from a heavy-haul railway line in China, and the results show that the proposed approach is effective and flexible.
      原文链接:
    • NETL
    • NSTL
    • Wiley-Hindawi

    TCN-SA: A Social Attention Network Based on Temporal Convolutional Network for Vehicle Trajectory Prediction

    Qin LiBingguang OuYifa LiangYong Wang...
    1.1-1.12页
    查看更多>>摘要：Vehicle trajectory prediction can provide important support for intelligent transportation systems in areas such as autonomous driving, traffic control, and traffic flow optimization. Predicting vehicle trajectories is an extremely challenging task that not only depends on the vehicle’s historical trajectory but also on the dynamic and complex social-temporal relationships of the surrounding traffic network. The trajectory of the target vehicle is influenced by surrounding vehicles. However, existing methods have shortcomings in considering both time dependency and interactive dependency between vehicles or insufficient consideration of the impact of surrounding vehicles. To address this issue, we propose a hybrid deep learning model based on a temporal convolutional network (TCN) that considers local and global interactions between vehicles. Specifically, we use a social convolutional pooling layer to capture local interaction features between vehicles and a multihead self-attention layer to capture global interaction features between vehicles. Finally, we combine these two features using an encoder-decoder structure to predict vehicle trajectories. Through experiments on the Next-Generation Simulation (NGSIM) public dataset and ablation experiments, we validate the effectiveness of our model.
      原文链接:
    • NETL
    • NSTL
    • Wiley-Hindawi

    Accurate Detection and Tracking of Small-Scale Vehicles in High-Altitude Unmanned Aerial Vehicle Bird-View Imagery

    Heshan ZhangXin TanMengwei FanCunshu Pan...
    1.1-1.17页
    查看更多>>摘要：Vehicle detection and tracking from unmanned aerial vehicles (UAVs) aerial images are among the main tasks of intelligent traffic systems. Especially in tasks with long distances, extensive backgrounds, and small objects, it increases the difficulty of localization and regression, which can easily lead to missed detections and false positives. This paper proposes a detection-based small-scale vehicle tracking framework that integrates an improved YOLOX network and the DeepSORT algorithm to address these issues. Based on the original YOLOX network, a shallow feature extraction network, 160 × 160 pixels, is added to enhance the ability to extract small-scale object features. A convolutional block attention module (CBAM) is inserted in front of the neck network to select crucial information for vehicle detection tasks while suppressing noncritical ones. EIoU_Loss is introduced as the bounding box regression loss function in training to speed up their convergence and improve the localization accuracy of the small objects. Furthermore, an image segmentation method is proposed to effectively reduce missed and false detection events. It divides the original high-definition image into multiple subimages, first detected and then reassembled. Finally, the improved YOLOX network is used as the detector of the DeepSORT to perform small-scale vehicle detection and tracking tasks in various traffic scenarios. Experiments show that the proposed method can significantly improve the detection accuracy of the network and effectively solve the problems of missed detection and false positives in small-scale vehicle tracking tasks in high-resolution aerial images captured by high-altitude UAVs. Significantly, the algorithm proposed in this paper has sufficient robustness for small-scale tracking tasks of aerial videos captured at different altitudes.
      原文链接:
    • NETL
    • NSTL
    • Wiley-Hindawi

    Revealing the Impacts of the Pandemic on Travel Behavior by Examining Pre- and Post-COVID-19 Surveys

    Zoltán György VargaTamás TettamantiDomokos Esztergár-KissMichela Le Pira...
    1.1-1.12页
    查看更多>>摘要：Recently, the topic of travel behavior and social media usage has been widely discussed. The current study specifically focuses on how specific factors, such as the sociodemographic variables, the number of friends, the social media usage, and the ICT usage, influence their travel patterns based on survey results conducted in pre- and post-COVID-19 times. The effect of the COVID-19 pandemic is taken into consideration to better understand the impact of restrictions on travel attitudes. Statistical analysis is carried out to investigate the survey data. The results show that the pandemic has made a huge impact on general travel behavior, especially in terms of transport mode choice shifting towards individual modes, such as car and walking. The location choice of the participants has a significant connection to the available transport mode and the price range of the place, together with the retrieved information from the ICT devices. Based on the results, it can be seen that the pandemic has deepened the number of close friendships, but younger people do not tend to choose trendy places anymore. In addition, the results show that there is no direct connection between the number of friends and the number of meetings, and the daily online meetings have not replaced all personal meetings.
      原文链接:
    • NETL
    • NSTL
    • Wiley-Hindawi

    Exploring the Behavior-Driven Crash Risk Prediction Model: The Role of Onboard Navigation Data in Road Safety

    Xiao-chi MaJian LuYiik Diew WongJaehyun (Jason) So...
    1.1-1.16页
    查看更多>>摘要：Driving behavior has frequently been overlooked in previous road traffic crash research. Hereby, abnormal (extreme) driving behavior data transmitted by the onboard navigation systems were collected for vehicles involved in traffic crashes, including sharp-lane-change, sharp-acceleration, and sudden-braking behaviors. Using these data in conjunction with expressway crash records, multiple classification learners were trained to establish a behavior-driven risk prediction model. To further investigate the influence of driving behavior on crash risk, partial dependence plots (PDPs) were applied. Regression analyses indicate that models have a stronger effect when derivative features such as frequency of specific deviant behavior, speed, and acceleration in the behavior process are included. The behavioral RUSBoost model surpasses other models, achieving an AUC prediction metric of 0.782 and outperforming traditional traffic-flow-driven machine learning models. PDP analysis demonstrates that the sudden-braking behavior is the leading contributory factor of expressway crashes, particularly when the acceleration exceeds 0.5 G. This study confirms the potential of predicting crash risks through augmenting behavior data from navigation software; the findings lay a foundation for countermeasures.
      原文链接:
    • NETL
    • NSTL
    • Wiley-Hindawi

