Pub Date : 2024-11-01DOI: 10.1016/j.trb.2024.103020
Yazan Safadi , Nikolas Geroliminis , Jack Haddad
Connectivity and digitalization will enable new control measures in urban air mobility operations and open new ways for integrating these measures in real-time traffic management. Hence, new control strategies can be designed to regulate both demand and supply of Low-Altitude Air city Transport (LAAT) systems. This can be achieved by adjusting aircraft departure times, and manipulating transfer aircraft flows at boundary air regions. In this research, new model-based control strategies are designed, where aircraft departure management and boundary control strategies are integrated. The aviation operation can benefit from the proposed flow-oriented control paradigm, which can balance the LAAT system’s supply and demand, i.e. controlling the transfer flow between airspace regions and simultaneously managing the aircraft departure (inflow). The current paper presents the development of different control strategies: Departure Controller (DC), Boundary Controller (BC), and integrated Departure and Boundary Controller (DBC), with supporting simulation results. The designed controllers are tested in a new LAAT framework that considers modeling and control of LAAT operation while capturing the microscopic and macroscopic levels simultaneously.
连通性和数字化将为城市空中交通运营提供新的控制措施，并为将这些措施整合到实时交通管理中开辟新的途径。因此，可以设计新的控制策略来调节低空城市航空运输（LAAT）系统的需求和供给。这可以通过调整飞机起飞时间和操纵边界空气区域的转移飞机流来实现。本研究设计了新的基于模型的控制策略，将飞机起飞管理和边界控制策略融为一体。提出的以流量为导向的控制范式可以平衡 LAAT 系统的供需，即控制空域区域间的转移流量，同时管理飞机的离港（流入），从而使航空运营受益。本文介绍了不同控制策略的发展情况：出发控制器 (DC)、边界控制器 (BC) 以及出发和边界综合控制器 (DBC)，并给出了仿真结果。设计的控制器在新的 LAAT 框架中进行了测试，该框架考虑了 LAAT 运行的建模和控制，同时捕捉微观和宏观层面。
{"title":"Integrated departure and boundary control for low-altitude air city transport systems","authors":"Yazan Safadi , Nikolas Geroliminis , Jack Haddad","doi":"10.1016/j.trb.2024.103020","DOIUrl":"10.1016/j.trb.2024.103020","url":null,"abstract":"<div><div>Connectivity and digitalization will enable new control measures in urban air mobility operations and open new ways for integrating these measures in real-time traffic management. Hence, new control strategies can be designed to regulate both demand and supply of Low-Altitude Air city Transport (LAAT) systems. This can be achieved by adjusting aircraft departure times, and manipulating transfer aircraft flows at boundary air regions. In this research, new model-based control strategies are designed, where aircraft departure management and boundary control strategies are integrated. The aviation operation can benefit from the proposed flow-oriented control paradigm, which can balance the LAAT system’s <em>supply</em> and <em>demand</em>, i.e. controlling the transfer flow between airspace regions and simultaneously managing the aircraft departure (inflow). The current paper presents the development of different control strategies: Departure Controller (DC), Boundary Controller (BC), and integrated Departure and Boundary Controller (DBC), with supporting simulation results. The designed controllers are tested in a new LAAT framework that considers modeling and control of LAAT operation while capturing the microscopic and macroscopic levels simultaneously.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 103020"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594073","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 : 2024-11-01DOI: 10.1016/j.trb.2024.102997
Shan Yang , Yang Liu
This study proposes a learning-based approach to tackle the challenge of joint adaptive routing in stochastic traffic networks with Connected Vehicles (CVs). We introduce a Markov Routing Game (MRG) to model the adaptive routing behavior of all vehicles in such networks, thereby incorporating both competitive route choices and real-time decision-making. We establish the existence of the Nash policy (i.e., optimal joint adaptive routing policy) within the MRG that enables vehicles to adapt optimally to real-time traffic conditions online through efficient communication. To enhance scalability, we innovate with a homogeneity-based mean-field approximation method and, based on that, further develop the Homogeneity-based Mean-Field Deep Reinforcement Learning (HMF-DRL) algorithm to learn the Nash policy within the MRG. Through numerical experiments on the Nguyen–Dupuis network, we demonstrate our algorithm’s ability to efficiently converge and learn the joint adaptive routing policy that significantly enhances traffic network efficiency. Furthermore, our study provides insights into the effects of travel demand, penetration of CVs, and levels of uncertainty on the performance of the joint adaptive routing policy. This paper presents a significant step towards improving network efficiency and reducing the travel time for a majority of vehicles amid uncertain traffic conditions.
