The deployment of Internet of Things and cyber-physical technologies leads to increased collection of spatiotemporal data. In the transportation domain, this data is being used in an expanding range of applications, e.g., to enhance road safety. The capture of changes to traffic signs, e.g., due to wear, road construction, accidents, or theft, helps ensure well-functioning and safe road networks. However, this process has so far been largely manual, making it costly and meaning that changes are often captured with considerable delays. To capture changes more cost-effectively and more frequently, we introduce AutoTracker, an automated reporting system that exploits low-cost cameras in vehicles to capture changes. The system features a three-layer architecture—perception, analysis, and storage—that incorporates vehicle motion dynamics for improved performance. The analysis layer integrates physical modeling, R-tree-based retrieval, and calibration components to refine detection results. We also introduce new datasets and evaluation metrics to assess the performance of AutoTracker. Experiments show that AutoTracker is capable of state-of-the-art accuracy and efficiency in traffic sign change reporting.

Today it is simple to collect and transmit GNSS data from vehicles with a high frequency. However, there is a storage and processing cost related to handling the data. Further, some data has limited value, e.g., redundant GNSS data from a vehicle stopped at an intersection. In this paper, sampling methods for GNSS data focusing on time, distance, speed, and heading changes are systematically analyzed. The goal is to retain only valuable data. A set of metrics is proposed to quantify the value of the data, e.g., no redundancy and retention of the spatial and temporal distributions. An existing commercial approach to GNSS-based travel time computation in road networks is used to measure if the sampled GNSS is accurate for this important purpose. The results show that sampling methods using individual properties, such as time, space, or speed, have their own strengths and weaknesses. However, with hybrid methods, it is possible to retain the strengths and eliminate most weaknesses. Using a large, real-world GNSS dataset, we show that a hybrid method that retains only 20 % of the original data can achieve travel time estimation with an error of just 1.0 – 1.3%.

The ship trajectories collected by the Automatic Identification System (AIS) are widely used in maritime applications. However, a significant issue with AIS data is that large AIS gaps occur. Existing trajectory imputation methods for AIS data have three main limitations: (1) the temporal aspect is ignored; (2) the methods fall short when dealing with complex ship movements; (3) the common-route assumption does not always hold. To overcome these limitations, we propose TrajImpMC, a tracking-based framework that uses polygon-based ship location estimates from multiple cameras to impute large AIS gaps. TrajImpMC combines speed constraints and Kalman filters, and can return imputed trajectories that contain both spatial and temporal information. Extensive experiments are conducted on real datasets. In terms of the quality of the imputed trajectories, TrajImpMC improves the RMSE errors by at least one order of magnitude over two existing state-of-the-art AIS imputation methods. In addition, a visual comparison shows that the imputed trajectories of TrajImpMC align very well with the real ship trajectories during AIS gaps. The code for this paper is available at: https://github.com/songwu0001/TrajImpMC.

Accurate vessel trajectory prediction facilitates improved navigational safety, routing, and environmental protection.However, existing prediction methods are challenged by the irregular sampling time intervals of the vessel tracking data from the global AIS system and the complexity of vessel movement.These aspects complicate model learning and generalization.To address these challenges and improve vessel trajectory prediction, we propose Multi-modAl Knowledge-Enhanced fRamework (MAKER) for vessel trajectory prediction.To contend better with the irregular sampling time intervals, MAKER features a Large language model-guided Knowledge Transfer (LKT) module that leverages pre-trained language models to transfer trajectory-specific contextual knowledge effectively.To enhance the ability to learn complex trajectory patterns, MAKER incorporates a Knowledge-based Self-paced Learning (KSL) module.This module employs kinematic knowledge to progressively integrate complex patterns during training, allowing for adaptive learning and enhanced generalization.Experimental results on two vessel trajectory datasets show that MAKER can improve the prediction accuracy of state-of-the-art methods by 12.08%-17.86%.

Monitoring roundabouts and signalized intersections in a road network is important, e.g., to reduce travel time and greenhouse gas emissions. The monitoring of such junctions is a challenging problem, and current approaches mainly use high-cost solutions for a selected few. In this work, we present a framework for the automated identification and monitoring of all junctions in a road network. The framework utilizes detailed trajectory data or high-level segment-based data to compute travel time and energy consumption for all turn directions. These metrics are then aggregated per junction to enable a fair comparison between roundabouts and intersections. The aggregated metric is used to provide an overview of all junctions and to pinpoint those performing poorly. An analysis of 1,394 junctions using 334,081 trajectories quantifies the different benefits of roundabouts and intersections, e.g., the travel time in roundabouts varies little, and turns are 21% to 155% more energy-consuming than going straight in intersections. Further, the aggregated junction metric makes it simple to monitor all analyzed junctions and detect the worst-performing. The analysis also clearly shows the benefits of trajectory data over segment-based data for junction monitoring.

