Traffic Data Aggregation Research Articles

Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach

Real-time crash prediction helps identify and prevent the occurrence of traffic crash. For years, various real-time crash prediction models have been investigated to provide effective information for proactive traffic management. When building real-time crash prediction model, a suitable variable space together with a specific time interval for traffic data aggregation and an appropriate modelling algorithm should be applied. Regarding the intercorrelation problem with variable space, comprehensive real-time crash prediction model considering available traffic data characteristics in applicable circumstances needs to be explored. Taking Xi’an G3001 Expressway as study area, real road traffic and accident data during the period from January 2014 to January 2019 on this expressway are applied for real-time crash prediction. To better capture traffic flow characteristics on expressway and improve the practicality of real-time crash prediction model, two new variables (segment difference coefficient and lane difference coefficient) describing the smoothness and continuity of traffic flow in spatial dimension are developed and incorporated in building the crash prediction model to solve the intercorrelation problem with variable space. Random forest (RF) is then adopted to specify the quantitative relationship between specific variable and crash risk. Real-time crash prediction model based on support vector machine (SVM) using new composed variable space is built. The results show that simplified variable space could contribute to the same classification power in currently used real-time crash prediction models compared with traditional variable space. Moreover, the prediction model based on SVM reaches an accuracy level of 0.9, which performs better than other currently used prediction models.

Secure Real-Time Traffic Data Aggregation With Batch Verification for Vehicular Cloud in VANETs

The vehicular cloud provides many significant advantages to Vehicular ad-hoc Networks (VANETs), such as unlimited storage space, powerful computing capability and timely traffic services. Traffic data aggregation in the vehicular cloud, which can aggregate traffic data from vehicles for further processing and sharing, is very important. Incorrect traffic data feedback may affect traffic safety; therefore, the security of traffic data aggregation should be ensured. In this paper, by using the property of data recovery in the message recovery signature (MRS), we propose a secure real-time traffic data aggregation scheme for vehicular cloud in VANETs. In the proposed scheme, the validity of vehicles’ signatures are verified, and then the original traffic data is recovered from signatures. Moreover, the proposed scheme supports batch verification for multiple vehicles’ signatures. Due to advantages of the MRS, security features such as data confidentiality, privacy preservation and reply attack resistance are preserved. In addition, the comparison and simulation results indicate that the proposed scheme is superior in comparison to previous schemes with respect to the communication and computational cost.

A Stochastic Data-Based Traffic Model Applied to Vehicles Energy Consumption Estimation

A new approach to estimate traffic energy consumption via traffic data aggregation in (speed and acceleration) probability distributions is proposed. The aggregation is done on each segment composing the road network. In order to reduce data occupancy, clustering techniques are used to obtain meaningful classes of traffic conditions. Different times of the day with similar speed patterns and traffic behavior are thus grouped together in a single cluster. Different energy consumption models based on the aggregated data are proposed to estimate the energy consumption of the vehicles in the road network. For validation purposes, a microscopic traffic simulator is used to generate the data and compare the estimated energy consumption to the measured one. A thorough sensitivity analysis with respect to the parameters of the proposed method (i.e., number of clusters and size of the distributions support) is also conducted in simulation. Finally, a real-life scenario using floating car data is analyzed to evaluate the applicability and the robustness of the proposed method.

Visual analysis of traffic data via spatio-temporal graphs and interactive topic modeling

Analyzing of traffic data is an important task for urban planners and managers. Traffic data have the spatio-temporal characteristics, which can reflect the variation of the presence of vehicle in different places over time as well as traffic flow dynamics among different places. The analysis of large-scale GPS trajectory data is a very challenging research due to the complexity of data and the need to extract useful information under cover in data. In this study, we combine temporal and geospatial aggregation of traffic data for obtaining key areas and creating legible traffic flow maps; meanwhile, we make full use of the topic model to capture latent semantic information. Nevertheless, most of the topic models always encounter the plague of choosing the optimal number of topics and cannot easily incorporate numerous types of user feedback. To tackle these problems, we propose an interactive topic modeling equipped with various interactive capabilities which empowers users to explore data from different levels of detail. Finally, we design and implement an interactive visual analytics prototype system based on the spatio-temporal graphs and the interactive topic modeling. The feasibility and validity of our system is demonstrated by conducting two case studies with a real-world traffic data in Hangzhou.

