Lane Changing Control to Reduce Traffic Load Effect on Long-Span Bridges

Long span bridges are critical parts of a nation’s infrastructure network and congested traffic loading is the governing form of traffic loading. Groups of trucks travelling in conveys are created when fast-er moving vehicles, such as cars, change lane. In this research the authors investigate how the control of these lane-changing events can help reduce the traffic load effects on long span bridges. Real traffic data is used to simulate a traffic stream on a virtual road and bridge using a microsimulation model. Various lane-changing restrictions are examined and compared to the typical case of free lane changing. It is shown that restriction of lane changing is an effective means of reducing long-span bridge traffic load effect. This result may assist bridge owners in implementing measures to prolong the life of existing infrastructure

Identification of Lane-Change Maneuvers in Real-World Drivings with Hidden Markov Model and Dynamic Time Warping

For the introduction of new automated driving functions, the systems need to be verified extensively. A scenario-driven approach has become an accepted method for this task. But to verify the functionality of an automated vehicle in the simulation in a certain scenario such as a lane change, relevant characteristics of scenarios need to be identified. This, however, requires to extract these scenarios from real-world drivings accurately. For that purpose, this work proposes a novel framework based on a set of unsupervised learning methods to identify lane-changes on motorways. To represent various types of lane changes, the maneuver is split up into primitive driving actions with an Hidden Markov Model and Divisive Hierarchical Clustering. Based on this, lane change maneuvers are identified using Dynamic-Time-Warping. The presented framework is evaluated with a real-world test drive and compared to other baseline methods. With a f1 score of 98.01\% in lane-change identification, the presented approach shows promising results

Lane changing control to reduce traffic load effect on long-span bridges

Long span bridges are critical parts of a nation’s infrastructure network and congested traffic loading is the governing form of traffic loading. Groups of trucks travelling in conveys are created when fast-er moving vehicles, such as cars, change lane. In this research the authors investigate how the control of these lane-changing events can help reduce the traffic load effects on long span bridges. Real traffic data is used to simulate a traffic stream on a virtual road and bridge using a microsimulation model. Various lane-changing restrictions are examined and compared to the typical case of free lane changing. It is shown that restriction of lane changing is an effective means of reducing long-span bridge traffic load effect. This result may assist bridge owners in implementing measures to prolong the life of existing infrastructure

AI-based framework for automatically extracting high-low features from NDS data to understand driver behavior

Our ability to detect and characterize unsafe driving behaviors in naturalistic driving environments and associate them with road crashes will be a significant step toward developing effective crash countermeasures. Due to some limitations, researchers have not yet fully achieved the stated goal of characterizing unsafe driving behaviors. These limitations include, but are not limited to, the high cost of data collection and the manual processes required to extract information from NDS data. In light of this limitations, the primary objective of this study is to develop an artificial intelligence (AI) framework for automatically extracting high-low features from the NDS dataset to explain driver behavior using a low-cost data collection method. The author proposed three novel objectives for achieving the study's objective in light of the identified research gaps. Initially, the study develops a low-cost data acquisition system for gathering data on naturalistic driving. Second, the study develops a framework that automatically extracts high- to low-level features, such as vehicle density, turning movements, and lane changes, from the data collected by the developed data acquisition system. Thirdly, the study extracted information from the NDS data to gain a better understanding of people's car-following behavior and other driving behaviors in order to develop countermeasures for traffic safety through data collection and analysis. The first objective of this study is to develop a multifunctional smartphone application for collecting NDS data. Three major modules comprised the designed app: a front-end user interface module, a sensor module, and a backend module. The front-end, which is also the application's user interface, was created to provide a streamlined view that exposed the application's key features via a tab bar controller. This allows us to compartmentalize the application's critical components into separate views. The backend module provides computational resources that can be used to accelerate front-end query responses. Google Firebase powered the backend of the developed application. The sensor modules included CoreMotion, CoreLocation, and AVKit. CoreMotion collects motion and environmental data from the onboard hardware of iOS devices, including accelerometers, gyroscopes, pedometers, magnetometers, and barometers. In contrast, CoreLocation determines the altitude, orientation, and geographical location of a device, as well as its position relative to an adjacent iBeacon device. The AVKit finally provides a high-level interface for video content playback. To achieve objective two, we formulated the problem as both a classification and time-series segmentation problem. This is due to the fact that the majority of existing driver maneuver detection methods formulate the problem as a pure classification problem, assuming a discretized input signal with known start and end locations for each event or segment. In practice, however, vehicle telemetry data used for detecting driver maneuvers are continuous; thus, a fully automated driver maneuver detection system should incorporate solutions for both time series segmentation and classification. The five stages of our proposed methodology are as follows: 1) data preprocessing, 2) segmentation of events, 3) machine learning classification, 4) heuristics classification, and 5) frame-by-frame video annotation. The result of the study indicates that the gyroscope reading is an exceptional parameter for extracting driving events, as its accuracy was consistent across all four models developed. The study reveals that the Energy Maximization Algorithm's accuracy ranges from 56.80 percent (left lane change) to 85.20 percent (right lane change) (lane-keeping) All four models developed had comparable accuracies to studies that used similar models. The 1D-CNN model had the highest accuracy (98.99 percent), followed by the LSTM model (97.75 percent), the RF model (97.71 percent), and the SVM model (97.65 percent). To serve as a ground truth, continuous signal data was annotated. In addition, the proposed method outperformed the fixed time window approach. The study analyzed the overall pipeline's accuracy by penalizing the F1 scores of the ML models with the EMA's duration score. The pipeline's accuracy ranged between 56.8 percent and 85.0 percent overall. The ultimate goal of this study was to extract variables from naturalistic driving videos that would facilitate an understanding of driver behavior in a naturalistic driving environment. To achieve this objective, three sub-goals were established. First, we developed a framework for extracting features pertinent to comprehending the behavior of natural-environment drivers. Using the extracted features, we then analyzed the car-following behaviors of various demographic groups. Thirdly, using a machine learning algorithm, we modeled the acceleration of both the ego-vehicle and the leading vehicle. Younger drivers are more likely to be aggressive, according to the findings of this study. In addition, the study revealed that drivers tend to accelerate when the distance between them and the vehicle in front of them is substantial. Lastly, compared to younger drivers, elderly motorists maintain a significantly larger following distance. This study's results have numerous safety implications. First, the analysis of the driving behavior of different demographic groups will enable safety engineers to develop the most effective crash countermeasures by enhancing their understanding of the driving styles of different demographic groups and the causes of collisions. Second, the models developed to predict the acceleration of both the ego-vehicle and the leading vehicle will provide enough information to explain the behavior of the ego-driver.Includes bibliographical references

