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A hierarchical control framework for alleviating network traffic bottleneck congestion using vehicle trajectory data 基于车辆轨迹数据的分层控制框架缓解网络交通瓶颈拥塞
IF 2.8 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2270428
Lei Wei , Peng Chen , Yu Mei , Jian Sun , Yunpeng Wang
Traffic bottlenecks significantly influence the operation efficiency of large-scale road networks. Developing advanced control strategies for bottleneck optimization is a cost-efficient and critical way to deal with network congestion. However, the state-of-the-art studies on network congestion control focus on the topology level, which may fail to relieve congestion by addressing the root cause of bottleneck. This study proposed a hierarchical control framework for alleviating network traffic bottleneck congestion using vehicle trajectory data. First, the bottleneck-related sub-network (BRS) was identified by tracing vehicle trajectories upstream and downstream of the bottleneck based on the traffic flow propagation. Then, a hierarchical control framework was proposed for BRS optimization. Specifically, in the outer layer, i.e., the gating control layer, the multigated intersections in BRS were controlled via a multimemory deep Q-network approach to optimize the network traffic distribution. In the inner layer, i.e., the coordinated control layer, local intersection controllers were coordinated by adjusting the dynamic input and output streams of the bottleneck under the guidance of the outer layer controller, which helps balance the traffic pressure within BRS and avoids congestion transferring in the network. Both simulation and field experiments were conducted to verify the performance of the proposed hierarchical framework. Results reveal that the framework can effectively relieve network traffic congestion with decreased queue length and travel time.
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引用次数: 0
Eco-friendly platooning operation algorithm of the electric vehicles 电动汽车的环保队列运行算法
IF 2.8 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2209911
Joonwon Jang , Sung Il Kwag , Young Dae Ko
Platooning is one of the promising technologies that maximizes the power efficiency of electric vehicles by decreasing the distances between the vehicles. Along with the development of autonomous driving technology, platooning is expected to be commercialized. Recent studies on the operation of platooning focused on power-efficient maintenance of platooning. However, power-efficient operation strategy is also needed for practical applications. Therefore, this study deals with platooning operations that can maximize the power efficiency of electric vehicles in various operational situations. In order to derive the operation method, a mathematical model structured with an objective function that minimizes power consumption is developed. To derive the solution of the mathematical model, a hybrid genetic algorithm is applied. The numerical experiments on four different operational situations are performed to verify the validity of the model and the solution procedure. The four situations consider overall situation that can happen during the platooning stage. The stages are formation, disassembly, join and breakaway of vehicles of platoon. Those four situations are decided upon since they can represent the general situation that can happen during platooning. As a result, the power-efficient driving patterns of electric vehicles are identified. After the development of electric and systematic technology, operational technology for platooning will collaborate for the further improvement. Therefore, throughout consideration of the formation of platooning, technology will expand the sustainability of technological development.
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引用次数: 0
Handling inevitable collision states by Advanced Driver Assistance Systems functions: software-in-the-loop performance assessment of an injury risk-based logic in a “lane departure” scenario 通过高级驾驶辅助系统功能处理不可避免的碰撞状态:在“车道偏离”场景下,基于伤害风险逻辑的软件在环性能评估
IF 2.8 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2277713
Michelangelo-Santo Gulino , Krzysztof Damaziak , Anita Fiorentino , Dario Vangi
