German Highways Research Articles

Method for Systematic Assessment of Mobile Network Coverage for Logistic Applications on the German Highway

Smart logistics, combining the capabilities of logistics with methods and techniques of the Internet of Things, Information and Communication Technologies, and the highest levels of automation are key to addressing the challenges of the 21st century and minimizing emissions while maximizing logistic performance. High-performance cellular networks are a prerequisite to fully using and leveraging their possibilities. These communication networks were developed based on the need for voice communication and streaming services. While the upcoming requirements are included in the latest versions of cellular networks, the existing infrastructure requires significant improvements and will have to adapt significantly. This study evaluates the performance of the current state of implementation of cellular networks on the German highway experimentally and analytically. The known indicators RSRP, RSSI, and RSRQ are analyzed spatially, over time, and for different driving conditions. The results indicate a high level of spatial correlation and a sufficient level of confidence, which are needed to ensure consistency and repeatability of these measurements. The procedure and the results can be used to assess the suitability of cellular networks for smart logistics applications and continuously monitor their improvement. The results indicate the status of the cellular network on the German highway which is worse compared to the network operator’s self-assessment.

Manual drivers’ experience and driving behavior in repeated interactions with automated Level 3 vehicles in mixed traffic on the highway

In the near future, conditionally automated vehicles (CAVs; SAE Level 3) will travel alongside manual drivers (≤ SAE level 2) in mixed traffic on the highway. It is yet unclear how manual drivers will react to these vehicles beyond first contact when they interact repeatedly with multiple CAVs on longer highway sections or even during entire highway trips. In a driving simulator study, we investigated the subjective experience and behavioral reactions of N = 51 manual drivers aged 22 to 74 years (M = 41.5 years, SD = 18.1, 22 female) to driving in mixed traffic in repeated interactions with first-generation Level 3 vehicles on four highway sections (each 35 km long), each of which included three typical speed limits (80 km/h, 100 km/h, 130 km/h) on German highways. Moreover, the highway sections differed regarding the penetration rate of CAVs in mixed traffic (within-subjects factor; 0%, 25%, 50%, 75%). The drivers were assigned to one of three experimental groups, in which the CAVs differed regarding their external marking, (1) status eHMI, (2) no eHMI, and (3) a control group without information about the mixed traffic. After each highway section, drivers rated perceived safety, comfort, and perceived efficiency. Drivers were also asked to estimate the penetration rate of CAVs on the previous highway section. In addition, we analyzed drivers’ average speed and their minimum time headways to lead vehicles for each speed zone (80 km/h, 100 km/h, 130 km/h) as well as the percentage of safety critical interactions with lead vehicles (< 1 s time headway). Results showed that manual drivers experienced driving in mixed traffic, on average, as more uncomfortable, less safe and less efficient than driving in manual traffic, but not as dangerous. A status eHMI helps manual drivers identify CAVs in mixed traffic, but the eHMI had no effect on manual drivers’ subjective ratings or driving behavior. Starting at a level of 25% Level 3 vehicles in mixed traffic, participants' average speed decreased significantly. At the same time, the percentage of safety critical interactions with lead vehicles increased with an increasing penetration rate of CAVs. Accordingly, additional measures may be necessary in order to at least keep the existing safety level of driving on the highway.

