Transportation Research Part B-Methodological最新文献

{"title":"Joint resource exchange and pricing for intercity multimodal transport systems","authors":"Xiaoshu Ding,&nbsp;Sisi Jian","doi":"10.1016/j.trb.2025.103184","DOIUrl":"10.1016/j.trb.2025.103184","url":null,"abstract":"<div><div>Intercity multimodal transportation systems hold great promise for enhancing travel, boosting regional economies, and promoting sustainability. However, realizing their full potential hinges on effective cooperation among transport service providers (TSPs), which current practices often lack. This study proposes a novel framework to address this challenge by leveraging price competition through mobility resource exchange. Our framework considers both independent TSP operations and collaborative resource sharing, incorporating the price elasticity of demand to determine optimal resource exchange and pricing strategies. Through comprehensive analysis of Nash equilibrium outcomes under various capacity conditions, we identify strategies that maximize total profit for participating TSPs. Furthermore, we introduce an ex-post revenue sharing rule to ensure equitable profit allocation among alliance members. Our analytical findings prove that a resource exchange alliance consistently outperforms a no alliance scenario in terms of total profit, particularly in competitive markets with ample resources. Remarkably, the alliance can even achieve the first-best profit level, typically attainable only through perfect coordination. Numerical studies using real-world data further validate the viability of the proposed resource exchange framework. Results demonstrate that a resource exchange alliance not only ensures equitable benefit distribution but also consistently enhances profits across all price elasticity scenarios, highlighting its potential to revolutionize intercity multimodal transportation.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"194 ","pages":"Article 103184"},"PeriodicalIF":5.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480032","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":"Optimizing automotive maritime transportation in Ro-Ro and container shipping","authors":"Lu Zhen ,&nbsp;Jingwen Wu ,&nbsp;Shuaian Wang ,&nbsp;Siyu Li ,&nbsp;Miaomiao Wang","doi":"10.1016/j.trb.2025.103175","DOIUrl":"10.1016/j.trb.2025.103175","url":null,"abstract":"<div><div>This study investigates an automotive maritime transportation planning problem, considering roll-on/roll-off (Ro-Ro) shipping and container shipping. Automobiles are distributed from a manufacturer to overseas dealers through maritime transportation. This transportation process involves three key decisions: the choice of maritime transportation mode for the automobiles, the shipping volume of the ships, and the routes of the ships. We investigate the above decisions and explore how to allocate automobiles to ships and ports to minimize total transportation costs. Considering the differences between Ro-Ro shipping and container shipping, we propose a mixed integer linear programming model to optimize maritime transportation plans for automobile distribution. We design a column generation algorithm to solve the model, in which an acceleration tactic is proposed to shorten the time required to resolve the pricing problem. The effectiveness of our algorithm is validated using experiments with both real and synthetic data based on an ocean shipping case and an offshore case. The computational results show that our algorithm can yield an optimal solution in a significantly shorter time than the CPLEX solver. Furthermore, we draw managerial implications from our sensitivity analyses that can be useful to automobile manufacturers. In addition, three extensions that consider additional real-world factors are discussed to generalize the findings to more generic contexts.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"194 ","pages":"Article 103175"},"PeriodicalIF":5.8,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465249","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":"Improving transportation network redundancy under uncertain disruptions via retrofitting critical components","authors":"Kai Qu ,&nbsp;Xiangyi Fan ,&nbsp;Xiangdong Xu ,&nbsp;Grani A. Hanasusanto ,&nbsp;Anthony Chen","doi":"10.1016/j.trb.2025.103174","DOIUrl":"10.1016/j.trb.2025.103174","url":null,"abstract":"<div><div>Improving redundancy is one way of enhancing transportation network resilience by providing travelers with more alternative travel options in case of disastrous events. This paper studies an alternative means of improving network redundancy via retrofitting critical components at the strategical level, which is less constrained by the land use limitation and is less costly compared to building new infrastructures. We define redundancy-oriented network retrofit problem (RNRP) as to seek the retrofit resource allocation scheme that minimizes the loss of network redundancy under uncertain disastrous events. The lack of explicit formulation of network redundancy poses a challenge in the model development. We explore using the linear regression to approximate the loss of network redundancy function. We establish a stochastic programming (RNRP-SP) model and further a distributionally robust optimization (RNRP-DRO) model, corresponding to cases with different available information of potential disruptions. With the approximate loss of redundancy function, we show how to reformulate the two models and develop algorithms to efficiently solve the reformulated approximate models. We conduct numerical experiments in the realistic Winnipeg network of Canada to demonstrate the effectiveness of the retrofit scheme in improving redundancy. The retrofit schemes determined from the developed models are shown to generate better performance in improving redundancy compared with several heuristic approaches. We also show that the solution algorithms can produce high-quality solutions within a shorter time as compared to benchmark methods.