For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.

Data-Driven Intelligent Transportation Systems: A Survey

Automatic detection of drowsiness in EEG records based on multimodal analysis.

Safety is key to civil aviation. To further improve its already respectable safety records, the airline industry is transitioning towards a proactive approach which anticipates and mitigates risks before incidents occur. This approach requires continuous monitoring and analysis of flight operations; however, modern aircraft systems have become increasingly complex to a degree that traditional analytical methods have reached their limits – the current methods in use can only detect ‘hazardous’ behaviors on a pre-defined list; they will miss important risks that are unlisted or unknown. This paper presents a novel approach to apply data mining in flight data analysis allowing airline safety experts to identify latent risks from daily operations without specifying what to look for in advance. In this approach, we apply a Gaussian Mixture Model (GMM) based clustering to digital flight data in order to detect flights with unusual data patterns. These flights may indicate an increased level of risks under the assumption that normal flights share common patterns, while anomalies do not. Safety experts can then review these flights in detail to identify risks, if any. Compared with other data-driven methods to monitor flight operations, this approach, referred to as ClusterAD-DataSample, can (1) better establish the norm by automatically recognizing multiple typical patterns of flight operations, and (2) pinpoint which part of a detected flight is abnormal. Evaluation of ClusterAD-DataSample was performed on two sets of A320 flight data of real-world airline operations; results showed that ClusterAD-DataSample was able to detect abnormal flights with elevated risks, which make it a promising tool for airline operators to identify early signs of safety degradation even if the criteria are unknown a priori.

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Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring

Misunderstanding of driver correction behaviors is the primary reason for false warnings of lane-departure-prediction systems. We proposed a learning-based approach to predict unintended lane-departure behaviors and chances of drivers to bring vehicles back to the lane. First, a personalized driver model for lane-departure and lane-keeping behavior is established by combining the Gaussian mixture model and the hidden Markov model. Second, based on this model, we developed an online model-based prediction algorithm to predict the forthcoming vehicle trajectory and judge whether the driver will act a lane departure behavior or correction behavior. We also develop a warning strategy based on the model-based prediction algorithm that allows the lane-departure warning system to be acceptable for drivers according to the predicted trajectory. In addition, the naturalistic driving data of ten drivers were collected to train the personalized driver model and validate this approach. We compared the proposed method with a basic time-to-lane-crossing (TLC) method and a TLC-directional sequence of piecewise lateral slopes (TLC-DSPLS) method. Experimental results show that the proposed approach can reduce the false-warning rate to 3.13% on average at 1-s prediction time.

A Learning-Based Approach for Lane Departure Warning Systems With a Personalized Driver Model

In this paper, we propose a new framework for discriminating the initial maneuver of a lane-crossing event from a driver correction event, which is the primary reason for false warnings of lane departure prediction systems (LDPSs). The proposed algorithm validates the beginning episode of the trajectory of driving signals, i.e., whether it will cause a lane-crossing event, by employing driver behavior models of the directional sequence of piecewise lateral slopes (DSPLS) representing lane-crossing and driver correction events. The framework utilizes only common driving signals and allows the adaptation scheme of driver behavior models to better represent individual driving characteristics. The experimental evaluation shows that the proposed DSPLS framework has a detection error with as low as a 17% equal error rate. Furthermore, the proposed algorithm reduces the false-warning rate of the original lane departure prediction system with less tradeoff for the correct prediction.

On the Use of Stochastic Driver Behavior Model in Lane Departure Warning

This paper develops an onboard decision module for issuing appropriate warnings when the equipped vehicle's traveling trajectory is irregular. Potential vehicle accidents exist if the driver is distracted by fatigue, drowsiness, food, phone use, and talking or is under the influence of alcohol or drugs, etc. Usual freeway accidents include lateral and rear-end collisions. Detecting vehicle behavior is a good way to measure the security of driving. This paper presents two modules. An unexpected lane departure avoidance module is utilized to prevent lateral collision. This module issues warnings when the vehicle approaches an irregular departure from the middle of the lane. Radial basis probability networks are applied to distinguish between normal lane change and lane departure. A rear-end collision avoidance module is used to issue warnings to avoid longitudinal accidents. This module reflects driver perceptions of environmental influence with a warning value and the warning threshold by neural networks and fuzzy membership functions, respectively. The proposed modules are satisfactory according to simulations and field tests.