The unprecedented availability of new types of data coupled with the invention of new technologies combine to enable entirely new or higher-resolution services that in turn enable more rational and data-driven processes. We consider the overall process of vehicular transportation and, more specifically, the process of deciding which route to follow when having to reach a destination. Early solutions modeled a road work as a graph, used sparse in-road sensor data to assign weights to graph edges, and then applied improved versions of Dijkstra's algorithm to find routes with the lowest sums of edge weights. Since then, massive vehicle trajectory data has become available. When coupled with new technologies, this data enables entirely new and higher-resolution routing services that in turn enable better routing. For more than a decade, the authors have engaged in research aimed at exploiting trajectory data to enable better routing. The resulting technologies were developed outside a DBMS. Here, we cover aspects of this research. Further, we challenge the community to develop DBMS support for these and other aspects of routing.

AIS data from ships is excellent for analyzing single-ship movements and monitoring all ships within a specific area. However, the AIS data needs to be cleaned, processed, and stored before being usable. This paper presents a system consisting of an efficient and modular ETL process for loading AIS data, as well as a distributed spatial data warehouse storing the trajectories of ships. To efficiently analyze a large set of ships, a raster approach to querying the AIS data is proposed. A spatially partitioned data warehouse with a granularized cell representation and heatmap presentation is designed, developed, and evaluated. Currently the data warehouse stores 312 million kilometers of ship trajectories and more than 8 billion rows in the largest table. It is found that searching the cell representation is faster than searching the trajectory representation. Further, we show that the spatially divided shards enable a consistently good scale-up for both cell and heatmap analytics in large areas, ranging between 354% to 1164% with a 5x increase in workers

Recent advances, especially in deep learning, allow to effectively detect ship targets in surveillance videos. However, the translation of these detections to the real-world locations of ships has not been sufficiently explored. The common approach in the literature is using a transformation matrix to convert a pixel to a real-world coordinate. However, this approach has three shortcomings: first, a set of reference point pairs has to be manually prepared to establish the matrix; second, the matrix always maps a pixel to the same real-world coordinate, ignoring that there is no one-to-one correspondence between discrete pixel coordinates and continuous real-world coordinates; third, this approach can only work with one camera. In light of this, we propose a technique PixelToRegion that explicitly takes into account the uncertainty in coordinate conversion by mapping each pixel to a spatial polygon. Next, we propose a new algorithm MCbSLE that can estimate ship locations using pixel sets from multiple cameras. The precision of location estimation by MCbSLE is enhanced through spatial intersection between polygons from different cameras. Experiments are conducted under 16 carefully designed multi-camera settings to evaluate MCbSLE w.r.t. four factors: different ports, the number of cameras, the distance between cameras, and camera headings. Results on one-day ship trajectory data show that (1) an 79.8% accuracy in the number of coordinates can be achieved by MCbSLE when there are no more than 10 ships in camera views; (2) using multiple cameras can improve the precision of location estimation by one order of magnitude compared with using one camera.

In this paper, we present an architecture for mobility data spaces enabling trustworthy and reliable data operations along with its main constituent parts. The architecture makes use of a data lake for scalable storage of diverse mobility data sets, on top of which separate computing and storage layers are implemented to allow independent scaling with a data operations toolbox providing all data operations. Furthermore, to cater for mobility analytics, machine learning and artificial intelligence support, an edge analytics suite is provided that encompasses distributed algorithms for mobility analytics and federated learning, thereby exploiting edge computing technologies. In turn, this is supported by a resource allocator that monitors the energy consumption of data-intensive operations and provides this information to the platform for intelligent task placement in edge devices, aiming at energy-efficient operations. As a result, an end-to-end platform is proposed that combines data services and infrastructure services towards supporting mobility application domains, such as urban and maritime.

While many countries are developing appropriate actions towards a greener future and moving towards adopting sustainable mobility activities, the real-time management and planning of innovative transportation facilities and services in urban environments still require the development of advanced mobile data management infrastructures. Novel green mobility solutions, such as electric, hybrid, solar and hydrogen vehicles, as well as public and gig-based transportation resources are very likely to reduce the carbon footprint. However, their successful implementation still needs efficient spatio-temporal data management resources and applications to provide a clear picture and demonstrate their effectiveness. This paper discusses the major data management challenges, open issues, and application opportunities closely related to urban green mobility. Additionally, it reports on recent successful experiences and challenging research questions. Furthermore, it highlights the global benefits one can expect when developing green mobility and emphasizes how mobile data infrastructures and services will play a crucial role in achieving these goals.