Understanding Micro-Level Lane Change and Lane Keeping Driving Decisions: Harnessing Big Data Streams from Instrumented Vehicles

It is important to get a deeper understanding of instantaneous driving behaviors, especially aggressive and extreme driving behaviors such as hard acceleration, as they endanger traffic efficiency and safety by creating unstable flows and dangerous situations. The aim of the dissertation is to understand micro-level instantaneous driving decisions related to lateral movements such as lane change or lane keeping events on various roadway types. The impacts of these movements are fundamental to microscopic traffic flow and safety. Sufficient geo-referenced data collected from connected vehicles enables analysis of these driving decisions. The “Big Data” cover vehicle trajectories, reported at 10 Hz frequency, and driving situations, which make it possible to establish a framework.The dissertation conducts several key analyses by applying advanced statistical modeling and data mining techniques. First, the dissertation proposes an innovative methodology for identifying normal and extreme lane change events by analyzing the lane-based vehicle positions, e.g., sharp changes in distance of vehicle centerline relative to the lane boundaries, and vehicle motions captured by the distributions of instantaneous lateral acceleration and speed. Second, since surrounding driving behavior influences instantaneous lane keeping behaviors, the dissertation investigates correlations between different driving situations and lateral shifting volatility, which quantifies the variability in instantaneous lateral displacements. Third, the dissertation analyzes the “Gossip effect” which captures the peer influence of surrounding vehicles on the instantaneous driving decisions of subject vehicles at micro-level. Lastly, the dissertation explores correlations between lane change crash propensity or injury severity and driving volatility, which quantifies the fluctuation variability in instantaneous driving decisions.The research findings contribute to the ongoing theoretical and policy debates regarding the effects of instantaneous driving movements. The main contributions of this dissertation are: 1) Quantification of instantaneous driving decisions with regard to two aspects: vehicle motions (e.g., lateral and longitudinal acceleration, and vehicle speed) and lateral displacement; 2) Extraction of critical information embedded in large-scale trajectory data; and 3) An understanding of the correlations between lane change outcomes and instantaneous lateral driving decisions

Characterizing Queue Dynamics at Signalized Intersections From Probe Vehicle Data

Probe vehicles instrumented with location-tracking technologies have become increasingly popular for collecting traffic flow data. While probe vehicle data have been used for estimating speeds and travel times, there has been limited research on predicting queuing dynamics from such data. In this research, a methodology is developed for identifying the travel lanes of the GPS-instrumented vehicles when they are standing in a queue at signalized intersections with multilane approaches. In particular, the proposed methodology exploits the unequal queue lengths across the lanes to infer the specific lanes the probe vehicles occupy. Various supervised and unsupervised clustering methods were developed and tested on data generated from a microsimulation model. The generated data included probe vehicle positions and shockwave speeds predicated on their trajectories. Among the tested methods, a Bayesian approach that employs probability density functions estimated by bivariate statistical mixture models was found to be effective in identifying the lanes. The results from lane identification were then used to predict queue lengths for each travel lane. Subsequently, the trajectories for non-probe vehicles within the queue were predicted. As a potential application, fuel consumption for all vehicles in the queue is estimated and evaluated for accuracy. The accuracies of the models for lane identification. queue length prediction, and fuel consumption estimation were evaluated at varying levels of demand and probe-vehicle market penetrations. In general, as the market penetration increases, the accuracy improves. For example. when the market penetration rate is about 40%, the queue length estimation accuracy reaches 90%. The dissertation includes various numerical experiments and the performance of the models under numerous scenarios