Intelligent Transportation Systems Data Research Articles

Mapping dynamic road emissions for a megacity by using open-access traffic congestion index data

The high populations of vehicles in global megacities have raised serious mobility and sustainability challenges, such as traffic congestion, air pollution deterioration and energy consumption issues. Detailed emission inventories at the link level are a prerequisite to accurately quantifying ambient pollution concentrations and identifying hotspots of human exposure within urban areas. The increasing adoption of intelligent transportation system data in smart-city initiatives worldwide has offered unprecedented opportunities for improving transportation air quality management. Based on the open-access traffic congestion index, we construct a high-resolution emission inventory of hourly fluxes of air pollutants and carbon dioxide from on-road vehicles over the whole road network in Shenzhen, China. Fine-grained quantification of ‘excess’ emissions from rush-hour traffic is explored, finding varied emission enhancement (14.3–30.4%) for different pollutants, as well as 24.3–26.8% and 19.6–22.0% ‘excess’ consumption for gasoline and diesel, respectively, in a central business district of Shenzhen during the rush hour periods. Also, we quantified the impacts of freight activities on pollutant emissions that freight activities can largely increase the street-level emission rates of nitrogen oxides, fine particulate matter and carbon dioxide, in which heavy-duty trucks share up to 50% of the total emissions of these species. This study provides a novel approach of high-resolution traffic emission inventory construction that can be potentially utilized in many types of cities, particularly for cities suffering from data-sparse situations, and further strengthen the intelligence and accuracy of vehicle emission management.

Integrating NFV and ICN for Advanced Driver-Assistance Systems

Advanced driver-assistance systems (ADASs) have been proposed as an alternative to driverless vehicles to provide support for automotive vehicle decisions. As a significant driving force for ADASs, the augmented reality (AR) provides comprehensive location-based content services for in-vehicle consumers. With the increase in request for information sharing, the current standalone mode of ADASs needs a shift to the multiuser sharing mode. In this article, to address the high mobility and real time requirements of ADASs in 5G environments, and also to address the resource orchestration and service management of big data in intelligent transportation systems, we integrate the information-centric network (ICN) and the network function virtualization (NFV) with ADASs to support an efficient AR-assisted content sharing and distribution. This integration eliminates the imbalance between the content requests and the resource limitation by splitting the virtual resources and providing an on-demand network and resource slicing in ADASs. We propose an incentive trading model for assistance content caching services and also propose a novel mechanism for optimal content cache allocation. Our extensive evaluation confirms that our proposed mechanism outperforms the past literature in terms of the cache hit ratio and latency.

A Review of Service Reliability Measures for Public Transportation Systems

The aim of this paper is to review various public transport reliability measures including the factors responsible for causing variability in the travel time. A four quadrant approach is used to summarize various reliability measures. These indicators use both Intelligent Transportation System (ITS) data and the stated preference data to measure reliability. Further various supply and demand side factors causing uncertainties in travel time are listed and discussed. In the end a brief case study on two routes of Bus Rapid Transit System(BRTS) of Ahmedabad is reported to apply and test the reviewed travel time measures on the ITS data.

Big Data in ITS: Concept, Case Studies, Opportunities, and Challenges

Intelligent transportation systems (ITS) typically rely on massive sources of data to monitor the transportation system, estimate its current state, forecast its future state, and react with a set of control and management strategies in real time. This requires the prompt processing of a variety of data sources that is consistently changing with time and typically challenging to combine (and make sense of) in real time. This paper presents thoroughly the studies related to big data applications in ITS and its associated challenges.

Spatio-temporal Anomaly Detection in Intelligent Transportation Systems

With the increased availability of real-time data streams in different domains comes the opportunity of using this data to provide valuable insights into the performance of the systems generating such data. In this paper, we are proposing an anomaly detection method to be applied on road traffic data in intelligent transportation systems. The proposed scheme is based on multi-channel singular spectrum analysis (MSSA), and aims to characterize the spatio-temporal properties of the transportation network. By simultaneously analyzing the spatial and temporal attributes of the network, the proposed anomaly detection scheme is able to detect contextual and collective anomalies that are otherwise undetectable using only spatial or temporal anomaly detection techniques. This is indicated through the results shown in the experiments and results section.

