51 research outputs found
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
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
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
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
Investigating Lane-Free Traffic with a Dynamic Driving Simulator
Lane-free traffic (LFT) is a new traffic system that relies on connected and
automated vehicles (CAV) to increase road capacity and utilization by removing
traditional lane markings using coordinated maneuvering of CAVs in LFT
strategies. LFT is based on two main principles: upstream nudging and vehicles
moving without adhering to any lane markings. By leveraging CAV capabilities to
communicate and exchange information, LFT represents a promising future traffic
system. While current research uses LFT simulations in two-dimensional space,
driving simulators are necessary to investigate human behavior and perceived
safety in LFT. This paper proposes a conceptual framework for LFT driving
simulations and describes the assumptions, requirements, and recent
technological developments that make it possible to investigate the human
perspective and acceptance of LFT. Additionally, we propose a scenario matrix
that can act as a test guide to building driving simulation scenarios for the
LFT.Comment: This paper was presented at IEEE ITSC 202
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