Nattering on Neonatal Transport With a Dear Old Friend, Air Medical Journal

It was the time of dreams and adventure. It was the time when everything was imaginable, and, even more than that, everything was possible. It was the summer of the rite of passage, the overcoming of adolescence that the “maturity examination” at the end of liceo certified, delivering the passport for adulthood that began with enrollment at the university. During the summer of 1972, everything was allowed—even my father, who was perhaps a bit crazy or at least too optimistic, giving me his old blue sedan for the epic journey that would take me from the shores of the Genoese Mediterranean to the ends of the world toward that mythical North Cape whose location was unknown by almost all of the other graduates that year. 1972 was the time when the world was boiling, shaking with great ideas and ideals, but it was also an era in which news emerged slowly, spread more by insinuating itself than by overwhelming everything and everyone as it happens today. Some news stations created more legends than certainties, and for this reason they were extremely attractive and full of charm. My peers and I all longed to listen to RTL 208, Radio Luxembourg, but it almost never reached Italy because of the Alps according to 1 of our peers, although this person later boasted of being able to listen to it using sophisticated equipment recovered from who knows where. Some of our older brothers and sisters said that 208 transmitted from the sea, from something similar to a pirate ship that was unmanned and adrift but unreachable and unsinkable. This station broadcast rock and roll never heard before—legends, in fact! Legends who were capable of nourishing the restlessness of young hearts. The first time I listened to 208, finally being certain of its existence, was while driving on the long and flat German highways toward Denmark and then up toward the clear skies and the long shadows of Scandinavia, which were rare in the latitudes of the Mediterranean Sea, and then the blond girls who populated our youthful dreams and who watched us from the walls of our rooms. When the prehistoric battery-powered radio we were carrying on board picked up 208, it was broadcasting “Have You Ever Seen the Rain” by Creedence Clearwater Revival, a famous war song that has accompanied me often since that day.

Enhanced service life of existing infrastructure buildings by use of concrete screw anchors as post-installed shear reinforcement

In the last decades, traffic on railways and roads has increased significantly in Central Europe. On the other hand, the existing infrastructure mainly origins from the late 1950s to the 1970s. This applies also for the existing bridge and tunnel infrastructure which is now up to 60 years old. Due to this age and new standards in the current codes, quite often the amount of shear reinforcement does not fulfil the current design code rules. As a replacement of existing structures is often highly unsustainable, methods to improve the shear strength are needed. Therefore, a new system for post-installed shear reinforcement by the use of concrete screws was developed at the University of Innsbruck in the last years. The idea is to use concrete screw anchors - already known as anchoring element - aspost-installed reinforcement. In over 60 shear tests with girders and slabs the excellent applicability of concrete screw anchors as post-installed shear reinforcement has been proven. Also shear tests with cyclic loading were performed prove the use for dynamically loaded structures, such as bridges. Based on these test results a design approach was determined to meet the Eurocode-2 assessment standards.Several pilot-projects with concrete screws have been realised in Austria and Germany. For example a rail-road-bridge over a German highway with a remaining service life of zero years was strengthened to a new service life of over 20 years. Also a city-centre tunnel in Munich which was erected below a historic building is currently strengthened under ongoing traffic in the tunnel using concrete screw anchors as post-installed reinforcement.

Bayesian Traffic State Estimation Using Extended Floating Car Data

Traffic state estimation is a challenging task due to the collection of sparse and noisy measurements from specific points of the traffic network. The emergence of Connected and Automated Vehicles (CAVs) provides new capabilities for traffic state estimation using extended floating car data such as position, speed and spacing information. In this work we propose a Bayesian Traffic State Estimation (BTSE) methodology for estimating the traffic density based on extended floating car data. BTSE utilizes the Bayesian paradigm to express any prior information to derive probability distributions of the traffic density of different road segments of the traffic network. Two variations of the BTSE methodology are developed to handle the offline and online estimation problem. The BTSE methodology is evaluated both using realistic SUMO micro-simulations for M25 Highway, London, U.K., and a real-life vehicle-trajectory dataset from German highways, extracted from videos recorded by drones. The efficiency and accuracy of the BTSE methodology is compared to an existing methodology in the literature. We present results for the estimation performance of the methods showing that the Bayesian methodology consistently results in lower mean absolute percentage error than the compared literature method. The BTSE methodology yields high-quality estimation results even for a low penetration rate of CAVs (e.g. 5%).

