Pub Date : 2025-02-08DOI: 10.1016/j.trb.2025.103162
Hannan Tureci-Isik , Murat Köksalan , Diclehan Tezcaner-Öztürk
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.
{"title":"Interactive biobjective optimization algorithms and an application to UAV routing in continuous space","authors":"Hannan Tureci-Isik , Murat Köksalan , 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}
Pub Date : 2025-02-06DOI: 10.1016/j.trb.2025.103161
Shaocheng JIA , S.C. WONG , Wai WONG
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.
{"title":"Adaptive signal control at partially connected intersections: A stochastic optimization model for uncertain vehicle arrival rates","authors":"Shaocheng JIA , S.C. WONG , 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}
Pub Date : 2025-02-05DOI: 10.1016/j.trb.2025.103154
Mengjie Li , Haoning Xi , Chi Xie , Zuo-Jun Max Shen , Yifan Hu
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 higher than those under the deterministic scenario, indicating the significance of incorporating uncertain and future information into the operational decisions of carsharing systems.
{"title":"Real-time vehicle relocation, personnel dispatch and trip pricing for carsharing systems under supply and demand uncertainties","authors":"Mengjie Li , Haoning Xi , Chi Xie , Zuo-Jun Max Shen , 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}
Pub Date : 2025-02-01DOI: 10.1016/j.trb.2024.103147
Xiaoxu Chen , Zhanhong Cheng , Alexandra M. Schmidt , Lijun Sun
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.
{"title":"Conditional forecasting of bus travel time and passenger occupancy with Bayesian Markov regime-switching vector autoregression","authors":"Xiaoxu Chen , Zhanhong Cheng , Alexandra M. Schmidt , 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}
Pub Date : 2025-02-01DOI: 10.1016/j.trb.2024.103134
Jiangbo Yu , Michael F. Hyland
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.
{"title":"Interpretable State-Space Model of Urban Dynamics for Human-Machine Collaborative Transportation Planning","authors":"Jiangbo Yu , 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}
Pub Date : 2025-02-01DOI: 10.1016/j.trb.2024.103129
Thijs Dekker , Prateek Bansal , Jinghai Huo
When estimating multinomial logit (MNL) models where choices are made from a large set of available alternatives computational benefits can be achieved by estimating a quasi-likelihood function based on a sampled subset of alternatives in combination with ‘McFadden’s correction factor’. In this paper, we theoretically prove that McFadden’s correction factor minimises the expected information loss in the parameters of interest and thereby has convenient finite (and large sample) properties. That is, in the context of Bayesian estimation the use of sampling of alternatives in combination with McFadden’s correction factor provides the best approximation of the posterior distribution for the parameters of interest irrespective of sample size. As sample sizes become sufficiently large consistent point estimates for MNL can be obtained as per McFadden’s original proof. McFadden’s correction factor can therefore effectively be applied in the context of Bayesian MNL models. We extend these results to the context of mixed multinomial logit models (MMNL) by using the property of data augmentation in Bayesian estimation. McFadden’s correction factor minimises the expected information loss with respect to the augmented individual-level parameters, and in turn also for the population parameters characterising the shape and location of the mixing density in MMNL. Again, the results apply to finite and large samples and most importantly circumvent the need for additional correction factors previously identified for estimating MMNL models using maximum simulated likelihood. Monte Carlo simulations validate this result for sampling of alternatives in Bayesian MMNL models.
