Transition Operator Research Articles

An economic analysis of a multi-modal transportation system with ride-sourcing services and multi-class users

This paper proposes a tractable bi-level model to explore the mode choice of travelers and optimal operation decisions of a public transit operator and a ride-sourcing operator under a multi-modal transportation system. In such a multi-modal system, users may travel by bus, metro, and/or ride-sourcing service. The lower-level network model is utilized to capture the equilibrium mode choices with the consideration of travelers’ heterogeneity in terms of the value of time (VOT), where deterministic user equilibrium (DUE) and stochastic user equilibrium (SUE) are formulated and compared. In the upper-level model, subject to the DUE/SUE, the two operators optimize their operation decisions in the notion that the profit is maximized or the total system cost is minimized. The existence and uniqueness/non-uniqueness of the multi-modal mode choice equilibrium are analyzed. The optimal operation decisions are also compared under different operation regimes. Our results show that SUE model provides a more conservative estimate of the ride-sourcing operator’s profit and total system travel cost compared to DUE. Besides, the total system cost can be reduced while the profit of the ride-sourcing company can be increased under appropriate operation decisions of the public transit operator and the ride-sourcing operator. Sensitivity analysis further indicates that travelers with longer total travel distance tend to use short-distance ride-sourcing services, which suggests that the ride-sourcing operator should dispatch more vehicles to suburbs located in remote areas to enhance service quality.

Logics for Epistemic Actions: Completeness, Decidability, Expressivity

We build and study dynamic versions of epistemic logic. We study languages parameterized by an action signature that allows one to express epistemic actions such as (truthful) public announcements, completely private announcements to groups of agents, and more. The language 𝓛(Σ) is modeled on dynamic logic. Its sentence-building operations include modalities for the execution of programs, and for knowledge and common knowledge. Its program-building operations include action execution, composition, repetition, and choice. We consider two fragments of 𝓛(Σ). In 𝓛1(Σ), we drop action repetition; in 𝓛0(Σ), we also drop common knowledge. We present the syntax and semantics of these languages and sound proof systems for the validities in them. We prove the strong completeness of a logical system for 𝓛0(Σ) and the weak completeness of one for 𝓛1(Σ). We show the finite model property and, hence, decidability of 𝓛1(Σ). We translate 𝓛1(Σ) into PDL, obtaining a second proof of decidability. We prove results on expressive power, comparing 𝓛1(Σ) with modal logic together with transitive closure operators. We prove that a logical language with operators for private announcements is more expressive than one for public announcements.

Artificial Intelligence Assisted Intelligent Adjustment Method for Urban Rail Transit Train Operation

<p>The operation of intercity rail transit has greatly relieved the pressure of urban traffic. In order to improve the operation quality and passenger carrying capacity, the scheduling strategy of urban rail needs to be timely adjusted according to the passenger flow and other disturbing factors, especially the traffic control problems brought by the outbreak of the epidemic. In this paper, according to the epidemic situation and the characteristics of peak passenger flow in the morning and evening, an optimization model is designed to minimize the travel cost of passengers and the daily cost of the urban rail operation company. The optimal solution of the model is found through the reinforcement learning algorithm. Finally, based on the parameters of Shijiazhuang Metro, the optimal train scheduling scheme is obtained through simulation, which verifies the effectiveness of the research method in this paper.</p> <p> </p>

Dynamic Clustering Meeting Points Strategy to Improve Operational Service Capability of Flex-Route Transit

To improve the operational service capability of flex-route transit under the condition of uncertain and inhomogeneous passenger travel demand, a dynamic meeting points optimization solution is proposed. This solution can set dynamic pickup and drop-off meeting points according to real-time travel demand. The flex-route transit operation mode is described in detail and is divided into a two-stage optimization problem. Mixed integer programming is employed to formulate the problem with a twofold objective—(1) serve as many requests as possible; and (2) minimize the time cost of the accepted passengers. An improved memetic solution algorithm is proposed for the model constructed. Computational results based on real-world cases show that the dynamic clustering meeting points solution breaks the space constraints of the original pickup and drop-off points of passengers and can significantly reduce the proportion of passenger reservation requests rejected without increasing operation cost.

