Transportation Datasets Research Articles

Discovering temporal changes in hierarchical transportation data: Visual analytics & text reporting tools

Analyzing important changes to massive transportation datasets like national bottleneck statistics, passenger data for domestic flights, airline maintenance budgets, or even publication data from the Transportation Research Record can be extremely complex. These types of datasets are often grouped by attributes in a tree structure hierarchy. The parent–child relationships of these hierarchical datasets allow for unique analytical opportunities, including the ability to track changes in the dataset at different levels of granularity, over time or between versions. For example, analysts can use hierarchies to uncover changes in the patterns of passengers flying in the United States over the last ten years, breaking down the data by states, cities, airports, and number of passengers. Exploring changes in travel patterns over time can help carriers make better decisions regarding their operations and long-range planning.This paper describes TreeVersity2, a web-based data comparison tool that provides users with information visualization techniques to find what has changed in a dataset over time. TreeVersity2 enables users to explore data that can be inherently hierarchical or not (by categorizing them by their attributes). An interactive textual reporting tool complements the visual exploration when the amount of data is very large. The results of two case studies conducted with transportation domain experts along with the results of an exit questionnaire are also described. TreeVersity2 preloaded with several demo datasets can be found at (http://treeversity.cattlab.umd.edu) along with several example videos.

FEASIBILITY OF INTEGRATED TRANSPORT NETWORK MODEL IN LITHUANIA

Availability of up-to-date and detailed spatial data on transport networks is very important for sustainable development. As Directive 2007/2/EC of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE) targets transport networks data as one of the first priority data themes to be harmonized across Europe, the specification for transport network dataset has been developed and adopted as guidance document for all Member States. The specification provides a common model for road, rail, air, water and cable transport networks and related infrastructure. Integrated transport network database can be used for routing of vehicles, taking into account all limitations of the road network, for transport planning, emergency services. In order to achieve the benefits related to common use of up-to-date information and to interoperable data for these tasks, all transport network elements must be accurately mapped in a consistent logical network. Unfortunately, in Lithuania there is no single continuous and extensive spatial data set (geographic database) of transport networks. The authors analysed the possibilities of building joint INSPIRE databases from different transport datasets, and indicated the best data sources for road and railway transport, as well as actions of improvement have to be taken. The research and successful experiment has showed that the more extensive INSPIRE transport data model can be implemented using ArcGIS data model. This concept and the model will serve as an important prop for development of policy of national and transboundary spatial data integration in countries with similar situation. INSPIRE implementation guidelines are compatible with national interest and must be followed, but technical feasibility and cost-benefit considerations must be taken into account.

Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning

Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a deep architecture that consists of two parts, i.e., a deep belief network (DBN) at the bottom and a multitask regression layer at the top. A DBN is employed here for unsupervised feature learning. It can learn effective features for traffic flow prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To the best of our knowledge, this is the first paper that applies the deep learning approach to transportation research. To incorporate multitask learning (MTL) in our deep architecture, a multitask regression layer is used above the DBN for supervised prediction. We further investigate homogeneous MTL and heterogeneous MTL for traffic flow prediction. To take full advantage of weight sharing in our deep architecture, we propose a grouping method based on the weights in the top layer to make MTL more effective. Experiments on transportation data sets show good performance of our deep architecture. Abundant experiments show that our approach achieved close to 5% improvements over the state of the art. It is also presented that MTL can improve the generalization performance of shared tasks. These positive results demonstrate that deep learning and MTL are promising in transportation research.