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"194 ","pages":"Article 103174"},"PeriodicalIF":5.8,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453591","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":"Pricing in the stochastic bottleneck model with price-sensitive demand","authors":"Qiumin Liu ,&nbsp;Vincent A.C. van den Berg ,&nbsp;Erik T. Verhoef ,&nbsp;Rui Jiang","doi":"10.1016/j.trb.2025.103176","DOIUrl":"10.1016/j.trb.2025.103176","url":null,"abstract":"<div><div>We analyse time-varying tolling in the stochastic bottleneck model with price-sensitive demand and uncertain capacity. We find that price sensitivity and its interplay with uncertainty have important implications for the effects of tolling on travel costs, welfare and consumers. We evaluate three fully time-variant tolls and a step toll proposed in previous studies. We also consider a uniform toll, which affects overall demand but not trip timing decisions. The first fully time-variant toll is the ‘first-best’ toll, which varies non-linearly over time and results in a departure rate that also varies over time. It raises the generalised price (i.e. the sum of travel cost and toll), thus lowering demand. These outcomes differ fundamentally from those found for first-best pricing in the deterministic bottleneck model. We call the second toll ‘second-best’: it is simpler to design and implement as it maximises welfare under the constraint that the departure rate is constant over time. While a constant rate is optimal without uncertainty, it is not under uncertain capacity. Next, ‘third-best’ tolling adds the further constraint to the second-best that the generalised price should stay the same as without tolling. It attains a lower welfare and higher expected travel cost than the second-best scheme, but a lower generalised price. All our other tolls raise the price compared to the no-toll case.</div><div>In our numerical study, when there is less uncertainty: the second-best and third-best tolls achieve welfares closer to that of the first-best toll, and the three schemes become identical without uncertainty. As the degree of uncertainty falls, the uniform and single-step tolls attain higher welfare gains. Also, when demand becomes more price-sensitive, the uniform and single-step tolls attain relatively higher welfare gains. Our step toll would lower the generalised price without uncertainty but raises it in our stochastic setting.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"194 ","pages":"Article 103176"},"PeriodicalIF":5.8,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453614","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}

{"title":"A contextual framework for learning routing experiences in last-mile delivery","authors":"Huai Jun (Norina) Sun,&nbsp;Okan Arslan","doi":"10.1016/j.trb.2025.103172","DOIUrl":"10.1016/j.trb.2025.103172","url":null,"abstract":"<div><div>This paper presents a contextual framework for solving the experience-driven traveling salesman problem in last-mile delivery. The objective of the framework is to generate routes similar to historic high-quality ones as classified by operational experts by considering the unstructured and complex features of the last-mile delivery operations. The framework involves learning a transition weight matrix and using it in a TSP solver to generate high quality routes. In order to learn this matrix, we use descriptive analytics to extract and select important features of the high-quality routes from the data. We present a <em>rule-based method</em> using such extracted features. We then introduce a factorization of the transition weight matrix by features, which reduces the dimensions of the information to be learned. In the predictive analytics stage, we develop (1) <em>Score Guided Coordinate Search</em> as a derivative-free optimization algorithm, and (2) <em>label-guided methods</em> inspired by supervised learning algorithms for learning the routing preferences from the data. Any hidden preferences that are not obtained in the descriptive analytics are captured at this stage. Our approach allows us to blend the advantages of different facets of data science in a single collaborative framework, which is effective in generating high-quality solutions for a last-mile delivery problem. We test the efficiency of the methods using a case study based on Amazon Last-Mile Routing Challenge organized in 2021. A preliminary version of our rule-based method received the third place and a $25,000 award in the challenge. In this paper, we improve the learning performance of our previous methods through predictive analytics, while ensuring that the methods are effective, interpretable and flexible. Our best performing algorithm improves the performance of our rule-based method on an out-of-sample testing dataset by more than 23.1%.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"194 ","pages":"Article 103172"},"PeriodicalIF":5.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429003","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}