Traffic Big Data in the Development of Intelligent Transportation System

With the continuous development and advancement of computer technology, big data guarantees the establishment of an urban intelligent transportation system, a solid environmental basis to reform its application, and the construction of a deeply integrated data mechanism for big data-driven traffic management. This review paper briefly elaborates on the basic characteristics and sources of traffic big data as well as expound on the problems and application mechanisms of big data in intelligent transportation systems.

Fine-grained vehicle emission management using intelligent transportation system data

The increasing adoption of intelligent transportation system (ITS) data in smart-city initiatives worldwide has offered unprecedented opportunities for improving transportation air quality management. In this paper, we demonstrate the effective use of ITS and other traffic data to develop a link-level and hourly-based dynamic vehicle emission inventory. Our work takes advantage of the extensive ITS infrastructure deployed in Nanjing, China (6600 km2) that offers high-resolution, multi-source traffic data of the road network. Improved than conventional emission inventories, the ITS data empower the strength of revealing significantly temporal and spatial heterogeneity of traffic dynamics that pronouncedly impacts traffic emission patterns. Four urban districts account for only 4% of the area but approximately 30%–40% of vehicular emissions (e.g., CO2 and air pollutants). Owing to the detailed resolution of road network traffic, two types of emission hotspots are captured by the dynamic emission inventory: those in the urban area dominated by urban passenger traffic, and those along outlying highway corridors reflecting inter-city freight transportation (especially in terms of NOX). Fine-grained quantification of emissions reductions from traffic restriction scenarios is explored. ITS data-driven emission management systems coupled with atmospheric models offer the potential for dynamic air quality management in the future.

SMART TSS: Defining transportation system behavior using big data analytics in smart cities

A smart city improves the quality of its citizens by providing access to ubiquitous services. Intelligent Transportation Systems (ITS) have a fundamental role in transforming a metropolitan area into a smart city. In the past two decades, many applications of ITS, e.g. city-wide traffic management and monitoring, smart parking, public transportation information services (bus, train, taxi, plane, etc.), logistics, real-time traffic, road speed limit monitoring and management etc., are deployed in smart cities. The sensors or mobile objects in ITS constantly generate mobility data and the scale at which this data is generated is witnessing an exponential increase in volumes. To store and subsequently analyze such massive data generated by sensors, new architectures are needed which are primarily designed for working with big data. In this work, we propose a big data analytics architecture for ITS. The proposed architecture has a built-in storage and analysis capability to work with ITS data and is composed of four modules, namely (1) Big Data Acquisition and Preprocessing Unit (2) Big Data Processing Unit (3) Big Data Analytics Unit and (4) Data Visualization Unit. A detailed analysis of ITS big data for monitoring the average speed of a vehicle at w.r.t. the time attribute is provided. The proposed architecture is evaluated using Hadoop thus validating the proof of concept. The empirical results are encouraging and open directions for future research.

Learning Low-Dimensional Representation of Bivariate Histogram Data

With an increasing amount of data in intelligent transportation systems, methods are needed to automatically extract general representations that accurately predict not only known tasks but also similar tasks that can emerge in the future. Creation of low-dimensional representations can be unsupervised or can exploit various labels in multi-task learning (when goal tasks are known) or transfer learning (when they are not) settings. Finding a general, low-dimensional representation suitable for multiple tasks is an important step toward knowledge discovery in aware intelligent transportation systems. This paper evaluates several approaches mapping high-dimensional sensor data from Volvo trucks into a low-dimensional representation that is useful for prediction. Original data are bivariate histograms, with two types—turbocharger and engine—considered. Low-dimensional representations were evaluated in a supervised fashion by mean equal error rate (EER) using a random forest classifier on a set of 27 1-vs-Rest detection tasks. Results from unsupervised learning experiments indicate that using an autoencoder to create an intermediate representation, followed by $t$ -distributed stochastic neighbor embedding, is the most effective way to create low-dimensional representation of the original bivariate histogram. Individually, $t$ -distributed stochastic neighbor embedding offered best results for 2-D or 3-D and classical autoencoder for 6-D or 10-D representations. Using multi-task learning, combining unsupervised and supervised objectives on all 27 available tasks, resulted in 10-D representations with a significantly lower EER compared to the original 400-D data. In transfer learning setting, with topmost diverse tasks used for representation learning, 10-D representations achieved EER comparable to the original representation.

