2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)最新文献_第3页

Capturing and Exploring Unobserved Heterogeneity in Traffic Crash Injuries in China: A Deep Learning Approach 捕捉和探索中国交通碰撞伤害的未观察到的异质性:一种深度学习方法

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101376

Titong Jiang, Wenhao Hu, Yahui Liu, Lingyun Xiao

{"title":"Capturing and Exploring Unobserved Heterogeneity in Traffic Crash Injuries in China: A Deep Learning Approach","authors":"Titong Jiang, Wenhao Hu, Yahui Liu, Lingyun Xiao","doi":"10.1109/ICITE56321.2022.10101376","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101376","url":null,"abstract":"Traffic crash injuries have often been considered a leading threat to human lives. To alleviate this problem, crash data are used to explore various factors of injuries which resulted in accidents with both statistical models and deep learning models. Unobserved heterogeneity, as a result of the complexity of crashes, is inevitable in injury severity analysis. However, it is overlooked in most deep learning models. In this study, TabVAE, a deep learning model which accounts for unobserved heterogeneity, is proposed to analyze the severity of injuries. Data from a total of 3,445 crashes in China, involving 5,384 passengers, are collected and analyzed. The experimental results suggest that the proposed TabVAE model can better capture unobserved heterogeneity. Model comparison demonstrates that TabVAE outperforms classical statistical methods and deep learning models. Additionally, the proportion of unobserved heterogeneity within data and the importance of explanatory variables are identified. The results show that energy dissipation is the key factor to the injury of passengers, and that vehicle damage serves as the most important indicator of energy dissipation. Furthermore, Advanced Driver Assistant System contributes significantly to the unobserved heterogeneity of crashes since it alters the way of energy dissipation. Therefore, addressing unobserved heterogeneity caused by ADAS systems is essential for future accident analysis works.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125633915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research Infrastructure for Real-Time Computer Vision Applications in Unmanned Aerial Systems 实时计算机视觉在无人机系统中的应用研究基础

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101390

Luigi Raphael I. Dy, Kristoffer B. Borgen, Qilei Zhang, John H. Mott

{"title":"Research Infrastructure for Real-Time Computer Vision Applications in Unmanned Aerial Systems","authors":"Luigi Raphael I. Dy, Kristoffer B. Borgen, Qilei Zhang, John H. Mott","doi":"10.1109/ICITE56321.2022.10101390","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101390","url":null,"abstract":"As the use and applications of Unmanned Aerial Systems (UAS) grow, the need for rapid and efficient research and development arises. UAS aspects such as sensing, navigation, and collision avoidance need to be evaluated. To facilitate this, research infrastructure comprising a simulator and a sizeable indoor testing center has been proposed and built. The discussed infrastructure aims to boost research progress with a focus on low-power computer vision solutions for UAS applications. This research and development process optimizes available resources by eliminating suboptimal solutions early. Leveraging a motion capture system, precise and accurate metrics can be used to evaluate and compare competing solutions prior to real-world testing. Additionally, various scenarios and environments can be recreated at relatively low cost in simulation or indoors, providing researchers with rigorous testing opportunities. Competitions are currently held to pit teams from around the world to produce effective solutions for UAS tasks. Beginning with simple problems, tasks of increasing complexity will be introduced commensurate with previous successes. While it is acknowledged that simulation and indoor testing cannot completely replace real-world testing, this infrastructure setup provides an invaluable opportunity for UAS researchers.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122733321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proxy Model Construction for Aerodynamic Effects of High-Speed Trains in Tunnels and Research on Optimization Method of Key Parameters of High-Speed Railway 高速列车在隧道中的气动效应代理模型构建及高速铁路关键参数优化方法研究

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101463

Kexin Zhang, N. Xu, Aijun Cheng

{"title":"Proxy Model Construction for Aerodynamic Effects of High-Speed Trains in Tunnels and Research on Optimization Method of Key Parameters of High-Speed Railway","authors":"Kexin Zhang, N. Xu, Aijun Cheng","doi":"10.1109/ICITE56321.2022.10101463","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101463","url":null,"abstract":"With the increasing speed of high-speed railway operations, the air pressure change of the train in the tunnel, aerodynamic effects on the high-speed train seriously affect the passenger aerodynamic comfort. A smaller tunnel blockage ratio and a higher vehicle sealing index can reduce the change of air pressure in the vehicles when trains are crossing each other in tunnel and improve the ride comfort, but it significantly increases the economic cost. With the ride comfort as a requirement, how to determine the tunnel clearance area and vehicle sealing index to minimize the cost is a key concern for high-speed railway design. In this paper, from the design of high-speed railway, firstly, on the basis of obtaining limited data samples of vehicle interior pressure change by simulation method, the proxy model of tunnel clearance area, vehicle sealing index and 3s extremum of vehicle interior pressure change is constructed based on Kriging method and their accuracy is analyzed; then, with the tunnel clearance area and vehicle sealing index as variables, based on the proxy model and optimization design theory, the proxy model construction of aerodynamic effect in the tunnel of high-speed railway and the optimization method of key parameters of high-speed railway are studied, which provides an important theoretical basis for the tunnel and vehicle design, and is of great significance to improve the passenger ride comfort of high-speed railway and reduce the manufacturing cost.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115150547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Routing Design and Departure Time Determination of Customized Buses Based on Reserved Trip Request 基于预定行程请求的定制客车路线设计与出发时间确定

