Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science最新文献

{"title":"Toward dynamic path recommender system based on social network data","authors":"F. Rehman, A. Lbath, Mohamed Abdur Rahman, Saleh M. Basalamah, Imad Afyouni, Akhlaq Ahmad, Syed Osama Hussain","doi":"10.1145/2674918.2674927","DOIUrl":"https://doi.org/10.1145/2674918.2674927","url":null,"abstract":"With the advancement of mobile technologies, more and more people are connected to social networks such as Facebook and Twitter. Social networks allow users to share diversity of information including spatio-temporal data either publicly or within their community of interest in realtime. Particularly, by analyzing social network data streams and then validating the content, one can extract knowledge about dynamic road conditions for a given city. This paper presents a dynamic path recommender system that helps users finding optimized routes in dynamic environments based on social network data. The system collects geo-tagged social network data from which relevant knowledge is extracted for identifying constraints such as accidents, weather conditions, and congestions. Moreover, by continuously collecting moving user's geo-tagged data, the system can also identify the traffic flow as well as roads' conditions. As soon as the system identifies and validates a given constraint, it can notify affected users and recommend an adapted route from their current position to the destination. A proof of concept of the system will be shown through three example scenarios.","PeriodicalId":249764,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116817130","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}

{"title":"Realtime response of shortest path computation","authors":"Lin Qi, Markus Schneider","doi":"10.1145/2674918.2674924","DOIUrl":"https://doi.org/10.1145/2674918.2674924","url":null,"abstract":"Computing the shortest path between two locations in a network is an important and fundamental problem that finds applications in a wide range of fields. This problem has attracted considerable research interest and led to a plethora of algorithms. However, existing approaches have two main drawbacks: complete path computation before movement and re-processing when node failure occurs. In this paper, two novel algorithms, RSP (Realtime Shortest Path) and RSP+ (Realtime Shortest Path Plus), are proposed to handle both shortcomings. We perform a network pre-processing to ensure a constant time response of retrieving the shortest route for an arbitrary node to an important set of destinations. RSP+ further divides the complete path into smaller partial paths, which can then be computed in parallel. Besides, considering the continuous changes of the network, like traffic jams and road constructions, where certain paths are blocked, a fast recovery method to efficiently find the best alternative route is integrated into RSP+. Empirical studies have shown that RSP+ can achieve an average query processing time of 0.8 microseconds. Besides, the effectiveness of the recovery mechanism demonstrates that alternative routes can be obtained to avoid unavailable areas.","PeriodicalId":249764,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131310956","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}

{"title":"TeleAgro+: analysis framework for agricultural telematics data","authors":"Johannes Lauer, Ludwig Richter, Timothy Ellersiek, A. Zipf","doi":"10.1145/2674918.2674925","DOIUrl":"https://doi.org/10.1145/2674918.2674925","url":null,"abstract":"This paper provides a framework description for movement data analysis of agricultural telematics data. The framework implements interfaces for interacting with data providers and integrates performant spatial databases for passing storage intense analysis methods on complex datasets. Preprocessing steps include methods to efficiently handle raw data to filter erroneous records and detect noisy outliers. While previously mentioned steps are described briefly, the focus is on an approach to automatically extract geographic features such as field boundaries from the filtered data. This is done using the supervised k-Nearest Neighbors classification method to determine the specific working-mode of the machine and differentiate between field work and road. To find and construct meaningful metrics leading to the required objective, a so-called Space-Time-Cube was utilized which depicts trajectories from agricultural machines in 3dimensional space.","PeriodicalId":249764,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122608603","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}