A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data

Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth (MR-PFP) algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives (HAR), CombineFileInputFormat (CFIF), and Sequence Files (SF), to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth (FP-growth) algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth (PFP) algorithm in efficiency and scalability.

A distributed platform for big data analysis in smart cities: combining Intelligent Transportation Systems and socioeconomic data for Montevideo, Uruguay

This article proposes a platform for distributed big data analysis in the context of smart cities. Extracting useful mobility information from large volumes of data is crucial to improve decision-making processes in smart cities. This article introduces a framework for mobility analysis in smart cities combining Intelligent Transportation Systems and socioeconomic data for the city of Montevideo, Uruguay. The efficiency of the proposed system is analyzed over a distributed computing infrastructure, demonstrating that the system scales properly for processing large volumes of data for both off-line and on-line scenarios. Applications of the proposed platform and case studies using real data are presented, as examples of the valuable information that can be offered to both citizens and authorities. The proposed model for big data processing can also be extended to allow using other distributed (e.g. grid, cloud, fog, edge) computing infrastructures.

Geovisualization of Archived ITS Data-Case Studies

Although transportation data have a spatial component by nature, this component generally has not been fully utilized when it comes to intelligent transportation system (ITS) archived data. This paper demonstrates the use of geographic information system (GIS) to spatially visualize and analyze transportation management center (TMC)-related performance measures derived from archived ITS data from a traffic management center in Texas, namely, San Antonio's TransGuide. The paper discusses three case studies. The first case study involved a performance evaluation of an automatic incident detection algorithm deployed by many traffic management centers after a few years of implementation in the field. The overall detection rate was found to be about 20%, and the overall false rate was about 0.005%. The second case study summarizes work completed to address quality control issues associated with a very large archived ITS data set composed of some 3.4 billion 20-s loop detector data records. There were some 1.6 billion (about 48%) speed, volume, and occupancy records that had a quality control flag. Approximately 1.5 billion flagged records were “valid” records, and the remaining 126 million (about 3.7%) flagged records were “abnormal” records. In the third case study, the researchers evaluated the data completeness both at the aggregate level (by server) and at a more detailed individual detector level. At the server level, the completeness rate varied from 95% to 100%. At the individual detector level, the analysis showed that, on average, the completeness rate for all detectors was about 80%. The researchers utilized GIS to prepare maps showing the spatial distribution of incident detection rate, false alarm rate, quality control flags, and data completeness rate. It is worth mentioning that all of the analysis was performed at a detailed segment-by-segment level, which is by itself a unique addition. The researchers recommend implementing the demonstrated spatial analysis of the archived ITS data. This will allow TMC officials to identify spots with any abnormal behavior, whether it is at the corridor level or even at the segment level.

