Transit Network Research Articles

How do dockless bikesharing services affect public transit and taxi use? Evidence from 36 central cities in China

Despite the rapid growth in the popularity of technology-enabled dockless bikesharing services, comprehensive research on their causal effects on other modes of transportation is still scarce. To fill this gap, this study exploits the staggered entry of Ofo and Mobike into 36 Chinese central cities as a quasi-natural experiment using a difference-in-differences approach to examine the overall impact of dockless bikesharing services on bus, rail, and taxi use. The results show that the entry of dockless bikesharing services significantly reduced city bus ridership by 5.1% and taxi passenger miles by 6.4%, but had no significant impact on rail ridership overall. Further heterogeneity analysis shows that a higher proportion of BRT vehicles can attenuate the negative impact of bikesharing services on bus ridership, while bikesharing seems to promote rail transit use in cities with low population densities and limited rail transit networks. Moreover, the reduction effect of bikesharing services on taxi passenger miles was driven by a decrease in average trip distance, with the unintended consequence of total taxi deadheading increased by 9.7%. This study provides important and valuable references for city governments to better utilize bikesharing services to develop efficient and sustainable urban transportation systems.

Effects of the COVID-19 pandemic on use and perception of shared e-scooters

Micro-mobility, such as electric scooters (e-scooters) and bicycles, has gained attraction in recent years by offering reduced emissions, decreased congestion, and improved public health. However, due to the COVID-19 pandemic, travel behavior and mode choices have changed. As such, shared micro-mobility are likely more vulnerable to these changes, as a relatively new mode, with less habitual riders and their shared nature perceived as a higher risk. The objective of this paper is to understand how preferences and perceptions of micro-mobility changed before, during, and potentially after the pandemic, and relative to quarantine behavior. Shared e-scooter trip data (N = 2604) before and during the pandemic, and survey data (N = 134) during the pandemic, were collected in one small- to mid-sized US city. Number of e-scooter trips decreased while duration of trips increased during the pandemic. People who only left their houses for essential activities and/or work reported using public transit and transportation network companies (e.g., Uber) significantly less during the pandemic, but there was no association between their use of micro-mobility and timeframes of the pandemic. People who left their houses similar to before the pandemic did not significantly change their mode choices during the pandemic. Most respondents were neutral in opinion about sanitary levels of shared micro-mobility, suggesting an opportunity to attract riders through new hygiene initiatives. Micro-mobility operators and jurisdictions can use these insights on perception and use of shared transport modes as travel behavior adjusts post pandemic.

Assessing the dynamic resilience of Urban Rail Transit Networks during their evolution using a ridership-weighted network.

The assessment of the resilience of Urban Rail Transit Networks (URTNs) and the analysis of their evolutionary characteristics during network growth can help in the design of efficient, safe, and sustainable networks. However, there have been few studies regarding the change of resilience in long-term network development. As for the existing resilience studies, they rarely consider the entire cycle of accident occurrence and repair; furthermore, they ignore the changes in network transportation performance during emergencies. Moreover, the measurement metrics of the important nodes have not been comprehensively considered. Therefore, to remedy these deficiencies, this paper proposes a URTN dynamic resilience assessment model that integrates the entire cycle of incident occurrence and repair, and introduces the network transport effectiveness index E(Gw) to quantitatively assess the network resilience. In addition, a weighted comprehensive identification method of the important nodes (the WH method) is proposed. The application considers the Xi'an urban rail transit network (XURTN) during 2011-2021. The obtained results identify the resilience evolutionary characteristics during network growth. And longer peripheral lines negatively affect the resilience of XURTN during both the attack and the repair processes. The central city network improves the damage index Rdam and the recovery index Rrec by up to 123.46% and 11.65%, respectively, over the overall network. In addition, the WH method can comprehensively and accurately identify the important nodes in the network and their evolutionary characteristics. Compared to the single-factor and topological strategies, the Rdam is 1.17%~178.89% smaller and the Rrec is 1.68%~84.81% larger under the WH strategy. Therefore, this method improves the accuracy of the important node identification. Overall, the insights from this study can provide practical and scientific references for the synergistic development of URTN and urban space, the enhancement of network resilience, and the protection and restoration of important nodes.

