Transportation Research Part E-Logistics and Transportation Review最新文献

{"title":"Physics-informed neural network for cross-dynamics vehicle trajectory stitching","authors":"Keke Long ,&nbsp;Xiaowei Shi ,&nbsp;Xiaopeng Li","doi":"10.1016/j.tre.2024.103799","DOIUrl":"10.1016/j.tre.2024.103799","url":null,"abstract":"<div><div>High-accuracy long-coverage vehicle trajectory data can benefit the investigations of various traffic phenomena. However, existing datasets frequently contain broken trajectories due to sensing limitations, which impedes a thorough understanding of traffic. To address this issue, this paper proposes a Physics-Informed Neural Network (PINN)-based method for stitching broken trajectories. The proposed PINN-based method enhances traditional neural networks by integrating physics priors, including vehicle kinematics and boundary conditions, aiming to provide information beyond training domain and regularization, thus increasing method accuracy and extrapolation ability for cross-dynamics scenarios (e.g., extrapolating from low-speed training data to reconstruct high-speed trajectories). Two publicly available vehicle trajectory datasets, NGSIM and HighSIM, were adopted to validate the proposed PINN-based method, and four biased training scenarios were designed to assess the PINN-based method’s extrapolation ability. Results indicate that the PINN-based method demonstrated superior performance regarding trajectory stitching accuracy and consistency compared to benchmark models. The dataset processed using our proposed PINN-based method has been made publicly available online to support the traffic research community. Additionally, this PINN-based approach can be applied to a broader range of scenarios that include physics-based priors.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103799"},"PeriodicalIF":8.3,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the influence of bargaining power in shipping: An empirical study on iron ore freight market","authors":"Satya Sahoo ,&nbsp;Pierre Cariou","doi":"10.1016/j.tre.2024.103811","DOIUrl":"10.1016/j.tre.2024.103811","url":null,"abstract":"<div><div>Shipping freight contracts are heterogeneous services surrounded by asymmetric information and traded in over-the-counter markets, where the physical market agents help match ship operators and charterers. These contracts create opportunities for negotiations tailored to traders’ needs. Despite the ubiquitous role of bargaining in establishing freight contracts, the shipping literature lacks systematic empirical investigation of pricing mechanisms from a bargaining perspective. This article seeks to unveil the influence of the bargaining power of ship operators and charterers in shipping, offering insights for industry players in individual iron ore voyage charter contracts across two pivotal routes: Brazil-China and Australia-China, spanning January 2013 to June 2023. The results show evidence of how the bargaining power of the ship operators and charterers fluctuates over time and is shaped by contractual terms, traders’ characteristics, and macroeconomic factors, which can be helpful for the players to improve their respective bargaining power. Academically, this study offers an operationalizable price bargaining framework in the context of freight markets, that could initiate a conversation among scholars to consider shipping as a valuable case study for empirically exploring pricing mechanisms through bargaining, thus enriching the broader bargaining theory landscape.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103811"},"PeriodicalIF":8.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic route optimization under dynamic ground risk uncertainties for safe drone delivery operations","authors":"Bizhao Pang ,&nbsp;Xinting Hu ,&nbsp;Wei Dai ,&nbsp;Kin Huat Low","doi":"10.1016/j.tre.2024.103717","DOIUrl":"10.1016/j.tre.2024.103717","url":null,"abstract":"<div><div>The rise of unmanned aircraft systems (UAS) for urban drone delivery introduces significant risks, particularly the potential for crash-induced fatalities on the ground. A crucial strategy to address this challenge is through risk assessment and mitigation of flight routes that consider the stochastic nature of urban populations. Traditional strategies treat drone flight route approval and execution independently, which fall short in such a dynamic risk environment where plans deemed safe at the strategic approval stage may later prove hazardous, and vice versa. To address these intricacies, this paper introduces a novel two-stage stochastic optimization model that integrates strategic route feasibility assessment with tactical route selection and timing adjustments. A unique aspect of our model is the implementation of a risk penalty that effectively bridges decisions between the two stages, thereby reducing the likelihood of decision errors caused by stochastic variations. Through extensive simulations within Singapore’s urban context, our model demonstrates a risk reduction by an average of 36.13%, which significantly outperforms traditional methods. This performance consistency across 100 simulated urban scenarios proved the robustness and broad applicability of our model. Furthermore, our model shows an 18% improvement in resolving potential decision errors, with the stochastic solution further affirming a notable risk decrease of 27.18%. Our research enhances the domain of UAS risk-based stochastic decision making and provides opportunities for automated flight approval, drone fleet management, and urban airspace management.