Electric passenger car transport and passenger car life cycle inventories in ecoinvent version 3

PurposeDue to the large environmental challenges posed by the transport sector, reliable and state-of-the art data for its life cycle assessment is essential for enabling a successful transition towards more sustainable systems. In this paper, the new electric passenger car transport and vehicle datasets, which have been developed for ecoinvent version 3, are presented.MethodsThe new datasets have been developed with a strong modular approach, defining a hierarchy of datasets corresponding to various technical components in the vehicle. A vehicle is therefore modelled by linking together the various component datasets. Also, parameters and mathematical formulas have been introduced in order to define the amount of exchanges in the datasets through common transport and vehicle characteristics. This supports users in the choice of the amount of exchanges and enhances the transparency of the dataset.ResultsThe new transport dataset describes the transport over 1 km with a battery electric passenger car taking into account the vehicle production and end of life, the energy consumption due to the use phase, non-exhaust emissions, maintenance and road infrastructure. The dataset has been developed and is suitable for a compact class vehicle.ConclusionsA new electric passenger car transport dataset has been developed for version 3 of the ecoinvent database which exploits modularisation and parameters with the aim of facilitating users in adapting the data to their specific needs. Apart from the direct use of the transport dataset for background data, the various datasets for the different components can also be used as building blocks for virtual vehicles.

Forecasting spatiotemporal impact of traffic incidents for next-generation navigation systems

The advances in sensor technologies enable real-time collection of high-fidelity spatiotemporal data on transportation networks of major cities. In this paper, using two real-world transportation datasets: (1) incident and (2) traffic data, we address the problem of predicting and quantifying the impact of traffic incidents. Traffic incidents include any nonrecurring events on road networks, such as accidents, weather hazard or road construction. By analyzing archived incident data, we classify incidents based on their features (e.g., time, location, type of incident). Subsequently, we model the impact of each incident class on its surrounding traffic by analyzing the archived traffic data at the time and location of the incidents. Consequently, in real-time, if we observe a similar incident (from real-time incident feeds), we predict and quantify its impact on the surrounding traffic using our models. This information, in turn, can help drivers to effectively avoid impacted areas in real-time. To be useful for such real-time navigation application, and unlike current approaches, we study the dynamic behavior of incidents and model the impact as a quantitative time varying spatial span. In addition, we study a novel approach that improves our classification method by analyzing traffic density around the incident area and the initial behavior of the incident. We evaluated our approach with very large traffic and incident datasets collected from Los Angeles County and the results show by utilizing our impact prediction approach in the navigation system, precision of the travel time calculation can be improved by up to 67 %.

Modeling Flow and Solute Transport in Irrigation Furrows

This paper presents an internally coupled flow and solute transport model for free-draining irrigation furrows. Furrow hydraulics is simulated with a numerical zero-inertia model and solute transport is computed with a numerical cross-section averaged advection-dispersion model. A procedure for integrating the furrow volumetric cumulative intake integral in the context of a hydraulic model is presented. Two hydraulic and solute transport data sets collected in sloping free-draining test furrows were used in model evaluation. Soil intake and hydraulic parameters were estimated with a simple approach that matches simulated and measured flow depth hydrographs. The field-scale Weighted Mean Relative Residual (WMRR) between measured and model predicted flow depth hydrographs are 22.0% and 29.0% for the two data set. Furthermore, it is shown that the WMRR of 29.0% reduces to 16.0%, when only the error associated with the downstream end computational node is excluded. This suggests that a significant fraction of the error is related to the form of the downstream boundary condition used. It also shows that the effect of the downstream boundary condition does not extend to a large segment of the flow upstream. The longitudinal dispersion coefficient is approximated with an explicit equation as a function of the hydraulic and geometric variables. Model evaluation is conducted in three steps: (1) cumulative intakes and intake rates computed with the numerical formulation presented here were compared with a subsurface flow model, HYDRUS-2D; (2) solute breakthrough curves computed with the coupled flow and transport model were compared with those from exact analytical solutions for applicable conditions; and (3) model predicted solute breakthrough curves were compared with those obtained from field measurements. Overall the results suggest that the coupled flow and transport model is a useful irrigation and fertigation system management and evaluation tool.