Metro track geometry car routing problem with periodic demands: a case study in Beijing, China

Because of their curvature, age, track type and maintenance history, metro tracks should be inspected periodically during the planning horizon. Metro companies operate track geometry cars to periodically collect measurement data from the tracks by crossing the urban rail transit network. Optimizing the schedule for the track geometry car is a challenge: in addition to minimizing travelling distance, the inspection time interval of the same line should be arranged as equally as possible within the planning horizon. This article proposes a mathematical model for the track geometry car routing problem with periodic demands (TGCRP-PD). To effectively address this problem, a memetic algorithm-based metaheuristic is adopted. The proposed solution approach is applied to a real-world case. The numerical results show that the proposed method could save a dead mileage of 295.016 km (accounting for 48.882%) and greatly improve the level of service intervals.

A novel cross-bearing fault diagnosis method based on pseudo-label transitive domain adaptation networks

Transfer across bearings produces a greater domain shift than transfer across working conditions (WCs). Because different bearings may have differences in structural parameters, measurement environments, and WCs, a direct transfer may significantly lower the diagnostic accuracy of the target bearing. A novel fault diagnosis method based on pseudo-label transitive domain adaptation networks (PLTDANs) is proposed to address this problem. First, empirical selection criteria are offered to ensure that appropriate intermediate domains are selected and can better bridge the source and target domains. The intermediate-domain-added TDAN can split the direct transfer process into source-intermediate and intermediate-target transfer diagnoses. This division allows for the gradual correction of the domain shift. Second, the cross-domain pseudo-label constraint (CDPLC) is proposed to select high-confidence intermediate domain samples and generate corresponding pseudo-labels. Pseudo-labels highlight the health status of intermediate domain samples. The application of CDPLC aims to minimize the cumulative error of the TDAN. A cross-bearing fault diagnosis experiment demonstrated the PLTDAN's effectiveness.

A method framework for identifying digital resource clusters in software ecosystems

A popular form of modern software development involves co-creation and sharing of digital resources (e.g., API and SDK) by third-party developers in software ecosystems. In doing so, digital resources, software development projects, and developer organizations are networked together and form digital resource clusters (DRCs), which can be closely or loosely related. Management science and innovation research has focused on how organizations use DRCs to innovate their software products and services. However, the network structures of software ecosystems are spatiotemporally complex since digital resources are transitively, heterogeneously, and temporally related. In existing literature, the extent of spatiotemporal complexity has impeded the empirical identification of DRCs. Our research devises a method framework consisting of two steps toward identifying DRCs using machine learning, which we found to be well suited to representing the spatiotemporal characteristics of networked digital resources. First, we devise a spatiotemporal network embedding method that learns and represents temporal, transitive, and heterogeneous networks of software ecosystems. Second, we devise a clustering method that identifies DRCs using the output of our embedding method as input. The performance test experiment results show that our devised method framework is superior to existing conventional methods at identifying DRCs.

Assessment, Prediction, and Mitigation of Metro-Induced Vibrations for an Opera Theatre Adjacent to a Station: A Case Study

Rapid urban expansion and the development of urban rail transit networks have led to a deteriorating vibration environment along metro lines. These long-term vibrations pose significant challenges to adjacent buildings, such as opera theatres, and to the well-being of nearby residents. Consequently, there is a critical need for vibration evaluation and the implementation of mitigation solutions. This work provides a numerical investigation into the dynamics of vibrations observed in an opera theatre located above a metro station. A unified coupling method, known as the train-track-station-solum-opera model, is proposed and validated with field experiments. By employing contact theory, deformation coordination criteria, and spring elements, various components are meticulously modeled and coupled. Using this unified coupled approach, metro-induced vibrations at the opera theatre are predicted and evaluated. Additionally, vibration control measures are employed from the perspectives of transfer paths and vibration receivers to mitigate and isolate excessive theatre vibrations. The results, based on a case where the distance between the metro line and the opera theatre is 42 m, demonstrate that metro operations can lead to vibrations exceeding acceptable limits at the opera theatre near the metro station. Therefore, it is imperative that vibrations are assessed before constructing vibration-sensitive buildings along metro lines and that mitigation measures are implemented to meet specifications. In this work, the application of extruded polystyrene (XPS) plates and optimization of building structures effectively reduced excessive theatre vibrations by 1–2.5 dB, offering viable attenuation options without requiring modifications to the existing metro system.