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103717"},"PeriodicalIF":8.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban mobility foundation model: A literature review and hierarchical perspective","authors":"Zhen Zhou ,&nbsp;Ziyuan Gu ,&nbsp;Xiaobo Qu ,&nbsp;Pan Liu ,&nbsp;Zhiyuan Liu ,&nbsp;Wenwu Yu","doi":"10.1016/j.tre.2024.103795","DOIUrl":"10.1016/j.tre.2024.103795","url":null,"abstract":"<div><div>An urban mobility system serves as a highly intricate and nonlinear mega-system facilitating the movement of people, goods, and services across spatio-temporal<!--> <!-->domains. This complexity stems from factors such as intricate interactions between transportation supply and demand, and the inherent stochastic nature of an open, heterogeneous, and adaptable system. Successfully comprehending and navigating this system presents a challenge. Yet, a remarkable opportunity emerges with the growing availability of multi-source data in urban mobility and various sectors, combined with the recent advancements in large-scale machine learning (ML) models. In this paper, we introduce a novel conceptual framework, the HUGE (Hierarchically Unified GEnerative) foundation model, to address multifaceted computational tasks and decision-making problems embedded in urban mobility systems. We delve into the core technologies and their seamless integration to realize this framework, highlighting its potential to harness substantial data analytics, hierarchical ML methodologies, and domain-specific knowledge. The conceived framework has the potential to revolutionize urban mobility system planning, design, construction, and management in a digital and intelligent manner.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103795"},"PeriodicalIF":8.3,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Food transportation and price impacted by diesel price and truck-driver shortage pre-, amid and post pandemic","authors":"Aichih Jasmine Chang ,&nbsp;Fuqin Zhou ,&nbsp;Nesreen El-Rayes ,&nbsp;Jim Shi","doi":"10.1016/j.tre.2024.103794","DOIUrl":"10.1016/j.tre.2024.103794","url":null,"abstract":"<div><div>The food transportation and distribution industry has been radically disrupted over the last few years, especially amid the COVID-19 pandemic. Food prices, for example, have been seen to increase considerably in the wave of economic inflation. As two main driving factors in the context of food transportation, (1) the rising diesel prices and (2) the prevailing shortage of truck drivers have posed threatening challenges, leading to a substantial surge in transportation costs and subsequently contributing to higher food market prices. Based on collected data consolidated from multiple sources, this study conducts a comprehensive analysis to elucidate the impact of diesel prices and driver availability on food prices. To this end, we have curated a panel dataset encompassing key variables such as diesel prices, truck driver availability, and food prices for the most popular food commodities (i.e., apples, potatoes, onions, and tomatoes) pre-, amid, and post-pandemic of COVID-19. Employing fixed effects regression, this paper specifically investigates the extent to which the surge in fuel prices and truck-driver availability has contributed to the overall increase in food prices in the United States fresh food market. With high statistical significance, the experiment results show that the rising diesel prices and the shortage of truck drivers’ availability have a significant positive impact on food price margin, <em>ceteris paribus</em>. The contributions of this study are multifold. First, our study enriches the food price literature by specifically considering the two fundamental root factors: truck-driver availability and diesel price. Second, this study provides data-driven empirical analysis to unveil how diesel prices, driver availability, and the significant impact of the pandemic drive food prices. Third, considering the impact of COVID-19, the food price sensitivity to diesel prices and driver availability obtained from this study renders practical guidelines for policy implications, especially in the age of a devastating pandemic.