Detecting traffic signs on images has many applications within the transportation area, e.g., speed limit detection, navigation, asset management, and autonomous vehicles. A significant challenge in detecting traffic signs is that a large set of labeled images are needed to train, test, and validate an object detector. In this paper, we demonstrate a webapp that enables traffic-sign detection of 169 common traffic signs and downloads the results. Users can contribute to improving object detection by (1) annotating user-uploaded images, (2) verifying traffic signs detected on existing imagery, or (3) donating new imagery by uploading images, e.g., taken with smartphones. The set of detected traffic signs is stepwise increased by correcting mistakes in object detection using the online verification part of the system. The user can immediately download all verified traffic-sign objects. Users can also download 43,995 traffic-sign objects from 378 different classes. This existing set of traffic-sign objects is updated nightly with objects two or more users have verified. The demonstration includes detecting traffic signs on imagery uploaded by the audience and downloading the traffic sign detected, e.g., to be used internally in an organization.

Accurate estimation of shipping CO2 emissions is important for developing regulations to combat the greenhouse effect. Many shipping CO2 emissions models have been proposed in the past decades. However, most of them are only validated for a few specific ships, and there is a lack of data-driven validation and comparison of these models on a large scale. To fill this gap, this study proposes a general evaluation framework to quantitatively validate and compare different emission models. This framework is based on data integration of three types of data sources: ship technical details, AIS trajectory, and weather. Along with emission models, these data are fed into three carefully-designed modules that perform analysis at both grid and trajectory level as well as use annually aggregated fuel consumption ground truth. Extensive experiments are conducted on one-month data from 1,571 ships passing Danish waters to demonstrate the utility of the framework and insights into the accuracy of five popular CO2 emission models are presented.

The large search companies have very clearly shown that full-text search on very large datasets can be executed efficiently. In this paper, we show how querying spatio-temporal trajectory data can be converted to a full-text search problem. This allows for the reuse of efficient data and index structures from the full-text domain. The core idea is to convert a trajectory into a document consisting of spatial and temporal terms. For example, spatial terms are municipality names, zip codes, or road-network segment numbers. Temporal terms are, for example, morning, weekday, spring, and 2020. Using a dataset consisting of +62 million trajectories (24.9 billion GPS points) we show how to query this dataset efficiently. These queries cover spatial, temporal, and spatio-temporal queries.

Images are an important data source for many applications such as autonomous driving and map annotations. However, it is expensive to collect large imagery sets and annotate objects such as traffic signs. In the paper, we present a web solution where a user efficiently can label traffic signs by verifying or correcting the labels provided by an object-detection model. All verified/corrected objects can immediately be downloaded by the user for usage in for example semi-supervised or supervised learning. The tool is available via the URL https://gomap.cs.aau.dk

Detection of fishing activities in trajectory data is important for authorities to develop fishery management policies and combat illegal, unreported, and unregulated (IUU) fishing at sea. However, the complex movement patterns of fishing activities challenge existing trajectory segmentation approaches, which may not identify complete fishing activities. In light of this, we propose a window-based trajectory segmentation algorithm which aims to detect fishing activities as completely as possible. Firstly, we introduce a visualization-based technique TPoSTE to help design features characterizing different movement patterns. Secondly, a window-based segmentation algorithm WBS-RLE is proposed to split a trajectory into fishing and non-fishing segments. WBS-RLE first utilizes a pre-trained classifier to label windows in a trajectory as fishing or non-fishing, then it uses the run-length encoding technique to merge those labeled windows into complete fishing activities. The effectiveness of our approach and its advantages over existing approaches are evaluated on a real-world trajectory dataset.

All large ships are by international law required to provide their position, speed, and course while sailing. This data is called AIS data. Several maritime organizations make this data freely available. In this paper, we present two approaches to querying AIS data. The first approach combines the individual AIS data records into trajectories and the second approach is to combine many trajectories into heat maps. The first approach is well suited, e.g., to find the complete route of a few ships or study how many ships are navigating in a smaller area known to be complicated to sail. The heat-map approach is particularly well suited to provide an overview of ship movements in large areas. For the trajectory approach, we introduce and define a novel way to query AIS data called a trident query. This query type is developed in close collaboration with domain experts. The core idea with a trident query is to visualize route choices. The heat-map approach works both for user-defined areas and for predefined Areas Of Interest (AOI) cells. The trajectory approach is difficult to scale and we show how the trajectories can be simplified to make querying and visualization more efficient. We present data on a map and statistical details are provided in graphs and tables, e.g., the distribution of ship types and ship dimensions (length, width, and draught). End-users can filter on attributes such as ship IDs, ship types, and ship dimensions for both the trajectory and heap-map approaches.

