Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science最新文献

Small Traffic Sign Detection Through Selective Feature Fusion Based Faster R-CNN With Arc-Softmax Loss 基于Arc-Softmax损失的快速R-CNN选择性特征融合小交通标志检测

Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science Pub Date : 2019-11-05 DOI: 10.1145/3357000.3366142

Site Li, Yang Gu, Zhichao Song, Tengfei Xing, Yiping Meng, Pengfei Xu, Runbo Hu, Tiancheng Zhang, Ge Yu, Hua Chai

{"title":"Small Traffic Sign Detection Through Selective Feature Fusion Based Faster R-CNN With Arc-Softmax Loss","authors":"Site Li, Yang Gu, Zhichao Song, Tengfei Xing, Yiping Meng, Pengfei Xu, Runbo Hu, Tiancheng Zhang, Ge Yu, Hua Chai","doi":"10.1145/3357000.3366142","DOIUrl":"https://doi.org/10.1145/3357000.3366142","url":null,"abstract":"Traffic signs are basic and important elements in maps. They are related to traffic regulations, profoundly affecting/managing the travel mode of human beings and efficiency of vehicle running. Traffic sign mining technology is applied in many research fields such as traditional map update, high-precision map establishment and automatic driving. Image based traffic sign identification technology has the advantages of low cost and high efficiency over manual processing mode, and traffic sign detection has thus become a significant task with the pacing advancement of autonomous driving. However, many common object detection methods cannot be directly applied to this task, as the size of traffic signs are very small yet they vary considerably. Due to such characteristics, features of traffic signs are difficult to capture, and are harder to discriminate between classes. To address this problem, we proposed a selective feature fusion based Faster R-CNN with Arc-Softmax loss, which optimizes the detection performance from the two following ways: network structure and loss function. We discover that each Faster R-CNN layer is only capable of detecting targets within a certain size range. By carefully selecting and combining different layers' feature maps, we can extract features that effectively represent traffic signs of various sizes. Then, Arc-Softmax loss penalizes the angular distances between the feature vectors of different signs, and their corresponding weight vectors of the last fully connected layers, thereby encouraging intra-class compactness and inter-class separability between learned features. Extensive analysis and experiments on the challenging Tsinghua-Tencent 100K benchmark demonstrate the superiority and implementation simplicity of our proposed method. Code will be made publicly available.","PeriodicalId":153340,"journal":{"name":"Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"279 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121818859","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

Relative Reachability Analysis as a Tool for Urban Mobility Planning: Position Paper 相对可达性分析作为城市交通规划的工具:立场文件

Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science Pub Date : 2019-11-05 DOI: 10.1145/3357000.3366139

Theodoros Chondrogiannis, M. Nascimento, Panagiotis Bouros

{"title":"Relative Reachability Analysis as a Tool for Urban Mobility Planning: Position Paper","authors":"Theodoros Chondrogiannis, M. Nascimento, Panagiotis Bouros","doi":"10.1145/3357000.3366139","DOIUrl":"https://doi.org/10.1145/3357000.3366139","url":null,"abstract":"There is a plethora of user-oriented route planning applications and systems that enable the computation of the fastest journey between two locations using different transportation modes, e.g., car, public transport, walking, bicycle. While useful for individuals, they are of limited interest to a class of users that may be interested in a more global and comparative view of transportation systems in general. In this context, we adopt the view of an urban planner. Urban planners may be interested in queries such as \"if a new transit stop was to be introduced in a given location, would that bring the travel time to a given point-of-interest (POI) or area-of-interest (AOI) by bus closer to the travel time by car, therefore improving air quality and/or overall traffic congestion?\" To answer queries such as this one, as well as many other interesting ones, we propose the concept of relative reachability which aims at measuring how efficient a given transportation mode is (or may be) in comparison to other competing modes. We discuss the challenges associated with the computation of relative reachability of POIs (or AOIs) within a city, which reveal directions for interesting research in spatial data management towards better informed urban mobility planning processes.","PeriodicalId":153340,"journal":{"name":"Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131324580","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: 2

Shortest-Path Diversification through Network Penalization: A Washington DC Area Case Study 通过网络惩罚实现最短路径多样化:华盛顿特区案例研究

Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science Pub Date : 2019-11-05 DOI: 10.1145/3357000.3366137

Danhong Cheng, Olga Gkountouna, Andreas Züfle, D. Pfoser, C. Wenk

引用次数: 11

Map matching when the map is wrong: Efficient on/off road vehicle tracking and map learning 地图错误时的地图匹配:高效的道路车辆跟踪和地图学习

Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science Pub Date : 2019-11-05 DOI: 10.1145/3357000.3366143