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103794"},"PeriodicalIF":8.3,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining supervised learning and local search for the multicommodity capacitated fixed-charge network design problem","authors":"Charly Robinson La Rocca ,&nbsp;Jean-François Cordeau ,&nbsp;Emma Frejinger","doi":"10.1016/j.tre.2024.103805","DOIUrl":"10.1016/j.tre.2024.103805","url":null,"abstract":"<div><div>The multicommodity capacitated fixed-charge network design problem has been extensively studied in the literature due to its wide range of applications. Despite the fact that many sophisticated solution methods exist today, finding high-quality solutions to large-scale instances remains challenging. In this paper, we explore how a data-driven approach can help improve upon the state of the art. By leveraging machine learning models, we attempt to reveal patterns hidden in the data that might be difficult to capture with traditional optimization methods. For scalability, we propose a prediction method where the machine learning model is called at the level of each arc of the graph. We take advantage of off-the-shelf models trained via supervised learning to predict near-optimal solutions. Our experimental results include an algorithm design analysis that compares various integration strategies of predictions within local search algorithms. We benchmark the ML-based approach with respect to the state-of-the-art heuristic for this problem. The findings indicate that our method can outperform the leading heuristic on sets of instances sampled from a uniform distribution.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103805"},"PeriodicalIF":8.3,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable production strategies of loss-averse competitive manufacturers with reference dependence under cap-and-trade policy","authors":"Yu Zhang,&nbsp;Yuyin Yi","doi":"10.1016/j.tre.2024.103809","DOIUrl":"10.1016/j.tre.2024.103809","url":null,"abstract":"<div><div>This study examines the joint optimization decisions on production quantity and sustainable investment of two competing low-carbon manufacturers with loss aversion and reference dependence under the cap-and-trade policy. We focus on exploring the impacts of manufacturers’ risk attitude, encompassing factors like the degree of loss aversion and reference dependence, the cap-and-trade policy, and heterogeneous characteristics on optimal decisions and their expected utilities. The main findings are as follows. First, loss aversion and reference dependence have completely opposite effects on manufacturers’ production and emission reduction decisions. Specifically, production quantity will become more conservative. Interestingly, the investment to reduce emissions may actually become more aggressive. In addition, setting a lower reference level presents an opportunity for firms to generate greater profits compared to the risk-neutral scenario. Second, affected by loss aversion and reference dependence, manufacturers’ sustainable investment may decrease with carbon trading price, which is different from the conclusion of the existing research. Third, the total expected utility of heterogeneous manufacturers decreases with the difference of loss aversion and reference dependence. Finally, we extend our analysis to the impacts of sustainable investment, initial carbon emissions associated with emission reduction and heterogeneous market prices. This paper enriches the research on carbon emission reduction and loss aversion with reference dependence, and provides some managerial insights for practice from the perspective of government and enterprise operation.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103809"},"PeriodicalIF":8.3,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reverse logistics for electric vehicles under uncertainty: An intelligent emergency management approach","authors":"Sunil Kumar Jauhar ,&nbsp;Apoorva Singh ,&nbsp;Sachin Kamble ,&nbsp;Sunil Tiwari ,&nbsp;Amine Belhadi","doi":"10.1016/j.tre.2024.103806","DOIUrl":"10.1016/j.tre.2024.103806","url":null,"abstract":"<div><div>The frequency and intensity of global disasters, including the COVID-19 pandemic, and natural disasters such as earthquakes, floods, and wildfires, are increasing, necessitating effective emergency logistics management. Climate change significantly contributes to these events, emphasizing the importance of limiting human and environmental impacts. The transportation sector, particularly the automobile industry, ranks second in global carbon emissions, highlighting the need to adopt electric vehicles (EVs) to reduce emissions and minimize the impact of climate change. However, this has led to an increase in demand for lithium-ion batteries. During emergencies, end-of-life (EOL) battery management through reverse logistics is essential because recycling EOL batteries can recover valuable raw materials, decrease landfill waste and costs, and support environmental sustainability. This study proposed a two-phase method for intelligent emergency EV battery reverse logistics management. The first phase employed machine learning to address unpredictable battery demands, whereas the second phase proposed a multi-objective model to minimize carbon emissions through efficient order allocation during uncertain emergencies. The model considers carbon emissions and defect rates as sources of uncertainty, current regulations, and customer environmental awareness. The model is solved using the weighted sum and ε-constraint methods, resulting in non-dominant solutions. The findings indicate that combining the selection of third-party reverse logistics providers (3PRLPs) with optimal order allocation for recycling old batteries during emergencies effectively minimizes environmental impacts and combats climate change.