Timetable Data Research Articles

Sound event detection of passing trains using convolutional recurrent neural network (CRNN)

Acoustic measurements near railway infrastructure typically involve precise identification and labelling of train passages in time series of noise measurements. The labelling of events is done manually by an acoustician, by cross-referencing several sources of information, such as train timetable data, audio data, images and/or videos. In this paper, we present a method which automatically detects and labels the passage of trains in front of a microphone using a convolutional recurrent neural network (CRNN). The advantage of this type of model is that it can be trained using existing data (time series of LAeq,1s and 1/3 octave band with a good labelling of passing trains); which has been widely available for a number of years. The results are promising if limited to binary classification but would require further development if the aim included the recognition and categorisation of various train types.

Estimation of schedule preference and crowding perception in urban rail corridor commuting: An inverse optimization method

This paper introduces an inverse optimization method to uncover commuters’ schedule preference and crowding perception based on aggregated observations from smart card data for an urban rail corridor system. The assessment of time-of-use preferences typically involves the use of econometric models of discrete choice based on detailed travel survey data. However, discrete choice models often struggle with potential endogeneity issues in behavioral observations when estimating individual samples from massive transit data with limited exogenous identifying information. This motivates us to employ an equilibrium modeling approach to capture the dynamism hidden in commuters’ departure time decision-making from aggregations. Assuming user optimality in observed choices, an inverse optimization method is proposed to find a set of preference parameters in the stochastic user equilibrium-based morning commuting model with heterogeneous commuters so that the resulting equilibrium pattern best approximates the observed departure rate distribution over time. The proposed inverse optimization problem can be formulated by a bi-level programming model and a sensitivity analysis-based solution framework is further designed for model estimation. Lastly, the smart card data and train timetable data from the rail corridor along the Beijing Subway Batong Line are synthesized for a case study to estimate commuters’ departure time choice preferences during morning peak periods, as well as to validate the robustness and practicality of the proposed method.

Visualizing the Influence of New Public Transport Infrastructure on Travel Times

Due to the global climate change and increasing traffic volumes in cities, a shift from individual to public and multimodal transport is aspired. Travel time is one of the most important aspects for many people when choosing their mode of transportation. This leads to the requirement that changes in travel times have to be considered when planning new public transport infrastructure. This research paper presents and compares different techniques for visualizing the impact of new lines in existing public transport networks on travel times. The general approach of simulating timetable data and calculating intermodal travel times considering public transport and walking is being applied to two current infrastructure projects in the city of Bonn and the surrounding region. The created maps generally aim to visualize the spread in travel times between existing and extended transportation networks discretized by different spatial units such as rectangles or postal code areas. In comparison to other common methods which typically require two maps for two different scenarios (e.g. in case of isochrones), our approach gives the opportunity to combine all relevant information within one map. It is also shown how to apply bivariate choropleth maps for displaying travel times and how to visualize improvements in the accessibility of multiple target points of interest at once.

Combining travel behavior in metro passenger flow prediction: A smart explainable Stacking-Catboost algorithm

Accurately predicting short-term passenger flow is essential to optimize operation resources and improve transportation services in urban metro systems. However, it has become a challenging problem due to spatial-temporal demand fluctuations and heterogeneous passengers’ travel behavior, i.e., the interaction between the departure and arrival passengers. In this paper, we develop an explainable Stacking-Catboost model for passenger flow prediction combining the passenger’s return probability computation. The model explores several basic ensemble learning models and the best stacking strategy. To better characterize the macroscopic spatiotemporal travel patterns other than the micro individual travel behavior, several relevant variables such as train operation characteristics, the nearby bus stations, and points of interest are considered. Ablation studies are conducted to investigate the utility of each component of the proposed model. An explainable analysis is performed to interpret information from “black box” models and quantify the contribution of each feature. We present a real-world case study conducted in the Beijing metro, demonstrating that our proposed method achieves significant improvements over existing techniques in hourly prediction tasks. Specifically, our approach outperforms the CategoricalBoosting (CatBoost) by up to 11.11 % and the Random Forest (RF) by up to 12.90 %, showcasing the effectiveness of incorporating macroscopic spatiotemporal travel patterns and micro individual travel behavior to enhance short-term forecasting accuracy. The global interpretability of models reveals key factors that impact performance, such as returning passenger flow, historical travel demand, and timetable data. Our results offer valuable insights into short-term forecasting challenges and provide a framework for leveraging explainable artificial intelligence to improve transportation services in urban metro systems.

