Emergency vehicle lane pre-clearing: From microscopic cooperation to routing decision making

Wu, Jin−Jei; Kulcsár, Balázs; Ahn, Soyoung; Qu, Xiaobo

doi:10.1016/j.trb.2020.09.011

Cited by 73 publications

(43 citation statements)

References 61 publications

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“…After longitudinal regulations, the normal lanes are well prepared with larger feasible lane‐changing gaps, and the subsequent issue is how and when vehicles on the work zone lane merge into normal lanes. Here, we applied the sorting algorithm (Wu et al., 2020), which could find optimal trajectories to transform a multi‐lane vehicular platoon from any permutation to any desired permutation through cooperative driving.…”

Section: Methodsmentioning

confidence: 99%

“…Note that in each movement step, a vehicle can only move to an adjacent cell. More details can be found in the previous work of the authors (Wu et al., 2020).…”

Section: Methodsmentioning

confidence: 99%

“…Unlike our previous work in Wu et al. (2020), which mainly addresses the vehicle platooning problem at signalized intersections, the objective in this study is to achieve a vehicular permutation in which all vehicles drive on the normal lanes. The Manhattan distance is used as the heuristic function when finding the shortest path in the modeled graph as shown in Figure 4.…”

Section: Solving Algorithmmentioning

confidence: 99%

“…Wu et al. (2020) proposed a pre‐clearing strategy to prioritize emergency vehicles by developing an optimal trajectory for surrounding CVs. Chen et al.…”

Section: Introductionmentioning

confidence: 99%

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A platoon regulation algorithm to improve the traffic performance of highway work zones

Cao

et al. 2021

Computer aided Civil Eng

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This paper presents a cooperative traffic control strategy to increase the capacity of nonrecurrent bottlenecks such as work zones by making full use of the spatial resources upstream of work zones. The upstream area is divided into two zones: the regulation and the merging areas. The basic logic is that a large gap is more efficient in accommodating merging vehicles than several small and scattered gaps with the same total length. In the regulation area, a nonlinear programming model is developed to balance both traffic capacity improvements and safety risks. A two‐step solving algorithm is proposed for finding optimal solutions. In the merging area, the sorting algorithm is used to design lane‐changing trajectories based on the regulated platoons. A case study is conducted, and the results indicate that the proposed model is able to significantly improve work zone capacity with minor disturbances to the traffic.

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Section: Methodsmentioning

confidence: 99%

“…Note that in each movement step, a vehicle can only move to an adjacent cell. More details can be found in the previous work of the authors (Wu et al., 2020).…”

Section: Methodsmentioning

confidence: 99%

Section: Solving Algorithmmentioning

confidence: 99%

“…Wu et al. (2020) proposed a pre‐clearing strategy to prioritize emergency vehicles by developing an optimal trajectory for surrounding CVs. Chen et al.…”

Section: Introductionmentioning

confidence: 99%

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A platoon regulation algorithm to improve the traffic performance of highway work zones

Cao

et al. 2021

Computer aided Civil Eng

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“…Heuristic searching algorithms also help UGVs achieve excellent performance in the DARPA Urban Challenge, where hybrid A * [36] and A * [37] were used in Stanford University's UGV and KIT's UGV, while AD * was used in Boss, the winning vehicle [38]. In [39], a rule-based A * algorithm is presented to find the shortest path in the graphical domain for the emergency vehicle lane preclearing problem. In [40], the deterministic A * algorithm with a stepwise strategy is applied to solve a cooperative sorting problem for connected and automated vehicles in a multilane platoon.…”

Section: Heuristic Searching Algorithmsmentioning

confidence: 99%

Autonomous Last-Mile Delivery Based on the Cooperation of Multiple Heterogeneous Unmanned Ground Vehicles

Ding

et al. 2021

Mathematical Problems in Engineering

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With the development of e-commerce, the last-mile delivery has become a significant part of customers’ shopping experience. In this paper, an autonomous last-mile delivery method using multiple unmanned ground vehicles is investigated. Being a smart logistics service, it provides a promising solution to reduce the delivery cost, improve efficiency, and avoid the spread of airborne diseases, such as SARS and COVID-19. By using a cooperation strategy with multiple heterogeneous robots, contactless parcel delivery can be carried out within apartment complexes efficiently. In this paper, the last-mile delivery with heterogeneous UGVs is formulated as an optimization problem aimed at minimizing the maximum makespan to complete all tasks. Then, a heuristic algorithm combining the Floyd’s algorithm and PSO algorithm is proposed for task assignment and path planning. This algorithm is further realized in a distributed scheme, with all robots in a swarm working together to obtain the best task schedule. A good solution with an optimized makespan is achieved by considering the constraints of various robots in terms of speed and payload. Simulations and experiments are carried out and the obtained results confirm the validity and applicability of the developed approaches.

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Novel traffic congestion detection algorithms for smart city applications

Bawaneh

Simon

2022

Concurrency and Computation

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Traffic congestion detection (TCD) techniques are becoming a critical component of traffic management systems. They can be considered a pre-step to address traffic jam problems, providing useful input for traffic management systems to predict and avoid traffic congestions' unwanted effects. In this research article, two novel real-time congestion detection algorithms are proposed, the TCD algorithm and the ensemble based traffic congestion detection (EB-TCD) algorithm. TCD can detect the congestion based on one traffic feature (i.e., speed, occupancy, or flow). TCD first cleans and preprocesses the traffic data. After, it computes the absolute of the first derivative of each sample in the traffic feature to determine its anomaly score. Then, the anomaly likelihood of each sample is computed to classify it as an anomaly or normal sample. On the other hand, EB-TCD utilizes the information contained in multiple traffic features in parallel by proposing an ensemble technique to combine the anomaly scores coming out from the different traffic features in one unified score. EB-TCD computes an anomaly score based on each traffic feature separately. Then, it votes for each sample in each traffic feature as an anomaly or not (from the corresponding traffic feature's point of view). After, the weight for each traffic feature is determined by studying the dissimilarity between their anomaly scores. These weights show how much each traffic feature explains the traffic behavior. Finally, the results are combined to form a unified anomaly score for each sample. The proposed statistical-based techniques are able to detect changes in traffic patterns while mitigating the effect of noise in traffic data. Moreover, the proposed algorithms' parameters were tuned and tailored for fast detection, which is a crucial feature for traffic management systems since fast detection accelerates decision-making. The computational complexity analysis has shown the simplicity of the proposed algorithms. Furthermore, the evaluation results have shown that the two algorithms outperform other widespread methods from the literature in terms of false alarm rate and detection time while keeping the detection rate at the same high level.

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Emergency vehicle lane pre-clearing: From microscopic cooperation to routing decision making

Cited by 73 publications

References 61 publications

A platoon regulation algorithm to improve the traffic performance of highway work zones

A platoon regulation algorithm to improve the traffic performance of highway work zones

Autonomous Last-Mile Delivery Based on the Cooperation of Multiple Heterogeneous Unmanned Ground Vehicles

Novel traffic congestion detection algorithms for smart city applications

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