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Cities allocate dedicated road space to bicycles in favor of active-mode road users. For urban environments with a mass bicycle volume, bicycle traffic congestion is likely to occur. Hence, a thorough understanding of bicycle traffic flow is necessary for the assessment of cycling infrastructure and the development of traffic management strategies considering cycling efficiency. This study aims to investigate bicycle flow characteristics using microscopic traffic simulation. As bicycle flow performance is subject to the non-lane-based movement strategy and the behavioral heterogeneity among cyclists, various scenarios with different microsimulation settings are evaluated. Ultimately, we derive the functional form fundamental diagrams and macroscopic fundamental diagrams using a curve-fitting approach and an analytical method, respectively. Important macroscopic traffic flow parameters, such as capacity, critical speed, critical density, backward wave speed, etc., are estimated. The results show that lane width, overtaking incentive, and desired speed distribution are factors that affect bicycle flow performance. The distinct features of bicycle flow under different traffic states are identified by discussing the simulation outcome and comparing the estimated flow parameters. The findings can be utilized by future research regarding large-scale bicycle traffic flow modeling and control.

Monetary markets serve as established resource allocation mechanisms, typically achieving efficient solutions with limited information. However, they are susceptible to market failures, particularly under the presence of public goods, externalities, or inequality of economic power. Moreover, in many resource-allocating contexts, money faces social, ethical, and legal constraints. Consequently, artificial currencies and non-monetary markets are increasingly explored, with Karma emerging as a notable concept. Karma, a non-tradeable, resource-inherent currency for prosumer resources, operates on the principles of contribution and consumption of specific resources. It embodies fairness, near incentive compatibility, Pareto-efficiency, robustness to population heterogeneity, and can incentivize a reduction in resource scarcity. The literature on Karma is scattered across disciplines, varies in scope, and lacks conceptual clarity and coherence. Thus, this study undertakes a comprehensive review of the Karma mechanism, systematically comparing its resource allocation applications and elucidating overlooked mechanism design elements. Through a systematic mapping study, this review situates Karma within its literature context, offers a structured design parameter framework, and develops a road map for future research directions.

Abstract As more and more trajectory data become available, their analysis creates unprecedented opportunities for traffic flow investigations. However, observed physical quantities like speed or acceleration are often measured having unrealistic values. Furthermore, observation devices have different hardware and software specifications leading to heterogeneity in noise levels and limiting the efficiency of trajectory reconstruction methods. Typical strategies prune, smooth, or locally modify vehicle trajectories to infer physically plausible quantities. The filtering strength is usually heuristic. Once the physical quantities reach plausible values, additional improvement is impossible without ground truth data. This paper proposes an adaptive physics-informed trajectory reconstruction framework that iteratively detects the optimal filtering magnitude, minimizing local acceleration variance under stable conditions and ensuring compatibility with feasible vehicle acceleration dynamics and common driver behavior characteristics. Assessment is performed using both synthetic and real-world data. Results show a significant reduction in the speed error and invariability of the framework to different data acquisition devices. The last contribution enables the objective comparison between drivers with different sensing equipment.

Connected and automated vehicles (CAVs) attract much attention due to their unique behavioral characteristics and the promise to transform road transport systems as we know them today. However, the behavior of such vehicles on the road is not homogeneous since each manufacturer offers different implementations. Cooperation is essential to homogenize the traffic flow of CAVs and prevent delays on congested networks with high demand. This work proposes a rule-based decentralized on- off cooperation strategy at merging locations on freeway networks using CAVs as mobile actuators. The proposed strategy handles bilateral conflicts at on- ramps between vehicles with conflicting trajectories, taking actions at the vehicle level in a lateral or longitudinal direction, to minimize the disturbance for the following vehicles. Furthermore, heterogeneity among such vehicles is assessed through different parameter distributions among CAVs to demonstrate the importance of ensuring homogeneous dynamics in the vehicle operation design. Microscopic simulation is used to demonstrate the robustness of the proposed strategy and the value of homogeneous dynamics. The results show that cooperation of CAVs decreases the average network delay time by up to 46%. Homogeneous driving behavior amplifies the benefit of the cooperative strategy significantly.

Fully actuated signal controls are becoming increasingly popular in modern urban environments, attempting to reduce congestion locally, synchronize flows, or prioritize specific types of vehicles. This trend is expected to grow as more vehicles are expected to communicate via Vehicle-to-Infrastructure (V2I) communication. The presented dataset contains cleaned observations from a fully actuated signal control system with priority for public transportation. Time series data of traffic signals that regulate vehicle, public transportation, bicycle, and pedestrian traffic flows are available, showing where a traffic signal operates in a red or green phase. Also, loop detector data representing the occupancy at several locations at an urban intersection in Zurich, Switzerland is available. The data of all traffic signals and loop detectors corresponds to January and February 2019 and has a resolution of 1 s.Recent advances in transportation science show novel approaches for signalized intersections, but most publications assess their methodology on self-collected or simulated data. Therefore, the presented dataset aims at facilitating the development, calibration, and validation of novel methodological developments for modeling, estimation, forecasting, and other tasks in traffic engineering. Furthermore, it can be used as a real-world benchmark dataset for objectively comparing different methodologies.

Understanding complicated city traffic patterns has been recognized as a critical goal by twenty-first-century urban planners and traffic management systems, resulting in a significant rise in the quantity and variety of traffic data gathered. For example, in a growing number of large cities, taxi firms have begun collecting metadata for each vehicle trip, such as origin, destination, and travel duration. Taxi data offer information on traffic patterns, allowing the study of urban flow—what will traffic look like between two sites on a particular day and time in the future? This paper proposes a method based on sparse GPS probe data, that focuses on allocating travel time data to the different links traveled between GPS observations. This model incorporates the progressive spatial correlations between the links in a network. The main goal of this work is to show how we can consider progressive spatial correlations and improve our results more realistically with a simple adjustment in the previously known parametric methods. For estimating arterial travel time, the methodology is applied to a case study for the partial network of New York City-based on the data collected from the taxicabs in New York City, providing the locations of origins, destinations, and travel times. The model estimates quarter-hourly averages of urban link travel times using OD trip data. This study proposes a more accurate approach for estimating link travel times, that fully utilizes the partial information received from taxi data in cities.

A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. The idea of using the same type of sensors along large networks is not feasible; as a result, data fusion from different sources for the same location should be performed. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex for several reasons, such as variable specifications per sensor type, different noise levels, and heterogeneous data inputs. To assess sensor accuracy and propose a fusion methodology, we organized a video measurement campaign in an urban test area in Zurich, Switzerland. The work focuses on capturing traffic conditions regarding traffic flows and travel times. The video measurements are processed (a) manually for ground truth and (b) with an algorithm for license plate recognition. Additional processing of data from established thermal imaging cameras and the Google Distance Matrix allows for evaluating the various sensors’ accuracy and robustness. Finally, we propose an estimation baseline MLR (multiple linear regression) model (5% of ground truth) that is compared to a final MLR model that fuses the 5% sample with conventional loop detector and traffic signal data. The comparison results with the ground truth demonstrate the efficiency and robustness of the proposed assessment and estimation methodology.