The downward trend in the number of fatalities and serious injuries related to road accidents depends on the implementation of increasingly performing Advanced Driver Assistance Systems (ADAS) in the circulating fleet. The greatest benefit of the adoption of ADASs like Autonomous Emergency Braking (AEB) consists in limiting the frequency of impacts. However, in Inevitable Collision States (ICSs), the decrease in impact closing speed guaranteed by the AEB may not reduce the Injury Risk (IR) for the occupants: IR is a function of the vehicle’s velocity change in the collision (ΔV) – a combination of impact closing speed and impact eccentricity. The work virtually analyses, in lane departure ICS scenarios, the performance of an adaptive steering and braking intervention logic based on instantaneous IR minimization. The adaptive logic reduces IR compared to the absence of intervention (down to 80 times lower) and to the AEB (down to 40 times lower) by leading the ego vehicle toward eccentric impact configurations. It is highlighted that full activation of the steer-by-wire system in 0.3 s allows the adaptive logic to also reduce the frequency of impacts; it is further evidenced that employing a function capable of modulating the braking level to minimize IR entails disadvantages from the IR perspective compared to the AEB: efficient intervention strategies on the steering are the only alternative for increasing the safety provided by high-performance ADASs. Finally, compared to previous literature, the study highlights high efficiencies of the adaptive logic in a wide range of ICS scenarios.
摘要与道路交通事故相关的死亡和重伤人数的下降趋势取决于在循环车队中日益执行的高级驾驶辅助系统(ADAS)的实施。采用自动紧急制动(AEB)等自动驾驶辅助系统的最大好处在于限制了碰撞的频率。然而,在不可避免的碰撞状态(ics)中,AEB保证的碰撞关闭速度的降低可能不会降低乘员的伤害风险(IR): IR是车辆在碰撞中速度变化的函数(ΔV) -碰撞关闭速度和碰撞偏心的组合。该工作虚拟分析了车道偏离ICS场景下基于瞬时红外最小化的自适应转向和制动干预逻辑的性能。与没有干预(降低了80倍)和AEB(降低了40倍)相比,自适应逻辑通过引导自我车辆进入偏心撞击配置,降低了IR。值得强调的是,在0.3秒内完全激活线控转向系统,使自适应逻辑也可以减少碰撞的频率;进一步证明,从红外角度来看,与AEB相比,采用能够调节制动水平以最小化红外的功能会带来缺点:高效的转向干预策略是提高高性能ADASs安全性的唯一选择。最后,与之前的文献相比,本研究强调了自适应逻辑在广泛的ICS场景中的高效率。关键词:ΔVactuation时间自主紧急制动aeb碰撞闭合速度扫描时间速度变化披露声明作者未报告潜在利益冲突。注1 https://transport.ec.europa.eu/news/road-safety-eu-fatalities-below-pre-pandemic-levels-progress-remains-too-slow-2023-02-21_en2 https://www.acea.auto/figure/average-age-of-eu-vehicle-fleet-by-country/3欧盟法规第661/20094号https://www.nissan-global.com/EN/INNOVATION/TECHNOLOGY/ARCHIVE/AUTONOMOUS_EMERGENCY_STEERING_SYSTEM/5 http://iglad.net/
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引用次数: 0
Comparing the performance of metaheuristics on the Transit Network Frequency Setting Problem 比较公交网络班次设置问题的元追求法性能
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-08-29 DOI: 10.1080/15472450.2024.2392722
İlyas Cihan Aksoy, Mehmet Metin Mutlu
The Transit Network Frequency Setting Problem (TNFSP), an NP-Hard combinatorial optimization problem, has been frequently addressed in previous investigations, most of which employ metaheuristics. ...
公交网络频率设置问题(TNFSP)是一个 NP-硬组合优化问题,在以往的研究中经常被提及,其中大多数都采用了元启发式方法。...
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引用次数: 0
Scene adaptation in adverse conditions: a multi-sensor fusion framework for roadside traffic perception 不利条件下的场景适应:用于路边交通感知的多传感器融合框架
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-08-19 DOI: 10.1080/15472450.2024.2390844
Kong Li, Zhe Dai, Chen Zuo, Xuan Wang, Hua Cui, Huansheng Song, Mengying Cui
Robust roadside traffic perception requires integrating the strengths of multi-source sensors under various adverse conditions, which is challenging but indispensable for formulating effective traf...
强大的路边交通感知需要在各种不利条件下整合多源传感器的优势,这虽然具有挑战性,但对于制定有效的交通管理方案却不可或缺。
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引用次数: 0
Activity-based and agent-based transport model of Melbourne: an open multi-modal transport simulation model for Greater Melbourne 基于活动和代理的墨尔本交通模型:大墨尔本地区开放式多模式交通模拟模型
IF 3.6 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-07-11 DOI: 10.1080/15472450.2024.2372894
Afshin Jafari, Dhirendra Singh, Alan Both, Mahsa Abdollahyar, Lucy Gunn, Steve Pemberton, Billie Giles-Corti
Activity- and agent-based models for simulating transport systems have attracted significant attention in recent years. However, building these types of models at a city-wide level and including mo...
近年来,基于活动和代理的交通系统仿真模型备受关注。然而,在整个城市范围内建立这类模型,并将更多的人纳入其中,并不容易。
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引用次数: 0
Optimizing the ground intra-city express delivery network: An integrated multiple centrality assessment, multi-criteria decision-making, and multi-objective integer programming model 优化地面市内快递网络:综合多重中心性评估、多标准决策和多目标整数编程模型
IF 2.8 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-07-03 DOI: 10.1080/15472450.2022.2157211

