{"title":"Quantifying the potential of data-driven mobility support systems","authors":"Lukas Rottkamp, Matthias Schubert","doi":"10.1145/3423457.3429366","DOIUrl":"https://doi.org/10.1145/3423457.3429366","url":null,"abstract":"When traveling it is often necessary to take a detour, for example to find an on-street parking opportunity or a charging station. Numerous systems intending to reduce time or other resources spent on such detours have been presented. An example are methods guiding drivers to free on-street parking opportunities. However, the question of how much can actually be saved by using such solutions when compared to the status quo remains largely unanswered. Often, the cost attached to these detours is unclear. In this work, we present a generalized approach to answer these questions: A methodology consisting of an evaluation environment powered by real-world data and implementations of different scenarios. We then illustrate our proposal by using it to quantify the potential of an optimal assistant for finding on-street parking opportunities. We further show how to generate synthetic but realistic parking data when real-world data is not available.","PeriodicalId":129055,"journal":{"name":"Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117350786","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}
Christopher V. H. Nielsen, Simon Makne Randers, B. Yang, N. Agerholm
{"title":"Estimating travel speed distributions of paths in road networks using dual-input LSTMs","authors":"Christopher V. H. Nielsen, Simon Makne Randers, B. Yang, N. Agerholm","doi":"10.1145/3423457.3429364","DOIUrl":"https://doi.org/10.1145/3423457.3429364","url":null,"abstract":"Thanks to recent advances in sensor technologies, detailed travel speed information is becoming increasingly available. Such data provide a solid data foundation to capture traffic uncertainty, e.g., in the form of travel speed distributions. We study the problem of estimating travel speed distributions of paths in a road network using vehicle trajectory data. Given a path and a departure time, we aim at estimating the travel speed distribution of the path. To this end, we propose a dual-input long-short term memory (DI-LSTM) model. We introduce two new gates with the purpose of combining two input distributions in every iteration, where one distribution is an edge' distribution, and the other is the distribution of the pre-path until the edge, which is obtained from the previous DI-LSTM unit. Empirical studies on a large trajectory dataset offer insight into the design properties of the DI-LSTM and demonstrate that DI-LSTM out-performs classic LSTM, especially for long paths.","PeriodicalId":129055,"journal":{"name":"Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128100712","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}
Sadegh Motallebi, Hairuo Xie, E. Tanin, K. Ramamohanarao
{"title":"Streaming route assignment with prior temporal traffic data","authors":"Sadegh Motallebi, Hairuo Xie, E. Tanin, K. Ramamohanarao","doi":"10.1145/3423457.3429369","DOIUrl":"https://doi.org/10.1145/3423457.3429369","url":null,"abstract":"The cost of traffic congestions has been significantly high in many countries. Traffic congestion can be minimized by coordinated route allocation to maximize the traffic efficiency in the whole road network. Unfortunately, the existing traffic management systems cannot achieve this type of optimization as vehicles tend to follow the shortest/fastest routes to their destinations. Such individually optimized routes may cause significant congestions in a road network. In the coming era of connected autonomous vehicles, traffic management systems can have access to a huge volume of prior temporal traffic data that depicts the historical traffic conditions collected at regular time intervals. This type of data provides great opportunities for traffic optimization at the network level. We propose a new route assignment algorithm for the era of connected autonomous vehicles. Our algorithm optimizes traffic based on real-time traffic conditions and prior temporal traffic data. Our experiments show that the proposed algorithm can improve traffic efficiency by up to 10% over the state-of-the-art algorithm.","PeriodicalId":129055,"journal":{"name":"Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117107410","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":"Near real time analytic processing of traffic data streams","authors":"Paulo Pintor, R. L. C. Costa, José Moreira","doi":"10.1145/3423457.3429365","DOIUrl":"https://doi.org/10.1145/3423457.3429365","url":null,"abstract":"Location data is vital for traffic management and for transportation and urban planning, but also benefits people in daily life, helping on decisions related to route planing and on the use of public transportation. Although historical data can provide insights on expected traffic volume at a certain region and time, predictions based solely on historical data fail to deal with events like street works and traffic-accidents. In this work, we use real time information together with historical data to predict traffic by road segment in the near future. The paper outlines the architecture of the system, the data model and the prediction method. Preliminary results using real world data on taxi positions show that using stochastic processes is a promising approach for short-term traffic forecasting.","PeriodicalId":129055,"journal":{"name":"Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125737460","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":"VAE-Info-cGAN: generating synthetic images by combining pixel-level and feature-level geospatial conditional inputs","authors":"Xuerong Xiao, Swetava Ganguli, Vipul Pandey","doi":"10.1145/3423457.3429361","DOIUrl":"https://doi.org/10.1145/3423457.3429361","url":null,"abstract":"Training robust supervised deep learning models for many geospatial applications of computer vision is difficult due to dearth of class-balanced and diverse training data. Conversely, obtaining enough training data for many applications is financially prohibitive or may be infeasible, especially when the application involves modeling rare or extreme events. Synthetically generating data (and labels) using a generative model that can sample from a target distribution and exploit the multi-scale nature of images can be an inexpensive solution to address scarcity of labeled data. Towards this goal, we present a deep conditional generative model, called VAE-Info-cGAN, that combines a Variational Autoencoder (VAE) with a conditional Information Maximizing Generative Adversarial Network (InfoGAN), for synthesizing semantically rich images simultaneously conditioned on a pixel-level condition (PLC) and a macroscopic feature-level condition (FLC). Dimensionally, the PLC can only vary in the channel dimension from the synthesized image and is meant to be a task-specific input. The FLC is modeled as an attribute vector, a, in the latent space of the generated image which controls the contributions of various characteristic attributes germane to the target distribution. During generation, a is sampled from U[0, 1], while it is learned directly from the ground truth during training. An interpretation of a to systematically generate synthetic images by varying a chosen binary macroscopic feature is explored by training a linear binary classifier in the latent space. Experiments on a GPS trajectories dataset show that the proposed model can accurately generate various forms of spatio-temporal aggregates across different geographic locations while conditioned only on a raster representation of the road network. The primary intended application of the VAE-Info-cGAN is synthetic data (and label) generation for targeted data augmentation for computer vision-based modeling of problems relevant to geospatial analysis and remote sensing.","PeriodicalId":129055,"journal":{"name":"Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114983999","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}