MaaS for the masses: Potential transit accessibility gains and required policies under Mobility-as-a-Service

Transit accessibility, the conditions and distance under which people have access to transit services, is one of the key indicators to assess the performance of cities' transit systems. The more people can access the transit system, the better its performance in terms of social equity (e.g., more equal access to jobs, education, and other opportunities). To inform policymakers and support decision-making, it is crucial to measure potential transit accessibility changes of transport investments. Due to the paucity of available data, however, calculating and monitoring transit accessibility is a difficult task. Anchored in SDG 11 for more ‘Sustainable Cities and Communities’, the UN has thus proposed a simplified, globally applicable indicator for the performance of cities’ transit systems (SDG 11.2.1) that measures the share of the population living in a walking distance of 500 m to the transit system. Building on this definition and leveraging open data sources, we analyze potential transit accessibility gains under Mobility-as-a-Service (MaaS) in Metro Manila, Philippines. We show that the integration of paratransit (i.e., jeepneys) into the transit network could almost triple access to transit from 23.9 % to 65.0 %. The integration of micro-mobility (i.e., e-scooter and bicycles) as a feeder mode could further increase this share significantly (to 97.9 % and 99.9 %, respectively). We outline and discuss evidence-based policy recommendations to exploit this potential and foster a sustainable development under MaaS. Finally, we conclude with a research agenda for micro-mobility and MaaS in developing countries, a topic which has been widely overlooked in the scientific literature so far.

Minimal Pinning Control for Oscillatority of Boolean Networks.

In this article, minimal pinning control for oscillatority (i.e., instability) of Boolean networks (BNs) under algebraic state space representations method is studied. First, two criteria for oscillatority of BNs are obtained from the aspects of state transition matrix (STM) and network structure (NS) of BNs, respectively. A distributed pinning control (DPC) from these two aspects is proposed: one is called STM-based DPC and the other one is called NS-based DPC, both of which are only dependent on local in-neighbors. As for STM-based DPC, one arbitrary node can be chosen to be controlled, based on certain solvability of several equations, meanwhile a hybrid pinning control (HPC) combining DPC and conventional pinning control (CPC) is also proposed. In addition, as for NS-based DPC, pinning control nodes (PCNs) can be found using the information of NS, which efficiently reduces the high computational complexity. The proposed STM-based DPC and NS-based DPC in this article are shown to be simple and concise, which provide a new direction to dramatically reduce control costs and computational complexity. Finally, gene networks are simulated to discuss the effectiveness of theoretical results.

A sight of self-assembly mechanism in fish oil oleogels: Phase transition, crystal structure and non-covalent interaction

Fish oils contain ω-3 polyunsaturated fatty acids (PUFAs), but easily cause quality deterioration due to the oxidation. Beeswax-based oleogels could wrap fish oils by beeswax self-assembly. The phase transition, crystal structure and non-covalent interaction were investigated to reveal the self-assembly mechanism from the perspective of beeswax and oil phase characteristics. The results indicated that high unsaturation degree, PUFAs and beeswax additions promoted phase transition, SFC and stable crystal networks. The changes of crystal structures were ascribed to the polymorphism and polymorphic transition. β-Polymorphs could form crystal networks, and β′-polymorphs could influence the size of crystal chains or clusters as well as crystalline domains. Crystalline domain sizes affected crystal morphologies and network structures, including plate-like structures and multi-layer porous structures. UFAs could involve the beeswax self-assembly to change structure characteristics by van der Waals forces and π-π stacking. The OBC remained 100%, when beeswax additions reached more than 6%. Hence, beeswax additions, PUFA contents and unsaturation degree all influenced the self-assembly mechanism and adjusted the macroscopic properties of oleogels.