{"title":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science","authors":"Xin Chen","doi":"10.1145/2674918","DOIUrl":"https://doi.org/10.1145/2674918","url":null,"abstract":"In the near future, vehicles, travelers, and the infrastructure will collectively have billions of sensors that can communicate with each other. Transportation systems, due to their distributed/mobile nature, can become the ultimate test-bed for a ubiquitous (i.e., embedded, highly-distributed, and sensor-laden) computing environment of unprecedented scale. This environment will enable numerous novel applications and order of magnitude improvement of the performance of existing applications. Information technology is the foundation for implementing new strategies, particularly if they are to be made available in real-time to wireless devices in vehicles or in the hands of people. Contributing are increasingly more sophisticated geospatial and spatio-temporal information management capabilities. Human factors, technology adoption and use, user feedback and incentives for collaborative behavior are areas of technology policy central to the success of this ubiquitous computing environment. \u0000 \u0000The emerging discipline of Computational Transportation Science (the science behind Intelligent Transportation Systems) combines computer science and engineering with the modelling, planning, and economic aspects of transportation planning and engineering to leverage developments in the above domains. By taking advantage of ubiquitous computing, Computational Transportation Science applications can help create more efficient, equitable, livable and sustainable transportation systems and communities. \u0000 \u0000The seventh International Workshop on Computational Transportation Science (IWCTS) is particularly timely given the prominence of self-driving and connected vehicle technologies in the global auto industry's near-term growth strategies. We build upon the success of previous workshops to continue to focus on computation, knowledge discovery, and technology aspects of transportation systems while welcoming research papers in computer science, transportation science, urban and regional planning, the automotive arena, civil engineering, robotics, geography, geoinformatics, and other related disciplines.","PeriodicalId":249764,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116380525","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}

{"title":"Deriving driver-centric travel information by mining delay patterns from single GPS trajectories","authors":"Richard Brunauer, K. Rehrl","doi":"10.1145/2674918.2674922","DOIUrl":"https://doi.org/10.1145/2674918.2674922","url":null,"abstract":"Crowd-sourcing approaches for generating accurate real time travel information for road networks is promising but still challenging. For example, travel speeds, even if derived from highly sampled GPS trajectories, have limitations in their interpretability for more sophisticated travel information such as traffic-related delays or level of service (LOS) information. The proposed algorithm in this work analyzes the flow characteristics of individual vehicles by deriving and classifying delays into LOS relevant (e.g. queuing traffic) and LOS non-relevant delay patterns (e.g. stopping at a crosswalk). In contrast to other approaches, the proposed algorithm works on single GPS trajectories collected from individual vehicles (e.g. floating car data - FCD), without the necessity to average travel speeds or travel times of multiple vehicles for reliable LOS estimation. Applied to sample GPS trajectories from test drives the algorithm reaches an overall recognition rate of 82.0% for delay classes slight delay, massive delay, single stop, and multi stops. The recognized delay patterns are capable to distinguish between LOS relevant and LOS non-relevant delays at high accuracy for subsequent delay and LOS information. The recognition of LOS non-relevant single stops reaches a rate close to 100.0%.","PeriodicalId":249764,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114527737","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}

{"title":"Euler histogram tree: a spatial data structure for aggregate range queries on vehicle trajectories","authors":"Hairuo Xie, E. Tanin, L. Kulik, P. Scheuermann, Goce Trajcevski, Maryam Fanaeepour","doi":"10.1145/2674918.2674921","DOIUrl":"https://doi.org/10.1145/2674918.2674921","url":null,"abstract":"This work addresses the problem of aggregation of trajectories data. Specifically, we propose a tree-based data structure for counting vehicle trajectories by mapping them into a set of spatial histograms with different granularities. We also present an approach for processing spatio-temporal range queries by aggregating the histograms in the query rectangles. The proposed methodology can be used for preserving the privacy of vehicle drivers by maintaining aggregated trajectory data. In addition, as we show, it can be used to handle the well-known distinct counting problem. Experimental results show that the new data structure achieves a high level of accuracy in query results and consistently outperforms the leading histogram-based approach.","PeriodicalId":249764,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127498839","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}

{"title":"NormSTAD flight analysis: visualizing air traffic patterns over the United States","authors":"S. Ayhan, Brendan C. Fruin, F. Yang, M. Ball","doi":"10.1145/2674918.2674919","DOIUrl":"https://doi.org/10.1145/2674918.2674919","url":null,"abstract":"In this paper, we describe the NormSTAD Flight Analysis Tool, a novel interactive visualization application that can be used to examine historical data and derive insights on trends, find anomalies and perform strategic planning. A web-based visualization is demonstrated which allows users to analyze flight data and make discoveries pertaining to their 4D trajectories which include their time, distance, altitude, and speed. Unique patterns discovered in this application could result in less fuel consumption by air carriers and more efficient management of departure and arrivals by air traffic controllers. The application consumes ASDI data, product of the Enhanced Traffic Management System (ETMS), then normalizes flight attributes including distance, speed, altitude, and time along the flight path. This information is then displayed on a line chart which can be customized through filtering, coloring and selection. Attributes pertaining to selected flights can be viewed in a details on demand fashion. The result is a both intuitive and visually appealing visualization with the goals of spotting trends and revealing outliers.","PeriodicalId":249764,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124189767","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}