Shared micromobility and public transport integration - A mode choice study using stated preference data

This paper uses stated preference data collected in the city of Rotterdam and discrete choice modelling techniques to study the relationship between public transport and shared micromobility. It assumes a hypothetical condition of integrated systems and studies the relationships of complement and competition between these modes. The findings suggest that shared micromobility modes are viable alternatives as egress modes for metro trips. Shared micromobility can be seen as a complement to metro, yet shared e-mopeds proved to also be a viable option as individual modes for long-distance trips. Different characteristics proved to be important in choices in this context: frequency of public transport use, previous use of shared micromobility, and age. Considering the results obtained, collaboration between shared micromobility and transit operators might benefit them as well as travellers. Collaborations should be designed so that they help travellers to decrease total travel time, even if it implies longer egress legs. However, the costs of these shared modes should not be as high as to prevent travellers to use them as egress alternatives. Finally, young travellers and frequent transit users could be specifically targeted, as they showed to have a better perception of shared micromobility.

Revealing the Influence of the Fine-Scale Built Environment on Urban Rail Ridership with a Semiparametric GWPR Model

There is a causal interaction between urban rail passenger flow and the station-built environment. Analyzing the implicit relationship can help clarify rail transit operations or improve the land use planning of the station. However, to characterize the built environment around the station area, existing literature generally adopts classification factors in broad categories with strong subjectivity, and the research results are often shown to have case-specific applicability. Taking 154 stations on 8 rail transit lines in Xi’an, China, as an example, this paper uses the data sources of multiple open platforms, such as web map spatial data, mobile phone data, and price data on house purchasing and renting, then combines urban land classification in the China Urban Land Classification and Planning and Construction La1d Standard to classify the land use in the station area using structural hierarchy. On the basis of extracting fine-grained factors of the built environment, a semi-parametric Geographically Weighted Poisson Regression (sGWPR) model is used to analyze the correlation and influence between the variation of passenger flow and environmental factors. The results show that the area of Class II residential land (called R2) is the basis for generating passenger flow demand during morning and evening peak periods; The connection intensity between rail transit station area and bus services has a significant impact on commuters’ utilization level of urban rail transit. Furthermore, two scenarios in practical applications will be provided as guidance according to the research results. This study provides a general analytical framework using urban multi-source data to study the internal relationship and impact between the built environment of urban rail transit stations and passenger flow demand.

Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems

Archimedes Optimization Algorithm (AOA) is a new physics-based optimizer that simulates Archimedes principles. AOA has been used in a variety of real-world applications because of potential properties such as a limited number of control parameters, adaptability, and changing the set of solutions to prevent being trapped in local optima. Despite the wide acceptance of AOA, it has some drawbacks, such as the assumption that individuals modify their locations depending on altered densities, volumes, and accelerations. This causes various shortcomings such as stagnation into local optimal regions, low diversity of the population, weakness of exploitation phase, and slow convergence curve. Thus, the exploitation of a specific local region in the conventional AOA may be examined to achieve a balance between exploitation and exploration capabilities in the AOA. The bird Swarm Algorithm (BSA) has an efficient exploitation strategy and a strong ability of search process. In this study, a hybrid optimizer called AOA-BSA is proposed to overcome the limitations of AOA by replacing its exploitation phase with a BSA exploitation one. Moreover, a transition operator is used to have a high balance between exploration and exploitation. To test and examine the AOA-BSA performance, in the first experimental series, 29 unconstrained functions from CEC2017 have been used whereas the series of the second experiments use seven constrained engineering problems to test the AOA-BSA’s ability in handling unconstrained issues. The performance of the suggested algorithm is compared with 10 optimizers. These are the original algorithms and 8 other algorithms. The first experiment’s results show the effectiveness of the AOA-BSA in optimizing the functions of the CEC2017 test suite. AOABSA outperforms the other metaheuristic algorithms compared with it across 16 functions. The results of AOABSA are statically validated using Wilcoxon Rank sum. The AOABSA shows superior convergence capability. This is due to the added power to the AOA by the integration with BSA to balance exploration and exploitation. This is not only seen in the faster convergence achieved by the AOABSA, but also in the optimal solutions found by the search process. For further validation of the AOABSA, an extensive statistical analysis is performed during the search process by recording the ratios of the exploration and exploitation. For engineering problems, AOABSA achieves competitive results compared with other algorithms. the convergence curve of the AOABSA reaches the lowest values of the problem. It also has the minimum standard deviation which indicates the robustness of the algorithm in solving these problems. Also, it obtained competitive results compared with other counterparts algorithms regarding the values of the problem variables and convergence behavior that reaches the best minimum values.