James Murphy, Yuanyuan Pao, Albert Yuen

{"title":"Map matching when the map is wrong: Efficient on/off road vehicle tracking and map learning","authors":"James Murphy, Yuanyuan Pao, Albert Yuen","doi":"10.1145/3357000.3366143","DOIUrl":"https://doi.org/10.1145/3357000.3366143","url":null,"abstract":"Given a sequence of possibly sparse and noisy GPS traces and a map of the road network, map matching algorithms can infer the most accurate trajectory on the road network. However, if the road network is wrong (for example due to missing or incorrectly mapped roads, missing parking lots, misdirected turn restrictions or misdirected one-way streets) standard map matching algorithms fail to reconstruct the correct trajectory. In this paper, an algorithm to tracking vehicles able to move both on and off the known road network is formulated. It efficiently unifies existing hidden Markov model (HMM) approaches for map matching and standard free-space tracking methods (e.g. Kalman smoothing) in a principled way. The algorithm is a form of interacting multiple model (IMM) filter subject to an additional assumption on the type of model interaction permitted, termed here as semi-interacting multiple model (sIMM) filter. A forward filter (suitable for real-time tracking) and backward MAP sampling step (suitable for MAP trajectory inference and map matching) are described. The framework set out here is agnostic to the specific tracking models used, and makes clear how to replace these components with others of a similar type. In addition to avoiding generating misleading map matching trajectories, this algorithm can be applied to learn map features by detecting unmapped or incorrectly mapped roads and parking lots, incorrectly mapped turn restrictions and road directions.","PeriodicalId":153340,"journal":{"name":"Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"384 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131406354","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: 10

Alternative Routes for Next Generation Traffic Shaping 下一代流量整形的备选路线

Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science Pub Date : 2019-11-05 DOI: 10.1145/3357000.3366141

Florian Barth, S. Funke

引用次数: 6

Online Stochastic Prediction of Mid-Flight Aircraft Trajectories 飞行中飞机轨迹的在线随机预测

Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science Pub Date : 2019-11-05 DOI: 10.1145/3357000.3366144

Y. Pan, M. Nascimento, J. Sander

{"title":"Online Stochastic Prediction of Mid-Flight Aircraft Trajectories","authors":"Y. Pan, M. Nascimento, J. Sander","doi":"10.1145/3357000.3366144","DOIUrl":"https://doi.org/10.1145/3357000.3366144","url":null,"abstract":"Online trajectory prediction is central to the function of air traffic control of improving the flow of air traffic and preventing collisions, particularly considering the ever-increasing number of air travellers. In this paper, we propose an approach to predict the mid-flight trajectory of an aircraft using models learned from historical trajectories. The main idea is based on Hidden Markov Models, representing the location of aircraft as states and weather conditions as observations. Using our approach, one is able to make predictions of future positions of currently mid-flight aircraft for each minute into the future, optionally concatenating these positions to form the remaining predicted trajectory of an aircraft. We evaluated the effectiveness of the proposed approach using a dataset of historical trajectories for flights over the USA. Using prediction accuracy metrics from the aviation domain, we demonstrated that our approach could accurately predict trajectories of mid-flight aircraft, achieving an effectiveness improvement of 26% in horizontal error and 32% in vertical error over baseline models with virtually no loss in prediction efficiency.","PeriodicalId":153340,"journal":{"name":"Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"300 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122096803","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: 2

Road Map Generation and Feature Extraction from GPS Trajectories Data 基于GPS轨迹数据的道路地图生成与特征提取

Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science Pub Date : 2019-11-05 DOI: 10.1145/3357000.3366140

Tariq Alsahfi, Mousa Almotairi, R. Elmasri, Bader Alshemaimri

{"title":"Road Map Generation and Feature Extraction from GPS Trajectories Data","authors":"Tariq Alsahfi, Mousa Almotairi, R. Elmasri, Bader Alshemaimri","doi":"10.1145/3357000.3366140","DOIUrl":"https://doi.org/10.1145/3357000.3366140","url":null,"abstract":"Road maps are important in our personal lives and are widely used in many different applications. Therefore, an up-to-date road map is essential. The huge amount of GPS data collected from moving objects provides an opportunity to generate an up-to-date road map. In this paper, we propose a novel method to generate road maps using GPS trajectories that is accurate with good coverage area, has a minimum number of vertices and edges, and several details of the road network. Our algorithm starts by identifying the locations of intersections using a line simplification algorithm with spatial-constraints and grid-based method. Then, it creates graph connectivity information to connect intersections and build road segments. In addition, our algorithm extracts road features such as turn restrictions, average speed, road length, road type, and the number of cars traveling in a specific portion of the road. To demonstrate the accuracy of our proposed algorithm, we conduct experiments using two real data sets and compare our results with two baseline methods. The comparisons indicate that our algorithm is able to achieve higher F-score in terms of accuracy and generates a detailed road map that is not overly complex.","PeriodicalId":153340,"journal":{"name":"Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130379508","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: 4

Quantifying the Impact of Autonomous Vehicles using Microscopic Simulations 使用微观模拟量化自动驾驶汽车的影响

Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science Pub Date : 2019-11-05 DOI: 10.1145/3357000.3366145

Hairuo Xie, E. Tanin, S. Karunasekera, Jianzhong Qi, Rui Zhang, L. Kulik, K. Ramamohanarao

引用次数: 11

Destination Signs in OpenStreetMap: Quality Assessment and Instrumentation for Routing OpenStreetMap中的目的地标志:路由的质量评估和仪器

Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science Pub Date : 2019-11-05 DOI: 10.1145/3357000.3366146

J. Rapp, Florian Barth, S. Funke

引用次数: 0

Convoy Detection using Sequence Alignment 使用序列对齐的车队检测

Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science Pub Date : 2019-11-05 DOI: 10.1145/3357000.3366138

Kai Li, Mark McKenney

引用次数: 0