{"title":"Revisiting McFadden’s correction factor for sampling of alternatives in multinomial logit and mixed multinomial logit models","authors":"Thijs Dekker , Prateek Bansal , Jinghai Huo","doi":"10.1016/j.trb.2024.103129","DOIUrl":"10.1016/j.trb.2024.103129","url":null,"abstract":"<div><div>When estimating multinomial logit (MNL) models where choices are made from a large set of available alternatives computational benefits can be achieved by estimating a quasi-likelihood function based on a sampled subset of alternatives in combination with ‘<em>McFadden’s correction factor</em>’. In this paper, we theoretically prove that McFadden’s correction factor minimises the expected information loss in the parameters of interest and thereby has convenient finite (and large sample) properties. That is, in the context of Bayesian estimation the use of sampling of alternatives in combination with McFadden’s correction factor provides the best approximation of the posterior distribution for the parameters of interest irrespective of sample size. As sample sizes become sufficiently large consistent point estimates for MNL can be obtained as per McFadden’s original proof. McFadden’s correction factor can therefore effectively be applied in the context of Bayesian MNL models. We extend these results to the context of mixed multinomial logit models (MMNL) by using the property of data augmentation in Bayesian estimation. McFadden’s correction factor minimises the expected information loss with respect to the augmented individual-level parameters, and in turn also for the population parameters characterising the shape and location of the mixing density in MMNL. Again, the results apply to finite and large samples and most importantly circumvent the need for additional correction factors previously identified for estimating MMNL models using maximum simulated likelihood. Monte Carlo simulations validate this result for sampling of alternatives in Bayesian MMNL models.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"192 ","pages":"Article 103129"},"PeriodicalIF":5.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788858","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}
Pub Date : 2025-02-01DOI: 10.1016/j.trb.2024.103135
Nina Wiedemann , Christian Nöbel , Lukas Ballo , Henry Martin , Martin Raubal
The lack of cycling infrastructure in urban environments hinders the adoption of cycling as a viable mode for commuting, despite the evident benefits of (e-)bikes as sustainable, efficient, and health-promoting transportation modes. Bike network planning is a tedious process, relying on heuristic computational methods that frequently overlook the broader implications of introducing new cycling infrastructure, in particular the necessity to repurpose car lanes. In this work, we call for optimizing the trade-off between bike and car networks, effectively pushing for Pareto optimality. This shift in perspective gives rise to a novel linear programming formulation towards optimal bike network allocation. Our experiments, conducted using both real-world and synthetic data, testify the effectiveness and superiority of this optimization approach compared to heuristic methods. In particular, the framework provides stakeholders with a range of lane reallocation scenarios, illustrating potential bike network enhancements and their implications for car infrastructure. Crucially, our approach is adaptable to various bikeability and car accessibility evaluation criteria, making our tool a highly flexible and scalable resource for urban planning. This paper presents an advanced decision-support framework that can significantly aid urban planners in making informed decisions on cycling infrastructure development.
{"title":"Bike network planning in limited urban space","authors":"Nina Wiedemann , Christian Nöbel , Lukas Ballo , Henry Martin , Martin Raubal","doi":"10.1016/j.trb.2024.103135","DOIUrl":"10.1016/j.trb.2024.103135","url":null,"abstract":"<div><div>The lack of cycling infrastructure in urban environments hinders the adoption of cycling as a viable mode for commuting, despite the evident benefits of (e-)bikes as sustainable, efficient, and health-promoting transportation modes. Bike network planning is a tedious process, relying on heuristic computational methods that frequently overlook the broader implications of introducing new cycling infrastructure, in particular the necessity to repurpose car lanes. In this work, we call for optimizing the trade-off between bike and car networks, effectively pushing for Pareto optimality. This shift in perspective gives rise to a novel linear programming formulation towards optimal bike network allocation. Our experiments, conducted using both real-world and synthetic data, testify the effectiveness and superiority of this optimization approach compared to heuristic methods. In particular, the framework provides stakeholders with a range of lane reallocation scenarios, illustrating potential bike network enhancements and their implications for car infrastructure. Crucially, our approach is adaptable to various bikeability and car accessibility evaluation criteria, making our tool a highly flexible and scalable resource for urban planning. This paper presents an advanced decision-support framework that can significantly aid urban planners in making informed decisions on cycling infrastructure development.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"192 ","pages":"Article 103135"},"PeriodicalIF":5.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816580","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}
Pub Date : 2025-02-01DOI: 10.1016/j.trb.2024.103151
Zhijia Tan , Dian Sheng , Yafeng Yin
Shore power (SP) is an effective way to cut carbon emissions at ports by replacing fuel oil for docked ships. The adoption of SP by ships hinges on the onboard transformer setup cost and the cost saving from SP utilization in comparison with fuel oil. The allocation of SP capacity at ports influences the availability of SP-equipped berths and, along with conventional berths, incurs potential service delays. Misallocation can actually increase port emissions. This paper addresses the SP capacity allocation problem in a general container shipping network with multiple ports and a ship fleet. The service congestion or capacity-dependent waiting time at berths is considered, which results in strategic choices or choice equilibrium of ships on SP adoption. The emission quantity at each port is affected by the choice equilibrium of ships. For the benchmark case with a single port, we analytically identify a threshold SP capacity above which emissions decrease, below which a counterintuitive increase occurs. For the general shipping network, assuming government covers transformer setup costs, we develop an exact method to determine the critical level for each port to ensure emission reductions. A case study based on the Yangtze River is conducted to illustrate the analytical results.