{"title":"Markov game for CV joint adaptive routing in stochastic traffic networks: A scalable learning approach","authors":"Shan Yang , Yang Liu","doi":"10.1016/j.trb.2024.102997","DOIUrl":"10.1016/j.trb.2024.102997","url":null,"abstract":"<div><div>This study proposes a learning-based approach to tackle the challenge of joint adaptive routing in stochastic traffic networks with Connected Vehicles (CVs). We introduce a Markov Routing Game (MRG) to model the adaptive routing behavior of all vehicles in such networks, thereby incorporating both competitive route choices and real-time decision-making. We establish the existence of the Nash policy (i.e., optimal joint adaptive routing policy) within the MRG that enables vehicles to adapt optimally to real-time traffic conditions online through efficient communication. To enhance scalability, we innovate with a homogeneity-based mean-field approximation method and, based on that, further develop the Homogeneity-based Mean-Field Deep Reinforcement Learning (HMF-DRL) algorithm to learn the Nash policy within the MRG. Through numerical experiments on the Nguyen–Dupuis network, we demonstrate our algorithm’s ability to efficiently converge and learn the joint adaptive routing policy that significantly enhances traffic network efficiency. Furthermore, our study provides insights into the effects of travel demand, penetration of CVs, and levels of uncertainty on the performance of the joint adaptive routing policy. This paper presents a significant step towards improving network efficiency and reducing the travel time for a majority of vehicles amid uncertain traffic conditions.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 102997"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594053","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 : 2024-11-01DOI: 10.1016/j.trb.2024.103103
H. Michael Zhang , Yafeng Yin , Henry X. Liu
{"title":"Preface to ISTTT Special Issue Volume 189, Transportation Research Part B","authors":"H. Michael Zhang , Yafeng Yin , Henry X. Liu","doi":"10.1016/j.trb.2024.103103","DOIUrl":"10.1016/j.trb.2024.103103","url":null,"abstract":"","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 103103"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594111","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 : 2024-11-01DOI: 10.1016/j.trb.2024.102991
Rui Yao, Kenan Zhang
Mobility-as-a-service (MaaS) provides seamless door-to-door trips by integrating different transport modes. Although many MaaS platforms have emerged in recent years, most of them remain at a limited integration level. This study investigates the assignment and pricing problem for a MaaS platform as an intermediary in a multi-modal transportation network, which purchases capacity from service operators and sells multi-modal trips to travelers. The analysis framework of many-to-many stable matching is adopted to decompose the joint design problem and to derive the stability condition such that both operators and travelers are willing to participate in the MaaS system. To maximize the flexibility in route choice and remove boundaries between modes, we design an origin–destination pricing scheme for MaaS trips. On the supply side, we propose a wholesale purchase price for service capacity. Accordingly, the assignment problem is reformulated and solved as a bi-level program, where MaaS travelers make multi-modal trips to minimize their travel costs meanwhile interacting with non-MaaS travelers in the multi-modal transport system. We prove that, under the proposed pricing scheme, there always exists a stable outcome to the overall many-to-many matching problem. Further, given an optimal assignment and under some mild conditions, a unique optimal pricing scheme is ensured. Numerical experiments conducted on the extended Sioux Falls network also demonstrate that the proposed MaaS system could create a win-win-win situation—the MaaS platform is profitable and both traveler welfare and transit operator revenues increase from a baseline scenario without MaaS.