Transitions between quasi-stationary states in traffic systems: Cologne orbital motorways as an example

Traffic systems can operate in different modes. In a previous work, we identified these modes as different quasi-stationary states in the correlation structure. Here, we analyze the transitions between such quasi-stationary states, i.e. how the system changes its operational mode. In the longer run this might be helpful to forecast the time evolution of correlation patterns in traffic. Taking Cologne orbital motorways as an example, we construct a state transition network for each quarter of 2015 and find a seasonal dependence for those quasi-stationary states in the traffic system. Using the PageRank algorithm, we identify and explore the dominant states which occur frequently within a moving time window of 60 days in 2015. To the best of our knowledge, this is the first study of this type for traffic systems.

Cooperative bus holding and stop-skipping: A deep reinforcement learning framework

The bus control problem that combines holding and stop-skipping strategies is formulated as a multi-agent reinforcement learning (MARL) problem. Traditional MARL methods, designed for settings with joint action-taking, are incompatible with the asynchronous nature of at-stop control tasks. On the other hand, using a fully decentralized approach leads to environment non-stationarity, since the state transition of an individual agent may be distorted by the actions of other agents. To address it, we propose a design of the state and reward function that increases the observability of the impact of agents’ actions during training. An event-based mesoscopic simulation model is built to train the agents. We evaluate the proposed approach in a case study with a complex route from the Chicago transit network. The proposed method is compared to a standard headway-based control and a policy trained with MARL but with no cooperative learning. The results show that the proposed method not only improves level of service but it is also more robust towards uncertainties in operations such as travel times and operator compliance with the recommended action.

Exploring Activity Patterns and Trip Purposes of Public Transport Passengers from Smart Card Data

With the development of technology, there is an increasing number of automatically collected data sources applied in human mobility research. Although these data sets can record travel spatiotemporal information, the semantical information of travel cannot be reflected, e.g., activity pattern or trip purpose. In this paper, we proposed a methodological framework to explore the activity patterns and trip purposes of public transit riders using smart card data in an unsupervised way. First, the heuristic rules are proposed to identify home/working activity based on the multidays travel regularity of passengers. Second, we use a modified latent Dirichlet allocation (LDA) model to explore the activity patterns for the remaining activities based on four activity attributes (including arrival time, duration, day of the week, and destination station functional attribute). In this model, the trip attributes of each passenger are considered as a word in the document with specific topics that correspond to different activity characteristics, based on which the trip purpose of each topic is inferred to interpret travel behavior. The proposed methodology is demonstrated using transit smart card data from Beijing. The performance of our model is compared with two baselines based on perplexity and the result shows that our model achieved the best. Besides, the proportions of inferred trip purposes are compared to the values from travel survey in 2020 Beijing Transport Development Annual Report. The reliability of the results is further confirmed. This work can be extended to other automated travel datasets without ground-truth labels and used to understand and predict travel demand.Practical ApplicationsThis paper proposes an integrated methodological framework to using trip records to explore activity behavior and infer the trip purpose of public transport passenger from smart card data in an unsupervised way. The methodology is implemented on the smart card data in Beijing and demonstrated the availability. The results show that the performance of our model is superior to the other two baselines, moreover, the proportions of inferred different trip purposes are approximate to the ground-truth data from travel survey in the 2020 Beijing Transport Development Annual Report. The methodology can be extended to other automated travel datasets without ground-truth labels and the result can be used to understand and predict travel demand. The study makes it possible to enrich human mobility data, which eventually would be meaningful for comprehensive city transport planning and management and can initiate a new wave of innovative applications in the public transit network, such as passenger portrayal and targeted advertising.

