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Accurate forecasting of subway passenger flows is considered essential for the development of efficient train schedules. However, transport capacity constraints as well as station congestion can be caused by unexpected concerns with trains or power supply, which endanger passenger safety. Predicting passenger flows at the time of a fault is particularly challenging due to the low probability of failure and the complexity of the factors involved. In addition, deviation from the observed value may be resulted by the point-in-time prediction of passenger flow, thus affecting the efficiency of passenger flow control measures. To address this concern, a three-stage A-LSTM prediction model utilizing an attention mechanism and a double-layer LSTM (Long Short-Term Memory) neural network has been proposed. The model is used to map the impact of fault events on subway transport capacity with respect to delays onto the inbound passenger flow. By analyzing the data from the subway system in a metropolitan city of China, the range of passenger flow fluctuations in 10-minute intervals will be precisely predicted and applied to different subway stations., Author(s): Yichao Pu [1]; Xiangdong Xu [2]; Qianqi Fan (corresponding author) [2,3]; Shengyu Zhang [4]; Jilai Chen [5] 1. Introduction Subway systems around the world are expanding, and they mainly [...]

Timely and accurate prediction of bus passenger flow plays a crucial role in uncovering real-time traffic demand, presenting an essential and formidable challenge in the realm of bus scheduling and management. The extensive application of deep learning methods in transit passenger flow prediction can be attributed to their exceptional ability to effectively capture spatiotemporal features, resulting in superior performance. However, prevailing deep learning models in transit passenger flow prediction tend to ignore the data enhancement. Additionally, the predominant focus on a single station in the prediction task presents challenges in effectively capturing the spatiotemporal features of the entire network. A model named TSD-ST is proposed to better accomplish the task of predicting short-term transit passenger flow at multistation. The TSD-ST model leverages time series decomposition for data enhancement. Simultaneously, in addition to considering the adjacency graph, the similarity of all the stations of the entire transit network is also considered and uses multigraph convolution and graph fusion modules. This approach enables the TSD-ST model to effectively capture spatiotemporal dependencies. Experiments based on real-world bus transit datasets confirm that the TSD-ST model shows better performance in prediction tasks at 30-min, 60-min, and 90-min time scales, with an average improvement of 21.87%. The effectiveness of each component has been verified through ablation experiments., Author(s): Gang Luo [1]; Haoxuan Kuang [1]; Dongran Zhang [1]; Kunxiang Deng [1]; Jun Li (corresponding author) [1] 1. Introduction Accurate and timely multistation short-term passenger flow prediction is important [...]

With governments worldwide emphasizing environmental protection and the global focus on carbon reduction, the battery electric vehicle (BEV) industry has developed rapidly. An urban transportation network with BEVs as the main form of transportation will soon become mainstream. Motivated by the abovementioned background, a BEV transportation network design problem is investigated, and a network design model is established. The model aims to minimize the system travel time of BEV transportation networks and optimize the government's lane expansion scheme (the location and number of lanes) under a limited budget. To consider the travel characteristics of BEV drivers, the charging time, range anxiety, and bounded rationality factors are simultaneously incorporated into the model. A heuristic algorithm is designed based on the active set algorithm to obtain the local optimal solution to the actual-scale problem. Moreover, a cutting-plane method is used to convert the original problem into a different form, and a column generation technique is embedded in the abovementioned algorithm to avoid the enumeration of paths. Sensitivity analyses of different levels of rationality of BEV drivers and government investment scales are performed. The experimental results demonstrate that the model and algorithm can effectively solve the problem and provide decision support for the government in formulating transportation infrastructure construction policies., Author(s): Xu Xin [1]; Tao Zhang [1]; Cui Li [2]; Yanran Liu (corresponding author) [3]; Lingyu Gao [4]; Yuchuan Du [5] 1. Introduction As reported by the World Meteorological Organization [...]