{"title":"Design an intermediary mobility-as-a-service (MaaS) platform using many-to-many stable matching framework","authors":"Rui Yao, Kenan Zhang","doi":"10.1016/j.trb.2024.102991","DOIUrl":"10.1016/j.trb.2024.102991","url":null,"abstract":"<div><div>Mobility-as-a-service (MaaS) provides seamless door-to-door trips by integrating different transport modes. Although many MaaS platforms have emerged in recent years, most of them remain at a limited integration level. This study investigates the assignment and pricing problem for a MaaS platform as an intermediary in a multi-modal transportation network, which purchases capacity from service operators and sells multi-modal trips to travelers. The analysis framework of many-to-many stable matching is adopted to decompose the joint design problem and to derive the stability condition such that both operators and travelers are willing to participate in the MaaS system. To maximize the flexibility in route choice and remove boundaries between modes, we design an origin–destination pricing scheme for MaaS trips. On the supply side, we propose a wholesale purchase price for service capacity. Accordingly, the assignment problem is reformulated and solved as a bi-level program, where MaaS travelers make multi-modal trips to minimize their travel costs meanwhile interacting with non-MaaS travelers in the multi-modal transport system. We prove that, under the proposed pricing scheme, there always exists a stable outcome to the overall many-to-many matching problem. Further, given an optimal assignment and under some mild conditions, a unique optimal pricing scheme is ensured. Numerical experiments conducted on the extended Sioux Falls network also demonstrate that the proposed MaaS system could create a win-win-win situation—the MaaS platform is profitable and both traveler welfare and transit operator revenues increase from a baseline scenario without MaaS.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 102991"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594049","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 : 2024-11-01DOI: 10.1016/j.trb.2024.103023
Pu Xu , Tian-Liang Liu , Qiong Tian , Bingfeng Si , Wei Liu , Hai-Jun Huang
This paper introduces an inverse optimization method to uncover commuters’ schedule preference and crowding perception based on aggregated observations from smart card data for an urban rail corridor system. The assessment of time-of-use preferences typically involves the use of econometric models of discrete choice based on detailed travel survey data. However, discrete choice models often struggle with potential endogeneity issues in behavioral observations when estimating individual samples from massive transit data with limited exogenous identifying information. This motivates us to employ an equilibrium modeling approach to capture the dynamism hidden in commuters’ departure time decision-making from aggregations. Assuming user optimality in observed choices, an inverse optimization method is proposed to find a set of preference parameters in the stochastic user equilibrium-based morning commuting model with heterogeneous commuters so that the resulting equilibrium pattern best approximates the observed departure rate distribution over time. The proposed inverse optimization problem can be formulated by a bi-level programming model and a sensitivity analysis-based solution framework is further designed for model estimation. Lastly, the smart card data and train timetable data from the rail corridor along the Beijing Subway Batong Line are synthesized for a case study to estimate commuters’ departure time choice preferences during morning peak periods, as well as to validate the robustness and practicality of the proposed method.
{"title":"Estimation of schedule preference and crowding perception in urban rail corridor commuting: An inverse optimization method","authors":"Pu Xu , Tian-Liang Liu , Qiong Tian , Bingfeng Si , Wei Liu , Hai-Jun Huang","doi":"10.1016/j.trb.2024.103023","DOIUrl":"10.1016/j.trb.2024.103023","url":null,"abstract":"<div><div>This paper introduces an inverse optimization method to uncover commuters’ schedule preference and crowding perception based on aggregated observations from smart card data for an urban rail corridor system. The assessment of time-of-use preferences typically involves the use of econometric models of discrete choice based on detailed travel survey data. However, discrete choice models often struggle with potential endogeneity issues in behavioral observations when estimating individual samples from massive transit data with limited exogenous identifying information. This motivates us to employ an equilibrium modeling approach to capture the dynamism hidden in commuters’ departure time decision-making from aggregations. Assuming user optimality in observed choices, an inverse optimization method is proposed to find a set of preference parameters in the stochastic user equilibrium-based morning commuting model with heterogeneous commuters so that the resulting equilibrium pattern best approximates the observed departure rate distribution over time. The proposed inverse optimization problem can be formulated by a bi-level programming model and a sensitivity analysis-based solution framework is further designed for model estimation. Lastly, the smart card data and train timetable data from the rail corridor along the Beijing Subway Batong Line are synthesized for a case study to estimate commuters’ departure time choice preferences during morning peak periods, as well as to validate the robustness and practicality of the proposed method.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 103023"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594005","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 : 2024-11-01DOI: 10.1016/j.trb.2024.103051
Yuhao Liu , Zhibin Chen , Xiaolei Wang
The notorious phenomenon of bus bunching prevailing in uncontrolled bus systems produces irregular headways and downgrades the level of service by increasing passengers’ expected waiting time. Modular autonomous vehicles (MAVs), due to their ability to split and merge en route, have the potential to help both late and early buses recover from schedule deviation while providing continuous service. In this paper, we propose a novel bus bunching alleviation strategy for MAV-aided transit systems. We first consider a soft vehicle capacity constraint and establish a continuum approximation (CA) model (Model I) to capture the system dynamics intertwined with the MAV splitting and merging operations, and then establish an infinite-horizon stochastic optimization model to determine the optimal splitting and merging strategy. To capture the reality that passengers may fail to board an overcrowded bus, we propose a second model (Model II) by extending Model I to accommodate a hard vehicle capacity constraint. Based on the characteristics of the problem, we develop a customized deep Q-network (DQN) algorithm with multiple relay buffers and a penalized ruin state applicable for both models to optimize the strategy for each MAV. Numerical results show that the strategy obtained via the DQN algorithm is an effective bunch-proof strategy and has a better performance than the myopic strategy for MAV-aided systems and the two-way-looking strategy for conventional bus systems. Sensitivity analyses are also conducted to examine the effectiveness and benefits of the proposed strategy across different operation scenarios.