Logical Scenarios Parameterization for Automated Vehicle Safety Assessment: Comparison of Deceleration and Cut-In Scenarios From Japanese and German Highways

This study compares real-traffic deceleration and cut-in scenarios, which were established as critical to automated vehicles (AVs) safety, between Japanese and German highway trajectory datasets. Both scenarios were extracted from two different traffic data previously collected in Japan with both instrumented vehicles and fixed cameras over highways (SAKURA dataset) and in Germany with drones (highD dataset). Five vehicle kinematic variables (lateral and longitudinal distances, velocities, and accelerations) were used to parameterize both scenarios and compared them between datasets using correlation and intersection objective measures and safety metrics: Time-to-Collision and Time Headway. Despite the differences in the rule of the road (e.g. speed limits left- and right-hand traffic), road design, and data sources between the two countries, data comparison results revealed significant correlations and intersections of parameters distribution for both scenarios. The Time-to-Collision significantly overlapped between countries for both scenarios. However, differences in the Time Headway indicate that the safety distance varied across both countries, suggesting that safety assessment methodologies need to be tailored to different environments and regions to ensure safety. These results highlight the potential to develop safety indicators applicable at the international level and warrant further data collection and comparative studies that support the development of harmonized, widely applicable, and region-neutral AVs safety assessment methodologies.

Reformations-Atlas. Die Reformation in Mitteldeutschland ed. by Markus Hein and Armin Kohnle

Reviewed by: Reformations-Atlas. Die Reformation in Mitteldeutschland ed. by Markus Hein and Armin Kohnle Jeffrey Jaynes Reformations-Atlas. Die Reformation in Mitteldeutschland. Edited by Markus Hein and Armin Kohnle. Wettin-Löbejün: Verlag Janos Stekovics, 2018. 212 pp. The 500-year anniversary of the Protestant Reformation prompted several aids for studying this important period. Reformations-Atlas is one of the best. It features seventy-six maps ranging from local city plans to world maps that depict the current scope of Lutheran and Reformed influence respectively. It concentrates, however, on numerous regional maps that sketch Saxony's Reformation heartland. This coffee-table-style book is a more than an atlas as it features images, timelines, and short introductory essays that elevate its scholarly contribution. The book is organized into eight chapters, beginning with the religious and political landscape that shaped the Reformation in "middle Germany." A helpful map of principal German highways shows how Leipzig, more than Wittenberg, stood at the crossroads of trade networks, fueling its book trade and the influence of its university. Other maps depict the church patrons honored across the breadth of Saxony (the customary saints, Mary, and the Trinity) and popular pilgrimage sites. The chapter concludes with maps and essays that focus on the two Wettin princes, Elector Frederick and Duke George, heirs of the Leipzig Partition (1485), and their commitments to the traditional faith. The next two chapters attend more biographically to Martin Luther, addressing his background (chapter 2) and the early stages of the Reformation (chapter 3). An excellent city plan and description of Luther's academic home at Erfurt highlights this section. Several itinerary maps are included, notably Luther's route to and from Rome, his journey to the Diet of Worms, and then to the Wartburg. Chapter 3 also addresses the Wittenberg Reformation broadly, with attention to Philip Melanchthon, Justus Jonas, and even Thomas Müntzer. A fascinating map chronicles the introduction of evangelical preachers and pastors into the towns of Saxony and adjacent communities, followed by an up-close analysis of the evangelical movement in Leisnig. This chapter concludes with the critical year [End Page 92] of 1525 featuring maps of Müntzer's career, the "Peasants' War," and the plundering of regional monasteries. Programmatic dimensions of reform appear in the next three chapters: Ernestine Saxony (chapter 4), Albertine Saxony (chapter 5), and the German Empire (chapter 6). This section opens with a detailed map of the "magisterial Reformation" by identifying when various German cities and territories adopted their first church ordinances (Kirchenordnungen). Specific phases and visitors are depicted on a map that chronicles the first visitations under Elector John (Ernestine Saxony) to later efforts under Duke Maurice (Albertine Saxony). These evangelical visitations differed significantly from earlier diocesan visitations, described for the bishoprics of Meissen (1522) and Merseburg (1524). In addition, chapters 4 and 5 include more local expressions of reform like the first visitations in Zwickau, or the efforts of Elisabeth von Rochlitz (1537). Visitations in the 1530s also targeted Anabaptist communities, and their presence across Saxony is represented. Chapter 6 redirects the focus to wider aspects of imperial politics, tracing the various leagues and alliances created to secure particular positions. This chapter concludes with representations of the Saxon theaters of conflict during the Schmalkaldic War, and the realignment of Wettin lands in the Wittenberg Capitulation. The final two chapters gauge the influence of the Reformation in middle Germany. The print culture that shaped the Reformation is on full display (chapter 7), featuring an array of images. One helpful map illustrates the translations of Luther's Small Catechism across most of Europe before 1600. Music receives good exposure in this chapter, with maps that note the distribution of hymnals and the creation of numerous Kantoreien. The final chapter shifts to the spread of the Lutheran Reformation to Scandinavia and across the Atlantic. Movements inside of Germany are also featured in maps of various Pietist traditions, from the seventeenth to the nineteenth centuries. One could hope for similar detailed atlases of other German regions and beyond. Encountering a stimulating book like this one generates ideas about additional things to include, such as the [End Page 93] influence of the...