Optimizing public transport transfers by integrating timetable coordination and vehicle scheduling

Transfer optimization in public transport (PT) networks can be achieved through coordinated timetabling and vehicle scheduling. Traditionally, the coordinated timetabling problem is solved first before proceeding to the vehicle scheduling problem. The integration of these two problems can help further reduce the total operation cost and improve the level of service, especially when timetables of different PT lines are well-coordinated at transfer stations. This work addresses the integrated PT timetable coordination and vehicle scheduling problem while ensuring that each PT line is dispatched with an even headway. We first separately formulate two integer linear programming models for the timetable coordination and vehicle scheduling problems. Next, the two models are integrated into a bi-objective integer linear programming model for the integrated timetable coordination and vehicle scheduling problem. For small size PT networks, the model can be solved by using an ɛ-constraint method, together with off-the-shelfoptimization solvers. For large-size problems, two constraint-reduction procedures are developed to reduce the number of redundant constraints so as to reduce the computation complexity and improve the solution process. Finally, the models and solution method are applied to a numerical example and a real-world bus rapid transit (BRT) network in Chengdu, China. Computation results show that the solution generated by the sequential optimization approach is usually dominated by the Pareto-optimal solutions generated by the integrated optimization approach. Our findings suggest that it is not a wise decision to use the solution generated by the sequential optimization approach or the solution with the minimum fleet size generated by the integrated optimization approach. For practical implementation, it is recommended to choose the solution that has a fleet size of one more vehicle than the minimum fleet size.

ULTRA: Unlimited Transfers for Efficient Multimodal Journey Planning

We study a multimodal journey planning scenario consisting of a public transit network and a transfer graph that represents a secondary transportation mode (e.g., walking, cycling, e-scooter). The objective is to compute Pareto-optimal journeys with respect to arrival time and the number of used public transit trips. Whereas various existing algorithms can efficiently compute optimal journeys in either a pure public transit network or a pure transfer graph, combining the two increases running times significantly. Existing approaches, therefore, typically only support limited walking between stops by either imposing a maximum transfer distance or requiring the transfer graph to be transitively closed. To overcome these shortcomings, we propose a novel preprocessing technique called unlimited transfers (ULTRA): given an unlimited transfer graph, which may represent any non–schedule based transportation mode, ULTRA computes a small number of transfer shortcuts that are provably sufficient for computing a Pareto set of optimal journeys. These transfer shortcuts can be integrated into a variety of state-of-the-art public transit algorithms, establishing the ULTRA-query algorithm family. Our extensive experimental evaluation shows that ULTRA improves these algorithms from limited to unlimited transfers without sacrificing query speed. This is true not just for walking, but also for faster transfer modes, such as bicycle or car. Compared with the state of the art for multimodal journey planning, the fastest ULTRA-based algorithm achieves a speedup of an order of magnitude. Funding: This work was supported by Deutsche Forschungsgemeinschaft [Grant WA 654/23-2]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0198 .

Dynamic state of low-Reynolds-number turbulent channel flow.

We numerically study the dynamic state of a low-Reynolds-number turbulent channel flow from the viewpoints of symbolic dynamics and nonlinear forecasting. A low-dimensionally (high-dimensionally) chaotic state of the streamwise velocity fluctuations emerges at a viscous sublayer (logarithmic layer). The possible presence of the chaotic states is clearly identified by orbital instability-based nonlinear forecasting and ordinal partition transition network entropy in combination with the surrogate data method.

Multi-objective multi-agent deep reinforcement learning to reduce bus bunching for multiline services with a shared corridor

Bus bunching is a long-standing problem in transit operation and ruining the regularity of transit service. In a typical urban transit network setting of multiple lines with a shared corridor, bus bunching becomes more frequent as there is more uncertainty inside the shared corridor. While multi-agent reinforcement learning (MARL) has been a promising scheme for learning efficient control policy in a multi-agent system, few studies have explored its applicability in multi-line transit control scenarios. In this study, we focus on a basic transit network where there are two bus lines with a shared corridor. An efficient MARL framework is proposed to learn multi-line bus holding control to avoid bus bunching. Specifically, we design observation and reward functions that incorporate multi-line information. In addition, a preference weights producer is introduced to update the objective weights towards a good trajectory evaluation during daily transit operation. In this way, we handle the multi-objective issue in multi-line control. In experimental studies, we validate the superiority of the method in real-world bus lines. Results show that the state and reward augmented with multi-line information benefit MARL in multi-line bus control. Besides, by updating preference weights towards less passenger waiting time, the regularity of transit service is further improved.

