Transportation Research Part B-Methodological最新文献

{"title":"Alleviating bus bunching via modular vehicles","authors":"Yuhao Liu ,&nbsp;Zhibin Chen ,&nbsp;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}

{"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 ,&nbsp;Jean-Patrick Lebacque ,&nbsp;Negin Alisoltani ,&nbsp;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}

{"title":"Bus stop spacing with heterogeneous trip lengths and elastic demand","authors":"Ayush Pandey,&nbsp;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}

{"title":"Network macroscopic fundamental diagram-informed graph learning for traffic state imputation","authors":"Jiawei Xue,&nbsp;Eunhan Ka,&nbsp;Yiheng Feng,&nbsp;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>¹</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}

{"title":"A novel mobility consumption theory for road user charging","authors":"Michiel C.J. Bliemer ,&nbsp;Allister Loder ,&nbsp;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}

{"title":"Learning to generate synthetic human mobility data: A physics-regularized Gaussian process approach based on multiple kernel learning","authors":"Ekin Uğurel ,&nbsp;Shuai Huang ,&nbsp;Cynthia Chen","doi":"10.1016/j.trb.2024.103064","DOIUrl":"10.1016/j.trb.2024.103064","url":null,"abstract":"<div><div>Passively-generated mobile data has grown increasingly popular in the travel behavior (or human mobility) literature. A relatively untapped potential for passively-generated mobile data is synthetic population generation, which is the basis for any large-scale simulations for purposes ranging from state monitoring, policy evaluation, and digital twins. And yet, this significant potential may be hindered by the growing sparsity or rate of missingness in the data, which stems from heightened privacy concerns among both data vendors and consumers (users of service platforms generating individual mobile data). To both fulfill the great potential and to address sparsity in the data, there is a need to develop a flexible and scalable model that can capture individual heterogeneity and adapt to changes in mobility patterns. We propose a conditional-generative Gaussian process framework that learns kernel structures characterizing individual mobile data and can provably replicate observed patterns. Our approach integrates physical knowledge to regularize the framework such that the generated data obeys constraints imposed by the built and natural environments (such as those on velocity and bearing). To capture travel behavior heterogeneity at the individual level, we propose a data-driven multiple kernel learning approach to determine the optimal composite kernel for every user. Our experiments demonstrate that: (1) the impact of kernel choice on mobility metrics derived from synthetic data is non-negligible; (2) physics-regularization not only reduces model bias but also improves uncertainty estimates associated with the predicted locations; and (3) the proposed method is robust and generalizes well to varying individuals and modes of travel.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 103064"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593875","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":"Microscopic Discontinuities Disrupting Hydrodynamic and Continuum Traffic Flow Models","authors":"Benjamin Coifman","doi":"10.1016/j.trb.2024.103068","DOIUrl":"10.1016/j.trb.2024.103068","url":null,"abstract":"<div><div>This paper explores short duration disturbances in the traffic stream that are large enough to impact the traffic dynamics and disrupt stationarity when establishing the fundamental diagram, FD, but small enough that they are below the resolution of conventional vehicle detector data and cannot be seen using conventional methods. This empirical research develops the Exclusionary Vehicle Aggregation method (EVA) to extract high fidelity time series data from conventional loop detectors and then extends the method to measure the standard deviation of headways in a given fixed time sample, stdevh. Using loop detector data spanning 18 years and five sites, all of the sites show that samples with low stdevh tend towards a triangular FD while samples with high stdevh tend towards a concave FD that falls inside the triangular FD. The stdevh is also shown to be strongly correlated with the duration of the longest headway within the sample. The presence of a long headway means the state is perceptively different over the sample and thus, the measurement is non-stationary. A review of the earliest FD literature by Greenshields finds strong supporting evidence for these trends. Collectively, the loop detector and historical FD results span over 75 years of empirical traffic data.</div><div>Based on the EVA analysis, this work offers the following insights: the shape of equilibrium FD appears to be triangular and that conventional detector data mask critical features needed by hydrodynamic traffic flow models, HdTFM. Because the driver behind a long headway can act independent of their leader, the long headways can correspond to unobserved boundary conditions that generate kinematic waves. If these boundaries were detected many HdTFM could accommodate them, especially multi-class models. But the stochastic nature of the long headways also challenges the predictive abilities of deterministic HdTFM. Perhaps the largest of these challenges is driver agency- the driver behind a long headway can maintain it, resulting in signals propagating downstream or they can close the gap, resulting in signals propagating upstream. Meanwhile, this work provides a test for stationary conditions to help ensure an empirical FD supports the assumptions placed upon it.