    A Coordinated Allocation Method for Right-Turn Strategy at Signalized Intersections with Optimal Pedestrian and Vehicle Delays

    Yu BaiWanyan LuoJinjun Tang
    1.1-1.12页
    查看更多>>摘要：With strict enforcement of pedestrian right of way at all intersections, the inappropriate right-turn resource allocation from a spatial and temporal perspective will lead to a reduction in the operational efficiency of the intersection. In this paper, three spatiotemporal resource allocation schemes for right-turning vehicles are proposed, considering the vehicle and pedestrian traffic efficiency in all directions of the intersection. To minimize vehicle and pedestrian delay at the intersection individually, an optimization model is established with the effective green time of each phase and three schemes as decision variables. A right-turn vehicle and pedestrian conflict delay model is developed based on the pedestrian-vehicle interaction behavior as the constraints of the optimization model. The NSGA-II algorithm is used to solve the model, and the quantitative criteria for the exclusive right-turn lane and phase are obtained by sensitivity analysis. The results of this paper can be used as a guide for traffic design and for planning and controlling the operation of right-turning vehicles at intersections.
      原文链接:
    • NETL
    • NSTL
    • Wiley-Hindawi

    Analysis of the Severity of Accidents on Rural Roads Using Statistical and Artificial Neural Network Methods

    Mohammad HabibzadehPooyan AyarMohammad Hassan MirabimoghaddamMahmoud Ameri...
    1.1-1.18页
    查看更多>>摘要：This study assesses the relationship that existed between various variables and their subvariables on rural roads in Qom, Iran, using statistical analysis and calculates the relationship between the considered factors and accident severity. A logit model was applied to determine the factors affecting the severity of accidents. In addition, two artificial neural network (ANN) models were developed using two kinds of learning methods to train neurons to select the best result. The results of modeling and analysis of accidents using various techniques revealed that each technique, depending on its purpose, examined the severity of accidents from a different point of view and represented various outcomes. Finally, the performance of the proposed models was validated utilizing other mathematical models. As a result, putting the output results together, the best measures can be suggested to increase the safety of people on rural roads. The outcomes of this study may aid these service providers in strategic planning and policy framework.
      原文链接:
    • NETL
    • NSTL
    • Wiley-Hindawi

    Analysis of Traffic Volume and Travel-Time Relationship Using Continuous One-Hour Values on Urban Expressway

    Motohiro FujitaAtsuki ArataniShinji YamadaJing Zhao...
    1.1-1.12页
    查看更多>>摘要：To make more efficient use of the expanded freeway and urban expressway networks, various measures such as bottleneck management and wide-area congestion pricing based on traffic data obtained from traffic detectors, including traffic volume and travel time, have been considered. Generally, the congestion status of the data varies from day to day. This study proposes a method for analyzing a graph of traffic volume and travel time to visually and intuitively grasp the change in the daily traffic situation using continuous one-hour values. These values are continuously generated hourly values obtained by shifting data every minute. Twenty-four hours 1 minute data for 128 days on 32 segments with detectors in the Nagoya Expressway Network in Japan were used to draw a continuous one-hour value graph. A number of graphs showed loops of continuous one-hour values with congestion and a smooth variation characteristic of values over time. These graphs provide an accurate estimate of the daily maximum one-hour traffic volumes and facilitate a sequential understanding of the congestion pattern changes on successive route segments. Hourly travel-time prediction models were constructed to macroscopically examine congestion measures over a range of several hours. These models were fabricated with high accuracy using multiple regression analysis based on the characteristics of continuous one-hour values. Exploratory predictive analysis of hourly travel-time models has allowed us to study and discuss various congestion factors in road structures and traffic flows, and it has been found to be easy to grasp the phenomenon and ensure accuracy and operability.
      原文链接:
    • NETL
    • NSTL
    • Wiley-Hindawi

    Applying the Operational Design Domain Concept to Vehicles Equipped with Advanced Driver Assistance Systems for Enhanced Safety

    Heejin KangYoseph LeeHarim JeongGiok Park...
    1.1-1.14页
    查看更多>>摘要：Advanced driver assistance systems (ADASs) assist drivers by alerting them of the occurrence of events based on the sensing capabilities of the vehicle, reducing the effort required by drivers. Most vehicles that are recently launched vehicles have been endowed with ADAS, thereby significantly reducing traffic accidents. However, the Insurance Institute for Highway Safety has reported that traffic accidents caused by driver negligence may increase as drivers have become accustomed to using ADAS. Therefore, drivers must be provided with sufficient information on the appropriate use of ADAS through user manuals. In this study, the regulations regarding the presentation of the operational design domain (ODD) in ADAS user manuals were analyzed. The results indicated that most user manuals do not sufficiently specify the ODD, which is claimed important by various organizations for ensuring safe driving. Additionally, the expression of the limitations and performance of ADAS is ambiguous because most countries are not regulated to explicitly present the ODD when writing ADAS user manuals. Therefore, in this study, the ODD guidelines for presenting ADAS specific to vehicle manufacturers and governments have been outlined in addition to guidelines for drivers on using ADAS. These guidelines can contribute to the development of clear ADAS user manuals, which in turn can ensure the safe driving of ADAS-equipped vehicles.
      原文链接:
    • NETL
    • NSTL
    • Wiley-Hindawi

    Retracted: A Way to Automatically Generate Lane Level Traffic Data from Video in the Intersections

    Journal of Advanced Transportation
    1.1-1.1页
      原文链接:
    • NETL
    • NSTL
    • Wiley-Hindawi