Single-cell resolution analysis reveals the preparation for reprogramming the fate of stem cell niche in cotton lateral meristem

BackgroundSomatic embryogenesis is a major process for plant regeneration. However, cell communication and the gene regulatory network responsible for cell reprogramming during somatic embryogenesis are still largely unclear. Recent advances in single-cell technologies enable us to explore the mechanism of plant regeneration at single-cell resolution.ResultsWe generate a high-resolution single-cell transcriptomic landscape of hypocotyl tissue from the highly regenerable cotton genotype Jin668 and the recalcitrant TM-1. We identify nine putative cell clusters and 23 cluster-specific marker genes for both cultivars. We find that the primary vascular cell is the major cell type that undergoes cell fate transition in response to external stimulation. Further developmental trajectory and gene regulatory network analysis of these cell clusters reveals that a total of 41 hormone response-related genes, including LAX2, LAX1, and LOX3, exhibit different expression patterns in the primary xylem and cambium region of Jin668 and TM-1. We also identify novel genes, including CSEF, PIS1, AFB2, ATHB2, PLC2, and PLT3, that are involved in regeneration. We demonstrate that LAX2, LAX1 and LOX3 play important roles in callus proliferation and plant regeneration by CRISPR/Cas9 editing and overexpression assay.ConclusionsThis study provides novel insights on the role of the regulatory network in cell fate transition and reprogramming during plant regeneration driven by somatic embryogenesis.

Timetable synchronization of the last several trains at night in an urban rail transit network

To improve passenger accessibility and reduce the travel time cost at night, this paper aims to optimize the synchronization of the last several trains’ timetables on each line of an urban rail transit (URT) network. A space-time network is constructed to describe the train flow and passenger flow in the URT network, based on which an integer programming model is formulated. To effectively solve the proposed model, we decompose the problem into two levels and propose an iterative algorithm. In the upper level, an adaptive large neighbourhood search (ALNS) method is developed to generate new train timetables in the URT network, which are then evaluated by the passenger flow problem in the lower level, and the optimization-evaluation iteration continues until the termination conditions are met. The method and algorithm are applied to a large-scale instance using data for the Wuhan URT network at night, which involve 9 urban rail transit lines and over 119,000 passengers, and the results show that the number of inaccessible passengers is reduced by 27.53%, and the average travel time cost is reduced by 2.70%, compared with the original timetable. Then, the experimental results on a small-scale instance show that the proposed algorithm can find a near-optimal solution in a short time, which illustrates the effectiveness and efficiency of the ALNS method.

Integrated optimization of rolling stock allocation and train timetables for urban rail transit networks: A benders decomposition approach

We investigate the integrated optimization of rolling stock allocation and train timetables (RATT) in an urban rail transit network with multiple connected lines and rolling stock depots. Different from most existing research on single-line cases, we consider that the rail manager plans to allocate a certain fleet of rolling stock to depots to implement the operational timetables of multiple lines, where each depot can serve more than one line but has limited capacity. By means of the construction of a space–time network, we formulate RATT into a bi-objective mixed-integer linear programming (MILP) model. The model can simultaneously generate the rolling stock allocation plan and train schedules for the considered lines, with the aim of optimizing both the investment cost of rolling stock and the service quality of passengers. Due to the computational complexity of large-scale real-world instances, we develop a Benders decomposition-based solution algorithm, which decomposes the MILP into a rolling stock assignment problem and a set of independent subproblems, i.e., a train scheduling subproblem for each line. As the subproblems also contain integral variables, we further prove that a large portion of the subproblems, after proper reformulation, has the integrality property and can be solved rapidly via linear relaxation. We test our integrated approach and solution algorithms on real-world instances from the Beijing rail transit network. The results show that our solution approach can obtain near optimal solutions, while the commercial solvers cannot even return feasible solutions on real-world instances. Compared to the existing plan in the Beijing metro, our RATT approach can improve the service quality by 17.6%, on average, using the same fleet size of rolling stock.