This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data. Electric buses are a compelling candidate to replace diesel buses for their environmental and social benefits. However, the state-of-art models to evaluate the value of bus electrification require granular and bespoke data on bus operation that can be difficult to procure. This strict requirement on data and modeling can hinder potential collaborators on bus electrification, such as electric vehicle manufacturers or electricity providers, from selecting the candidate public transit agencies in the initial phase of the market investigation. Our valuation tool uses General Transit Feed Specification, a standard data format used by transit agencies worldwide, to provide high-level guidance on developing a prioritization strategy for electrifying a bus fleet. We develop physics-informed machine learning models to evaluate energy consumption, carbon emissions, health impacts, and the total cost of ownership for each transit route. We demonstrate our tool with a case study of the bus lines in the Greater Boston and Milan metropolitan areas. While the capital cost of electric buses and the price of diesel fuel may impact the economic benefit of electrification, we expect significant improvements in health and environmental performances. We estimate about 50% health cost reduction by electrifying only 4 routes out of 15 to 19 routes in each city. For Boston and Milan, some routes are estimated to produce less than 5% of the diesel bus emissions and 14%, respectively, for which investment in electrification can be prioritized., Author(s): Upadhi Vijay [1]; Soomin Woo (corresponding author) [1]; Scott J. Moura [1]; Akshat Jain [2]; David Rodriguez [3]; Sergio Gambacorta [4]; Giuseppe Ferrara [5]; Luigi Lanuzza [6]; Christian Zulberti [...]

Due to continuous urban sprawl, large-scale bus network design has become a major challenge in urban transport planning. The continuous increase in urban population and scale makes the factors considered in the urban route network design increasingly complex. Contemporary public transportation network design problems are based more on efficiency goals such as the accessibility and comfort of the transportation network, which increases the difficulty of analyzing the problem. Bus network design is not only an NP-hard (nondeterministic polynomial) problem but also a multivariable and multiobjective problem. This paper focuses on the bivariate and multiobjective bus network design problem of route generation and station selection. This paper proposes an algorithm called the Pseudo Force Field. By combining the idea of Particle Swarm Optimization (PSO) and the properties of the force field, a feasible route generation scheme is provided for the design of the bus network. The algorithm does not need to determine the end station and has a high degree of completion of the demand. This solves the problem of the selection of terminal stations in large-scale road network design. On this basis, the article combines Genetic Algorithm (GA) and Pareto frontier to provide a new route optimization algorithm and proves the effectiveness of the algorithm. The model has achieved theoretical results in the design of the bus route network in the megacity of Shenzhen, China., Author(s): Yiming Chen [1]; Yanhong Gu (corresponding author) [1]; Zhengdong Huang [2]; Tianhong Zhao [2]; Liangyuan Guo [1] 1. Introduction Public transportation is one of the important ways to achieve [...]

Rail transit network design is an important strategic problem in determining the layout of infrastructure and improving operating performance. A core transit network with multiclass rail transit systems has been constructed in many metropolitan areas worldwide. In this study, we aimed to expand an existing network to shorten travel time and improve service quality under the restriction of limited transport supply. We formulate the studied problem as a mixed-integer linear model to obtain optimal construction links, the number of trains required on each link, and the path selected by each traveler such that the weighted sum of total costs from the perspective of travelers, operators, and investors is minimized. The formulated model is path-based, where feasible paths for each traveler are generated to describe the full door-to-door journey, including the first/last mile, transfers, and multiclass transit modes. Owing to the complexity of the network design problem and because it is impractical to enumerate all feasible paths for each traveler in real-size problems, we propose a column generation-based algorithm to find both tight lower bounds and good-quality solutions efficiently by considering only a subset of feasible paths. We prove that the pricing subproblem in column generation can be decomposed into multiple shortest path problems, which can be solved efficiently and separately, based on O/D pairs instead of individual travelers. A rail transit network along a metropolitan corridor was studied as an example. Multiple computational experiments were conducted, and the results illustrate the validity and practicality of the proposed methodology for solving the problem., Author(s): Anjun Li [1,2]; Dian Wang [1,2]; Qiyuan Peng [1,2]; Lisha Wang (corresponding author) [3] 1. Introduction To alleviate travel issues in urban areas and mitigate climate change, many metropolitan [...]