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103806"},"PeriodicalIF":8.3,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic collaborative truck-drone delivery with en-route synchronization and random requests","authors":"Haipeng Cui ,&nbsp;Keyu Li ,&nbsp;Shuai Jia ,&nbsp;Qiang Meng","doi":"10.1016/j.tre.2024.103802","DOIUrl":"10.1016/j.tre.2024.103802","url":null,"abstract":"<div><div>Coordinated truck and drone delivery is gaining popularity in logistics as it can greatly reduce operation costs. However, existing studies on related operations management problems typically ignore the following important features: (i) the random appearance of requests, which require operators to dynamically respond to the requests; and (ii) the decisions of optimal launch and retrieval locations for trucks and drones instead of fixed to customer locations, which can significantly impact the overall time costs. To tackle these challenges, this study investigates the dynamic collaborative truck-drone routing problem with randomly arriving requests and synchronization on routes. We model the problem as a Markov Decision Process (MDP) and solve the MDP via a reinforcement learning (RL) approach. The proposed RL approach determines: (i) whether each request should be serviced upon arrival, (ii) which truck or drone should be assigned for the request, and (iii) the optimal en-route take-off and landing positions for paired trucks and drones. We further employ a framework of decentralized learning and centralized dispatching in RL to increase performance. Numerical experiments are conducted to assess the proposed solution approach on instances generated based on both the Solomon dataset and real-world operational data of a logistics operator in Singapore over several benchmark algorithms under various battery endurance levels of drones and distinct transportation scenarios including node-based dynamic collaborative truck-drone routing problem, dynamic non-collaborative truck and drone routing problem, and dynamic vehicle routing problem. The results show that our RL solution outperforms the benchmark algorithm in total profit by an average of 28.03 %, and our en-route takeoff and landing scenario outperforms the benchmark scenarios in total profit by an average of 8.43 % in multi-day instances. Additionally, compared to the traditional node-based landing scenario, employing our en-route takeoff and landing strategy can save 0.9 h/(drone*day) of waiting time on average.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103802"},"PeriodicalIF":8.3,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do Smart Loading Zones help reduce traffic congestion? A causal analysis in Pittsburgh","authors":"Tao Tao ,&nbsp;Sean Qian","doi":"10.1016/j.tre.2024.103796","DOIUrl":"10.1016/j.tre.2024.103796","url":null,"abstract":"<div><div>Rising demand for ride-hailing services and e-commerce delivery intensifies competition for urban curbside spaces, leading to uncoordinated travel behavior, increased traffic congestion and social costs. One possible solution to address those issues is Smart Loading Zones (SLZs), equipped with advanced technologies to optimize curbside use. Yet, the real-world impact of SLZs on traffic flow is unclear due to a lack of real-world data and rigorous studies investigating SLZ’s causal effect on traffic speed. With granular speed data and real-world implementations of SLZs from Pittsburgh, PA, this study applies the regression discontinuity design method to rigorously examine the causal impact of SLZs on traffic speed in the downtown network. The results showed that the introduction of SLZs could enhance the traffic speed of the nearby road segments by 4.5%, while controlling for the underlying trend of speed and multiple influential factors such as time, weather, and road characteristics. In addition, SLZs with a short length could statistically improve traffic speed but those with a long length exert no significant effect. These heterogenous effects might be attributed to the weak enforcement at the time of SLZ deployment in Pittsburgh. The results confirmed the overall positive impact of SLZs on improving congestion. However, policies such as effective dimension planning and robust enforcement policies are essential to maximize the benefits of SLZs.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"192 ","pages":"Article 103796"},"PeriodicalIF":8.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}