Maintaining a database with the type, location, and direction of traffic signs is a labor-intensive part of asset management for many road authorities. Today there are high-quality cameras in cell-phones that can add location (EXIF) metadata to the images. This makes it efficient and cheap to collect large geo-located imagery datasets. Detecting traffic signs from imagery is also much simpler today due to the availability of several high-quality open-source object-detection solutions. In this paper, we use the detection of traffic signs to find both the location and the direction of physical traffic signs. Five approaches to cluster the detections are presented. An extensive experimental evaluation shows that it is important to consider both the location and the direction. The evaluation is done on a novel dataset with 21,565 images that is available free for download. This includes the ground-truth location of 277 traffic signs and all source code. The conclusion is that traffic signs are detected with an F1 score of 0.8889, a location accuracy of 5.097-meter (MAE), and a direction accuracy of ± 11.375°(MAE). Only data from two trips are needed to get these results.

The continued spread of electric vehicles raises new challenges for the supporting digital infrastructure. For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel time as well as energy use. We envision a two-tier architecture to produce such predictions. First, a routing and travel-time-prediction subsystem generates a suggested route and predicts how the speed will vary along the route. Next, the expected energy use is predicted from the speed profile and other contextual characteristics, such as weather information and slope.

Action cameras and smartphones have made it simple and cheap to collect large imagery datasets from the road network while driving. At the same time, several frameworks, e.g., Detectron2 and the TensorFlow Object Detection API, have made it fairly easy to build object-detection models for your imagery datasets. In this paper, we use the Detectron2 framework to detect 18 different common traffic signs from 351.469 images. The purpose is to automate the asset management of traffic signs in large road networks. A task that today often is done in a manual and labor-intensive manner. To improve the accuracy of determining the locations of traffic signs, we develop a new, general method that uses the size of the object detected (in pixels) and the camera's GPS position and heading. To further enhance the accuracy, multiple detections of the same physical traffic sign are clustered. The traffic-sign type and computed location are stored in a spatial data warehouse. The clustered locations are presented on a digital road network in a web app. This app allows visual inspection of the overall approach. We demonstrate that the accuracy of the computed locations is good, e.g., signs are placed on the correct side of the road or in/out of a roundabout.

The increasingly massive volumes of vehicle trajectory data that are becoming available hold the potential to enable more accurate vehicle travel-time estimation than hitherto possible. To enable such uses, we present a multi-threaded, in-memory trajectory store that supports efficient and accurate travel-time estimation for road-network paths based on network-constrained trajectories. The trajectory store employs advanced indexing to support so-called strict-path queries that retrieve all trajectories that traverse a given path to provide accurate travel-time estimations. As a key novel feature, the store is designed and implemented to exploit modern non-uniform memory access (NUMA) systems. We provide a detailed experimental study of the performance of the trajectory store using a synthetic trajectory data set based on real traffic data. The study shows that query latency can be halved compared to our baseline system.

Speed is a major killer in traffic. This paper presents the DriveLaB telematics platform that monitors drivers in real-time and provides feedback if they are speeding. The platform has been developed in collaboration with a major Danish insurance company and used in three larger field trials. The platform uses an Android/iOS smartphone app at the client side and does all data processing at the server-side, e.g., map-matching. The paper first provides an overview of the entire platform and then focuses on three major technical challenges: 1) A trip scoring algorithm that allows for comparison of scores across trip length. 2) A notification system that provides both positive and negative real-time feedback to the drivers, without leading the drivers' attention away from traffic. 3) Auto start of the cross-platform app to make it convenient for drivers to participate in the field trials. Three field trials, with 32 participants driving 57,933 km has been conducted. The results of these trials validates that trip scores can be compared across trip length. Further, we demonstrate that doing all data-processing on the server side is a viable approach also for real-time notifications and that drivers are notified with reasonable delays. Finally, we show that the client auto start is fast, robust, and convenient.

Large vehicle trajectory data sets can give detailed insight into traffic and congestion that is useful for routing as well as transportation planning. Making information from such data sets available to more users can enable applications that reduce travel time and fuel consumption. However, extracting such information efficiently requires deep knowledge of the underlying schema and indexing methods. To enable more users to extract information from trajectory data, we have developed an API that removes the need to be familiar with the schema. Furthermore, when giving access to trajectory data, privacy concerns often call for the application of anonymization methods before analysis results are made available. In our demonstration, owners of trajectory data are able to experiment with different levels of anonymization to see how this affects the quality of different types of trajectory analysis services implemented on top of a large trajectory data set.

