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The current city operational status. (a) presents the cumulative distribution function of the radius of gyration. (b) shows the four metro lines currently operating in Xi'an, of which the two red lines were opened before our experimental data, and the two green lines were opened later. (c) presents the distribution of grid average house price in 2015, which is used to characterize the index of development of grid.

The current city operational status. (a) presents the cumulative distribution function of the radius of gyration. (b) shows the four metro lines currently operating in Xi'an, of which the two red lines were opened before our experimental data, and the two green lines were opened later. (c) presents the distribution of grid average house price in 2015, which is used to characterize the index of development of grid.

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Context 1
... determine the grid size, with the above user trajectory data, we calculate each user's radius of gyration which is a metric to distinguish users' mobility patterns, and chose the distinct geographic distance which separates total users equally into two main groups as the grid size [34]. Figure 3(a) depicts the cumulative distribution function of the radius of gyration, in which users with the radius of gyration less than 1094 meters account for 50.2% of the total. Finally, we set each grid as a square with 1000 meters width and divide the study area into 29 × 29 grids. ...
Context 2
... the above in mind, the realistic metro network with 4 lines in Xi'an is presented in Figure 3(b), where the red lines represent the 2 lines that existed before October 2015, the green lines represent the subsequently opened 2 lines, and the dots represent stations. Mapping the above user stay points into grids, we calculate the OD trips between any two grids. ...
Context 3
... the above user stay points into grids, we calculate the OD trips between any two grids. Figure 3(c) presents the distribution of the average house price in 2015. The average house price of grid д i is used to characterize its index of development D i , which is applied to the calculation of social equity in Appendix C. ...
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... 6(c) is the result of considering only social equity. By comparing with Figure 3(c), we find that this metro line has passed through the grids with a high development level, which intuitively demonstrates the effectiveness of our method. ...

Citations

... Eective expansion design requires accurate demand predictions for new stations during the planning year. While previous studies have made signicant contributions to metro network expansion design using optimization models and reinforcement learning algorithms [22,27,28,33], they often overlook the demand prediction step. Many studies either use the current year's all-mode travel demand as a proxy for metro demand in the planning year or rely on unvalidated estimated demands. ...
... But to make their method feasible, they assumed that the network is a connected graph, which is not needed in our paper. The work [19] presented a RL-based method to solve the city metro network expansion problem. Our main difference lies in the different methods used to extract the information on PT graph. ...
Preprint
Designing Public Transport (PT) networks able to satisfy mobility needs of people is essential to reduce the number of individual vehicles on the road, and thus pollution and congestion. Urban sustainability is thus tightly coupled to an efficient PT. Current approaches on Transport Network Design (TND) generally aim to optimize generalized cost, i.e., a unique number including operator and users' costs. Since we intend quality of PT as the capability of satisfying mobility needs, we focus instead on PT accessibility, i.e., the ease of reaching surrounding points of interest via PT. PT accessibility is generally unequally distributed in urban regions: suburbs generally suffer from poor PT accessibility, which condemns residents therein to be dependent on their private cars. We thus tackle the problem of designing bus lines so as to minimize the inequality in the geographical distribution of accessibility. We combine state-of-the-art Message Passing Neural Networks (MPNN) and Reinforcement Learning. We show the efficacy of our method against metaheuristics (classically used in TND) in a use case representing in simplified terms the city of Montreal.
... Because of large power consumption or privacy concerns, GPS sensors are not installed on all vehicles or enabled by all mobile subscribers. This limits many applications that rely on group behavior analysis of a large amount of users, such as urban planning optimization [2], [24] and human mobility analysis [5], [6], etc. Second, in places with ...
... To further improve map matching performance, we exploit global heuristics observed from real driving scenarios, such as preferring the routes with more proportion of major roads, less frequency of turns and U-turns. To incorporate these heuristics, inspired by the recent advance of reinforcement learning (RL) approaches [24], [28]- [30], we customize the basic map matching model into a reinforcement learning framework. ...
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This paper presents a novel map matching framework that adopts deep learning techniques to map a sequence of cell tower locations to a trajectory on a road network. Map matching is an essential pre-processing step for many applications, such as traffic optimization and human mobility analysis. However, most recent approaches are based on hidden Markov models (HMMs) or neural networks that are hard to consider high-order location information or heuristics observed from real driving scenarios. In this paper, we develop a deep reinforcement learning based map matching framework for cellular data, named as DMM, which adopts a recurrent neural network (RNN) coupled with a reinforcement learning scheme to identify the most-likely trajectory of roads given a sequence of cell towers. To transform DMM into a practical system, several challenges are addressed by developing a set of techniques, including spatial-aware representation of input cell tower sequences, an encoder-decoder based RNN network for map matching model with variable-length input and output, and a global heuristics-driven reinforcement learning based scheme for optimizing the parameters of the encoder-decoder map matching model. Extensive experiments on a large-scale anonymized cellular dataset reveal that DMM provides high map matching accuracy and fast inference time.