{"title":"Interactive biobjective optimization algorithms and an application to UAV routing in continuous space","authors":"Hannan Tureci-Isik ,&nbsp;Murat Köksalan ,&nbsp;Diclehan Tezcaner-Öztürk","doi":"10.1016/j.trb.2025.103162","DOIUrl":"10.1016/j.trb.2025.103162","url":null,"abstract":"<div><div>We develop interactive optimization algorithms for biobjective problems with continuous nondominated frontiers to search for the most preferred solution of a decision maker who is assumed to have an underlying linear or quasiconvex preference function. We progressively acquire preference information from the decision maker through pairwise comparisons of efficient solutions. We keep reducing the search space based on the obtained preference information and the properties of the form of the preference function. Our algorithms provide a performance guarantee on the final solution's distance from the most preferred solution in the objective function space. We demonstrate the algorithms on complex Unmanned Air Vehicle routing problems in continuous space with nonconvex and continuous nondominated frontiers. We consider the objectives of minimizing the total distance traveled and minimizing the total radar detection threat. We simulate the preference function of the decision maker using several underlying preference functions. The interactive algorithms for all preference functions converge to solutions within the desired accuracies after a few pairwise comparisons.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"193 ","pages":"Article 103162"},"PeriodicalIF":5.8,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350867","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":"Adaptive signal control at partially connected intersections: A stochastic optimization model for uncertain vehicle arrival rates","authors":"Shaocheng JIA ,&nbsp;S.C. WONG ,&nbsp;Wai WONG","doi":"10.1016/j.trb.2025.103161","DOIUrl":"10.1016/j.trb.2025.103161","url":null,"abstract":"<div><div>Optimizing traffic signal control is crucial for improving efficiency in congested urban environments. Current adaptive signal control systems predominantly rely on on-road detectors, which entail significant capital and maintenance costs, thereby hindering widespread implementation. In this paper, a novel connected vehicle (CV)-based adaptive signal control (CVASC) framework is proposed that optimizes signal plans on a cycle-by-cycle basis without the need for on-road detectors, leveraging partial CV data. The framework comprises a consequential system delay (CSD) model, deterministic penetration rate control (DPRC), and stochastic penetration rate control (SPRC). The CSD model analytically estimates vehicle arrival rates and, consequently, the total junction delay, utilizing CV penetration rates as essential inputs. Employing the CSD model without considering CV penetration rate uncertainty results in fixed vehicle arrival rates and leads to DPRC. On the other hand, incorporating CV penetration rate uncertainty accounts for uncertain vehicle arrival rates, establishing SPRC, which poses a high-dimensional, non-convex, and stochastic optimization problem. An analytical stochastic delay model using generalized polynomial chaos expansion is proposed to efficiently and accurately estimate the mean, variance, and their gradients for the CSD model within SPRC. To solve DPRC and SPRC, a gradient-guided golden section search algorithm is introduced. Comprehensive numerical experiments and VISSIM simulations demonstrate the effectiveness of the CVASC framework, emphasizing the importance of accounting for CV penetration rate uncertainty and uncertain vehicle arrival rates in achieving optimal solutions for adaptive signal optimizations.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"193 ","pages":"Article 103161"},"PeriodicalIF":5.8,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143325364","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}

{"title":"Real-time vehicle relocation, personnel dispatch and trip pricing for carsharing systems under supply and demand uncertainties","authors":"Mengjie Li ,&nbsp;Haoning Xi ,&nbsp;Chi Xie ,&nbsp;Zuo-Jun Max Shen ,&nbsp;Yifan Hu","doi":"10.1016/j.trb.2025.103154","DOIUrl":"10.1016/j.trb.2025.103154","url":null,"abstract":"<div><div>In one-way carsharing systems, striking a balance between vehicle supply and user demand across stations poses considerable operational challenges. While existing research on vehicle relocation, personnel dispatch, and trip pricing have shown effectiveness, they often struggle with the complexities of fluctuating and unpredictable demand and supply patterns in uncertain environments. This paper introduces a real-time relocation-dispatch-pricing (RDP) problem, within an evolving time-state-extended transportation network, to optimize vehicle relocation, personnel dispatch, and trip pricing in carsharing systems considering both demand and supply uncertainties. Furthermore, recognizing the critical role of future insights in real-time decision making and strategic adaptability, we propose a novel two-stage anticipatory-decision rolling horizon (ADRH) optimization framework where the first stage solves a