Road networks are a type of spatial network, where edges may be associated with qualitative information such as road type and speed limit. Unfortunately, such information is often incomplete; for instance, OpenStreetMap only has speed limits for 13% of all Danish road segments. This is problematic for analysis tasks that rely on such information for machine learning. To enable machine learning in such circumstances, one may consider the application of network embedding methods to extract structural information from the network. However, these methods have so far mostly been used in the context of social networks, which differ significantly from road networks in terms of, e.g., node degree and level of homophily (which are key to the performance of many network embedding methods).We analyze the use of network embedding methods, specifi-cally node2vec, for learning road segment embeddings in road networks. Due to the often limited availability of information on other relevant road characteristics, the analysis focuses on leveraging the spatial network structure. Our results suggest that network embedding methods can indeed be used for deriving relevant network features (that may, e.g, be used for predicting speed limits), but that the qualities of the embeddings differ from embeddings for social networks.

Up to 50% of delay in traffic is due to non-reoccurring events such as traffic accidents. Accidents lead to delays, which can be costly for transport companies. Road authorities are also very interested in warning drivers about accidents, e.g., to reroute them. This paper presents a novel and efficient approach and system for uncovering effects from traffic accidents by dynamic integration of GPS, weather, and traffic-accident data. This integration makes it possible to explore and quantify how accidents affects traffic. Dynamic integration means that data is combined at query time as it becomes available. This is necessary, because data can be missing (weather station down) or late arriving (accident not officially reported by the police yet). Further, the integration can be parameterized by the user, e.g., distance to accident, which is important due to inaccuracy in reporting. We present the integrated data on a map and show the effectiveness of the integration by allowing users to interactively browse all accidents or pick a single accident to study it in very fine-grained details. Using information from 31 433 road accidents and 38 billion GPS records, we show that the proposed dynamic data integration scales so very large data sets.

The focus on reducing CO2 emissions from the transport sector is larger than ever. Increasingly stricter reductions on fuel consumption and emissions are being introduced by the EU, e.g., to reduce the air pollution in many larger cities. Large sets of high-frequent GPS data from vehicles already exist. However, fuel consumption data is still rarely collected even though it is possible to measure the fuel consumption with high accuracy, e.g., using an OBD-II device and a smartphone. This paper, presents a method for comparing fuel-consumption estimates using the SIDRA TRIP model with real fuel measures to determine if the fuel-consumption model is sufficiently accurate. The model is implemented using a 2D, a simple 3D, and a high-precision (H3D) road map of Denmark. The original 2D map is lifted to a 3D map using a Digital Elevation Model (DEM). Results show that introducing a 3D map improves the accuracy of fuel-consumption estimates with up to 40% on hilly roads. There is only very little improvement of the high-precision (H3D) map over the simple 3D map. The fuel consumption estimates are most accurate on flat terrain with average fuel estimates of up to 99% accuracy. The fuel estimates are most inaccurate uphill/downhill and when the vehicles accelerate at speeds above 50 km/h.

Speed is the major killer in traffic. The typical approach to enforce speed limits is by having the police monitor drivers and issue tickets when they are speeding. In this paper, we introduce a new platform where speeding is reduced by nudging. The three major approaches are to warn drivers if they are speeding, praise the drivers if they are driving within the speed limit, and grade each trip. The latter is used to rank drivers, e.g., drivers within a company are ranked according to their trip scores. We present the DriveLaB smartphone app that provides real-time feedback to the drivers. All computations are done at the server-side and we show how to compute real-time feedback and store trip data. In addition, we report on two field trials in the Copenhagen and Aalborg Areas where the platform is tested in collaboration with a major Danish insurance company.

Travel-time estimation for paths in a road network often relies on pre-computed histograms that are usually available on a road segment level. Then the pre-computed histograms of the segments of a path are convolved to obtain a histogram that estimates the travel time. With the growing sizes of trajectory datasets, it becomes possible to compute histograms for increasingly longer sub-paths. Since pre-computation is infeasible for all sub-paths in a road network, we propose computing histograms on-the-fly, i.e., during routing. Such an on-the-fly method must filter the underlying trajectory dataset by spatio-temporal predicates to obtain the relevant trajectories and offers the opportunity to apply additional filtering predicates to the trajectories with little overhead. We report on a study showing that considerable improvements in accuracy of the histograms obtained for paths can be obtained by choosing filtering predicates that not only adapt to the intended start of a trip, but also to the driver and the weather. We also make the cases for a sub-path partitioning based on segment categories since there are significant differences between road types when applying our on-the-fly method.

GPS data is often used for computing travel time in road networks. In addition, GPS data is often map matched to find the routes driven by vehicles. Today GPS data is collected with different sampling periods, however, both the computed travel times and the routes found by map matching algorithms actual depends on the sampling period. This paper proposes a generic approach to study how travel time and map matched routes vary with the sampling period. Two types of map matching algorithms are used, point based where each position is handled individually, and trajectory based where positions from a vehicle is consider a data stream. A baseline is created using a real-world data set of 455 million positions from 368 vehicles collected with a sampling period of 1 second. This data set is downsampled to 8 data sets with sampling periods between 2 and 120 seconds. This downsampling enables an apple-to-apple comparison of travel time computation and route restoration for different sampling periods. The main conclusion is that travel times are reasonably accurate if the sampling period is 5 second or below for the point-based method and 20 seconds or below for the trajectory-based method. GPS data collected with 60 second is to inaccurate to be used for computing travel times. Trajectory-based map matching works best if the sampling period is 20 seconds or below.