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 103068"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593876","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":"Stability analysis of a departure time choice problem with atomic vehicle models","authors":"Koki Satsukawa ,&nbsp;Kentaro Wada ,&nbsp;Takamasa Iryo","doi":"10.1016/j.trb.2024.103039","DOIUrl":"10.1016/j.trb.2024.103039","url":null,"abstract":"<div><div>In this study, we analyse the global stability of the equilibrium in a departure time choice problem using a game-theoretic approach that deals with atomic users. We first formulate the departure time choice problem as a strategic game in which atomic users select departure times to minimise their trip cost; we call this game the ‘departure time choice game’. The concept of the epsilon-Nash equilibrium is introduced to ensure the existence of pure-strategy equilibrium corresponding to the departure time choice equilibrium in conventional fluid models. Then, we prove that the departure time choice game is a weakly acyclic game. By analysing the convergent better responses, we clarify the mechanisms of global convergence to equilibrium. This means that the epsilon-Nash equilibrium is achieved by sequential better responses of users, which are departure time changes to improve their own utility, in an appropriate order. Specifically, the following behavioural rules are important to ensure global convergence: (i) the adjustment of the departure time of the first user departing from the origin to the corresponding equilibrium departure time and (ii) the fixation of users to their equilibrium departure times in order (starting with the earliest). Using convergence mechanisms, we construct evolutionary dynamics under which global stability is guaranteed. We also investigate the stable and unstable dynamics studied in the literature based on convergence mechanisms, and gain insight into the factors influencing the different stability results. Finally, numerical experiments are conducted to demonstrate the theoretical results.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 103039"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594006","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":"Sensor placement considering the observability of traffic dynamics: On the algebraic and graphical perspectives","authors":"Xinyue Hu,&nbsp;Yueyue Fan","doi":"10.1016/j.trb.2024.103057","DOIUrl":"10.1016/j.trb.2024.103057","url":null,"abstract":"<div><div>In this paper, we present a new sensor location model that aims to maximize observability of link densities in a dynamic traffic network described using a piecewise linear system of ordinary differential equations. We develop an algebraic approach based on the eigenstructure to determine the sensor location for achieving full observability with a minimal number of sensors. Additionally, a graphical approach based on the concept of structural observability is developed. By exploiting the special property of flow conservation in traffic networks, we derive a simple analytical result that can be used to identify observable components in a partially observable system, which is the main contribution of this paper. The graphical and algebraic properties of observability are then integrated into a sensor location optimization model considering a wide range of traffic conditions. Through numerical experiments, we demonstrate the good performance of our sensor deployment strategies in terms of the average observability and estimation errors.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 103057"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593874","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":"Combining time dependency and behavioral game: A Deep Markov Cognitive Hierarchy Model for human-like discretionary lane changing modeling","authors":"Kehua Chen ,&nbsp;Meixin Zhu ,&nbsp;Lijun Sun ,&nbsp;Hai Yang","doi":"10.1016/j.trb.2024.102980","DOIUrl":"10.1016/j.trb.2024.102980","url":null,"abstract":"<div><div><span>Human drivers take discretionary lane changes when the target lane is perceived to offer better traffic conditions. Improper discretionary lane changes, however, lead to traffic congestion or even crashes. Considering its significant impact on traffic flow efficiency and safety, accurate modeling and prediction of discretionary lane-changing (LC) behavior is an important component in microscopic traffic analysis. Due to the interaction process and driver behavior stochasticity<span>, modeling discretionary lane-changing behavior is a non-trivial task. Existing approaches include rule-based, utility-based, game-based, and data-driven ones, but they fail to balance the trade-off between modeling accuracy and interpretability. To address this gap, we propose a novel model, called Deep Markov Cognitive Hierarchy Model (DMCHM) which combines time dependency and behavioral game interaction for discretionary lane-changing modeling. Specifically, the lane-changing interaction process between the subject vehicle and the following vehicle in the target lane is modeled as a two-player game. We then introduce three dynamic latent variables for interaction aggressiveness, cognitive level, and payoffs based on the Hidden Markov Model<span>. The proposed DMCHM combines time dependency together with cognitive hierarchy behavioral games while preserving model interpretability. Extensive experiments on three real-world driving datasets demonstrate that DMCHM outperforms other game-theoretic baselines and has comparable performance with state-of-the-art deep learning methods in time and location errors. Besides, we employ SHAP values to present the model interpretability. The analysis reveals that the proposed model has good performance in discretionary LC prediction with high interpretability. Finally, we conduct an agent-based simulation to investigate the impact of various driving styles on macroscopic traffic flows. The simulation shows that the existence of massive aggressive drivers can increase traffic capacity because of small gaps during car-following, but inversely decrease discretionary LC rates. A balanced mixing of conservative and aggressive driving styles promotes discretionary LC frequencies since conservative car-following behaviors provide more spaces for LC. The codes can be found at </span></span></span><span><span>https://github.com/zeonchen/DMCHM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"189 ","pages":"Article 102980"},"PeriodicalIF":5.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594047","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}