Risky Driver Recognition with Class Imbalance Data and Automated Machine Learning Framework.

Identifying high-risk drivers before an accident happens is necessary for traffic accident control and prevention. Due to the class-imbalance nature of driving data, high-risk samples as the minority class are usually ill-treated by standard classification algorithms. Instead of applying preset sampling or cost-sensitive learning, this paper proposes a novel automated machine learning framework that simultaneously and automatically searches for the optimal sampling, cost-sensitive loss function, and probability calibration to handle class-imbalance problem in recognition of risky drivers. The hyperparameters that control sampling ratio and class weight, along with other hyperparameters, are optimized by Bayesian optimization. To demonstrate the performance of the proposed automated learning framework, we establish a risky driver recognition model as a case study, using video-extracted vehicle trajectory data of 2427 private cars on a German highway. Based on rear-end collision risk evaluation, only 4.29% of all drivers are labeled as risky drivers. The inputs of the recognition model are the discrete Fourier transform coefficients of target vehicle’s longitudinal speed, lateral speed, and the gap between the target vehicle and its preceding vehicle. Among 12 sampling methods, 2 cost-sensitive loss functions, and 2 probability calibration methods, the result of automated machine learning is consistent with manual searching but much more computation-efficient. We find that the combination of Support Vector Machine-based Synthetic Minority Oversampling TEchnique (SVMSMOTE) sampling, cost-sensitive cross-entropy loss function, and isotonic regression can significantly improve the recognition ability and reduce the error of predicted probability.

Policy support measures for widespread expansion of fast charging infrastructure for electric vehicles

Public fast charging infrastructure (FCI) is essential for the adoption of electric vehicles (EVs). To reach higher EV penetration, investments into the development of a comprehensive and widespread fast charging network are necessary. However, current investments in FCI are only profitable in specific locations resulting in a severe lack of deployments in most areas. The wish for rapid development of both, EVs and related charging opportunities, requires political support measures for FCI. This paper investigates various support measures regarding their contribution to a comprehensive expansion of FCI through profitability enhancement. We illustrate the impact of different support measures on the profitability of three different charging power categories at three different located charging sides along the German freeway. Besides the traffic volume, the profitability of FCI strongly depends on the location's surrounding charging facilities and population characteristics and decreases with increasing charging power. Currently available support measures such as investment subsidies or the exemption from the electricity tax do not contribute significantly to a widespread expansion of FCI. Changes in the demand charges have a higher potential to support nationwide investments in FCI.

Trajectory data based freeway high-risk events prediction and its influencing factors analyses