{"title":"Shore-power capacity allocation in a container shipping network under ships’ strategic behaviors","authors":"Zhijia Tan , Dian Sheng , Yafeng Yin","doi":"10.1016/j.trb.2024.103151","DOIUrl":"10.1016/j.trb.2024.103151","url":null,"abstract":"<div><div>Shore power (SP) is an effective way to cut carbon emissions at ports by replacing fuel oil for docked ships. The adoption of SP by ships hinges on the onboard transformer setup cost and the cost saving from SP utilization in comparison with fuel oil. The allocation of SP capacity at ports influences the availability of SP-equipped berths and, along with conventional berths, incurs potential service delays. Misallocation can actually increase port emissions. This paper addresses the SP capacity allocation problem in a general container shipping network with multiple ports and a ship fleet. The service congestion or capacity-dependent waiting time at berths is considered, which results in strategic choices or choice equilibrium of ships on SP adoption. The emission quantity at each port is affected by the choice equilibrium of ships. For the benchmark case with a single port, we analytically identify a threshold SP capacity above which emissions decrease, below which a counterintuitive increase occurs. For the general shipping network, assuming government covers transformer setup costs, we develop an exact method to determine the critical level for each port to ensure emission reductions. A case study based on the Yangtze River is conducted to illustrate the analytical results.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"192 ","pages":"Article 103151"},"PeriodicalIF":5.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918066","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}
Pub Date : 2025-02-01DOI: 10.1016/j.trb.2024.103136
Qianni Wang , Liyang Feng , Jiayang Li , Jun Xie , Yu (Marco) Nie
Entropy maximization is a standard approach to consistently selecting a unique class-specific solution for multi-class traffic assignment. Here, we show the conventional maximum entropy formulation fails to strictly observe the multi-class bi-criteria user equilibrium condition, because a class-specific solution matching the total equilibrium link flow may violate the equilibrium condition. We propose to fix the problem by requiring the class-specific solution, in addition to matching the total equilibrium link flow, also match the objective function value at the equilibrium. This leads to a new formulation that is solved using an exact algorithm based on dualizing the hard, equilibrium-related constraints. Our numerical experiments highlight the superior stability of the maximum entropy solution, in that it is affected by a perturbation in inputs much less than an untreated benchmark multi-class assignment solution. In addition to instability, the benchmark solution also exhibits varying degrees of arbitrariness, potentially rendering it unsuitable for assessing distributional effects across different groups, a capability crucial in applications concerning vertical equity and environmental justice. The proposed formulation and algorithm offer a practical remedy for these shortcomings.
{"title":"Entropy maximization in multi-class traffic assignment","authors":"Qianni Wang , Liyang Feng , Jiayang Li , Jun Xie , Yu (Marco) Nie","doi":"10.1016/j.trb.2024.103136","DOIUrl":"10.1016/j.trb.2024.103136","url":null,"abstract":"<div><div>Entropy maximization is a standard approach to consistently selecting a unique class-specific solution for multi-class traffic assignment. Here, we show the conventional maximum entropy formulation fails to strictly observe the multi-class bi-criteria user equilibrium condition, because a class-specific solution matching the total equilibrium link flow may violate the equilibrium condition. We propose to fix the problem by requiring the class-specific solution, in addition to matching the total equilibrium link flow, also match the objective function value at the equilibrium. This leads to a new formulation that is solved using an exact algorithm based on dualizing the hard, equilibrium-related constraints. Our numerical experiments highlight the superior stability of the maximum entropy solution, in that it is affected by a perturbation in inputs much less than an untreated benchmark multi-class assignment solution. In addition to instability, the benchmark solution also exhibits varying degrees of arbitrariness, potentially rendering it unsuitable for assessing distributional effects across different groups, a capability crucial in applications concerning vertical equity and environmental justice. The proposed formulation and algorithm offer a practical remedy for these shortcomings.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"192 ","pages":"Article 103136"},"PeriodicalIF":5.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816581","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}
Pub Date : 2025-02-01DOI: 10.1016/j.trb.2025.103157
Samson Ting , Thomas Lymburn , Thomas Stemler , Yuchao Sun , Michael Small