Y. Li, Yiqun Xie, Pengyue Wang, S. Shekhar, W. Northrop
{"title":"Significant lagrangian linear hotspot discovery","authors":"Y. Li, Yiqun Xie, Pengyue Wang, S. Shekhar, W. Northrop","doi":"10.1145/3423457.3429368","DOIUrl":"https://doi.org/10.1145/3423457.3429368","url":null,"abstract":"Given a collection of multi-attribute trajectories, an event definition, and a spatial network, the Significant Lagrangian Linear Hotspot Discovery (SLLHD) problem finds the paths where records in the trajectories tend to be events in the Lagrangian perspective. The SLLHD problem is of significant societal importance because of its applications in transportation planning, vehicle design, and environmental protection. Its main challenges include the potentially large number of candidate hotspots caused by the tremendous volume of trajectories as well as the non-monotonicity of the statistic measuring event concentration. The related work on the linear hotspot discovery problem is designed in the Eulerian perspective and focuses on point datasets, which ignores the dependence of event occurrence on trajectories and the paths where trajectories are. To solve this problem, we introduce an algorithm in the Lagrangian perspective, as well as five refinements that improve its computational scalability. Two case studies on real-world datasets and experiments on synthetic data show that the proposed approach finds hotspots which are not detectable by existing techniques. Cost analysis and experimental results on synthetic data show that the proposed approach yields substantial computational savings.","PeriodicalId":129055,"journal":{"name":"Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129530684","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":"Applying topographic features for identifying speed patterns using the example of critical driving","authors":"Antonios Karatzoglou","doi":"10.1145/3423457.3429362","DOIUrl":"https://doi.org/10.1145/3423457.3429362","url":null,"abstract":"Finding the right features represents an essential part when trying to identify patterns in spatiotemporal signals. This paper describes the concept of using the topographic properties prominence and isolation for recognizing critical driving patterns in speed signals. Experiments show that both features can help identify specific driving segments in the users' speed data such as harsh acceleration, abrupt braking as well as over-speeding phases.","PeriodicalId":129055,"journal":{"name":"Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131432623","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":"Predicting ambient traffic of a vehicle from road abrasion measurements using random forest","authors":"Christian Röger, I. Ismayilova","doi":"10.1145/3423457.3429367","DOIUrl":"https://doi.org/10.1145/3423457.3429367","url":null,"abstract":"The development and application of Intelligent Transportation Systems (ITSs) leads to a growing demand of traffic data. Floating Car Observers (FCOs) contribute by providing information about ambient traffic of vehicles while driving. We present an approach to implement an FCO that uses particulate matter sensors for obtaining road abrasion from cars driving ahead of a test vehicle. Using Random Forest (RF), we predict presence and absence of ambient traffic in the vicinity of test vehicle with particulate matter readings (PM01, PM2.5, PM10) as predictor variables. Results show that RF reaches prediction accuracy ranging from 86 to 99 percent for different train/test split options when analysing individual trajectories as well as 88 to 91 percent accuracy when analysing all trajectories combined. We face limitations mainly when merging single trajectories, due to different initial ambient particulate matter values. We conclude that presence and absence of ambient traffic are predictable using Random Forest with road abrasion values as predictor variables. Further, rainfall events (that may cause wash-off effects on roads) do not significantly change the accuracy of our classification. Optimisation of the model and the need of testing more diverse weather and road conditions remain open tasks for future research.","PeriodicalId":129055,"journal":{"name":"Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133072893","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":"Fast a on road networks using a scalable separator-based heuristic","authors":"Renjie Chen, C. Gotsman","doi":"10.1145/3423457.3429363","DOIUrl":"https://doi.org/10.1145/3423457.3429363","url":null,"abstract":"Fastest-path queries between two points in a very large road map is an increasingly important primitive in modern transportation and navigation systems, thus very efficient computation of these paths is critical for system performance and throughput. We present a novel method to compute an effective admissible heuristic for the fastest-path travel time between two points on a road map, which can be used to significantly accelerate the classical A* algorithm when computing fastest paths. Our basic method - called the Hierarchical Separator Heuristic (HSH) - is based on a hierarchical set of linear separators of the map represented by a binary tree, where all the separators are parallel lines in a specific direction. A preprocessing step computes a short vector of values per road junction based on the separator tree, which is then stored with the map and used to efficiently compute the heuristic at the online query stage. We demonstrate experimentally that this method scales well to any map size, providing a high-quality heuristic, thus very efficient A* search, for fastest-path queries between points at all distances - especially small and medium range. We show how to significantly improve the basic HSH method by combining separator hierarchies in multiple directions and by partitioning the linear separators. Experimental results on real-world road maps show that HSH achieves accuracy above 95% in estimating the true travel time between two junctions at the price of storing approximately 25 values per junction.","PeriodicalId":129055,"journal":{"name":"Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114669915","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}