Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles

Autonomous Vehicles as a Sensor: Simulating Data Collection Process

Urban traffic state estimation is pivotal in furnishing precise and reliable insights into traffic flow characteristics, thereby enabling efficient traffic management. Traditional traffic estimation methodologies have predominantly hinged on labor-intensive and costly techniques such as loop detectors and floating car data. Nevertheless, the relentless progression in autonomous driving technology has catalyzed an increasing interest in capitalizing on the extensive potential of on-board sensor data, giving rise to a novel concept known as "Autonomous Vehicles as a Sensor" (AVaaS). This paper innovatively refines the AVaaS concept by simulating the data collection process. We take real-world sensor attributes into account and employ more accurate estimation techniques based on the on-board sensor data. Such data can facilitate the estimation of high-resolution, link-level traffic states and, more extensively, online cluster- and network-level traffic states. We substantiate the viability of the AVaaS concept through a case study conducted using a real-world traffic simulation in Ingolstadt, Germany. The results attest to the ability of AVaaS in estimating both microscopic (link-level) and macroscopic (cluster- and network-level) traffic states, thereby highlighting the immense potential of the AVaaS concept in effecting precise and reliable traffic state estimation and also further applications.Comment: 15 pages, 11 figures, the 2024 TRB Annual Meetin

Model-based estimation of private charging demand at public charging stations

In recent years, many concepts have been developed on how to build a sufficient charging infrastructure to satisfy the demand of Battery Electric Vehicle (BEV) users. However, the focus of these approaches often lies on the spatial distribution of charging stations and the amount of charging demand is often given beforehand. In this paper, we describe a model to estimate the future private charging demand at public charging stations for different regions. Several aspects that influence the needed amount of charging stations are considered, e.g. a growing range of BEVs and the behavior of different user groups. For example, we distinguish between BEV users with or without a home charging possibility. The spatial distribution of these user groups is modeled using an agent-based approach, respecting sociodemographic properties. Forecasting the spread of BEVs strongly depends on the assumptions made regarding these influencing factors, where different current studies obtain deviant results. Therefore, in a case study for the city of Munich, we consider three different scenarios assuming a pessimistic, a realistic and an optimistic spread of BEVs in the year 2020. Additionally, we present a sensitivity analysis of the influencing factors and identify the ones that have the highest impact on the future charging demand: the overall adoption rate of BEVs is the parameter that influences the output the most. In fact, an adoption rate that is 10% higher than expected leads to an increase in charging demand of about 16%. This means, that our model strongly depends on reliable input data. The output of our model is the expected number of charging events requested in a certain region on an average day. Together with the average parking time and the temporal distribution of car arrivals at public charging stations, it is possible to obtain the necessary size of the charging infrastructure such that the demand can be satisfied even during peak hours. These results can be used as an input to existing optimization algorithms for the allocation of charging stations

Human T Lymphotropic Virus Type 1 protein Tax reduces histone levels

<p>Abstract</p> <p>Background</p> <p>Human T-Lymphotropic Virus Type-1 (HTLV-1) is an oncogenic retrovirus that causes adult T-cell leukemia/lymphoma (ATLL). The virally encoded Tax protein is thought to be necessary and sufficient for T-cell leukemogenesis. Tax promotes inappropriate cellular proliferation, represses multiple DNA repair mechanisms, deregulates cell cycle checkpoints, and induces genomic instability. All of these Tax effects are thought to cooperate in the development of ATLL.</p> <p>Results</p> <p>In this study, we demonstrate that histone protein levels are reduced in HTLV-1 infected T-cell lines (HuT102, SLB-1 and C81) relative to uninfected T-cell lines (CEM, Jurkat and Molt4), while the relative amount of DNA per haploid complement is unaffected. In addition, we show that replication-dependent core and linker histone transcript levels are reduced in HTLV-1 infected T-cell lines. Furthermore, we show that Tax expression in Jurkat cells is sufficient for reduction of replication-dependent histone transcript levels.</p> <p>Conclusion</p> <p>These results demonstrate that Tax disrupts the proper regulation of replication-dependent histone gene expression. Further, our findings suggest that HTLV-1 infection uncouples replication-dependent histone gene expression and DNA replication, allowing the depletion of histone proteins with cell division. Histone proteins are involved in the regulation of all metabolic processes involving DNA including transcription, replication, repair and recombination. This study provides a previously unidentified mechanism by which Tax may directly induce chromosomal instability and deregulate gene expression through reduced histone levels.</p

Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study

Multimodality is a main requirement for future Urban Traffic Control (UTC). For cities and traffic engineers to implement multimodal UTC, a holistic, multimodal assessment of UTC measures is needed. This paper proposes a Multimodal Performance Index (MPI), which considers the delays and number of stops of different transport modes that are weighted to each other. To determine suitable mode-specific weights, a case study for the German city Ingolstadt is conducted using the microscopic simulation tool SUMO. In the case study, different UTC measures (bus priority, coordination for cyclists, coordination for private vehicle traffic) are implemented to a varying extent and evaluated according to different weight settings. The MPI calculation is done both network-wide and intersection-specific. The results indicate that a weighting according to the occupancy level of modes, as mainly proposed in the literature so far, is not sufficient. This applies particularly to cycling, which should be weighted according to its positive environmental impact instead of its occupancy. Besides, the modespecific weights have to correspond to the traffic-related impact of the mode-specific UTC measures. For Ingolstadt, the results are promising for a weighting according to the current modal split and a weighting with incentives for sustainable modes

Data-driven Spatio-Temporal Scaling of Travel Times for AMoD Simulations

With the widespread adoption of mobility-on-demand (MoD) services and the advancements in autonomous vehicle (AV) technology, the research interest into the AVs based MoD (AMoD) services has grown immensely. Often agent-based simulation frameworks are used to study the AMoD services using the trip data of current Taxi or MoD services. For reliable results of AMoD simulations, a realistic city network and travel times play a crucial part. However, many times the researchers do not have access to the actual network state corresponding to the trip data used for AMoD simulations reducing the reliability of results. Therefore, this paper introduces a spatio-temporal optimization strategy for scaling the link-level network travel times using the simulated trip data without additional data sources on the network state. The method is tested on the widely used New York City (NYC) Taxi data and shows that the travel times produced using the scaled network are very close to the recorded travel times in the original data. Additionally, the paper studies the performance differences of AMoD simulation when the scaled network is used. The results indicate that realistic travel times can significantly impact AMoD simulation outcomes

Can Dedicated Lanes for Automated Vehicles on Urban Roads Improve Traffic Efficiency?

Connected and automated vehicles (CAVs) will behave fundamentally differently than human drivers. In mixed traffic, this could lead to inefficiencies and safety-critical situations since neither human drivers nor CAVs will be able to fully anticipate or predict surrounding traffic dynamics. Thus, some researchers proposed to separate CAVs from conventional vehicles by dedicating exclusive lanes to them. However, the separation of road infrastructure can negatively impact the system’s capacity. While the effects of CAV lanes were addressed for freeways, their deployment in urban settings is not yet fully understood. This paper systematically analyzes the effects of CAV-lanes in an urban setting accounting for the corresponding complexities. We employ microscopic traffic simulation to model traffic flow dynamics in a detailed manner and to be able to consider a wide array of supply-related characteristics. These concern intersection geometry, public transport operation, traffic signal control, and traffic management. Our study contributes to the existing literature by revealing the potential of CAV lanes in an urban setting while accounting for the behavioral and topological complexities. The results of this study can support decision-makers in the design of future urban transportation systems and to prepare cities for the upcoming era of automation in traffic