In China, many cities are building themselves the transit metropolis, and the reasonable evaluation of level of service (LOS) of public transport system (PTS) is one important aspect. However, to determine the overall LOS is hard because the distribution of service in PTS is not homogeneous with regard to time and space. To address this problem, this study proposes a general framework to determine the LOS scale of PTS based on the distribution of service quality. Under the framework, two classification methods are discussed. Method 1 uses two parameters, the mean and coefficient of variation to model the distribution, and Method 2 is an existing approach that only considers mean. Then the specific use of the framework is expounded for the service attribute of crowding, and Beijing subway line LOS is evaluated. The line LOS is divided into I-IV, whose threshold is expressed as a function of mean and coefficient of variation. The results show that 57.8% of the sample points are in the most crowding level IV in morning peak hours by Method 1, but 60.9% of sample points are in a comfortable level II by Method 2, and the former is more consistent with reality. In addition, it reveals which lines and time periods need to improve the service level. The research proves the feasibility of considering the service distribution to determine the overall LOS of PTS, and it is useful for capturing more detailed information of the system performance in time and space. This research can provide an approach for evaluating and helping to improve the overall service level of PTS for public transport authorities., Author(s): Min Wang [1]; Baohua Mao (corresponding author) [1]; Yanqiang Yang [1]; Ruijia Shi [2]; Junsheng Huang [1] 1. Introduction With the development of social economy and residents' living standards, [...]

With the development of the connected autonomous bus, the interactions between the bus and social vehicle during the mandatory lane changing for bus exiting become more diverse and complex. This research investigates the evolutionary dynamics of behavioral decision-making for the bus and social vehicle in different scenarios. The evolutionary game model for the connected autonomous bus and social vehicle is built, as do the human-driven bus and social vehicle, and the connected autonomous bus under different penetration rates and social vehicle. The results of numerical experiments reveal that the connected autonomous bus chooses to change lanes in most instances, and the strategies of the human-driven bus show conservative tendencies. Such tendencies are weakened when the connected autonomous bus and human-driven bus are mixed. As for the social vehicle in different scenarios, the strategies that balance overall traffic safety and efficiency are promoted. This research provides some references for intelligent decision-making of lane changing in urban public transportation., Author(s): Ronghan Yao [1]; Xiaojing Du (corresponding author) [1]; Wenyan Qi [1]; Li Sun [1] 1. Introduction Mandatory lane changing for bus exiting is one essential part in the daily [...]

Abstract. The merits of selected heuristics to obtain transit line structures are analyzed, applying them on a synthetic parametric description of a symmetric city that allows representation of different city [...]

Abstract. Efficient management of transportation systems requires accurate trip demand data. It is sometimes possible to track anonymized users through their commutes, accomplished through previous studies on smart cards, license [...]

Operational efficiency and stability are two critical aspects to measure bus systems. Influenced by many stochastic factors, buses always suffer from delay and bunching. Traditional studies focus on a single route and lack research on the systematic evaluation of bus network. In this paper, we propose a data-driven framework to analyze the efficiency and stability based on small granularity GPS trajectory data from the perspective of entire bus network. The IC card data and route data are used to extract the boarding passenger number and topological structure, respectively. The results show that the average headway of stations follows a lognormal distribution. Moreover, the distribution of arrival efficiency of stations is inhomogeneous and a small number of stations have large values. In addition, the relationships among average headway of stations, boarding passenger number, bus number, and complex network indicators are revealed. It is found that the average headway of station is negatively correlated with other indicators, which implies that complex network connections and more passenger flows could weaken the efficiency of bus operations. This paper provides a way to evaluate the operational performance of bus networks and could give help for monitoring and optimizing the daily operation of bus systems., Author(s): Hui Zhang [1]; Yanjun Liu [1]; Baiying Shi (corresponding author) [1]; Jianmin Jia [1]; Wei Wang [2]; Xiang Zhao [3] 1. Introduction Nowadays, public transport plays a growing role [...]