Impacts of photovoltaic and energy storage system adoption on public transport: A simulation-based optimization approach

Photovoltaic and energy storage system (PESS) adoption in public transport (PT) can offer a promising alternative towards reducing the charging and carbon emission costs of transit agencies. However, the quantitative impacts of PESS on operational cost, carbon emission cost, bus scheduling, and energy management in PT remain unclear. This study is performed with the aim of filling this research gap by presenting an impact analysis framework incorporating multisource datasets and a simulation-based optimization approach. A simulation is designed to capture bus scheduling and energy management on a day-to-day transit operation. A surrogate-based optimization method is adopted to determine an optimal PESS configuration for battery electric bus (BEB) charging stations. A case study is performed by introducing PESS at BEB charging stations in Beijing, China, leveraging historical weather and bus operational data. Compared with conventional charging stations, the novel transit system with PESS reduces the annual charging costs and carbon emissions of a single bus route in this case study by 17.6% and 8.8% on average, respectively. The results suggest that the recycling electricity price of PV generation is the key to influencing charging costs and carbon emissions. Battery capacities also impact the scheduling pattern of PESS considerably. Further comparison across five bus routes in Beijing reveals that the deployment locations of PESS are key decisions influencing the operational cost and carbon emissions in PT at the network level when the investment budget is limited.

Negotiated windfalls: Mapping how public actors pursue and share land-value capture in Nanterre-la-Folie, France

This article seeks to broaden the scope of discussions pertaining to land-value capture (LVC). Scholars have now accumulated considerable documentation and analyses on how public authorities can (and whether they should) capture land-value increases that stem from actions other than the landowner’s. Little attention has been paid, however, to how public actors might compete for, and share the fruit of LVC. Using the case study of Nanterre-la-Folie, a district located in the vicinity of a new train station within the Grand Paris metropolitan area, we draw attention to the interplay of a variety of public actors (transit operators, local governments, national government) as they negotiate their share of transit-related LVC. In doing so, we show that the largely agreed-upon narrative of LVC as public actors versus private ones leaves important aspects of planning in a blind spot. By drawing attention to public actors’ competition for transit-related LVC, we inform academic debates on metropolitan governance and highlight implications for the unfolding and outcomes of real-estate projects.

An open-source program for spatial decomposition of bus transit networks

Comparing transit operations and performance over time is often challenging due to changes in route identifiers and service patterns. It is particularly difficult if the analysis period spans a major network design change where bus routes are realigned and renamed. This paper presents a new open-source software package that simplifies longitudinal transit performance analysis by decomposing a bus network into block-length segments that remain stable over time. The package then matches transit networks across time periods using segment geometry rather than potentially unstable route or stop identifiers. This package provides an efficient method for transit planners, advocacy groups, policy makers and urban scientists to analyze changes in transit performance over time without any prior knowledge of network design changes.