Numerical simulation of bar and island morphodynamics in anabranching megarivers

[1] Bar and island morphodynamics in the world's largest anabranching rivers are investigated using a new numerical model of hydrodynamics, sediment transport, bank erosion, and floodplain development, operating over periods of several hundred years. Simulated channel morphology is compared to that of natural rivers and shown to be realistic, both in terms of the statistical characteristics of channel width, depth, and bar shape distributions, and mechanisms of unit bar, compound bar, and island evolution. Results demonstrate that bar and island stability may be sensitive to hydrologic regime, because greater variability in flood magnitude encourages the formation of emergent bars that can be stabilized by vegetation colonization. Simulations illustrate a range of mechanisms of unit bar generation that are linked to local bed or bank instabilities. This link may explain the reduced frequency of unit bars observed in some large anabranching rivers that are characterized by stable vegetated islands and slow rates of channel change. Model results suggest that the degree to which sand-sized bed material is carried in suspension likely represents an important control on bar morphodynamics and channel network evolution, because of its influence on sand transport direction. Consequently, differences in the partitioning of the total sand load between bed load and suspension may provide a partial explanation for contrasting styles of anabranching in the world's largest sand-bed rivers. These results highlight a need for spatially-distributed flow and sediment transport data sets from large rivers, in order to support improved parameterizations of sand transport mechanics in morphodynamic models.

Heat Transfer Scaling Close to the Wall for Turbulent Channel Flows

This work serves a two-fold purpose of briefly reviewing the currently existing literature on the scaling of thermal turbulent fields and, in addition, proposing a new scaling framework and testing its applicability. An extensive set of turbulent scalar transport data for turbulent flow in infinitely long channels is obtained using a Lagrangian scalar tracking approach combined with direct numerical simulation of turbulent flow. Two cases of Poiseuille channel flow, with friction Reynolds numbers 150 and 300, and different types of fluids with Prandtl number ranging from 0.7 to 50,000 are studied. Based on analysis of this database, it is argued that the value and the location of the maximum normal turbulent heat flux are important scaling parameters in turbulent heat transfer. Implementing such scaling on the mean temperature profile for different fluids and Reynolds number cases shows a collapse of the mean temperature profiles onto a single universal profile in the near wall region of the channel. In addition, the profiles of normal turbulent heat flux and the root mean square of the temperature fluctuations appear to collapse on one profile, respectively. The maximum normal turbulent heat flux is thus established as a turbulence thermal scaling parameter for both mean and fluctuating temperature statistics.

Influence of the regional topography on the remote emplacement of hydrothermal systems with examples of Ticsani and Ubinas volcanoes, Southern Peru

Present work studies the influence of the regional topography on the hydrothermal fluid flow pattern in the subsurface of a volcanic complex. We discuss how the advective transfer of heat from a magmatic source is controlled by the regional topography for different values of the averaged permeability. For this purpose, we use a 2-D numerical model of coupled mass and heat transport and new data sets acquired at Ticsani and Ubinas, two andesitic volcanoes in Southern Peru which have typical topography, justifying this approach. A remarkable feature of these hydrothermal systems is their remote position not centered on the top of the edifice. It is evidenced by numerous hot springs located in more than 10km distance from the top of each edifice. Upwelling of thermal water is also inferred from a positive self-potential anomaly at the summit of the both volcanoes, and by ground temperatures up to 37°C observed at Ticsani. Our model results suggest that the regional topographic gradient is able to significantly divert the thermal water flow and can lead to an asymmetric emplacement of the hydrothermal system even considering a homogeneous permeability of the edifice. Inside the thermal flow, the hydraulic conductivity increases with the decrease of temperature-related viscosity, focusing the flow towards the surface and creating a hydrothermal zone at a large lateral distance from the heat source. The location and temperature of the hot springs together with the water table position given by self-potential data can be used to constrain the average permeability of the edifice, a key parameter influencing fluid flow and associated advective heat transfer in the direction opposite to the regional topographic gradient. Our study allows to explain the emplacement of the hydrothermal systems at volcanoes with asymmetric edifices or even the absence of a shallow hydrothermal system. These results can be generalized to the study of non-volcanic hydrothermal systems.