<div><div>The gap acceptance framework is the theoretical basis for modelling traffic flow at intersections with a priority control. Reliable estimation methods for key gap acceptance parameters are important to more accurately predict key traffic performance measures such as capacity and delay. A notable challenge is that the critical gaps are not directly observable. Currently, the maximum likelihood estimator (MLE) is widely accepted as the most reliable method. In this research, we considered a Bayesian approach as an alternative framework for estimating gap acceptance parameters, which achieves a comparable performance to the MLE. We first formalised the gap acceptance statistical model and the estimand of interest, based on a Bayesian hierarchical formulation that naturally captures the variations between drivers. We then tested the performance of each method on simulated dataset, with the Bayesian posterior obtained through the No-U-Turn sampler, an adaptive Markov chain Monte Carlo algorithm. We showed that the MLE and the posterior mean as a point summary of the full posterior distribution have comparable performance, and both generally achieve a mean absolute error <span><math><mrow><mo>≤</mo><mn>0</mn><mo>.</mo><mn>2</mn></mrow></math></span> s for different major stream flow <span><math><msub><mrow><mi>q</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> in our experiment setup. In addition, we found that the standard error is higher for both low and high <span><math><msub><mrow><mi>q</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> so any point estimator is unlikely to be equally reliable across all level of <span><math><msub><mrow><mi>q</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span>’s. Furthermore, we also identified a potential issue when assuming consistent drivers and log-normally distributed critical gaps at high <span><math><msub><mrow><mi>q</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span>, as the heavy tail of the log-normal can result in unrealistic dataset. The full Bayesian approach also allows inherent uncertainty quantification, which we found to be well-calibrated, in the sense that the credible intervals obtained have roughly the correct frequentist coverage as per confidence intervals constructed with frequentist methods. From a traffic engineering point of view, quantifying uncertainties in gap acceptance parameters, whether using Bayesian or frequentist methods, is important as they induce uncertainties on intersection performance metrics such as capacity and delay, which will allow more informed decision-making for infrastructure investment. In addition, we also assessed the performance of Bayesian methods for more complicated statistical models, using a test scenario involving inconsistent driver behaviour, by jointly estimating the gap acceptance parameters and the inconsistency parameters. Lastly, we demonstrated the applicability of the proposed Bayesian framework to real data collect
{"title":"Estimating gap acceptance parameters with a Bayesian approach","authors":"Samson Ting , Thomas Lymburn , Thomas Stemler , Yuchao Sun , Michael Small","doi":"10.1016/j.trb.2025.103157","DOIUrl":"10.1016/j.trb.2025.103157","url":null,"abstract":"<div><div>The gap acceptance framework is the theoretical basis for modelling traffic flow at intersections with a priority control. Reliable estimation methods for key gap acceptance parameters are important to more accurately predict key traffic performance measures such as capacity and delay. A notable challenge is that the critical gaps are not directly observable. Currently, the maximum likelihood estimator (MLE) is widely accepted as the most reliable method. In this research, we considered a Bayesian approach as an alternative framework for estimating gap acceptance parameters, which achieves a comparable performance to the MLE. We first formalised the gap acceptance statistical model and the estimand of interest, based on a Bayesian hierarchical formulation that naturally captures the variations between drivers. We then tested the performance of each method on simulated dataset, with the Bayesian posterior obtained through the No-U-Turn sampler, an adaptive Markov chain Monte Carlo algorithm. We showed that the MLE and the posterior mean as a point summary of the full posterior distribution have comparable performance, and both generally achieve a mean absolute error <span><math><mrow><mo>≤</mo><mn>0</mn><mo>.</mo><mn>2</mn></mrow></math></span> s for different major stream flow <span><math><msub><mrow><mi>q</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> in our experiment setup. In addition, we found that the standard error is higher for both low and high <span><math><msub><mrow><mi>q</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> so any point estimator is unlikely to be equally reliable across all level of <span><math><msub><mrow><mi>q</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span>’s. Furthermore, we also identified a potential issue when assuming consistent drivers and log-normally distributed critical gaps at high <span><math><msub><mrow><mi>q</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span>, as the heavy tail of the log-normal can result in unrealistic dataset. The full Bayesian approach also allows inherent uncertainty quantification, which we found to be well-calibrated, in the sense that the credible intervals obtained have roughly the correct frequentist coverage as per confidence intervals constructed with frequentist methods. From a traffic engineering point of view, quantifying uncertainties in gap acceptance parameters, whether using Bayesian or frequentist methods, is important as they induce uncertainties on intersection performance metrics such as capacity and delay, which will allow more informed decision-making for infrastructure investment. In addition, we also assessed the performance of Bayesian methods for more complicated statistical models, using a test scenario involving inconsistent driver behaviour, by jointly estimating the gap acceptance parameters and the inconsistency parameters. Lastly, we demonstrated the applicability of the proposed Bayesian framework to real data collect","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"192 ","pages":"Article 103157"},"PeriodicalIF":5.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072519","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号