The frequent crash occurrences have caused massive loss of lives and properties all over the world. In order to improve traffic safety, it is vital to understand the relationships between traffic operation conditions and crash risk, and further implement safety countermeasures. Emerging studies have conducted the crash risk analyses using discrete and aggregated traffic data (e.g., loop detector data, probe vehicle data), where crash events were selected as the prediction target. However, traditional traffic sensing data obtained at segment level cannot describe the detailed operation conditions for the vehicle platoons near crash locations. Thus, more microscopic and high-resolution traffic sensing data are needed. In addition, considering the random occurrence feature of crashes, high-risk events should be paid more attentions given their higher occurrence probability and consistent causations with crashes, which could proactively reduce crash likelihood. In this study, HighD Dataset from German highways was utilized for the empirical analyses. First, high-risk events were obtained using safety surrogate measures with Modified Time to Collision (MTTC) less than 2 s. Traffic operation characteristics within 5 s prior to event occurrence were extracted based on vehicle trajectory data. Then, a total of three different logistic regression models were established, which are standard logistic regression model, random-effects logistic regression (RELR) model, and random-parameter logistic regression (RPLR) model. Among which, the RPLR model was showed to have the best fitness and prediction accuracy. The results showed that the disturbed traffic flows in both longitudinal and lateral directions have positive impacts on high-risk events occurrence. Besides, too close following distance between vehicles would lead to high-risk events. Moreover, RPLR models could provide a high prediction accuracy of 97 % for 2 s ahead of the high-risk events. Finally, potential safety improvement countermeasures and future application scenarios were also discussed.

Driver Situation Awareness and Perceived Sleepiness during Truck Platoon Driving – Insights from Eye-tracking Data

ABSTRACT Truck platoon driving technology uses vehicle-to-vehicle communication to allow one truck to follow another in an automated fashion. The first vehicle is operated manually, the second vehicle is driven semi-automatically once platoon-mode is activated. In this mode, the driver merely has to monitor traffic. Semi-automated driving in passenger cars has been shown to increase driver sleepiness and reduce situation awareness. The aim of the present study was to gain first insights whether this also applies to semi-automated platoon driving and whether platoon-specific situations pose special visual demands. In a first on-road experiment, ten professional truck drivers experienced a two-vehicle platooning system on a German highway as platoon follower or leader. In addition, all drivers conducted reference drives with a single truck. Driver situation awareness was measured with eye-tracking recordings, perceived sleepiness with subjective ratings. The results showed that the lead vehicle drivers kept their eyes less time on the road ahead as compared to normal truck driving. In particular in situations that required decoupling, drivers (in the lead vehicle as well as in the following vehicle) spent about 40% of fixations on the HMI. That is, situation awareness was reduced, amounting to potentially risky behavior, as the platoon goes blindfolded when both drivers attend to the display. Drivers did not report higher perceived sleepiness in semi-automated platoon drives than in the manual reference drives. Adequate solutions to reduce the time spent looking away from the road are required. Head-up displays should be investigated for this purpose, as they can simplify driver communication and present platoon-specific information while the eyes remain on the road.

Do drivers accept cooperative behavior of their automated vehicle on highways?

Automated cooperatively interacting vehicles will change the future of traffic completely. Such vehicles will be capable of planning and carrying out maneuvers based on vehicle-to-vehicle and vehicle-to-infrastructure communication, enabling a safer driving experience. However, this gain of safety will only be effective if drivers use and accept the decisions made by advanced automated technology. Especially when drivers are cognitively distracted, new strategies might be necessary, e.g., by further explaining the reason for a cooperative decision.In a driving simulator study, we investigated the acceptance of lane change maneuvers in cooperative situations carried out by an automated vehicle on a two-lane German highway. When the automated system detected a potential lane change ahead, it carried out one of three possible maneuvers: accelerate, decelerate, or maintain speed. Participants (N = 20) were asked whether they accepted the system’s behavior either while being cognitively distracted or in an attentive state. Thus, we investigated the influence of a cognitively demanding secondary task and, in addition, further situational characteristics (Scope of action, Criticality for the lane-changing vehicle, Display of intention) on the acceptance towards the system’s behavior. Moreover, participants had to rate the perceived situation’s criticality.Results underlined the importance of explicit indication of the intention to change lanes. Furthermore, increased cognitive load led to a higher acceptance of the defensive system behavior. This study contributes to the development of a user-centered interface and interaction strategy for cooperative interacting vehicles.