{"title":"Alleviating bus bunching via modular vehicles","authors":"Yuhao Liu , Zhibin Chen , Xiaolei Wang","doi":"10.1016/j.trb.2024.103051","DOIUrl":"10.1016/j.trb.2024.103051","url":null,"abstract":"<div><div>The notorious phenomenon of bus bunching prevailing in uncontrolled bus systems produces irregular headways and downgrades the level of service by increasing passengers’ expected waiting time. Modular autonomous vehicles (MAVs), due to their ability to split and merge en route, have the potential to help both late and early buses recover from schedule deviation while providing continuous service. In this paper, we propose a novel bus bunching alleviation strategy for MAV-aided transit systems. We first consider a soft vehicle capacity constraint and establish a continuum approximation (CA) model (Model I) to capture the system dynamics intertwined with the MAV splitting and merging operations, and then establish an infinite-horizon stochastic optimization model to determine the optimal splitting and merging strategy. To capture the reality that passengers may fail to board an overcrowded bus, we propose a second model (Model II) by extending Model I to accommodate a hard vehicle capacity constraint. Based on the characteristics of the problem, we develop a customized deep Q-network (DQN) algorithm with multiple relay buffers and a penalized ruin state applicable for both models to optimize the strategy for each MAV. Numerical results show that the strategy obtained via the DQN algorithm is an effective bunch-proof strategy and has a better performance than the myopic strategy for MAV-aided systems and the two-way-looking strategy for conventional bus systems. Sensitivity analyses are also conducted to examine the effectiveness and benefits of the proposed strategy across different operation scenarios.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 103051"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594007","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}
Urban traffic congestion remains a persistent issue for cities worldwide. Recent macroscopic models have adopted a mathematically well-defined relation between network flow and density to characterize traffic states over an urban region. Despite advances in these models, capturing the complex dynamics of urban traffic congestion requires considering the heterogeneous characteristics of trips. Classic macroscopic models, e.g., bottleneck and bathtub models and their extensions, have attempted to account for these characteristics, such as trip-length distribution and desired arrival times. However, they often make assumptions that fall short of reflecting real-world conditions. To address this, generalized bathtub models were recently proposed, introducing a new state variable to capture any distribution of remaining trip lengths. This study builds upon this work to formulate and solve the social optimum, a solution minimizing the sum of all users’ generalized (i.e., social and monetary) costs for a departure time choice model. The proposed framework can accommodate any distribution for desired arrival time and trip length, making it more adaptable to the diverse array of trip characteristics in an urban setting. In addition, the existence of the solution is proven, and the proposed solution method calculates the social optimum analytically. The numerical results show that the method is computationally efficient. The proposed methodology is validated on the real test case of Lyon North City, benchmarking with deterministic and stochastic user equilibria.