TreeVersity

Transportation data sets of all kinds are hierarchical in nature and therefore are easily represented in tree structures. Visually comparing the changes in these hierarchical data sets over time can be extremely difficult, even when the trees are relatively small. TreeVersity is a novel inter active visualization that allows users to detect and analyze changes in the structure and value of various components of the tree. TreeVersity uses dual comparison techniques (side by side and explicit differences) coupled with a tabular representation to help users understand and explore changes to the tree over time. TreeVersity uses carefully selected color palettes to show positive or negative absolute and relative value changes and glyphs that show these changes preattentively. A study illustrated the value of this visualization technique through various short case studies based on federal transportation budget data, airline maintenance budget data, and transportation publication data. Hypotheses for TreeVersity's future relevance for congestion trend analysis are provided.

An update of the sediment fluxes investigation in the Rio Cordon (Italy) after 25 years of monitoring

Quantification of bed-load transport in high-gradient mountain streams is important, but the field data needed to test transport models are scarce and difficult to obtain. In the present study, we describe the experimental station for monitoring water and sediment fluxes built in 1985 on the Rio Cordon, a small step-pool channel in the eastern Italian Alps. The measuring station consists of an inclined frame that separates fine from coarse sediments (D>20 mm), which are continuously measured by a series of ultrasonic sensors fitted above a storage area. The acquired 25-year dataset, which comprises a high-magnitude/ low-recurrence flood event, has allowed a magnitude-frequency analysis of bed-load volumes to be performed. Results from a combined frequency analysis of peak water discharge and total bed-load volumes are presented. In addition, the integration between the sediment transport dataset and the repeated surveys of sediment sources and of channel changes allowed us to assess the geomorphological effectiveness of different flood events. Despite the importance of the experimental station for making these bed-load observations possible, its maintenance costs are not low and these may have an impact on its future existence. At the same time, improving current instrumentation and future installations with novel technology would make the station an ideal location for calibrating surrogate techniques for sediment transport monitoring.

A MULTIPLE-SIZED TRANSPORT FORMULA FOR NONUNIFORM SEDIMENTS UNDER CURRENT AND WAVES

Nonuniform sediment transport exhibits difference from uniform sediment, even when the mean grain size is the same for both cases. The hiding, exposure, and armoring among different size fractions in the nonuniform bed material may significantly affect sediment transport, morphological change, bed roughness, wave dissipation, etc. It is necessary to develop multiple-sized sediment transport capacity formula to improve the accuracy and reliability of coastal analysis tools. The Wu et al. (2000) formula, which was developed for river sedimentation, is herein extended to calculate multiple-sized sediment transport under current and waves for coastal applications. This formula relates bed-load transport to the grain shear stress and suspended-load transport to the energy of the flow system. It considers the effect of bed material size composition in the hiding and exposure correction factor, which is omitted in many other existing formulas. Methods have been developed in this study to determine the bed shear stress due to waves only and combined current and waves, and in turn to compute the bed-load and suspended-load transport rates using the Wu et al. (2000) formula without changing its original formulation. The enhanced bed-load formula considers the effect of wave asymmetry on sediment transport, calculates the onshore and offshore bed-load transport rates separately and then derives the net transport rate, whereas the enhanced suspended-load formula calculates only the net transport rate due to the limit of available data. The formula has been tested using the single-sized and multiple-sized sediment transport data sets. The formula provides reliable predictions in both fractional and total transport rates. More than half of the test cases are predicted within a factor of 2 of the measured values, and more than 90% of the cases are within a factor of 5. This accuracy is generally reasonable for sediment transport under current and waves, which is very complex and little understood.

