An official website of the United States government
Accurate prediction of traffic flow dynamics is a key step towards effective congestion mitigation strategies. The dynamic nature of traffic flow and lack of comprehensive data coverage (e.g., availability of data at loop detector locations), however, have historically prevented accurate traffic state prediction, leading to the widespread utilization of reactive congestion mitigation strategies. The introduction of connected automated vehicles provides an opportunity to address this challenge. These vehicles rely on trajectory-level prediction of their surrounding traffic environment to plan a safe and efficient path. This study proposes a methodology to utilize the outcome of such predictions to estimate the future traffic state. Moreover, the same approach can be applied to data from connected vehicles for traffic state prediction. Since in many driving scenarios, more than one maneuver is feasible, it is more logical to predict the location of the vehicles in a probabilistic manner based on the probability of different maneuvers. The key contribution of this study is to introduce a methodology to convert such probabilistic trajectory predictions to aggregate traffic state predictions (i.e., flow, space–mean speed, and density). The key advantage of this approach (over directly predicting traffic state based on aggregated traffic data) is its ability to capture the interactions among vehicles to increase the accuracy of the prediction. The down side of this approach, on the other hand, is that any increase in the prediction horizon reduces the accuracy of prediction (due to the uncertainty in the vehicles’ interactions and the increase in the possibility of different maneuvers). At the microscopic level, this study proposes a probability based version of the time–space diagram, and at the macroscopic level, this study proposes probabilistic estimates of flow, density, and space–mean speed using the trajectory-level predictions. To evaluate the effectiveness of the proposed approach in predicting traffic state, the mean absolute percentage error for each probabilistic macroscopic estimate is evaluated on multiple subsamples of the NGSIM US-101 and I-80 data sets. Moreover, while introducing this novel traffic state prediction approach, this study shows that the fundamental relation among the average traffic flow, density, and space–mean speed is still valid under the probabilistic formulations of this study.
more »
« less
Improving Sensing Coverage in Vehicular Crowdsensing Using Location Diversity
The massive use of vehicles as a primary means of transportation as well the increasing adoption of vehicles’ on-board sensors represents a unique opportunity for sensing and data collection. However, vehicles tend to cluster in specific regions such as highways and a few popular roads, making their utilization for data collection in isolated regions with low-density traffic difficult. We address this problem by proposing an incentive mechanism that encourages vehicles to deviate from their pre-planned trajectories to visit these isolated places. At the core of our proposal is the idea of compensation based on participants’ location diversity, which allows for rewarding vehicles in low-density traffic areas more than those located in high-density ones. We model this problem as a non-cooperative game in which participants are the vehicles and their new trajectories are their strategies. The output of this game is a new set of stable trajectories that maximize spatial coverage. Simulations show our approach outperforms the approach that doesn't take into account participants’ location diversity in terms of spatial coverage and road utilization.
more »
« less
- Award ID(s):
- 1739409
- PAR ID:
- 10379190
- Editor(s):
- IEEE
- Date Published:
- Journal Name:
- Improving Sensing Coverage in Vehicular Crowdsensing Using Location Diversity
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Cycling as a green transportation mode has been promoted by many governments all over the world. As a result, constructing effective bike lanes has become a crucial task for governments promoting the cycling life style, as well-planned bike paths can reduce traffic congestion and decrease safety risks for both cyclists and motor vehicle drivers. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider key realistic government constraints: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization. In this paper, we propose a data-driven approach to develop bike lane construction plans based on large-scale real world bike trajectory data. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of the number of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. Finally, we deploy our system on Microsoft Azure, providing extensive experiments and case studies to demonstrate the effectiveness of our approach.more » « less
-
Advanced Air Mobility (AAM) operations are expected to transform air transportation while challenging current air traffic management practices. By introducing a novel market-based mechanism, we address the problem of on-demand allocation of capacity-constrained airspace to AAM vehicles with heterogeneous and private valuations. We model airspace and air infrastructure as a collection of contiguous regions (or sectors) with constraints on the number of vehicles that simultaneously enter, stay, or exit each region. Vehicles request access to airspace with trajectories spanning multiple regions at different times. We use the graph structure of our airspace model to formulate the allocation problem as a path allocation problem on a time-extended graph. To ensure that the cost information of AAM vehicles remains private, we introduce a novel mechanism that allocates each vehicle a budget of “air-credits” (an artificial currency) and anonymously charges prices for traversing the edges of the time-extended graph. We seek to compute a competitive equilibrium that ensures that: (i) capacity constraints are satisfied, (ii) a strictly positive resource price implies that the sector capacity is fully utilized, and (iii) the allocation is integral and optimal for each AAM vehicle given current prices, without requiring access to individual vehicle utilities. However, a competitive equilibrium with integral allocations may not always exist. We provide sufficient conditions for the existence and computation of a fractional-competitive equilibrium, where allocations can be fractional. Building on these theoretical insights, we propose a distributed, iterative, two-step algorithm that: (1) computes a fractional competitive equilibrium, and (2) derives an integral allocation from this equilibrium. We validate the effectiveness of our approach in allocating trajectories for the emerging urban air mobility service of drone delivery.more » « less
-
Safety and efficiency are primary goals of air traffic management. With the integration of unmanned aerial vehicles (UAVs) into the airspace, UAV traffic management (UTM) has attracted significant interest in the research community to maintain the capacity of three-dimensional (3D) airspace, provide information, and avoid collisions. We propose a new decision-making architecture for UAVs to avoid collision by formulating the problem into a multi-agent game in a 3D airspace. In the proposed game-theoretic approach, the Ego UAV plays a repeated two-player normal-form game, and the payoff functions are designed to capture both the safety and efficiency of feasible actions. An optimal decision in the form of Nash equilibrium (NE) is obtained. Simulation studies are conducted to demonstrate the performance of the proposed game-theoretic collision avoidance approach in several representative multi-UAV scenarios.more » « less
-
The ability to model and predict ego-vehicle's surrounding traffic is crucial for autonomous pilots and intelligent driver-assistance systems. Acceleration prediction is important as one of the major components of traffic prediction. This paper proposes novel approaches to the acceleration prediction problem. By representing spatial relationships between vehicles with a graph model, we build a generalized acceleration prediction framework. This paper studies the effectiveness of proposed Graph Convolution Networks, which operate on graphs predicting the acceleration distribution for vehicles driving on highways. We further investigate prediction improvement through integrating of Recurrent Neural Networks to disentangle the temporal complexity inherent in the traffic data. Results from simulation with comprehensive performance metrics support that our proposed networks outperform state-of-the-art methods in generating realistic trajectories over a prediction horizon.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCVE52871.2022.9742961