{"title":"Collective departure time allocation in large-scale urban networks: A flexible modeling framework with trip length and desired arrival time distributions","authors":"Mostafa Ameli , Jean-Patrick Lebacque , Negin Alisoltani , Ludovic Leclercq","doi":"10.1016/j.trb.2024.102990","DOIUrl":"10.1016/j.trb.2024.102990","url":null,"abstract":"<div><div>Urban traffic congestion remains a persistent issue for cities worldwide. Recent macroscopic models have adopted a mathematically well-defined relation between network flow and density to characterize traffic states over an urban region. Despite advances in these models, capturing the complex dynamics of urban traffic congestion requires considering the heterogeneous characteristics of trips. Classic macroscopic models, e.g., bottleneck and bathtub models and their extensions, have attempted to account for these characteristics, such as trip-length distribution and desired arrival times. However, they often make assumptions that fall short of reflecting real-world conditions. To address this, generalized bathtub models were recently proposed, introducing a new state variable to capture any distribution of remaining trip lengths. This study builds upon this work to formulate and solve the social optimum, a solution minimizing the sum of all users’ generalized (i.e., social and monetary) costs for a departure time choice model. The proposed framework can accommodate any distribution for desired arrival time and trip length, making it more adaptable to the diverse array of trip characteristics in an urban setting. In addition, the existence of the solution is proven, and the proposed solution method calculates the social optimum analytically. The numerical results show that the method is computationally efficient. The proposed methodology is validated on the real test case of Lyon North City, benchmarking with deterministic and stochastic user equilibria.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 102990"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594048","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 : 2024-11-01DOI: 10.1016/j.trb.2024.103022
Ayush Pandey, Lewis J. Lehe
This paper develops models of a bus route in which (i) stop spacing can vary; (ii) trip lengths are heterogeneous; (iii) demand is elastic; and (iv) passengers delay the bus. Since wider spacings make sufficiently long trips faster, and sufficiently short trips slower, they induce long trips and repel short trips. We explore two continuum-approximation models: one with fixed headways and another in which headways depend on the spacing. The pattern of induced/repelled trips means the ridership-maximizing spacing is shorter than the one that maximizes passenger-km traveled. The same pattern also makes the average trip length endogenous to spacing. In the model with endogenous headways, when spacing is very narrow, a rise in spacing can reduce the expected wait time by more than it increases the expected walk time. We draw several lessons for practice and use a discrete simulation to confirm results from the continuous approximation models.
{"title":"Bus stop spacing with heterogeneous trip lengths and elastic demand","authors":"Ayush Pandey, Lewis J. Lehe","doi":"10.1016/j.trb.2024.103022","DOIUrl":"10.1016/j.trb.2024.103022","url":null,"abstract":"<div><div>This paper develops models of a bus route in which (i) stop spacing can vary; (ii) trip lengths are heterogeneous; (iii) demand is elastic; and (iv) passengers delay the bus. Since wider spacings make sufficiently long trips faster, and sufficiently short trips slower, they induce long trips and repel short trips. We explore two continuum-approximation models: one with fixed headways and another in which headways depend on the spacing. The pattern of induced/repelled trips means the ridership-maximizing spacing is shorter than the one that maximizes passenger-km traveled. The same pattern also makes the average trip length endogenous to spacing. In the model with endogenous headways, when spacing is very narrow, a rise in spacing can reduce the expected wait time by more than it increases the expected walk time. We draw several lessons for practice and use a discrete simulation to confirm results from the continuous approximation models.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 103022"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594075","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 : 2024-11-01DOI: 10.1016/j.trb.2024.102996
Jiawei Xue, Eunhan Ka, Yiheng Feng, Satish V. Ukkusuri
Traffic state imputation refers to the estimation of missing values of traffic variables, such as flow rate and traffic density, using available data. It furnishes comprehensive traffic context for various operation tasks such as vehicle routing, and enables us to augment existing datasets (e.g., PeMS, UTD19, Uber Movement) for diverse theoretical and practical investigations. Despite the superior performance achieved by purely data-driven methods, they are subject to two limitations. One limitation is the absence of a traffic engineering-level interpretation in the model architecture, as it fails to elucidate the methodology behind deriving imputation results from a traffic engineering standpoint. The other limitation is the possibility that imputation results may violate traffic flow theories, thereby yielding unreliable outcomes for transportation engineers. In this study, we introduce NMFD-GNN, a physics-informed machine learning method that fuses the network macroscopic fundamental diagram (NMFD) with the graph neural network (GNN), to perform traffic state imputation. Specifically, we construct the graph learning module that captures the spatio-temporal dependency of traffic congestion. Besides, we develop the physics-informed module based on the -trapezoidal MFD, which presents a functional form of NMFD and was formulated by transportation researchers in 2020. The primary contribution of NMFD-GNN lies in being the first physics-informed machine learning model specifically designed for real-world traffic networks with multiple roads, while existing studies have primarily focused on individual road corridors. We evaluate the performance of NMFD-GNN by conducting experiments on real-world traffic networks located in Zurich and London, utilizing the UTD19 dataset 1. The results indicate that our NMFD-GNN outperforms six baseline models in terms of performance in traffic state imputation.
