Journal of Rail Transport Planning & Management最新文献

Due to increasing prices, growing environmental awareness, and resource scarcity, railway operations become increasingly more important. In today's conventional railway operations drivers are needed for driving trains. Drivers have a very responsible job to drive the trains safely. However, they work differently depending on the time of day. Due to illness and strikes, train journeys had to be cancelled in the past. Therefore, a driver takes a special role in today's railway operations. The operation in metros is different. Due to different system properties, unattended metro systems correspond the state of the art. In the future, technical developments will enable unattended operations on railway to substitute drivers, too. Although promising, several challenges need to be accomplished for a successful market launch of unattended trains. Next to several technical challenges, this could especially concern the acceptance among potential users. If a technology or a system is not accepted, it will most likely not be adopted. Most empirical literature regarding the acceptance of driverless or unattended vehicles is related to road and air, but not to railway transport. In this paper, we, therefore, present the results of an online survey that investigated the acceptance of unattended trains. The results of the survey indicate that people generally accept unattended trains. Numerous participants would use such trains. Furthermore, some sociodemographic factors, age, current usage of rail transport, and prior experience with unattended trains, affect the acceptance. However, the results show that people worry about cyberattacks. Overall, the implementation of unattended railway trains seems to be promising. This is an important finding for railway companies to drive the trend towards unattended trains.

Crew rescheduling is one of the most important factors one must consider during the recovery process following the disruption of a railway service. If it is not properly considered when the timetable is adjusted, it can jeopardise the prompt return to stable service. A new method, namely depth-first search crew recovery (DFSCR), is developed and proposed as a satisfactory way of dealing with the real-time crew rescheduling problem. DFSCR takes into account practical considerations of the railway environment and implements flexible, parameterised rescheduling constraints in the model to generate multiple solutions. The method has been tested on real-world scenarios and its capabilities are tested against two pre-existing methods. Sensitivity tests on five parameters (maximum permitted working hours, for example) are conducted and results indicate that small adjustments to the allowed ranges of parameter values can often generate acceptable solutions where there would have been none otherwise. This parameter relaxation is implemented via a feedback mechanism in DFSCR which takes results of a run of the algorithm to inform which parameters should be relaxed and by how much in order to maximise one's chances of finding a solution in the subsequent run of the algorithm.

The paper focuses on the possible capacity improvement of a segment between two stations of a single-track railway line by using of extended station switch point area and on methods assessing the capacity of such railway infrastructure, where a part of a single-track line segment being not equipped itself with e.g. line blocks becomes partially double-tracked. These methods can extend state-or-art general capacity assessment methods and allow more detailed assessment in this specific case. The research was conducted on three levels. The first is only analytic, the second is based on the developed own mesoscopic stochastic simulation model and the third is based on a stochastic microsimulation model elaborated by using the OpenTrack software. Divided solutions on each level can provide assessments with different demands on computation and model development. Stepwise solutions at the individual level may rationalize capacity assessment.

Network-level metro passenger flow prediction has always been one of the most important but most challenging scientific problems in intelligent transportation systems (ITS). However, conventional methods ignore the multi-dimensional topological relationships in the subway system or directly learn from the physical topological structure and fail to explore the spatial-temporal evolution of passenger flow fully. Moreover, due to the highly punctual operation feature, the metro system has high-degree controllability and regularity, which is usually not considered in the traditional model. To address this problem, we model a metro system based on various topological structures and propose a novel spatial-temporal multi-graph convolutional wavelet network (ST-MGCWN) with unique metro system state characteristics. The core of this approach is to combine the domain knowledge of transportation and deep learning models. To be precise, there are three main modules in the proposed framework: (i). The graph wavelet convolution captures the dynamic spatial dependence between nodes on the established multiple graphs and synthesizes topological features through the graph fusion module. (ii). The various approaches based on the gated recurrent model are designed to capture the long-term periodicity and short-term trends. (iii). The transformation of the metro state characteristics reflects the change of the train density on the operation diagram, which is approximated by the distribution of the local peaks of passenger flow. Experimental results show that our proposed model achieves up to about 5% improvement over the state-of-the-art approach for the network-level passenger flow prediction task. In addition, we conduct a detailed analysis of the contribution of each component to enhance interpretability and reliability.