Novel dynamic data aggregation scheme for WSN based intelligent vehicle systems

The data aggregation with multi-resources traffic information is a very important issue for intelligent vehicle systems (IVS). It can not only collect the most critical data from the traffic environment, but also prevent sensor network congestion. Unfortunately, we so far have no suitable solutions for WSN based IVSs. This paper presents an efficient approach for traffic data aggregation, applied in IVSs. At first, our approach adopts a hybrid network structure which is the combination of the chain structure and the unequal clusters structure. In this structure, all sensor nodes from the same cluster use parameters such as leading code and geographical position etc. to guarantee secure data aggregation. Furthermore, a method is introduced depending on creditability evaluation and reliability allocation so that the application layer can calculate aggregation results accurately, and then makes the decisions. Finally, the performance of the proposed scheme has been verified using simulation, showing that it is superior to similar protocol VLEACH (an improvement on LEACH) and ESDA (Efficient and Secure Data Aggregation protocol) such as the sensor nodes energy consumption and aggregation precision. The analysis result indicates that our scheme is effective and feasible in the next generation of sensor technologies of ITSs.

Exploring Bias in Traffic Data Aggregation Resulting from Transition of Traffic States

Transportation engineers and researchers heavily use traffic data, which are generally aggregated by predetermined time intervals (e.g., 5 to 15 min). The aggregation process often discards essential information of traffic state transition (e.g., breakdowns). However, the transition of traffic conditions within an aggregation interval is not well understood. This study explored traffic state transition from uncongested to congested regimes that occurred within a predetermined time interval. From two urban freeway locations in Norfolk, Virginia, traffic data archived at 15-min intervals were obtained. A heuristic method based on a Gaussian mixture model was developed to detect the aggregate traffic data that exhibit the transition of traffic states as well as to partition the data statistically into uncongested and congested traffic states. Results show a substantial difference in travel speed (approximately 20 mph) between the two states. In addition, these results illustrate that aggregating these different traffic conditions can cause substantial traffic data aggregation bias by lowering travel speed and flow rates, especially in high traffic flow situations. Finally, new insights into valid traffic data aggregation and speed–flow–concentration relationship development are discussed.

Integrating Real-Time Traffic Data in Road Safety Analysis

Traffic data aggregation has been a serious factor of inaccuracy in most road safety studies. The Average Annual Daily Traffic (AADT) has been the most commonly used measure to reflect traffic conditions. In this paper, we establish a framework for the integration of real-time traffic data in road safety analysis. To this end, we explore the effects of traffic parameters on type of road crash and on the injury level sustained by vehicle occupants. Univariate and ordered Probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de-France region, France. Empirical results indicate that multi-vehicle crashes tend to occur under low or very high traffic speeds, while single-vehicle crashes appeared to be largely geometry-dependent. Increasing traffic volume was found to have a consistently positive (i.e. decreasing) effect on injury severity, while speed appears to have a differential effect on severity depending on flow conditions. Also, while in higher traffic volumes higher traffic speeds aggravate severity outcomes, in lower traffic volumes speed does not significantly influence severity in a consistent pattern.

Realizing the Promise of Intelligent Transportation Systems (ITS) Data Archives

Intelligent transportation systems (ITS) are playing increasingly important roles in addressing traffic congestion, safety, and environmental concerns. Archiving and “reusing” the vast amounts of data initially collected by ITS for real-time operations holds the potential to significantly improve a wide range of transportation analyses. Effectively archiving and deriving information from ITS data requires the application of technology and algorithms recently developed and proven in research. This paper describes and summarizes this research, particularly in areas such as data warehousing, complex systems development, traffic data aggregation, traffic data imputation, and traffic data characterization. The paper concludes with a list of key future research needs required to allow expanded use of ITS data archives.