{"title":"Better transit routing by exploiting vehicle GPS data","authors":"D. Delling, G. Italiano, Thomas Pajor, Federico Santaroni","doi":"10.1145/2674918.2674923","DOIUrl":"https://doi.org/10.1145/2674918.2674923","url":null,"abstract":"Current public transport journey planners are mostly based on timetables, i.e., their planning assumes implicitly that all transit vehicles run on schedule. Unfortunately, this is not always the case, as unpredictable delays may occur frequently and for many unplanned reasons. In this scenario, deviations from the original schedule may have quite a negative impact on the quality of the journeys provided by timetable-based planners, as they increase the probability of missed connections and thus result in longer and unpredicted waiting times for the passengers. In this paper, we try to find effective solutions for mitigating this problem and evaluate them empirically on the metropolitan public transportation network of Rome (Italy), where delays are frequent. We first try to assess whether the availability of dynamic information on the geo-location of transit vehicles (via GPS data) may help to improve the quality of the journeys offered by a planner. The main findings of our experiments are that GSP data provides substantial benefits for short journeys, while it does not seem to provide enough insight to improve long journeys. In particular, even with the use of GPS data, a journey planner can still be rather unreliable for long journeys, as there can be large fluctuations between the times predicted by the planner and the actual travel times incurred by passengers. As a second contribution of our work, we provide and evaluate experimentally new routing algorithms to improve the reliability of public transport journey planners.","PeriodicalId":249764,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125252938","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}

{"title":"UPDetector: sensing parking/unparking activities using smartphones","authors":"Shuo Ma, O. Wolfson, Bo Xu","doi":"10.1145/2674918.2674929","DOIUrl":"https://doi.org/10.1145/2674918.2674929","url":null,"abstract":"Real-time information about vacant parking spaces is of paramount value in urban environments. One promising approach to obtaining such information is participatory sensing, i.e. detecting parking/unparking activities using smartphones. This paper introduces and describes multiple indicators, each of which provides an inconclusive clue for a parking or an unparking activity. As a result, the paper proposes a probabilistic fusion method which combines the output from different indicators to make more reliable detections. The proposed fusion method can be applied to inferring other similar high-level human activities that involve multiple indicators which output features asynchronously, and that involve concerns about power consumption. The proposed indicators and the fusion method are implemented as an Android App called UPDetector. Via experiments, we show that our App is both effective and energy-efficient in detecting parking/unparking activities.","PeriodicalId":249764,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121859169","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}

{"title":"A before and after study for travel time of unconventional intersections using probe data","authors":"Bo Xu, Matei Stroila, Jane Macfarlane","doi":"10.1145/2674918.2674926","DOIUrl":"https://doi.org/10.1145/2674918.2674926","url":null,"abstract":"In this paper we propose a method to evaluate the performance of an intersection using location traces collected from sampled vehicles. The performance is measured in terms of the times the vehicles spend traveling through the intersection. As a use case, we apply the method to conduct a before and after study for 1) two unconventional intersection projects including a continuous flow intersection (CFI) and a diverging diamond interchange (DDI), and 2) for two neighboring conventional intersections to factor out effects due to possible changes in the local traffic volume. The analysis shows that for the CFI project travel time is reduced after construction. On the other hand, we find two unexpected results: 1) for the DDI project, we observe an increase of travel time after construction and 2) for the CFI project, we find a decreased benefit during rush hours. We further translate the time reduction to economic benefit based on traffic volume and value of time to offer an understanding of ROI (Return on Investment) for the intersection modification construction costs.","PeriodicalId":249764,"journal":{"name":"Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116832961","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}