Measurement and Assessment of Driver Compliance with Variable Speed Limit Systems: Comparison of the United States and Germany

Active traffic management (ATM) systems are becoming increasingly important, particularly in urban areas aiming to combat recurrent and nonrecurrent congestion. ATM systems apply proactive strategies to improve safety and mobility, as well as to reduce emissions, noise, and fuel consumption. As one of the key elements in the ATM toolbox, variable speed limit (VSL) systems are being increasingly deployed around the United States. In the past, most VSL systems in Europe have included automated enforcement as part of the system, while systems in the United States have relied on traditional speed enforcement strategies. In the Portland, Oregon, metropolitan area, a new traffic- and weather-responsive VSL system has been deployed. It uses variable advisory speeds (VAS), allowing law enforcement to rely on the basic rule for enforcement. Thanks to the availability of high-resolution intelligent transportation systems data, it is possible to conduct post hoc analysis after VSL deployments. This paper focuses on driver compliance with displayed VAS messages. “Compliance” is defined as the difference between the displayed speed and the measured speed of traffic. In addition to analyzing more than 35 days at the Portland site, for comparison purposes, some additional analysis of a VSL site in Munich, Germany, is also included. The analysis of compliance for VSL–VAS deployments is useful for understanding the benefits of these important ATM strategies, assisting with targeting enforcement actions, and better understanding where and when to employ VSL–VAS systems.

A Novel Wireless Sensor Network Frame for Urban Transportation

The rapid progress in the research and development of electronics, sensing, signal processing, and communication networks has significantly advanced the state of applications of intelligent transportation systems (ITSs). However, efficient and low-cost methods for gathering information in large-scale roads are lacking. Consequently, wireless sensor network (WSN) technologies that are low cost, low power, and self-configuring are a key function in ITS. The potential application scenarios and design requirements of WSN for urban transportation (WSN-UT) are proposed in this work. A customized network topology is designed to meet the special requirements, and WSN-UT is specifically tailored for UT applications. WSN-UT enables users to obtain traffic and road information directly from the local WSN within its wireless scope instead of the remote ITS data center. WSN-UT can be configured according to different scenario requirements. A three-level subsystem and a configuration and service subsystem constitute the WSN-UT network frame, and the service/interface and protocol algorithms for every subsystem level are designed for WSN-UT.

고속도로 교통자료 품질 통합평가지표 개발

Evaluation of traffic data quality is a backbone of better traffic information and management systems because it directly affects the reliability of traffic information. This study developed an integrated index for evaluating the quality of archived intelligent transportation systems (ITS) data. Two novel indices including spatio-temporal consistency and severity of missing data were devised and integrated with existing indices such as availability and completeness. An evaluation framework was proposed based on the developed integrated index. Both analytical hierarchical analysis (AHP) technique and entropy method were adopted to derive mixed weighting values to be used for the integrated index. It is expected that the proposed methodology would be effectively used in enhancing the quality of traffic data as a part of traffic information system.

An analysis of four methodologies for estimating highway capacity from ITS data

With the recent advent of Intelligent Transportation Systems (ITS), and their associated data collection and archiving capabilities, there is now a rich data source for transportation professionals to develop capacity values for their own jurisdictions. Unfortunately, there is no consensus on the best approach for estimating capacity from ITS data. The motivation of this paper is to compare and contrast four of the most popular capacity estimation techniques in terms of (1) data requirements, (2) modeling effort required, (3) estimated parameter values, (4) theoretical background, and (5) statistical differences across time and over geographically dispersed locations. Specifically, the first method is the maximum observed value, the second is a standard fundamental diagram curve fitting approach using the popular Van Aerde model, the third method uses the breakdown identification approach, and the fourth method is the survival probability based on product limit method. These four approaches were tested on two test beds: one is located in San Diego, California, U.S., and has data from 112 work days; the other is located in Shanghai, China, and consists of 81 work days. It was found that, irrespective of the estimation methodology and the definition of capacity, the estimated capacity can vary considerably over time. The second finding was that, as expected, the different approaches yielded different capacity results. These estimated capacities varied by as much as 26 % at the San Diego test site and by 34 % at the Shanghai test site. It was also found that each of the methodologies has advantages and disadvantages, and the best method will be the function of the available data, the application, and the goals of the modeler. Consequently, it is critical for users of automatic capacity estimation techniques, which utilize ITS data, to understand the underlying assumptions of each of the different approaches.