{"title":"Network macroscopic fundamental diagram-informed graph learning for traffic state imputation","authors":"Jiawei Xue, Eunhan Ka, Yiheng Feng, Satish V. Ukkusuri","doi":"10.1016/j.trb.2024.102996","DOIUrl":"10.1016/j.trb.2024.102996","url":null,"abstract":"<div><div><span><span>Traffic state imputation refers to the estimation of missing values of traffic variables, such as flow rate and traffic density, using available data. It furnishes comprehensive traffic context for various operation tasks such as vehicle routing, and enables us to augment existing datasets (e.g., PeMS, UTD19, Uber Movement) for diverse theoretical and practical investigations. Despite the superior performance achieved by purely data-driven methods, they are subject to two limitations. One limitation is the </span>absence<span> of a traffic engineering-level interpretation in the model architecture, as it fails to elucidate the methodology behind deriving imputation results from a traffic engineering standpoint. The other limitation is the possibility that imputation results may violate traffic flow theories, thereby yielding unreliable outcomes for transportation engineers. In this study, we introduce NMFD-GNN, a physics-informed machine learning method that fuses the network macroscopic fundamental diagram (NMFD) with the graph neural network (GNN), to perform traffic state imputation. Specifically, we construct the graph learning module that captures the spatio-temporal dependency of traffic congestion. Besides, we develop the physics-informed module based on the </span></span><span><math><mi>λ</mi></math></span><span>-trapezoidal MFD, which presents a functional form of NMFD and was formulated by transportation researchers in 2020. The primary contribution of NMFD-GNN lies in being the first physics-informed machine learning model specifically designed for real-world traffic networks with multiple roads, while existing studies have primarily focused on individual road corridors. We evaluate the performance of NMFD-GNN by conducting experiments on real-world traffic networks located in Zurich and London, utilizing the UTD19 dataset </span><span><span><sup>1</sup></span></span>. The results indicate that our NMFD-GNN outperforms six baseline models in terms of performance in traffic state imputation.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 102996"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594052","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}
Building on the analogy between electrical energy and mobility, we propose a novel mobility consumption theory based on the idea of the required reserved space headway of vehicles while driving. In this theory, mobility is “produced” by road infrastructure and is “consumed” by drivers in a similar fashion to power that is produced in power plants and consumed by electrical devices. The computation of mobility consumption only requires travel distance and travel time as input, as well as two physical parameters that are readily available, namely vehicle length and reaction time. We argue that mobility consumption is a more comprehensive measure for road use than travel distance (or travel time) alone as it captures road use over both space and time. One application area for our mobility consumption theory that we look at in this study is road user charging. We propose mobility consumption as the basis of a new charging scheme, which we refer to as mobility-based charging. Impacts of mobility-based charging and distance-based charging are compared in two case studies. When considering only departure time choice in a simple bottleneck model, we show that mobility-based charging can reduce congestion akin a congestion pricing scheme, unlike distance-based charging. Further, when considering route choice, we show that distance-based charging can increase congestion as it encourages drivers to take shortcuts through routes with low capacity, while mobility-based charging mitigates this effect. The proposed mobility-based charging scheme is further capable of considering technological innovation in vehicle automation and carbon charging.
{"title":"A novel mobility consumption theory for road user charging","authors":"Michiel C.J. Bliemer , Allister Loder , Zuduo Zheng","doi":"10.1016/j.trb.2024.102998","DOIUrl":"10.1016/j.trb.2024.102998","url":null,"abstract":"<div><div>Building on the analogy between electrical energy and mobility, we propose a novel mobility consumption theory based on the idea of the required reserved space headway of vehicles while driving. In this theory, mobility is “produced” by road infrastructure and is “consumed” by drivers in a similar fashion to power that is produced in power plants and consumed by electrical devices. The computation of mobility consumption only requires travel distance and travel time as input, as well as two physical parameters that are readily available, namely vehicle length and reaction time. We argue that mobility consumption is a more comprehensive measure for road use than travel distance (or travel time) alone as it captures road use over both space and time. One application area for our mobility consumption theory that we look at in this study is road user charging. We propose mobility consumption as the basis of a new charging scheme, which we refer to as mobility-based charging. Impacts of mobility-based charging and distance-based charging are compared in two case studies. When considering only departure time choice in a simple bottleneck model, we show that mobility-based charging can reduce congestion akin a congestion pricing scheme, unlike distance-based charging. Further, when considering route choice, we show that distance-based charging can increase congestion as it encourages drivers to take shortcuts through routes with low capacity, while mobility-based charging mitigates this effect. The proposed mobility-based charging scheme is further capable of considering technological innovation in vehicle automation and carbon charging.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 102998"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594069","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}
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