Optimization of an intra-city express delivery network from three to two levels is of great interest to suppliers and customers for reducing costs and improving service efficiency. One feasible solution is to identify critical nodes in the three-level network and upgrade them as transshipment facilities in the two-level one. However, traditional optimization models seldom combine empirical business data, composite metrics, and objective evaluation rules. We proposed an approach integrating empirical data, multi-criteria decision-making methods based on the real-world application of the SF Express Chengdu branch. We also developed a mathematical optimization model using statistical and operations management techniques combined with logistics expertise for a location decision. First, the appropriateness of each service point as a candidate transshipment facility is evaluated from internal and external perspectives by applying multiple centrality assessment from complex network theory and fuzzy Technique for Order Preference by Similarity to an Ideal Solution, respectively. Second, 16 candidate transshipment facilities are selected by combining these two ways. Then, a multi-objective integer programming model is built to obtain the optimal number, locations of transshipment facilities, and the corresponding service points covered by each transshipment facility. Using this multi-methodologic approach, we show that the optimized two-level network is economically feasible and simply applicable, with the total cost and average delivery time reduced by 18.41% and 6 h, respectively. This article is of practical significance and provides an important reference for optimizing ground express service networks for other large cities.

优化市内快递网络,将其从三级降为两级,对供应商和客户降低成本和提高服务效率都具有重大意义。一个可行的解决方案是确定三级网络中的关键节点,并将其升级为二级网络中的转运设施。然而,传统的优化模型很少将经验业务数据、综合指标和客观评价规则结合起来。我们根据顺丰速运成都分公司的实际应用情况,提出了一种将经验数据、多标准决策方法相结合的方法。我们还利用统计和运营管理技术,结合物流专业知识,建立了一个数学优化模型,用于选址决策。首先,通过应用复杂网络理论中的多重中心性评估和与理想解相似性排序偏好模糊技术,分别从内部和外部角度评估了每个服务点作为候选转运设施的适当性。其次,结合这两种方法选出 16 个候选转运设施。然后,建立一个多目标整数编程模型,以获得最佳转运设施的数量、位置以及每个转运设施所覆盖的相应服务点。利用这种多方法,我们证明了优化后的两级网络在经济上是可行的,而且简单适用,总成本和平均交货时间分别降低了 18.41% 和 6 h。本文具有重要的现实意义,为其他大城市优化地面快递服务网络提供了重要参考。
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引用次数: 0
Proactive congestion management via data-driven methods and connected vehicle-based microsimulation 通过数据驱动方法和基于互联车辆的微观模拟进行积极的拥堵管理
IF 2.8 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-07-03 DOI: 10.1080/15472450.2022.2140047

Traffic congestion is a phenomenon that has been extensively explored by researchers due to its impact on reliability and safety. This research is focused on proactively detecting and mitigating congestion on freeways by fuzing conventional traffic data obtained from radar and loop detectors with newer sources, such as Bluetooth and connected vehicles (CV). Data-driven and signal-processing techniques are explored to develop algorithms that use near- or real-time traffic measurements to predict the onset and intensity level of traffic congestion. The developed algorithm can be applied to both conventional and low penetration CV-based datasets to identify four types of congestion, that is, normal, recurring, other non-recurring, and incident. This research also demonstrates the advantage of using CV-based travel time estimates to calibrate microsimulation models over fixed point-based derivations of travel time from spot speeds. Finally, a set of mitigation strategies consisting of speed harmonization and dynamic rerouting are implemented in the calibrated simulation network to demonstrate their effectiveness in proactively reducing recurring and non-recurring congestion. The final derived algorithm is effective in proactively predicting the onset of congestion and its intensity level, with an overall mean prediction error of 30.2%. A limitation to the algorithm’s methodology is that it cannot disentangle the type of congestion when two or more are occurring simultaneously and only predicts/classifies the anticipated highest level. However, this does not impair the user’s ability to readily deploy appropriate mitigation strategies to alleviate the predicted intensity of congestion.

由于交通拥堵对可靠性和安全性的影响,研究人员对这一现象进行了广泛的探讨。本研究的重点是通过将从雷达和环路探测器获得的传统交通数据与蓝牙和联网车辆(CV)等新数据源融合,主动检测和缓解高速公路上的拥堵现象。我们探索了数据驱动和信号处理技术,以开发使用近距离或实时交通测量来预测交通拥堵开始和严重程度的算法。所开发的算法可应用于基于 CV 的传统数据集和低渗透率数据集,以识别四种拥堵类型,即正常拥堵、经常性拥堵、其他非经常性拥堵和事故拥堵。这项研究还证明了使用基于 CV 的旅行时间估算来校准微观模拟模型的优势,而不是根据定点速度推导旅行时间。最后,在校准后的模拟网络中实施了一套由速度协调和动态改道组成的缓解策略,以证明其在主动减少经常性和非经常性拥堵方面的有效性。最终得出的算法能有效地主动预测拥堵的发生及其强度,总体平均预测误差为 30.2%。该算法方法的一个局限是,当同时发生两种或两种以上拥堵时,它无法区分拥堵类型,只能预测/分类预计的最高级别。不过,这并不影响用户随时部署适当的缓解策略,以减轻预测的拥堵强度。
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引用次数: 0
Fusion attention mechanism bidirectional LSTM for short-term traffic flow prediction 用于短期交通流预测的融合注意力机制双向 LSTM
IF 2.8 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-07-03 DOI: 10.1080/15472450.2022.2142049