New German Highway Infrastructure and the Impacts on Residential Real Estate Prices

Germany’s 1990 reunification necessitated highway connections between an underdeveloped region and a western country. The 2009/2013 German Autobahn highway A38 completion/additions alleviated congestion, enhanced connectivity, and increased pollution/noise. Using German residential real estate price data spanning 2007-2017, we find shorter drive times to the A38 raised prices; and direct proximity lowered prices. Our results exceed connectivity estimates of a German local roads study and some Western European highway studies, due to A38 newly linking the east/west of Germany. Our highway estimates are in the mid-range of German transit studies, implying German transit may impact real estate prices more than highways.

A Two-Layer Risky Driver Recognition Model With Context Awareness

For autonomous vehicles and intelligent connected vehicles, the real-time recognition of risky drivers can play an important role in traffic accident prevention. However, the external environment substantially impacts driving behavior and driving risk and is usually costly to acquire. Existing risky driver recognition models often ignore external environment information or assume this information is given. We propose two hierarchical two-layer context-aware machine learning structures. The first layer can speculate external context, for example, traffic states. The second layer recognizes risky drivers based on the contextual information speculated from the first layer. The German Highway Drone Dataset is used to establish risky driver recognition and traffic state recognition models. Rear-end collision risk and side collision risk are evaluated for each vehicle. Drivers with high collision risk are labeled as risky drivers. By analyzing vehicle trajectory data from three traffic states: free-flow, saturated, and congested, we find that traffic states have a significant influence on vehicle’s longitudinal speed, lateral speed, longitudinal acceleration/deceleration, and collision risk. Six classifiers, including SVM, KNN, RF, Adaboost, Extra trees, and XGBoost, are applied to train recognition models. Results show that the proposed structures can significantly improve model’s ability to recognize risky drivers.

Crash Risk Estimation Due to Lane Changing: A Data-Driven Approach Using Naturalistic Data

Road crashes are one of the critical issues in the transportation sector. Crash studies aim to establish the relationship of crash occurrences with driver, environment and traffic factors. Lack of a large disaggregate driving or accident data constrains the study of driver factors and driving maneuvers. Moreover, the usual outcome of these studies is the crash likelihood without accounting for the impact of a potential crash, thus leading to an incomplete interpretation of crash risk. New and innovative ways of data collection, such as drone videography and floating car data, are a promising candidate for a disaggregated analysis. Our study proposes a method for estimation of rear-end crash risk during a specific traffic state, using separate formulations of likelihood and severity. We quantify rear-end crash risk by weighing the likelihood by the potential severity of a collision, wherein we introduce a severity indicator for the rear-end crash. The methodology is applied to the highD dataset, a large naturalistic traffic dataset collected on German freeways. The proposed methodology allows for the analysis of risk with traffic state and lane-changing maneuver. The findings show that speed-drop is associated with increased crash risk. Also, lane changing is associated with higher rear-end crash risk, as compared to lane-keeping, during the free-flow as well as congestion traffic. The understanding of the evolution of the crash risk, due to the driving maneuvers under different traffic conditions, can be useful for real-time crash prediction and for devising traffic management strategies.

Collective Perception: A Safety Perspective

Vehicle-to-everything (V2X) communication is seen as one of the main enabling technologies for automated vehicles. Collective perception is especially promising, as it allows connected traffic participants to “see through the eyes of others” by sharing sensor-detected objects via V2X communication. Its benefit is typically assessed in terms of the increased object update rate, redundancy, and awareness. To determine the safety improvement thanks to collective perception, the authors introduce new metrics, which quantify the environmental risk awareness of the traffic participants. The performance of the V2X service is then analyzed with the help of the test platform TEPLITS, using real traffic traces from German highways, amounting to over 100 h of total driving time. The results in the considered scenarios clearly show that collective perception not only contributes to the accuracy and integrity of the vehicles’ environmental perception, but also that a V2X market penetration of at least 25% is necessary to increase traffic safety from a “risk of serious traffic accidents” to a “residual hypothetical risk of collisions without minor injuries” for traffic participants equipped with non-redundant 360° sensor systems. These results support the ongoing worldwide standardization efforts of the collective perception service.