First Fit Algorithm: A Graph Coloring Approach to Conflict-Free University Course Timetabling

Aims: Tackling scheduling issues with the most optimal graph coloring algorithm has consistently posed significant difficulties. The university scheduling problem can be expressed as a graph coloring problem, where courses are depicted as vertices and the connections between courses that have common students or teachers are represented as edges. Subsequently, the task at hand is to assign the vertices with the minimum number of colors. In order to accomplish this task, this paper present a graph coloring technique to conflict free university course timetabling using first fit algorithms.
 Methodology: The conflict graph is partitioned into a set of independent color classes to be assigned time slots and transformed into a conflict-free timetable. The Ladoke Akintola University of Technology (LAUTECH) University Course Timetabling Data was adopted. The allocation of venue based on the allotted time slots is done using first fit packing algorithm. The proposed model is implemented using Python programming language. The developed model had courses being represented as vertices and edges. The course conflict graph was created based on the acquired dataset using vertices-edges relationship diagram. The implemented model is evaluated in terms of Halstead complexity metrics: Program Volume (PV), Program Length (PL), Program Effort (PE), Program Difficulty (PD) and Execution Time (ET). The PV, PL, PE, PD and ET values obtained for the implemented model are 18.45kbits, 0.51, 1037684, 1.97 and 20.45 secs, respectively.
 Conclusion: The proposed model shows a significant improvement over the existing models by producing conflict-free course timetabling problem with better evaluation results. This work will be highly useful in solving various scheduling, optimization and NP-hard related computational problems.

Analysis of railway accessibility in Fujian Province and the influence of economic development on its spatial differentiation.

Understanding railway accessibility supports railway regulation and development, but few studies consider different perspectives. We study the spatial distribution of accessibility indicators at the county (city) scale in Fujian Province by spatiotemporal syntax and weighted average travel time using railway timetable data. Export trade, rich commercial activities, and high-speed rail had a significant positive effect on objective accessibility. Fuzhou, Sanming, and Longyan were main transfer centers. The most accessible nodes based on weighted average travel time formed a "U"-shaped corridor along the coast. The county-wide average accessibility was 1.72h. According to spatiotemporal syntax, local general public budget expenditure (0.758993) and export volume of goods-total retail sales of consumer goods (0.956257) had the most interactive impact, while according to weighted average accessibility, import volume of goods (0.618447) and per capita gross regional product-import volume of goods (0.878573) did. These results provide reference for transportation planning and regional development in Fujian Province.

Research on Case-Based Reasoning for Energy-Efficient Rail Transit Emergency Timetables

The continuous expansion of rail transit lines has led to a significant increase in energy consumption. Choosing an appropriate timetable based on external conditions of train operation can reduce energy consumption by more than 5%. When emergencies occur in rail transit, external conditions change, rendering the original timetable unable to meet energy-saving requirements. This paper primarily explores how to quickly determine an energy-efficient timetable based on historical energy-saving timetable data and current external conditions using case-based reasoning methods. The paper represents emergency event timetable cases using a framework approach, and then employs a retrieval algorithm based on the grey correlation degree method to search for cases. The energy-efficient timetable for the target case is selected from the retrieved source cases, considering the one with the lowest energy consumption.

Estimating Public Transportation Accessibility in Metropolitan Areas: A Case Study and Comparative Analysis

Accessibility-oriented public transportation planning can improve the operational efficiency of public transportation, guide orderly urban development, and alleviate issues such as traffic congestion, environmental pollution, and resource consumption in large cities. To promote the practical application and widespread adoption of public transportation accessibility estimating systems, this study proposes an improved public transport accessibility levels (PTAL) method. It innovatively incorporates residents’ preference indices for different modes of transportation and addresses the challenge of missing timetable data in the calculation process. Using actual data from Shenzhen, a case study is conducted to analyze the public transportation accessibility index and compare the results obtained through k-means clustering, the equal spacing method, the quantile method, and the application of the London PTAL method. The research findings indicate that the optimal number of clusters for public transportation accessibility index analysis in large cities is six when using clustering algorithms. Among the statistical analysis methods, the quantile method shows favorable performance. Furthermore, a comprehensive comparison of different classification methods confirms that the improved PTAL method offers better discrimination in estimating public transportation accessibility levels compared to the London PTAL method. The study concludes by providing guidance on how cities with different characteristics can reference the improved PTAL method.

A robustness assessment with passenger flow data of high-speed rail network in China

Many existing studies on the robustness assessment of high-speed rail (HSR) networks use HSR services derived from timetable data as an alternative indicator of actual passenger flow. This alternative method has inherent limitations because of the various capacities and seat allocations among different HSR services operated between the same station pairs, leading to a biased evaluation of network robustness. Using an actual ticket sales dataset and HSR timetable data, this study integrates passenger volume into the robustness evaluation of China's HSR network. Moreover, this study proposes systematic methods to assess the robustness of HSR networks at the station and large-scale network levels, with procedure indicators. We design random failure and target attack simulation scenarios in un-weighted, travel-time-weighted, and flow-weighted networks. Simulation is conducted from 2014 to 2016, and with a medium-and long-term HSR network plan in China. Using the methodological advances and empirical studies, we find that China's HSR network became less robust first, and then enhanced robustness strength during network expansion. The significance of integrating passenger flow data in the robustness assessment is confirmed, as the same attack strategy may affect the total travel time and the number of passengers delivered differently. In addition, the proposed indicators are useful for capturing the procedure failure of HSR networks and critical stations.