real-time RDP problem to make actionable decisions with future supply and demand distributions, while also incorporating anticipatory guidance from the second stage. The proposed RDP problem under the ADRH framework is then formulated as a stochastic nonlinear programming (SNP) model. However, the state-of-the-art commercial solvers are inadequate for solving the proposed SNP model due to its solution complexity. Thus, we customize a hybrid parallel Lagrangian decomposition (HPLD) algorithm, which decomposes the RDP problem into manageable subproblems. Extensive numerical experiments using a real-world dataset demonstrate the computational efficiency of the HPLD algorithm and its ability to converge to a near-globally optimal solution. Sensitivity analyses are conducted focusing on parameters such as horizon length, fleet size, number of dispatchers, and demand elasticity. Numerical results show that the profits under the stochastic scenario are 18<span><math><mtext>%</mtext></math></span> higher than those under the deterministic scenario, indicating the significance of incorporating uncertain and future information into the operational decisions of carsharing systems.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"193 ","pages":"Article 103154"},"PeriodicalIF":5.8,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143325369","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":"Conditional forecasting of bus travel time and passenger occupancy with Bayesian Markov regime-switching vector autoregression","authors":"Xiaoxu Chen ,&nbsp;Zhanhong Cheng ,&nbsp;Alexandra M. Schmidt ,&nbsp;Lijun Sun","doi":"10.1016/j.trb.2024.103147","DOIUrl":"10.1016/j.trb.2024.103147","url":null,"abstract":"<div><div>Accurate forecasting of bus travel time and passenger occupancy with uncertainty is essential for both travelers and transit agencies/operators. However, existing approaches to forecasting bus travel time and passenger occupancy mainly rely on deterministic models, providing only point estimates. In this paper, we develop a Bayesian Markov regime-switching vector autoregressive model to jointly forecast both bus travel time and passenger occupancy with uncertainty. The proposed approach naturally captures the intricate interactions among adjacent buses and adapts to the multimodality and skewness of real-world bus travel time and passenger occupancy observations. We develop an efficient Markov chain Monte Carlo (MCMC) sampling algorithm to approximate the resultant joint posterior distribution of the parameter vector. With this framework, the estimation of downstream bus travel time and passenger occupancy is transformed into a multivariate time series forecasting problem conditional on partially observed outcomes. Experimental validation using real-world data demonstrates the superiority of our proposed model in terms of both predictive means and uncertainty quantification compared to the Bayesian Gaussian mixture model.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"192 ","pages":"Article 103147"},"PeriodicalIF":5.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816577","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}

{"title":"Interpretable State-Space Model of Urban Dynamics for Human-Machine Collaborative Transportation Planning","authors":"Jiangbo Yu ,&nbsp;Michael F. Hyland","doi":"10.1016/j.trb.2024.103134","DOIUrl":"10.1016/j.trb.2024.103134","url":null,"abstract":"<div><div>Strategic Long-Range Transportation Planning (SLRTP) is pivotal in shaping prosperous, sustainable, and resilient urban futures. Existing SLRTP decision support tools predominantly serve forecasting and evaluative functions, leaving a gap in directly recommending optimal planning decisions. To bridge this gap, we propose an Interpretable State-Space Model (ISSM) that considers the dynamic interactions between transportation infrastructure and the broader urban system. The ISSM directly facilitates the development of optimal controllers and reinforcement learning (RL) agents for optimizing infrastructure investments and urban policies while still allowing human-user comprehension. We carefully examine the mathematical properties of our ISSM; specifically, we present the conditions under which our proposed ISSM is Markovian, and a unique and stable solution exists. Then, we apply an ISSM instance to a case study of the San Diego region of California, where a partially observable ISSM represents the urban environment. We also propose and train a Deep RL agent using the ISSM instance representing San Diego. The results show that the proposed ISSM approach, along with the well-trained RL agent, captures the impacts of coordinating the timing of infrastructure investments, environmental impact fees for new land development, and congestion pricing fees. The results also show that the proposed approach facilitates the development of prescriptive capabilities in SLRTP to foster economic growth and limit induced vehicle travel. We view the proposed ISSM approach as a substantial contribution that supports the use of artificial intelligence in urban planning, a domain where planning agencies need rigorous, transparent, and explainable models to justify their actions.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"192 ","pages":"Article 103134"},"PeriodicalIF":5.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867439","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}