Trajectory prediction for intelligent vehicles using spatial‐attention mechanism

It is of great interest for autonomous vehicles to predict the trajectory of other vehicles when planning a safe trajectory. To accurately predict the trajectory of the target vehicle, the interaction between vehicles must be considered. Interaction aware prediction methods track the previous trajectories of both the target vehicle and its surrounding vehicles. In this study, the authors consider trajectory prediction as a sequence-to-sequence prediction problem. They tackle this problem with an LSTM encoder–decoder framework. Moreover, they propose two spatial-attention mechanisms to account for the interaction between vehicles, i.e. context attention and lane attention. Spatial-attention mechanisms adopt the selective-attention mechanism of human drivers. They choose context vectors to help the model understand the surrounding environment better and thus improve its prediction accuracy. They evaluate the authors’ methods on the highD data set recorded in German highways with root mean squared error metric. Their experimental results show superior performance to other state-of-the-art methods. Code is available at https://github.com/momo1986/Spatial-attention.

The Channel as a Traffic Sensor: Vehicle Detection and Classification Based on Radio Fingerprinting

Ubiquitously deployed Internet of Things (IoT)- based automatic vehicle classification systems will catalyze data-driven traffic flow optimization in future smart cities and will transform the road infrastructure itself into a dynamically sensing Cyber-physical System (CPS). Although a wide range of different traffic sensing systems has been proposed, the existing solutions are not yet able to simultaneously satisfy the multitude of requirements, e.g., accuracy, robustness, cost-efficiency, and privacy preservation. In this paper, we present a novel approach, which exploits radio fingerprints - multidimensional attenuation patterns of wireless signals - for accurate and robust vehicle detection and classification. The proposed system can be deployed in a highly cost-efficient manner as it relies on off-the-shelf embedded devices which are installed into existing delineator posts. In a comprehensive field evaluation campaign, the performance of the radio fingerprinting-based approach is analyzed within an experimental live deployment on a German highway, where it is able to achieve a binary classification success ratio of more than 99% and an overall accuracy of 93.83% for a classification task with seven different classes.

Level of Service Evaluation Methods: Possible Adaptation for Lithuania

Level of Service is a quantitative measure to characterize operational conditions within a traffic stream. There is a set of factors affecting the Level of Service. These factors describe geometric parameters of the road, traffic conditions, traffic regulation conditions and base conditions. In this article, the Level of Service as a criterion for operational quality is described. Level of Service factors and performance measures are described on the basis of Highway Capacity Manual considering verifications done by countries for their own road conditions. German Highway Capacity Manual (HBS) and Highway Capacity Manual are compared for the purpose of possible adaptation for Lithuania.

Studying Car-Following Dynamics on the Basis of the HighD Dataset

A large-scale naturalistic vehicle trajectory dataset from German highways, highD, was used to investigate the car-following behavior of individual drivers. These data include trajectories of 110,000 vehicles recorded for a duration of 16.5 h. Solving a nonlinear optimization problem, the intelligent driver model and the optimal velocity model with two leaders in interaction were calibrated by minimizing the deviations between the observed and simulated gaps when following the prescribed leading vehicle. The obtained calibration errors ranged between 5.2% and 6.9%, which were slightly lower than previous findings. This was explained by the shorter highD trajectories, predominantly free-flow traffic, and the good precision metrics of this dataset. The optimal velocity model with multivehicle anticipation resulted in lower calibration errors. This confirmed that natural drivers take into account several leading vehicles ahead. The ratio between interdriver and intradriver variability was investigated by performing global and platoon calibrations. Intradriver variation accounted for a larger portion of the calibration errors than interdriver variation. We analyzed the acceleration time-series of the natural highD and artificial drivers using simulations of two car-following models. A new cumulative measure, proportional to the energy of the follower’s position time-series curve, was calculated both for natural and modeled drivers. Human drivers had higher energy and demonstrated more acceleration fluctuations, sometimes behaving irrationally. In contrast, artificial drivers followed the logical rules incorporated in the model, resulting in a smoother acceleration profile. This led to less fuel consumption and gas emissions.