Fuzzy Cluster Analysis: Pseudometrics and Fuzzy Clusters

Introduction. Clustering problems arise in various spheres of human activity. In cases where there are no initial data sufficient for statistical analysis or information obtained from experts is used, fuzzy models are proposed that take into account different types of uncertainty and more argumentatively reflect real situations that model systems of different purposes. Particular attention is drawn to invariance in problems with different types of data measured in different scales according to the classification of S. Stevens. It is known that when solving cluster analysis problems using the transitive closure operation with respect to the equivalence that is obtained, such connections between objects as similarity and dissimilarity are changed. Therefore, it is necessary to take into account the problem of adequacy when developing models and algorithms for solving problems of fuzzy cluster analysis. The purpose of the paper is an analyzing the problem of adequacy of the results of fuzzy cluster analysis on the introduction of metrics and pseudometrics on fuzzy sets in the presence of several qualitative and quantitative characteristics of objects. Propose an approach that ensures the adequacy of pseudometrics, that is, provides invariance with respect to permissible transformations of the values of fuzzy features, and also ensures the division of objects into equivalence classes without distorting the distance between them. Results. Axiomatic definitions of a fuzzy cluster and a fuzzy α level cluster are proposed, which are introduced as fuzzy sets of elements similar to certain elements of a given set, if the condition is met: the dissimilarity ratio must be an invariant pseudometric. This condition is ensured by the use of the linguistic correlation coefficient when calculating fuzzy relations of similarity and dissimilarity. Based on the definition of a fuzzy cluster of α level and threshold conorm, the distance between fuzzy clusters of α level is determined. Conclusions. The proposed approach can be the basis for the development of algorithms for solving cluster analysis problems. This provides a meaningful interpretation of the obtained clusters, and the possibility of clarifying the results in further studies of their structure. Keywords: fuzzy set, conorm, metric, pseudometric, fuzzy similarity relation, fuzzy cluster.

Research on the Evaluation System of Urban Rail Transit Operation Safety in the Context of Intelligent Transportation

<div>With the rapid development of the Internet and intelligent control technology, intelligent transportation has become a research hotspot in building a smart city. Under the background of intelligent transportation, it is particularly important to effectively evaluate the rail transit as the framework of urban public transport in this study, and fuzzy mechanism is introduced to optimize the support vector machine (SVM), and on this basis, analytic hierarchy process (AHP) and SVM are combined to improve the classification accuracy and improve the rail transit operation safety evaluation index system. The experimental results show that the classification accuracy of the fuzzy SVM combined with AHP is above 85% on all the datasets, and it can effectively eliminate the less-relevant indicators. In the actual evaluation of Shanghai Rail Transit safety, the prediction accuracy exceeded 80% and the highest reached 94.51%. Among them, the accuracy of management level and infrastructure were increased by 24.1% and 18.34%, respectively, indicating that this method can effectively screen the evaluation indicators. In the evaluation of Beijing Rail Transit, the accuracy rate of the combined algorithm reaches 95.67%, with high classification accuracy, which provides a reference direction for the establishment of the rail transit operation safety evaluation system.</div>

Routing Optimization of Regional Flexible Transit Under the Mixed Demand Mode

To meet trip demands and avoid transit capacity waste or shortages, this study investigates the routing optimization of flexible transit with time windows. We introduce the time penalty costs to accommodate the impacts of early and late vehicle arrivals on passengers’ satisfaction. A routing optimization model is developed to minimize the system operation costs and the costs incurred by passengers' time penalties. The problem is solved by a designed adaptive genetic algorithm that adopts an adaptive mutation strategy to dynamically adjust the mutation probability and mutation operator. The numerical experiments compare the results of the mixed demand model, in which vehicles can pick up and drop off passengers simultaneously, to those of the separate pick-up and delivery modes. Finally, a sensitive analysis is conducted to explore the impact of operational factors (vehicle speed, maximum one-way travel time, and weighting ratios between operating and penalty costs) on the system's performance (total costs, per capita mileage, and average seat occupancy rate). Our results confirm the advantages of the developed adaptive genetic algorithm over traditional ones with respect to the convergence speed and optimality gap. Moreover, the numerical results indicate that the mixed demand operation mode of transit reduces total costs by an average of 2.35% compared to separate pick-up and delivery modes. The results also reveal that an increase in the weights of the operating cost can reduce the total cost. The findings of this work can provide guidance to the operation of regional flexible transit.

