A Macro-Micro Approach to Reconstructing Vehicle Trajectories on Multi-Lane Freeways with Lane Changing

Vehicle trajectories can offer the most precise and detailed depiction of traffic flow and serve as a critical component in traffic management and control applications. Various technologies have been applied to reconstruct vehicle trajectories from sparse fixed and mobile detection data. However, existing methods predominantly concentrate on single-lane scenarios and neglect lane-changing (LC) behaviors that occur across multiple lanes, which limit their applicability in practical traffic systems. To address this research gap, we propose a macro-micro approach for reconstructing complete vehicle trajectories on multi-lane freeways, wherein the macro traffic state information and micro driving models are integrated to overcome the restrictions imposed by lane boundary. Particularly, the macroscopic velocity contour maps are established for each lane to regulate the movement of vehicle platoons, meanwhile the velocity difference between adjacent lanes provide valuable criteria for guiding LC behaviors. Simultaneously, the car-following models are extended from micro perspective to supply lane-based candidate trajectories and define the plausible range for LC positions. Later, a two-stage trajectory fusion algorithm is proposed to jointly infer both the car-following and LC behaviors, in which the optimal LC positions is identified and candidate trajectories are adjusted according to their weights. The proposed framework was evaluated using NGSIM dataset, and the results indicated a remarkable enhancement in both the accuracy and smoothness of reconstructed trajectories, with performance indicators reduced by over 30% compared to two representative reconstruction methods. Furthermore, the reconstruction process effectively reproduced LC behaviors across contiguous lanes, adding to the framework's comprehensiveness and realism

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

Privacy in trajectory micro-data publishing : a survey

We survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simple and frequent thanks to emerging information and communication technologies. The focus of our review is on privacy-preserving data publishing (PPDP), i.e., the publication of databases of trajectory micro-data that preserve the privacy of the monitored individuals. We classify and present the literature of attacks against trajectory micro-data, as well as solutions proposed to date for protecting databases from such attacks. This paper serves as an introductory reading on a critical subject in an era of growing awareness about privacy risks connected to digital services, and provides insights into open problems and future directions for research.Comment: Accepted for publication at Transactions for Data Privac

Spatiotemporal variability of water vapor investigated using lidar and FTIR vertical soundings above the Zugspitze

Water vapor is the most important greenhouse gas and its spatiotemporal variability strongly exceeds that of all other greenhouse gases. However, this variability has hardly been studied quantitatively so far. We present an analysis of a 5-year period of water vapor measurements in the free troposphere above the Zugspitze (2962 m a.s.l., Germany). Our results are obtained from a combination of measurements of vertically integrated water vapor (IWV), recorded with a solar Fourier transform infrared (FTIR) spectrometer on the summit of the Zugspitze and of water vapor profiles recorded with the nearby differential absorption lidar (DIAL) at the Schneefernerhaus research station. The special geometrical arrangement of one zenith-viewing and one sun-pointing instrument and the temporal resolution of both instruments allow for an investigation of the spatiotemporal variability of IWV on a spatial scale of less than 1 km and on a timescale of less than 1 h. The standard deviation of differences between both instruments sigma IWV calculated for varied subsets of data serves as a measure of variability. The different subsets are based on various spatial and temporal matching criteria. Within a time interval of 20 min, the spatial variability becomes significant for horizontal distances above 2 km, but only in the warm season (sigma IWV = 0.35 mm). However, it is not sensitive to the horizontal distance during the winter season. The variability of IWV within a time interval of 30 min peaks in July and August (sigma IWV > 0.55 mm, mean horizontal distance = 2.5 km) and has its minimum around midwinter (sigma IWV 5 km). The temporal variability of IWV is derived by selecting subsets of data from both instruments with optimal volume matching. For a short time interval of 5 min, the variability is 0.05 mm and increases to more than 0.5 mm for a time interval of 15 h. The profile variability of water vapor is determined by analyzing subsets of water vapor profiles recorded by the DIAL within time intervals from 1 to 5 h. For all altitudes, the variability increases with widened time intervals. The lowest relative variability is observed in the lower free troposphere around an altitude of 4.5 km. Above 5 km, the relative variability increases continuously up to the tropopause by about a factor of 3. Analysis of the covariance of the vertical variability reveals an enhanced variability of water vapor in the upper troposphere above 6 km. It is attributed to a more coherent flow of heterogeneous air masses, while the variability at lower altitudes is also driven by local atmospheric dynamics. By studying the short-term variability of vertical water vapor profiles recorded within a day, we come to the conclusion that the contribution of long-range transport and the advection of heterogeneous layer structures may exceed the impact of local convection by 1 order of magnitude even in the altitude range between 3 and 5 km

Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies (TR_C), Volume 145, 202