Increasingly large volumes of vehicle trajectory data are becoming available. This data holds the potential to offer detailed insight into important aspects of vehicular transportation and road networks. This insight can in turn be utilized to enable a range of important services. Specifically, we demonstrate a system that is capable of leveraging very large collections of GPS trajectories for enabling interactive analyses of traffic in road intersections, which are often bottlenecks in road networks. These analyses are able to provide detailed insight into the time-varying functioning of intersections, and they offer a solid, data-driven foundation for improving the capacity of intersections and the overall road network. The system enables more cost-effective analyses than what is possible with traditional techniques. Demonstration participants will gain first-hand experience with interactive analyses on top of a database of some 40 billion GPS records capturing more than a billion km of driving.

With the decreasing cost and growing size of main memory, it is increasingly relevant to utilize main-memory indexing for efficient query processing. We propose SPNET, which we believe is the first in-memory index for network-constrained trajectory data. To exploit the main-memory setting SPNET exploits efficient shortest-path compression of trajectories to achieve a compact index structure. SPNET is capable of exploiting the parallel computing capabilities of modern machines and supports both intra- and inter-query parallelism. The former improves response time, and the latter improves throughput. By design, SPNET supports a wider range of query types than any single existing index. An experimental study in a real-world setting with 1.94 billion GPS records and nearly 4 million trajectories in a road network with 1.8 million edges indicates that SPNET typically offers performance improvements over the best existing indexes of 1.5 to 2 orders of magnitude.

The combined battery capacity in electric vehicles (EVs) is considered an integral part of balancing a smart power grid in the future. In addition, EVs can reduce the usage of fossil fuels in the transport sector because EVs can be charged using electricity from renewable energy sources, such as wind turbines. To both enable a smart grid and the use of renewable energy, it is essential to know when and where an EV is plugged into the power grid and what battery capacity is available. In this paper, we present a generic spatio-temporal data-warehouse model for storing detailed information on all aspects of charging EVs, including integration with the electricity prices from a spot market. The proposed data warehouse is fully implemented and currently contains 2.5 years of charging data from 176 EVs. We describe the date warehouse model and the implementation including complex operations such as spatially identifying charging station usage patterns. Further, we give examples of novel analyses, e.g., how the free battery capacity in the fleet of EVs changes over the day and how users can save money by charging the EVs when the electricity price is the lowest.

Congestion is a major problem in many cities. In order to monitor and manage traffic, a number of different sensor types are used to collect traffic data. This includes GPS devices in the vehicles themselves as well as fixed Bluetooth sensors along the roads. Each sensor type has advantages and disadvantages. Where GPS has a wide coverage of the road network, Bluetooth sensors gather data from a much higher number of vehicles. In this paper we present Fog BAT, a system that combines GPS data with Bluetooth data. The goal of the system is to retain the advantages of both. We show how the data types are aligned to ensure that data from each sensor type is related to the exact same part of the road network and cover the same time period. Using very large real-world data sets, we use the system to compare travel speeds based on each data type, and how the use of both data types simultaneously can improve the accuracy of computed travel speeds and congestion levels.

We propose a system for calculating the personalized annual fuel consumption and CO2 emissions from transportation. The system, named CO2NNIE, estimates the fuel consumption on the fastest route between the frequent destinations of the user. The travel time and fuel consumption estimated are based on 3.8 billion GPS records from 16 thousand cars and 198 million records from 218 cars annotated with fuel consumption data, respectively. The fuel consumption estimates from the system are validated using fuel-pump data. We find that estimates have good accuracy, i.e., are generally within 10% of the actual fuel consumption (4.6% deviation on average). We conclude, that the system provides new detailed information on CO2 emissions and fuel consumption for any make and model.

Reduction in greenhouse gas emissions from transportation attracts increasing interest from governments, fleet managers, and individual drivers. Eco-routing, which enables drivers to use eco-friendly routes, is a simple and effective approach to reducing emissions from transportation. We present EcoSky, a system that annotates edges of a road network with time dependent and uncertain eco-weights using GPS data and that supports different types of eco-routing. Basic eco-routing returns the most eco-friendly routes; skyline eco-routing takes into account not only fuel consumption but also travel time and distance when computing eco-routes; and personalized eco-routing considers each driver's past behavior and accordingly suggests different routes to different drivers.