Exploration of the Ideological and Political Implementation Plan for the Course of "Urban Rail Transit Ticketing Management"

This research is based on the teaching practice process of urban rail transit operation management major in higher vocational colleges. Taking the course of Urban Rail Transit Ticketing Management as an example, focusing on the three teaching links before class, during class and after class, this research explores the curriculum ideological and political implementation plan from the seven stages of curriculum ideological and political teaching objectives, curriculum ideological and political teaching content, and curriculum ideological and political teaching situation creation, independent exploration, collaborative learning, effect evaluation, and after-school reflection. The teaching implementation plan indicates that this teaching model can effectively integrate value guidance into the knowledge transfer process and ability enhancement, enhance professional literacy, and achieve the goal of comprehensive education for all students in the ideological and political education curriculum.

New signal priority strategies to improve public transit operations in an urban corridor

By facilitating public busesʼ movement through traffic signal-controlled intersections, a transit signal priority (TSP) strategy can contribute to the reduction of queuing time at intersections. However, the traditional TSP also has a significant downside; it is detrimental to non-prioritized movements and other transport modes. This research proposes new TSP strategies that account for optimization for the person delay at an intersection along a corridor. This research focused both on major arterial roads and minor arterial roads, whereas the majority of the publications only addressed the major arterial approach. As part of this research, the bus schedule was also taken into consideration, whereas in other studies, the measure of giving priority was only the approach to the intersection. The proposed method is described in detail and also is implemented on arterial corridor in VISSIM. The study area simulation results indicated that the proposed TSP methods performed better than the conventional TSP.

State of the BART: Analyzing the Determinants of Bay Area Rapid Transit Use in the 2010s

Peaking on public transit—the concentration of ridership in peak times and directions into and out of central areas—has waxed in the U.S. over the past century, as public transit has lost more mode share at off-peak times, in off-peak directions, and among non-commute trips. A notable pre-pandemic manifestation of this chronic problem was on Bay Area Rapid Transit, the San Francisco Bay Area’s regional heavy rail system. While BART staved off an absolute ridership decline longer than most American transit operators in the mid- and late-2010s, it did so almost entirely due to peak gains in riders offsetting off-peak losses. As a result, the system experienced worsening passenger crowding at some times and places, expanding underutilization of capacity at many others, and the prospect of enormous expenditures to accommodate rising transbay passenger demand. To examine the factors driving transit use in the 2010s, we model peak and off-peak BART trips as a function of station area and system characteristics. We uniquely use origin–destination pairs as the unit of analysis in order to separately measure influences at both ends of the trip. We find that transfers and travel time most influence peak and off-peak BART ridership and that station-area employment and time-competitiveness with driving particularly influence peak patronage. Over time in our models, the associations between ridership and transit travel time weakened, while the associations between ridership and transfers, employment, and time-competitiveness with driving grew stronger. In sum, we find that the peaking problem plaguing public transit systems for decades worsened in the years leading up to the pandemic—on this one nationally significant U.S. transit system, at least—which poses potentially substantial financial challenges in the years ahead.

A multiple spatio‐temporal features fusion approach for short‐term passenger flow forecasting in urban rail transit

