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In this paper, we present a new data mining framework for discovering sequence effects. In particular, we focus on the sequences consisting of actions that are taken in chronological order, like sequences of clinical procedures or marketing actions. Each sequence is associated with a binary outcome, a success or a failure. We investigate the hypothesis that certain subsequences of actions contribute...
Vehicular Networks (VN) have a huge potential to increase roadway safety and traffic efficiency. Big Data analysis can be instrumental in realizing this potential and enhancing the Intelligent Transportation Systems (ITSs). We study the causes of road accidents using big real-time accidents data obtained from Florida Department of Transportation (FDOT) - District 4. The ultimate goal is to prevent...
In this paper we present a new Smart Online Vehicle Tracking System for Security Applications (AMOTSSA) and we describe how it can be modelled and implemented as a Big data application. In order to model AMOTSSA as a big data application, we argue our design choices that meets its specific data and processing needs and we present a set of data analytic algorithms that would achieve a set of investigation...
This paper is an early report from research undertaken to meet the needs of the General Directorate for National Roads and Motorways of Poland. They have defined the task of estimating the annual average Daily Traffic on a class of highways in the country, based on a very small number of daily traffic measurements undertaken throughout the year (typically one or two such measurements). We report the...
Subgraph isomorphism is a fundamental graph problem with many applications. Due to its NP-Hard nature, subgraph isomorphism in large dynamic graphs is considered as a challenging problem. In this paper, we present a distributed graph pruning algorithm (D-IDS) for dynamic graphs to enable efficient subgraph isomorphism. D-IDS continuously maintains the maximum dual simulation match in a dynamic graph...
When an optimization via simulation (OvS) procedure designed for known input distributions is applied to a problem with input uncertainty (IU), it typically does not provide the target statistical guarantee. In this paper, we focus on a discrete OvS problem where all systems share the same input distribution estimated from the common input data (CID). We define the CID effect as the joint impact of...
Improving the efficiency, safety, and cost of road systems is an essential social problem that must be solved as the number of drivers, and the size of mass transit systems increase. Methodologies used for the construction of traffic simulations need to be examined in the context of real world big traffic data. This data can be used to create models for vehicle arrivals, turning behavior, and traffic...
Effective management of land-side transportation provides the competitive advantage to port terminal operators in improving services and efficient use of limited space in an urban port. We present a hybrid simulation model that combines traffic-flow modeling and discrete-event simulation for land-side port planning and evaluation of traffic conditions for a number of what-if scenarios. We design our...
Traffic state estimation plays a significant role in intelligent transportation systems. For a specific road, traffic state varies at different times of a day. Therefore, to estimate real-time traffic state is difficult. This paper presents a probabilistic approach for traffic state estimation. In this approach, traffic state distribution and data point distribution are used to describe the pattern...
Based on the observation that the correlation between observed traffic at two measurement points or traffic stations may be time-varying, attributable to the time-varying speed which subsequently causes variations in the time required to travel between the two points, in this paper, we develop a modified Space-Time Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags for...
This paper presents a big data driven simulation-based dynamic traffic assignment (DTA) model by combining cellular signaling data, remote microwave sensors and license plate recognition (LPR) cameras data. First, the static origin destination (OD) matrix is estimated by using cellular signaling data. Second, remote microwave and LPR data are fused to calibrate parameters on the network supply side,...
A frequently raised argument against safely-driving automated vehicles is that they would not harmonize well with traffic flow—unrealistically large headways would invite other traffic participants to cut in and thus put passengers of following automated vehicles at risk. In order to test this hypothesis, we use real data of thousands of vehicles recorded in the United States as part of the Next Generation...
Intelligent Transportation Systems are an important aspect of our life and are going to become ubiquitous in the near future. Traffic flow prediction is a key component of any Intelligent Transportation Systems. This report uses Artificial Neural Network based models to predict short term traffic flow. Two new input parameters; temperature and truck flow has been introduced into a multi input parameters...
Concerning the increasing demand for intelligent and efficient urban vehicle systems with low cost maintainability and high passenger comfort, reliable methods are needed to model and to evaluate the imposed performances. The measurements, vibrations emerging on the wheels and the body, that has ben taken on a city bus are analyzed in the frequency domain. In this paper a parametric spectral analysis...
For design fair public service system using the weighted p-median problem exist some approaches which use reduction coefficients. These coefficients allow to take probability of failure/occupation of service center into account and to make the location-allocation model more realistic. If the nearest service center is occupied by some other user, the demand is served from the second nearest service...
The European Commission Decision C (2015) 6776 established the specific European Transport Specific Programme implementing Horizon 2020, Part 11 “Smart, green and integrated transport”, which promotes using clean transport methods, one of which is cycling. Although the number of web tools for creating and sharing cycling routes is constantly increasing, their added value is low.
This paper proposes a two-layer decision framework to model taxi drivers' customer-search behaviors within urban areas. The first layer models taxi drivers' pickup location choice decisions, and a Huff model is used to describe the attractiveness of pickup locations. Then, a path size logit (PSL) model is used in the second layer to analyze route choice behaviors considering information such as path...
The highway toll road system in many countries is incapable of providing the detailed route information of users, and drivers may choose alternative paths rather than the shortest path in the hope of saving the travel cost. Existing ambiguous path identification research is heavily dependent on vehicle detection sensors, and traffic assignment models using simulations. In this paper, we present a...
Successful traffic speed prediction is of great importance for the benefits of both road users and traffic management agencies. To solve the problem, traffic scientists have developed a number of time-series speed prediction approaches, including traditional statistical models and machine learning techniques. However, existing methods are still unsatisfying due to the difficulty to reflect the stochastic...
Cooperative Advanced Driving Assistance Systems (C-ADAS) require both efficient perception and communication technologies. Experimentation of such systems in real world conditions is needed before their deployment. However, this is a difficult task since it deals with complex road scenarios and substantial experimental cost. To limit ADAS real experimentation constraints, we propose the design and...
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