... In this paper, the parcel singulation task is formulated as Markov decision process(MDP) problem with a nonstationary environment [11,12]. In real-world circumstances, the input state space of MDP varies owing to the uncertainty of input parcels at each time-step. ...
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In the rapidly expanding logistics sector, parcel singulation has emerged as a significant bottleneck. To address this, we propose an automated parcel singulator utilizing a sparse actuator array, which presents an optimal balance between cost and efficiency, albeit requiring a sophisticated control policy. In this study, we frame the parcel singulation issue as a Markov Decision Process with a variable state space dimension, addressed through a deep reinforcement learning (RL) algorithm complemented by a State Space Standardization Module (S3). Distinct from previous RL approaches, our methodology initially considers the non-stationary environment during the problem modeling phase. To counter this challenge, the S3 module standardizes the dynamic input state, thereby stabilizing the RL training process. We validate our method through simulation experiments in complex environments, comparing it with several baseline algorithms. Results indicate that our algorithm excels in parcel singu-lation tasks, achieving a higher success rate and enhanced efficiency.
... [13] designed a URT network considering the competitive impact of alternative transport modes such as cars or existing slower public transportation systems. With the acceleration of urbanization, scholars on URT network design have shifted their focus from network design from scratch to extensions of existing networks [14]. For example, Chen et al. [15] proposed a bi-level programming model to obtain optimal links that are added to an existing URT network with the goal of optimizing its global accessibility and network efficiency. ...
... Constraint (12) ensures that new links are not added between stations belonging to the same line. Constraint (13) guarantees that at least one new link is added and Constraint (14) implies that the newly added links are undirected. Constraints (15) and (16) are the linearization results of constraint min ≤ J o u r n a l P r e -p r o o f ∑ ∑ • ∈ ∈ ≤ max that limit the minimal and maximal lengths of new links. ...
... (12) and (17), then ,∅ in Eq. end for 13. Compute ( ); 14. ...
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Adding new links to an existing urban rail transit (URT) network helps improve its operations by shortening passenger travel time under normal operations and disruptions. However, only a few studies have considered the impact of uncertain disruption occurrence stations on URT network design. This paper addresses this gap by proposing and solving a scenario model for determining the optimal scheme for adding new links to an existing URT network while considering the uncertainty of disruption occurrence stations. Numerical experiments are conducted on the Chengdu subway system to verify the effectiveness of the proposed model. Results indicate that disruptions occurring at a station result in an average performance loss of 0.61%. The scheme for adding new links obtained from this model helps improve network performance under normal operations and disruptions. The cumulative improved normal operating performance during the entire day and the ratio of improved weighted resilience metric are 1.638 and 24.59%, respectively. The solution of the proposed model is greatly affected by several parameters such as the total length of new links. Some useful suggestions for guiding URT network extensions are proposed based on the results of the sensitivity analysis of the above parameters.
... With the recent advancements in deep reinforcement learning algorithms, tasks involving sequential decision making and control, such as robotic control (Gu, Holly, Lillicrap, & Levine, 2017;Haarnoja, Pong, et al., 2018), and competitive games (Silver et al., 2016(Silver et al., , 2018(Silver et al., , 2017, have significantly benefited from this mathematical formalism (Nagabandi, Kahn, Fearing, & Levine, 2018;Wei, Mao, Zhao, Zou, & An, 2020). Unlike certain control engineering methods that primarily emphasize the integration of dynamical systems and reinforcement learning, our primary focus is on acquiring an optimal policy within entirely uncharted systems (Lewis, Vrabie, & Vamvoudakis, 2012). ...
... To determine the grid size, with the above user trajectory data, we calculate each user's radius of gyration which is a metric to distinguish users' mobility patterns, and chose the distinct geographic distance which separates total users equally into two main groups as the grid size [27], [28]. Figure 4 depicts the cumulative distribution function of the radius of gyration, in which users with the radius of gyration less than 1094 meters account for 50.2% of the total. ...