AbstractTimely and precise short‐term passenger flow forecasting (STPFF) is a key to intelligent urban rail transit (URT) operation. The uncertainty of passenger travel behaviors leads to passenger flow data showing high nonlinearities and complex spatio‐temporal correlation. Therefore, the ability to capture such correlations determines the prediction performance of the model. However, current methods either consider only the physical topology of URT network or lack the ability to model global spatial dynamics, making it difficult to obtain satisfactory predictions. Therefore, a Multiple Spatio‐Temporal Features Fusion (MSTFF) network is proposed for STPFF in URT networks. Specifically, the connectivity and passenger flow similarity relationships in URT networks are encoded as physical adjacency graph and virtual functional similarity graph, respectively, and then these two complementary graphs are input into two designed Residual Graph Convolutional Gated Recurrent Unit (RGCGRU) modules to extract their spatio‐temporal features. Second, an Attention‐based Gated Recurrent Unit (AGRU) module is designed to capture global dynamic evolutionary trends in both spatial and temporal dimensions. Finally, a fully connected neural network is used to fuse these features. Experiments on two real passenger flow datasets of URT show that MSTFF performs well on various prediction tasks (15, 30, 45, and 60 min).

Robust Dynamic Bus Control: a Distributional Multi-Agent Reinforcement Learning Approach

Bus system is a critical component of sustainable urban transportation. However, the operation of a bus fleet is unstable in nature, and bus bunching has become a common phenomenon that undermines the efficiency and reliability of bus systems. Recently research has demonstrated the promising application of multi-agent reinforcement learning (MARL) to achieve efficient vehicle holding control to avoid bus bunching. However, existing studies essentially overlook the robustness issue resulting from various events, perturbations and anomalies in a transit system, which is of utmost importance when transferring the models for real-world deployment/application. In this study, we integrate implicit quantile network and meta-learning to develop a distributional MARL framework -- IQNC-M -- to learn continuous control. The proposed IQNC-M framework achieves efficient and reliable control decisions through better handling various uncertainties/events in real-time transit operations. Specifically, we introduce an interpretable meta-learning module to incorporate global information into the distributional MARL framework, which is an effective solution to circumvent the credit assignment issue in the transit system. In addition, we design a specific learning procedure to train each agent within the framework to pursue a robust control policy. We develop simulation environments based on real-world bus services and passenger demand data and evaluate the proposed framework against both traditional holding control models and state-of-the-art MARL models. Our results show that the proposed IQNC-M framework can effectively handle the various extreme events, such as traffic state perturbations, service interruptions, and demand surges, thus improving both efficiency and reliability of the system.

Short-term Prediction of Suzhou Rail Transit Passenger Flow Based on Combination Model

With the increasing economic development of China, the country encourages to develop public transport strongly, and urban rail transit has become a choice for more and more cities. But for rail transit operations, passenger flow prediction is becoming more and more important and has become a key issue in transportation planning. However, the effect of a single model on predicting short-term passenger flow is not ideal. Therefore, this study proposes a combined model based on GA-BP neural network and forecasts the passenger flow of Suzhou Urban Rail Transit Line 1 according to weather, holidays, and other factors. Meanwhile, the study compares with the ARIMA and BP neural network models. The results show that the accuracy of GA-BP model improved by 6.06% and 8.69% respectively which compared with the former, and the results have improved the accuracy of passenger flow prediction effectively. It is proved that the combined model has certain practical value.

Prediction Study on the Alignment of a Steel-Concrete Composite Beam Track Cable-Stayed Bridge

Due to the alignment of track bridges directly affecting the safety and comfort of rail traffic operation, the alignment prediction of track bridges needs to be accurate. However, the structure of steel-concrete composite beam (SCCB) cable-stayed bridges is more complex, and the alignment prediction needs to be more accurate. To further improve the accuracy of alignment prediction for large-span SCCB track cable-stayed bridges, a method based on the response surface method (RSM) is proposed. In this paper, the Nanjimen Yangtze River Track Special Bridge was taken as a case for research. Considering the randomness of the influencing factors, the 95% confidence interval was obtained by using Monte Carlo (MC) sampling analysis, and the predicted values were within the confidence interval. The results show that the method integrates the confidence interval under each confidence level by simulating the long-term deformation of different years after bridge completion. The method could accurately predict the alignment of large-span SCCB track cable-stayed bridges, and thereby provide technical support for alignment control and ensure the safe and comfortable operation of rail transit.