Stochastic Modeling of Power Demand Due to EVs Using Copula

The driving patterns characterizing electric vehicles (EVs) are stochastic and, as a consequence, the electrical load due to EVs inherits their randomness. This paper presents a Monte Carlo procedure for the derivation of load due to EVs based on a fully stochastic method for modeling transportation patterns. Under the uncontrolled domestic charging scenario three variables are found to be crucial: the time a vehicle leaves home, the time a vehicle arrives home, and the distance traveled in between. A detailed transportation dataset is used to derive marginal cumulative distribution functions of the variables of interest. Since the variables are statistically dependent, a joint distribution function is built using a copula function. Subsequently, simulated EV trips are combined with a typical charging profile so that the energy contribution to the system is computed. The procedure is applied to analyze the effect of the EV load on the national power demand of The Netherlands under different market penetration levels and day/night electricity tariff scenarios.

Multi-domain anomaly detection in spatial datasets

A spatial anomaly captures a phenomenon occurring in a region which is vastly deviant in behavior with respect to the other normal observations. However, in reality this anomaly may impact other phenomena in the region across multiple domains, for example, crime is often linked to other sociopolitical factors or phenomenon such as poverty and education. Similarly, accidents in the region may be linked to other environmental factors such as weather and surface condition. So, finding anomalies across multiple domains is important in various applications. In this paper, we propose an approach for finding such a tangible anomalous window across multiple domains where window refers to the set of contiguous points in space, and since the window is multi-domain, there are several overlapping windows in the same space across domains. Our approach for finding anomalous window across the domains comprises the following steps: (1) single-domain anomaly detection: discovering anomalous window in each domain; (2) association rule mining: discovering relationship between the anomalous windows across domains using association rule mining; and (3) validation: validating the result using (a) Monte Carlo simulation, (b) correlation using lift and (c) ground truth evaluation. In addition, we also provide a probabilistic framework to evaluate the relationships between the spatial nodes as a postprocessing step. Finally, we provide a visualization technique for viewing the multi-domain anomalous window and the probabilistic relationships between the nodes. We provide detailed experimental results and comparisons with other approaches using real-world health ranking [51] and transportation datasets [50] with known ground truth windows. The results show that our approach is effective in finding the anomalies in multiple domains as compared to other approaches.

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

Network planning and traffic flow optimization require the acquisition and analysis of large quantities of data such as the network topology, its traffic flow data, vehicle fleet composition, emission measurements and so on. Data acquisition is an expensive process that involves household surveys and automatic as well as semiautomatic measurements performed all over the network. For example, in order to accurately estimate the effect of a certain network change on the total emissions produced by vehicles in the network, assessment of the vehicle fleet composition for each origin–destination pair is required. As a result, problems that optimize nonlocal merit functions become highly difficult to solve. One such problem is finding the optimal deployment of traffic monitoring units. In this article we suggest a new traffic assignment model that is based on the concept of shortest path betweenness centrality measure, borrowed from the domain of complex network analysis. We show how betweenness can be augmented in order to solve the traffic assignment problem given an arbitrary travel cost definition. The proposed traffic assignment model is evaluated using a high-resolution Israeli transportation data set derived from the analysis of cellular phones data. The group variant of the augmented betweenness centrality is then used to optimize the locations of traffic monitoring units, hence reducing the cost and increasing the effectiveness of traffic monitoring.

Two-Stage Data Mining Based Vehicle Navigation Algorithm in Urban Traffic Network

Along with the development of Intelligent Transportation System, traffic detectors collect numerous transportation state data in information databases and accumulate. Such data is greatly meaningful to the vehicle navigation. In this paper, we propose a noble two-stage algorithm about vehicle navigation by using data mining methods on the historical and current transportation dataset. This algorithm begins with picking sensitive data about start and end point in an urban traffic network, and data from related (or nearest) road fragments. Referring to current time and season, the algorithm gives an evaluation to every related road fragments and outputs a most reasonable route between start and end point. The experimental and theoretical analyzes show that this algorithm can form an efficient and effective route in reasonable time.