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Administrative divisions are regional divisions of the state for the purpose of hierarchical administration. In recent years, the process of urbanization has greatly promoted the urban development. This development is not only reflected in the expansion of urban areas but also in economic and social patterns. All these changes affect the way the urban operates. Then, a concern arising from the changing urban dynamics is that whether current administrative division accords with urban development? Existing studies conceptualize the urban space as the environment created by human activities, and elaborate the importance of urban boundaries respecting to human activities in urban management. Following this concept, we delineate the urban interior boundaries formed by human activities. Specifically, taking Xi’an in Shaanxi Province of China as an example, this study first explores the region-based human crowd mobility patterns to verify that human mobility can establish a stable correlation between regions, or capture the objective correlations between regions. Then, the above human crowd patterns have been found to be applicable for mining unusual urban regions from the perspective of anomaly detection, and empirical evidence has found that these regions are of great significance for understanding the urban spatial structure. Finally, we employ the community detection technology to naturally delimit the urban interior boundaries formed by human mobility, and make a comparison with the official urban boundaries. Some unexpected communities that are closely linked due to human activities appear from the results, and these findings help the urban planners re-examine the administrative division.
... In recent decades, deep learning (DL) and reinforcement learning (RL) have made remarkable progress in various fields, including dynamic scheduling [12,14], graph partitioning [17], and traffic flow prediction [18,19]. By combining the exceptional feature learning capabilities of deep learning with the decision control benefits of reinforcement learning, deep reinforcement learning (DRL) exhibits tremendous potential in gaming [33,34], urban road planning [43], and other domains. To enhance the adaptability of traffic lights in complex and dynamic traffic environments, some researchers are exploring the use of deep reinforcement learning technology for intelligent control of traffic lights [7,29,39], which can achieve superior performance compared to traditional methods. ...
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Traffic lights are crucial for urban traffic management, as they significantly impact congestion reduction and travel safety. Traditional methods relying on hand-crafted rules and operator experience are limited in their ability to adapt to changing traffic environments. To address this challenge, we have been exploring intelligent traffic light control using deep reinforcement learning techniques. However, current approaches often suffer from inadequate training data and unstable training processes, leading to suboptimal performance and real-world consequences. In this study, we propose RELight, a novel random ensemble reinforcement learning-based traffic light control framework. RELight effectively utilizes collected empirical data, ensuring a stable and efficient training process. To evaluate the performance of our proposed framework, we conducted a comprehensive set of experiments on a variety of datasets, including four synthetic datasets and a real traffic dataset collected from surveillance cameras at an intersection in Hangzhou, China. The results show that RELight outperforms existing approaches, demonstrating its superiority and potential for practical traffic light control applications. Graphical abstract
... An analysis on the change of the network in a course of a decade for example, can provide valuable information such as recognizing the area that is deemed suitable for public transportation service expansion especially if the network is predicted to enlarge and the public transportation service efficiency is presumed to be improved in the near future (Wei, Mao, Zhao, Zou, & An, 2020). These elements are crucial and often are caused by the increase demand of public transportation network and the accelerated growth of the network (Azzam, Klingauf, & Zock, 2013) caused by the rapid increase in global economy. ...
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Complex network theory is being widely used to study many real-life systems. One of the fields that can benefit from complex network theory approach is transportation network. In this paper, we briefly review the complex network theory method assimilated into transportation network research and the analysis it provided. It is irrefutable that complex network theory is capable to explain the structure, dynamic, node significance, performance as well as evolution of the transportation network.
... RL has been proposed for extending routes (Wei et al, 2020). Yoon and Chow (2020) proposed a similar approach at the route level that builds feasible routes in advance and include each route as options for sequential expansion. ...
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Mobility service route design requires potential demand information to well accommodate travel demand within the service region. Transit planners and operators can access various data sources including household travel survey data and mobile device location logs. However, when implementing a mobility system with emerging technologies, estimating demand level becomes harder because of more uncertainties with user behaviors. Therefore, this study proposes an artificial intelligence-driven algorithm that combines sequential transit network design with optimal learning. An operator gradually expands its route system to avoid risks from inconsistency between designed routes and actual travel demand. At the same time, observed information is archived to update the knowledge that the operator currently uses. Three learning policies are compared within the algorithm: multi-armed bandit, knowledge gradient, and knowledge gradient with correlated beliefs. For validation, a new route system is designed on an artificial network based on public use microdata areas in New York City. Prior knowledge is reproduced from the regional household travel survey data. The results suggest that exploration considering correlations can achieve better performance compared to greedy choices in general. In future work, the problem may incorporate more complexities such as demand elasticity to travel time, no limitations to the number of transfers, and costs for expansion.