GPS data recorded from driving vehicles is available from many sources and is a very good data foundation for answering traffic related queries. However, most approaches so far have not considered combining GPS data from many sources into a single data warehouse. Further, the integration of GPS data with fuel consumption data (from the so-called CAN bus in the vehicles) and weather data has not been done. In this paper, we propose a data warehouse design for handling GPS data, fuel consumption data, and weather data. The design is fully implemented in a running system using the PostgreSQL DBMS. The system has been in production since March 2011 and the main fact table contains today approximately 3.4 billion rows from 16 different data sources. We show that the system can be used for a number of novel traffic related analyses such as relating the fuel consumption of vehicles with the road network and road congestion.

Traffic researchers, planners, and analysts want a simple way to query the large quantities of GPS trajectories collected from vehicles. In addition, users expect the results to be presented immediately even when querying very large transportation networks with huge trajectory data sets. This paper presents a novel query type called sheaf, where users can browse trajectory data sets using a single mouse click. Sheaves are very versatile and can be used for location-based advertising, travel-time analysis, intersection analysis, and reachability analysis (isochrones). A novel in-memory trajectory index compresses the data by a factor of 12.4 and enables execution of sheaf queries in 40 ms. This is up to 2 orders of magnitude faster than existing work. We demonstrate the simplicity, versatility, and efficiency of sheaf queries using a real-world trajectory set consisting of 2.7 million trajectories (1.36 billion GPS records) and a network with 1.5 million edges.

The electric vehicle (EV) is an interesting vehicle type that can reduce the dependence on fossil fuels, e.g., by using electricity from wind turbines. A significant disadvantage of EVs is a very limited range, typically less than 200 km. This paper compares EVs to conventional vehicles (CVs) for private transportation using two very large data sets. The EV data set is collected from 164 vehicles (126 million rows) and the CV data set from 447 vehicles (206 million rows). Both data sets are collected in Denmark throughout 2012, with a logging frequency of 1 Hz. By comparing the two data sets, we observe that EVs are significantly slower on motorways, faster in cities, and drive shorter distances compared to CVs.

In traffic research, management, and planning a number of path-based analyses are heavily used, e.g., for computing turn-times, evaluating green waves, or studying traffic flow. These analyses require retrieving the trajectories that follow the full path being analyzed. Existing path queries cannot sufficiently support such path-based analyses because they retrieve all trajectories that touch any edge in the path. In this paper, we define and formalize the strict path query. This is a novel query type tailored to support path-based analysis, where trajectories must follow all edges in the path. To efficiently support strict path queries, we present a novel NET work-constrained TRAjectory index (NETTRA). This index enables very efficient retrieval of trajectories that follow a specific path, i.e., strict path queries. NETTRA uses a new path encoding scheme that can determine if a trajectory follows a specific path by only retrieving data from the first and last edge in the path. To correctly answer strict path queries existing network-constrained trajectory indexes must retrieve data from all edges in the path. An extensive performance study of NETTRA using a very large real-world trajectory data set, consisting of 1.7 million trajectories (941 million GPS records) and a road network with 1.3 million edges, shows a speed-up of two orders of magnitude compared to state-of-the-art trajectory indexes.

Vehicles in the US use approximately 1.4 billion liters of fuel a day. This number can be reduced by driving more fuel efficiently. Several sources provide fuel saving eco-driving advice, but the advices are often quite abstract. This paper uses a large set of high-frequent GPS and Controller Area Network Bus (CANBus) data from four similar vehicles to evaluate the eco-driving advice. The CANBus data provides information such as the fuel consumption per second and rounds per minute of the engine.

We present the INTRA system for interactive path-based traffic analysis. The analyses are developed in collaboration with traffic researchers and provide novel insights into conditions such as congestion, travel-time, choice of route, and traffic-flow. INTRA supports interactive point-and-click analysis, due to a novel and efficient indexing structure. With the web-site daisy.aau.dk/its/spqdemo/we will demonstrate several analyses, using a very large real-world data set consisting of 1.9 billion GPS records (1.5 million trajectories) recorded from more than 13 000 vehicles, and touching most of the road network in Denmark.

Reduction in greenhouse gas emissions from transportation is essential in combating global warming and climate change. Eco-routing enables drivers to use the most eco-friendly routes and is effective in reducing vehicle emissions. The EcoTour system assigns eco-weights to a road network based on GPS and fuel consumption data collected from vehicles to enable ecorouting. Given an arbitrary source-destination pair in Denmark, EcoTour returns the shortest route, the fastest route, and the eco-route, along with statistics for the three routes. EcoTour also serves as a testbed for exploring advanced solutions to a range of challenges related to eco-routing.