Area wide calculation of traffic induced CO2 emission in Seoul

To reduce emissions of Greenhouse Gas (GHGs) from automobiles, real on-road emission data should be required to help policy makers establish standards for reducing GHG emissions. Since general studies on calculating CO2 emissions from transportation sources have been based on petroleum consumption data, it has been difficult to analyze spatial activities and characteristics of transportation sources. In this study, we propose a method for calculating CO2 emissions from on-road transportation sources in Seoul. We focused on CO2 emission calculation by applying real traffic flow data and analyzed base emissions from the main roads and local streets. Because the emissions were calculated using a 1 km×1 km grid cell format, these data can be applied to other compatible transportation data sets for air pollution analysis, modal shift analysis, etc in the transportation sector.

Simplistic Geospatial Techniques in Analyzing Transportation Dynamics for Origin-Destination Container Movement Routes in the United States

Spatial analyses incorporating rudimentary techniques are essential to understand container transportation. Most transportation datasets are offered as raw disjoint data containing dissimilar attributes from different sources making them difficult to fathom any spatial linkages. Route optimization and market accessibility are increasingly becoming important and significant as newer container decongestion methods are put in place. Origin-Destination dichotomies can best be harnessed in the United States by optimizing routes based on available databases. In this study, the Public Use Waybill sample database from Surface Transporation Board were used as preliminary inputs for inland railway route optimization. The paper utilized an approach akin to one used in riverine management to optimize container routes. From the primer databases, in as much as most import containers originate from Asia and Europe and shipped to the U.S. through specific North American ports, a significant portion gets to inland markets through Pacific coast in the U.S. The main hub ports for import containers are Los Angeles/Long Beach and Seattle for transpacific trade and New York/New Jersey and Savannah for transatlantic trade. An anomalous and notably high volume flow can be observed between the Pacific coast and the Mississippi valley area including metropolitan areas like Minneapolis, Chicago, Kansas City, Memphis, Dallas, and Houston. Assessing movement in the Midwest to east U.S., most container traffic volume passes through New York/New Jersey and Norfolk to end in Chicago. It can thus be surmised that an in-depth preliminary assessment of container routing can provide a parametric basis for higher-level cost and multi-modal analyses.

Understanding the influence of slope on the threshold of coarse grain motion: Revisiting critical stream power

This paper investigates the slope-dependent variation in critical mean bed shear stress for coarse grain motion, and evaluates stream power per unit bed area as an alternative threshold parameter. Explanations for observed slope dependency and existing approaches for predicting the critical stream power per unit bed area are reviewed. An analysis of secondary bed-load transport datasets is used to examine the strength of associations between stream power per unit bed area, mean bed shear stress and mean velocity, with bed-load transport rate. Data from an original flume study are combined with secondary data from similar flume experiments to investigate the effect of slope on both critical stream power per unit bed area and critical mean bed shear stress. Results suggest that stream power per unit bed area is most closely correlated with bed-load transport rate, and also that critical stream power per unit bed area is less variable with slope than critical mean bed shear stress. Alternative solutions to approximating critical stream power are explored. These include: (1) modifying existing expressions for critical stream power to account for higher critical mean bed shear stresses at higher slopes, and (2) applying a constant dimensionless critical stream power criterion.

Integration of foreign trade and maritime transport statistics in Spain

This article aims to contribute to improving maritime trade data by analysing the possibility of integrating in a single enlarged databank two different databases: the Spanish foreign trade and maritime transport datasets. The methodology adopted consisted of studying the primary sources providing data compiled in the foreign trade and maritime traffic databases, analysing the electronic processes used by informants and data collectors and examining the linkages between the different electronic messages involved. Once the links between the trade and sea transport documents and electronic messages were found, a solution for integrating both databases was envisaged. The main outcome is a new proposed database including information on all export and import operations transported by sea. Compiled data for each record would include variables describing the trade operation (commodity, weight and value of the traded goods amongst other fields) as well as information on the transport route followed by each specific export or import flow. The new database would allow port planners to analyse traffic to and from ports’ hinterlands without resorting to estimations, Customs could correct errors affecting the Spanish foreign trade statistics and transport policy-makers, academics and practitioners would benefit from wider-ranging higher quality statistics.