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101443

Zhuxuan Cheng, Jieqiong Xiong, Tongfei Li, Yanyan Chen

{"title":"Routing Design and Departure Time Determination of Customized Buses Based on Reserved Trip Request","authors":"Zhuxuan Cheng, Jieqiong Xiong, Tongfei Li, Yanyan Chen","doi":"10.1109/ICITE56321.2022.10101443","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101443","url":null,"abstract":"This paper investigates an integrated optimization problem of routing design and departure time determination of customized buses based on demand-responsive service. The problem aims to optimize both a set of customized routes and the departure time of each route based on a set of reserved trip requests. A mixed integer programming model is formulated to address the problem. The model aims to minimize the sum of the operator cost of the bus company and the total travel cost of passengers so that the buses can be efficiently used to provide high-quality services to passengers. To solve the model, a departure time determination method based on a time window backpropagation process is first proposed to optimize the bus departure time for a given route. Then a variable neighborhood search (VNS) algorithm with customized operators is designed to generate the optimized routes. Finally, different numerical tests are conducted to verify the effectiveness of the proposed model and the algorithm, then the sensitivity analysis on key parameters follows.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132733789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved Multi-object Tracking Algorithm for Autonomous Driving Based on DeepSORT 基于深度排序的改进自动驾驶多目标跟踪算法

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101388

Xin Zhou, J. Cui, M. Qu

{"title":"An Improved Multi-object Tracking Algorithm for Autonomous Driving Based on DeepSORT","authors":"Xin Zhou, J. Cui, M. Qu","doi":"10.1109/ICITE56321.2022.10101388","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101388","url":null,"abstract":"Multi-object tracking (MOT) is one of the most important tasks of the vision perception module of autonomous driving, which provides crucial inputs for the downstream task modules such as motion prediction, planning and control. However, how to balance the efficiency and accuracy of MOT in autonomous driving scenario, has always been a great challenge. To address this issue, we try to improve the classical DeepSORT model which follows the Track-By-Detection paradigm from three perspectives of object detection, feature extraction and data association, so as to further improve its tracking accuracy while retaining its online, light-weight and high-efficiency advantages. First, a single-stage detection model Yolo-v5 is adopted to replace the two-stage model Faster R-CNN used in DeepSORT, which leads to significant improvement of detection efficiency and accuracy. Second, specified light-weight ShuffleNet-v2 in stead of time-consuming DenseNet is used to extract appearance feature from detected object bounding boxes to further enhance the efficiency of MOT. Finally, an efficient and accurate data association algorithm integrating cascade matching and IoU matching is proposed. Based on the cost matrix jointly constructed by appearance features, motion and shape, effective tracking of multiple objects is realized. A large number of experiments have been conducted to compare our improved MOT model proposed and the DeepSORT model in terms of many evaluation metrics, such as IDs, IDF1, MOTA, MOTP, etc. Moreover, the sensitivity of some hyper-parameters that have important influence on MOT efficiency is also analyzed. Experimental results show that the efficiency and accuracy of MOT can be effectively balanced by keeping the lightweight and efficient characteristics of the original DeepSORT model and further improving the performance of its three key components, i.e. object detection, feature extraction and data association.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133509142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Common Traffic Object Recognition Method Based on Roadside LiDAR 一种基于路边激光雷达的交通目标识别方法