Considerations about the Analysis of ITS Data of Bicycle Sharing Systems

Handling and managing data automatically collected by Intelligent Transport Systems (ITS) is a major opportunity and challenge for transport professionals nowadays. This study guides the management of smartcard data from public bikes by providing criteria to detect travel patterns that describe the specific use of bike-share systems and which cannot be encountered in other transport modes. The guidelines have been put into practice with data from the TusBic system in Santander, Spain.The major discovery that has resulted from the analysis of the data is the high number of records that describe very short trips that show the same terminal at origin and destination. An algorithm is proposed that assumes the users try and return the bike if this is not working properly, and pick a new one from the same terminal. In such cases, the records are joined to describe a unique trip by considering the origin as the pick-up instant of the first bike, and the destination, the instant at which the second bike has been returned.The indications presented in this article should be considered in future studies of demand and level of service of public bicycle systems since not only they can make a big difference in the accuracy of the results, but also they can provide interesting information regarding the management and design of the system. Therefore, they are of interest for different stakeholders such as politicians and decision makers, service planners, and agencies responsible for the operations and direct management of public bicycle systems.

Study on the Method of Road Transport Management Information Data Mining based on Pruning Eclat Algorithm and MapReduce

Road transport management information is a class of massive and correlation data in ITS (intelligent transportation systems), and its association rules data mining has important practical significance. In order to cover the shortage of the classical association rules optimized algorithm Eclat, this paper proposed and demonstrated that candidate sets which have the project as a prefix or suffix can be pruning calculated for both the properties. Then it proposed optimized method of frequent sets calculation-a method of parallel NEclat combining with cloud programming model. This method can solve the problem that Eclat algorithm cannot be calculated by pruning, and achieve a parallel compute. The practical application showed that, this method can reduce time waste by more than 40%, and it is suitable for the data mining of transport management information association rules.

A Generic Methodological Framework for Cyber-ITS: Using Cyber-infrastructure in ITS Data Analysis Cases

This paper presents a method to capture the computational intensity and computing resource requirements of data analysis in intelligent transportation systems (ITS). These requirements can be transformed into a generic methodological framework for Cyber-ITS, mainly consisting of region-based ITS data divisions and tasks scheduling for processing, to support the efficient use of cyber-infrastructure (CI). To characterize the computational intensity of a particular ITS data analysis, the computational transformation is performed by data-centric and operation-centric transformation functions. The application of this framework is illustrated by two ITS data analysis cases: multi-sensor data fusion for traffic state estimation by integrating rough set theory with Dempster-Shafer (D-S) evidence theory, and geospatial computation on Global Positioning System (GPS) data for advertising value evaluation. To make the design of generic parallel computing solutions feasible for ITS data analysis in these cases, an approach is developed to decouple the region-based division from specific high performance computer (HPC) architecture and implement a prototype of the methodological framework. Experimental results show that the prototype implementation of the framework can be applied to divide the ITS data analysis into a load-balanced set of computing tasks, therefore facilitating the parallelized data fusion and geospatial computation to achieve remarkable speedup in computation time and throughput, without loss in accuracy.

A Novel Traffic Flow Forecasting Method Based on the Artificial Neural Networks and Intelligent Transportation Systems Data Mining

Useful information often hides in traffic management system. To mine useful data, prior knowledge has been used to train the artificial neural network (ANN) to identify the traffic conditions in the traffic information forecasting. Subjective information has hence been introduced into the ANN model. To solve this problem, a new ANN model is proposed based on the data mining technology in this work. The Self-Organized Feature Map (SOFM) is firstly employed to cluster the traffic data through an unsupervised learning and provide the labels for these data. Then labeled data were used to train the GA-Chaos optimized RBF neural network. Herein, the GA-Chaos algorithm is used to train the RBF parameters. Experimental tests use practical data sets from the Intelligent Transportation Systems (ITS) to validate the performance of the proposed ANN model. The results show that the proposed method can extract the potential patterns hidden in the traffic data and can accurately predict the future traffic state. The prediction accuracy is beyond 95%.