Onboard Measurement and Warning Module for Irregular Vehicle Behavior

Traffic signal control is important for intersection safety and efficiency. However, most traffic signal control methods are designed for individual intersections or corridors. Although some adaptive control systems have been developed, the methods used are often proprietary and not published, making it difficult to evaluate their effectiveness. This study proposes an adaptive multi-input and multi-output traffic signal control method that not only can improve network-wide traffic operations in terms of reduced traffic delay and energy consumption, but also is more computationally feasible than existing centralized signal control methods. Considering intersection interactions, a linear dynamic traffic system model was built and adaptively updated to reflect how the signal control input of each intersection affects network-wide vehicle travel delay. Based on the system model, an adaptive linear-quadratic regulator (LQR) was designed to minimize both traffic delay and incremental changes in the control input. The proposed control method was evaluated in a microscopic traffic simulation environment with a 35-intersection network of Bellevue City, Washington. Simulation results show that the proposed method had shorter average traffic delays in the network when compared with the traffic delays controlled by the state-of-the-art max-pressure, self-organizing traffic lights, and independent deep Q network methods.

Optimizing Signal Timing Control for Large Urban Traffic Networks Using an Adaptive Linear Quadratic Regulator Control Strategy

Most existing models for prediction of pavement marking retroreflectivity have been developed from data collected in locations with similar weather conditions; therefore, the effect of different weather conditions, such as winter weather events, has not been extensively studied. This study develops degradation models that can predict retroreflectivity of durable pavement marking materials under various winter weather conditions. Piecewise multiple linear models are proposed to explicitly account for the effect of winter weather events in this study. In comparisons of the proposed models with conventional multiple linear models (MLMs) developed from the same set of data, the proposed models outperformed the MLMs, and the overall root-mean-square error improved from 204.6 mcd/m2/lux for MLMs to 106.5 mcd/m2/lux for piecewise MLMs. The proposed models also show robust and consistent results in predicting retroreflectivity of different materials in different states. The results indicate that the proposed meth...

Piecewise Multiple Linear Models for Pavement Marking Retroreflectivity Prediction Under Effect of Winter Weather Events

The visibility of pavement markings is an important aspect of a safe transportation system as the markings convey vital roadway warnings and guidance information to the traveling public. Therefore,...

Development and Implementation of a Pavement Marking Management System in Florida

The Georgia Department of Transportation (GDOT) has effectively utilized its pavement management system (PMS) to make informed, data-driven pavement maintenance decisions, including project selection, project prioritization, and funding allocation. Currently, due to funding shortages and the increasing reconstruction needs of its aging road network, GDOT is seeking to enhance its life-cycle cost analysis (LCCA) in pavement design to make the best investment in the pavements. A key question to address in conducting a reliable LCCA in pavement design is how long the pavements last. This question can be addressed by analyzing pavement condition data in the PMS. The objective of this project is to study the longevity of concrete pavements in Georgia using historical concrete pavement condition data. Also, this study provides a better understanding of the actual performance of various designs of concrete pavement.

Chieh Wang

Papers

Onboard Measurement and Warning Module for Irregular Vehicle Behavior

Optimizing Signal Timing Control for Large Urban Traffic Networks Using an Adaptive Linear Quadratic Regulator Control Strategy

Piecewise Multiple Linear Models for Pavement Marking Retroreflectivity Prediction Under Effect of Winter Weather Events

Development and Implementation of a Pavement Marking Management System in Florida

Georgia concrete pavement performance and longevity