Short term forecasting is essential and challenging in time series data analysis for traffic flow research. A novel deep learning architecture on short-term traffic flow prediction was presented in this work. In conventional model-driven prediction method, a critical deviation in prediction accuracy was occurred in face of large fluctuations in traffic flow, while machine and deep learning-based approaches performed well in accuracy study than conventional regression-based models. Moreover, a fusion attention mechanism bidirectional long short-term memory model (ATT-BiLSTM) was proposed due to its bidirectional LSTM (BiLSTM) and attention mechanism units. The model not only dealt with forward and backward dependencies in time series data, but also integrated the attention mechanism to improve the ability on key information representation. The BiLSTM layer was exploited to capture bidirectional temporal and spatial features dependencies from historical data. The proposed model was also trained and validated using freeway toll datasets from Humen Bridge. The results showed that compared with ARIMA and SVR models, the indicators of the proposed model have been significantly improved. The ablation experiments were conducted to evaluate the role of the attention mechanism module. Compared with BiLSTM, CNN and 1DCNN-ATT-BiLSTM models, the MAE, RMSE and MAPE indexes of proposed model were reduced by 0.6–5.9%, 1.6–4.7% and 0.6–22.8%, respectively. More accurate predictions were obtained by the proposed model. The research results are of great significance to improve the level of traffic management.

在交通流研究的时间序列数据分析中,短期预测是必不可少的,也是极具挑战性的。本研究提出了一种新颖的短期交通流预测深度学习架构。在传统的模型驱动预测方法中,面对交通流量的大幅波动,预测精度会出现临界偏差,而基于机器学习和深度学习的方法在精度研究中表现优于传统的回归模型。此外,由于双向 LSTM(BiLSTM)和注意力机制单元,一种融合注意力机制的双向长短期记忆模型(ATT-BiLSTM)被提出。该模型不仅处理了时间序列数据中的前向和后向依赖关系,还融合了注意力机制,提高了关键信息的表征能力。BiLSTM 层被用来捕捉历史数据中的双向时空特征依赖。此外,还使用虎门大桥的高速公路收费数据集对所提出的模型进行了训练和验证。结果表明,与 ARIMA 模型和 SVR 模型相比,所提模型的各项指标均有明显改善。为评估注意力机制模块的作用,进行了消融实验。与 BiLSTM、CNN 和 1DCNN-ATT-BiLSTM 模型相比,拟议模型的 MAE、RMSE 和 MAPE 指标分别降低了 0.6-5.9%、1.6-4.7% 和 0.6-22.8%。拟议模型获得了更准确的预测结果。这些研究成果对提高交通管理水平具有重要意义。
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引用次数: 0
Modeling the impact of COVID-19 on transportation at later stage of the pandemic: A case study of Utah 模拟 COVID-19 在大流行后期对运输的影响:犹他州案例研究
IF 2.8 3区 工程技术 Q3 TRANSPORTATION Pub Date : 2024-07-03 DOI: 10.1080/15472450.2022.2157212

The global COVID-19 pandemic has had a great impact on transportation across the United States. However, there is a lack of studies investigating the pandemic’s impact on vehicular traffic at the later stage of the pandemic. Therefore, this paper studies the change of freeway traffic patterns in two metropolitan counties in the State of Utah at the latter stage of the pandemic. We found that with the relaxation of travel restriction and the COVID vaccine, vehicular traffic has recovered to equaling, if not exceeding, pre-pandemic levels. Truck traffic is higher than the pre-pandemic level due to the growth of online shopping and on-demand delivery. To help responsive agencies to prepare for the near-future traffic pattern, a traffic prediction model based on an innovative approach integrating machine learning with graph theory is proposed. The evaluation shows that the proposed prediction model has a desirable performance. The mean absolute percentage prediction error is between 0.38% and 1.74% for different jurisdictions. On average, the modal outperforms the traditional long short-term memory model by 31.20% in terms of root mean squared prediction error.

全球 COVID-19 大流行对美国各地的交通产生了巨大影响。然而,目前还缺乏对大流行后期对车辆交通影响的研究。因此,本文研究了大流行后期犹他州两个大都市县高速公路交通模式的变化。我们发现,随着旅行限制的放宽和 COVID 疫苗的接种,车辆交通量已恢复到甚至超过大流行前的水平。由于网上购物和按需送货的增长,卡车交通量高于疫情发生前的水平。为了帮助应对机构为近期的交通模式做好准备,本文提出了一种基于机器学习与图论相结合的创新方法的交通预测模型。评估结果表明,所提出的预测模型具有理想的性能。不同辖区的平均绝对百分比预测误差介于 0.38% 和 1.74% 之间。平均而言,就均方根预测误差而言,该模型比传统的长短期记忆模型高出 31.20%。
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引用次数: 0
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Journal of Intelligent Transportation Systems
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