There are several safety questions associated with operating passenger trains and freight trains on shared-use rail corridors (SRCs). Among them are adjacent track accidents (ATA) in which derailed railroad equipment intrudes upon (“fouls”) adjacent tracks and is struck by another train on adjacent tracks. ATAs can occur in any multiple track territory, but they become more complex and potentially more hazardous on SRCs. ATAs can be broken down into three principal events: the initial derailment, an intrusion, and a train present on an adjacent track. Previous research established a foundation for addressing intrusion risk by qualitatively identifying the risk and potential mitigation measures and conducting preliminary quantitative intrusion probability analysis; however, a gap remains between current research on intrusion risk and a comprehensive risk assessment model for ATAs. This paper presents an index-based, semi-quantitative risk analysis framework to evaluate probability and consequence of ATAs. A new risk index system was developed to evaluate ATA risk by assigning levels of probability to the three principal events and the overall ATA consequence level to different track segments, thereby enabling comparison of relative ATA risk among these track segments. The levels of ATA probability and consequence are determined by various infrastructure, rolling stock, and operational factors identified in this research where each factor contributes risk scores that can be summed and converted to levels of probability and consequence. The magnitude of risk due to each factor is determined by their effect, i.e., whether it increases or decreases the probability and/or consequence. A case study based on a 320-km, modified real-world SRC is presented to demonstrate and validate the model. Higher operating speed, lack of containment or barriers, and higher initial derailment rate all significantly affect ATA risk. The model enables comparisons of the relative ATA risks among different track segments at a resolution not previously achieved. It can also be used to locate high-risk locations (risk hotspots) on a railroad corridor where ATA risk is high. This model also provides information pertinent to future improvements in quantification of ATA risk and research on mitigation measures.

Numerous studies suggest that built environments have impacts on transit ridership, but few consider the causal connection between them. The goal of this study is to examine the causal relationship between the built environment and subway ridership and then to re-examine the impacts. In the case of the Beijing subway system, we use the Bayesian Network learning approach to examine the causal relationship between built environment characteristics and ridership. Based on the causality analysis, we further explore the impact of the built environment on subway ridership using Ordinary Least Squares and Geographically Weighted Regression models. Findings reveal the causal impact of employment density and public transport accessibility on alighting ridership during the morning peak. The result of the correlation analysis between the morning-peak alighting ridership and other variables shows that higher employment density and public transport accessibility produce more travel demand. The regression model also indicates that the effects of the built environment on ridership vary across space. Last but not least, the results of the model performance tests show that the model constructed from the indicators obtained from the causality screening is reliable.

Automatic Train Operation (ATO) is well-known in urban railways and gets increasing interest from mainline railways at present to improve capacity and punctuality. A main function of ATO is the train trajectory generation that specifies the speed profile over the given running route considering the timetable and the characteristics of the train and infrastructure. This paper proposes and assesses different possible ATO architecture configurations through allocating the intelligent components on the trackside or onboard. The set of analyzed ATO architecture configurations is based on state-of-the-art architectures proposed in the literature for the related Connected Driver Advisory System (C-DAS). Results of the SWOT analysis highlight that different ATO configurations have diverse advantages or limitations, depending on the type of railway governance and the technological development of the existing railway signaling and communication equipment. In addition, we also use the results to spotlight operational, technological, and business advantages/limitations of the proposed ATO-over-ETCS architecture that is being developed by the European Union Agency for Railways (ERA) and provide a scientific argumentation for it.

Railway capacity is seemingly easy but truly complex concept due to its interaction with all the pillars of railways such as infrastructure, rolling stock, signaling and operation planning. Efficient capacity utilization is critical for railways worldwide but the literature lacks a comprehensive survey. This paper for the first time summarizes major research that have been done in the past two decades with more emphasis on those that have been published since 2010. Over 60 papers have been examined and their contributions are summarized. At first, definitions of capacity are presented followed by major factors affecting capacity utilization. Capacity assessment methods are classified into 3 categories of analytical, optimization and simulation. Analytical methods include two main approaches of UIC 406 and CUI that compress the timetable to identify how much capacity is utilized. These methods have been developed further by some researchers. Optimization models define objective functions and operational constraints of railway network. These models can be solved exactly by any generic optimization software or metaheuristic methods needs to be applied if the size of model is large. Capacity analysis by simulation are mostly done by special railway software that can mimic intrinsic real world operational conditions.

In this paper we define new concepts and metrics for improved understanding of causes to unpunctual trains. The metrics are denoted delay contribution and critical disturbance. Delay contribution can be interpreted as how much a specific disturbance contributes to the delay of a train and the critical disturbances can be interpreted as the disturbances that made the train become unpunctual. The metrics are applied in a test case with trains in southern Sweden. The results show that the metrics can provide a complementary view regarding causes to unpunctuality compared to standard methods and are able to pinpoint disturbances that made trains become unpunctual and separate them from disturbances that have less impact on the punctuality. The methods are useable in the continuous work to improve railway performance e.g., by prioritizing maintenance work that give best impact on punctuality.