In this paper, a complete platform used to compute travel times from GPS data is described. Two approaches to computing travel time are proposed one based on points and one based on trips. Overall both approaches give reasonable results compared to existing manual estimated travel times. However, the trip-based approach requires more GPS data and of a higher quality than the point-based approach. The platform has been completely implemented using open-source software. The main conclusion is that large quantity of GPS data can be managed, with a limited budget and that GPS data is a good source for estimating travel times, if enough data is available.

This paper describes an open, extendable, and scalable system that supports the delivery of context-dependent content to mobile users. The system enables users to receive content from multiple content providers that matches their demographic data, active profiles, and context such as location and time. The system also allows users to subscribe to specific services. In addition, it allows users to provide their own content and services, by either using the system's publicly available interface or by filling out one of the service-configuration templates.

The outcome of a test of an application that stores data in a database naturally depends on the state of the database. It is therefore important that test developers are able to set up and tear down database states in a simple and efficient manner In existing unit-test frameworks, setting up and tearing down such test fixtures is labor intensive and often requires copy-and-paste of code. This paper presents an extension to existing unit-test frameworks that allows unit tests to reuse data inserted by other unit tests in a very structured fashion. With this approach, the test fixture for each unit test can be minimized. In addition, the reuse between unit tests can speed up the execution of test suites. A performance test on a medium-size project shows a 40% speed up and an estimated 25% reduction in the number of lines of test code

In modern enterprises, almost all data is stored in relational databases. Additionally, most enterprises increasingly collaborate with other enterprises in long-running read-write workflows, primarily through XML-based data exchange technologies such as Web services. However, bidirectional XML data exchange is cumbersome and must often be hand-coded, at considerable expense. This paper remedies the situation by proposing RELAXML, an automatic and effective approach to bidirectional XML-based exchange of relational data. RELAXML supports re-use through multiple inheritance, and handles both export of relational data to XML documents and (re-)import of XML documents with a large degree of flexibility in terms of the SQL statements and XML document structures supported. Import and export are formally defined so as to avoid semantic problems, and algorithms to implement both are given. A performance study shows that the approach has a reasonable overhead compared to hand-coded programs.

The timestamps of now-relative bitemporal databases are modeled as growing, shrinking or rectangular regions. The shape of these regions makes it a challenge to design bitemporal operators that (a) are consistent with the point-based interpretation of a temporal database, (b) preserve the identity of the argument timestamps, (c) ensure locality and (d) perform efficiently. We identify the bitemporal split operator as the basic primitive to implement a wide range of advanced now-relative bitemporal operations. The bitemporal split operator splits each tuple of a bitemporal argument relation, such that equality and standard nontemporal algorithms can be used to implement the bitemporal counterparts with the aforementioned properties. Both a native database algorithm and an SQL implementation are provided. Our performance results show that the bitemporal split operator outperforms related approaches by orders of magnitude and scales well.

Previous approaches to implementing temporal DBMSs have assumed that a temporal DBMS must be built from scratch, employing an integrated architecture and using new temporal implementation techniques such as temporal indexes and join algorithms. However, this is a very large and time-consuming task. The paper explores approaches to implementing a temporal DBMS as a stratum on top of an existing non-temporal DBMS, rendering implementation more feasible by reusing much of the functionality of the underlying conventional DBMS. More specifically, the paper introduces three stratum meta-architectures, each with several specific architectures. Based on a new set of evaluation criteria, advantages and disadvantages of the specific architectures are identified. The paper also classifies all existing temporal DBMS implementations according to the specific architectures they, employ. It is concluded that a stratum architecture is the best short, medium, and perhaps even long-term, approach to implementing a temporal DBMS.

Transaction-time databases support access to not only the current database state, but also previous database states. Supporting access to previous database states requires large quantities of data and necessitates efficient temporal query processing techniques. Previously, we presented a log based storage structure and algorithms for the differential computation of previous database states. Timeslices-i.e., previous database states-are computed by traversing a log of database changes, using previously computed and cached timeslices as outsets. When computing a new timeslice, the cache will contain two candidate outsets: an earlier outset and a later outset. The new timeslice can be computed by either incrementally updating the earlier outset or decrementally "downdating" the later outset using the log. The cost of this computation is determined by the size of the log between the outset and the new timeslice. The paper proposes an efficient algorithm that identifies the cheaper outset for the differential computation. The basic idea is to compute the sizes of the two pieces of the log by maintaining and using a tree structure on the timestamps of the database changes in the log. The lack of a homogeneous node structure, a controllable and high fill factor for nodes, and of appropriate node allocation in existing tree structures (e.g., B/sup +/ trees, Monotonic B/sup +/ trees, and Append only trees) render existing tree structures unsuited for our use. Consequently, a specialized tree structure, the pointer-less insertion tree, is developed to support the algorithm. As a proof of concept, we have implemented a main memory version of the algorithm and its tree structure.