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101469

YuLin Xu, W. Liu, Yong Qi, Yifan Hu, Weibin Zhang

引用次数: 0

SaveDat: Spatio-Temporal Trajectory Compression by LSTM SaveDat: LSTM的时空轨迹压缩

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101477

Shay Horovitz, Guy Cohen, Dan Shmirer, Shir Boxer, Itai Blumenkrantz, Mike Lasry

{"title":"SaveDat: Spatio-Temporal Trajectory Compression by LSTM","authors":"Shay Horovitz, Guy Cohen, Dan Shmirer, Shir Boxer, Itai Blumenkrantz, Mike Lasry","doi":"10.1109/ICITE56321.2022.10101477","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101477","url":null,"abstract":"The emergence of automatic navigation systems brought an exponential increase in location data transmitted by vehicles to cloud services, which opens the door to optimizing storage and reducing communication of this data. In addition, location data and trajectories are often overlapping in many cases, such as navigation, fleet management, and logistics. Utilizing the collected data from many vehicles can contribute for location data compression sent from the vehicle to the service, but such data is held by the service side and transmitting it to the vehicle is inefficient in bandwidth and storage. As such, it would be advantageous if the vehicle could be made aware of common patterns of location-based trajectories in order to efficiently compress its location transmissions to the service or even mute while the expected trajectory keeps an acceptable error. We propose SaveDat - a Spatio-temporal Trajectory compression solution that discerns route patterns from location data at the service side and shares those patterns with vehicles in order to fit actual trajectories without sending location data or compressing it. Experiments present a compression ratio of up to 100:1 in typical scenarios.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115845400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective Mobile Charging Solution for Electric Vehciles in Smart Cities 智慧城市电动汽车高效移动充电解决方案

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101408

Shorooq Alaskar, Mohamed F. Younis

{"title":"Effective Mobile Charging Solution for Electric Vehciles in Smart Cities","authors":"Shorooq Alaskar, Mohamed F. Younis","doi":"10.1109/ICITE56321.2022.10101408","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101408","url":null,"abstract":"Range anxiety has become a major stumbling block for the adoption of electric vehicles (EVs) when it comes to long journeys. Aside from the extended charging period, most of the existing charging techniques require EVs to remain stationary while being charged. With the major advance in dynamic wireless charging technology, the Mobile Energy Disseminator (MED) has been introduced as a means to supply energy to an EV on the road. MED can provide energy to the vehicle during its motion without the need for larger batteries or an expensive change in infrastructure. However, MED is often associated with vehicles with predefined travel paths which forces an EV to alter its route to take advantage of the MED charging option. In this paper, we present RIMEC, a -Routing for -Increased Mobile Energy Charging algorithm that determines an optimized EV travel route for utilizing MEDs in order to maximize the amount of potential energy that can be harvested while minimizing the impact on travel time. The experiment results conducted in a realistic simulation scenario indicate that RIMEC not only achieves its design goals but also boosts the system throughput by serving a larger number of EV charging requests.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131854001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parking Reason Inference of Trucks Based on Trajectory Data 基于轨迹数据的货车停车原因推断

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101396

Meng Yuan

引用次数: 0

Interaction Design between Train Master Control Unit and ETBN and SDTv2 Application 列车主控单元与ETBN的交互设计及SDTv2的应用

2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2022-11-11 DOI: 10.1109/ICITE56321.2022.10101419

Zhaoling Gao, Wei Wang, Haoshang Zhou, Demin Wang

{"title":"Interaction Design between Train Master Control Unit and ETBN and SDTv2 Application","authors":"Zhaoling Gao, Wei Wang, Haoshang Zhou, Demin Wang","doi":"10.1109/ICITE56321.2022.10101419","DOIUrl":"https://doi.org/10.1109/ICITE56321.2022.10101419","url":null,"abstract":"At present, the train level network control communication mostly adopts the twisted Wire Train Bus (WTB) cable transmission. But its speed is slow and the amount of transmitted data is small, so it is difficult to meet the current requirements. Therefore, the high-speed and large amount of industrial Ethernet communication will become the mainstream of the future train level network control communication. It will research the train level network control communication based on the Train Real-time Data Protocol (TRDP) in combination with the requirements of the research project-200 km/h power centralized Electric Multiple Unit (EMU), and compare the international standards and the national standards of China in this paper. It will adopt the method of combining process data and message data to realize the data interaction requirements between the central control unit and the Ethernet Train Backbone Node (ETBN), acquire relevant data of Ethernet Train Backbone (ETB) nodes and vehicle formation information of the whole vehicle, and further acquire control data of the multiple connected vehicles. At the same time, transmit the control data and message data of the own vehicle to the multiple connected vehicles, so as to realize interconnection and mutual control between multiple connected vehicles. In order to improve the correctness and real-time of data transmission, it introduced the Secure Data Transmission protocol version2(SDTv2) for communication between the central control unit and other equipment in this project, and completed the interconnection test with the Ethernet card of Swiss Duagon company to ensure the safety and reliability of important data.","PeriodicalId":160149,"journal":{"name":"2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116430201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0