Improving service use through prediction modelling: a case study of a mathematics support centre

Abstract In higher education, student learning support centres are examples of walk-in services with nonstationary demand. For many centres, the major expenditure is tutor wages; thus, optimizing tutor numbers and ensuring value for money in this area are key. In University College Dublin, the mathematics support centre (MSC) has developed a software system, which electronically records the time each student enters the queue, their start time with a tutor and time spent with a tutor. In this paper, we show how data analysis of 25,702 student visits and tutor timetable data, spanning 6 years, is used to identify busy and quiet periods. Prediction modelling is then used to estimate the waiting time for future MSC visitors. Subsequently, we discuss how this is used for staffing optimization, i.e. to ensure there is sufficient coverage for busy times and no resource wastage during quieter periods. The analysis described resulted in the MSC reducing the number of queue abandonments and releasing funds from overstaffed hours to increase opening hours. The methods used are easily adapted for any busy walk-in service, and the code and data referenced are freely available: https://github.com/ehoward1/Math-Support-Centre-.

The Design of Augmented Reality Using Unity 3D Image Marker Detection for Smart Bus Transportation

Public bus transportation has become an integral part of society, but the disrup-tion of bus services is one of the major concerns. This project presents the devel-opment of Smart Bus Transportation using Augmented Reality (TRANSPAR) that was developed on a mobile application. One of the major issues with public transportation is on real-time responsiveness. Most bus schedules are presented online but customers still faced many failures. Some bus schedules are not updat-ed when changes happened through time. Some existing bus schedules system is fixed to the bus stations. This research is to identify the bus schedules and its routes characteristics. A 3D AR animation based on identified characteristics was designed using the Unity 3D image marker detection on a mobile Android plat-form. A smartphone application was developed using Vuforia and Google Fire-base. TRANSPAR shows an AR mobile application for acquiring the bus time-tables. The phone camera is applied for marker image detection and scanning the bus station’s images. AR and normal image scanner were designed. Google Fire-base Database is used to retrieve and store each timetable data for every bus sta-tion. Analysis of interactivity and benefits of TRANSPAR shows about 90% agreed on the use of AR and more than 76% agreed on its functionality based on 50 taken samples. This shows a positive impact on the designed TRANSPAR. The research is significant to encourage and experience the public with new tech-nological application for public transportation and it impact the society.

Linking Loyalty Card Data to Public Transport Data to Explore Mobility and Social Exclusion in the Older Population

Inequalities in the provision of public transport and the accessibility of both public transport services and key facilities can impact wellbeing and increase social exclusion. This study explores the relationship between the provision of public transport services, mobility and social exclusion by utilising loyalty card transaction data to estimate the activity spaces of the older population in the West Midlands (England) metropolitan area. Bus time table data were used to calculate bus service provision and travel times to retail areas. Regression analyses were then used to quantify the impacts that the provision and efficiency of public transport services have on the area of older cardholders’ activity spaces. The provision of public transport services was found to have a significant impact on the size of these activity spaces, along with several sociodemographic variables, including age, deprivation and health. The results suggested that access to reliable public transport increased the mobility levels of the older population and allowed for a wider range of retail opportunities to be accessed. Inequalities in the provision of public transport could therefore exacerbate the wider social issues that affect some of the most vulnerable groups of the population.

Estimating left behind patterns in congested metro systems: a Bayesian model

Purpose During rush hours, many passengers find it difficult to board the first train due to the insufficient capacity of metro vehicles, namely, left behind phenomenon. In this paper, a data-driven approach is presented to estimate left-behind patterns using automatic fare collection (AFC) data and train timetable data. Design/methodology/approach First, a data preprocessing method is introduced to obtain the waiting time of passengers at the target station. Second, a hierarchical Bayesian (HB) model is proposed to describe the left behind phenomenon, in which the waiting time is expressed as a Gaussian mixture model. Then a sampling algorithm based on Markov Chain Monte Carlo (MCMC) is developed to estimate the parameters in the model. Third, a case of Beijing metro system is taken as an application of the proposed method. Findings The comparison result shows that the proposed method performs better in estimating left behind patterns than the existing Maximum Likelihood Estimation. Finally, three main reasons for left behind phenomenon are summarized to make relevant strategies for metro managers. Originality/value First, an HB model is constructed to describe the left behind phenomenon in a target station and in the target direction on the basis of AFC data and train timetable data. Second, a MCMC-based sampling method Metropolis–Hasting algorithm is proposed to estimate the model parameters and obtain the quantitative results of left behind patterns. Third, a case of Beijing metro is presented as an application to test the applicability and accuracy of the proposed method.

Navigability assessment of large-scale redesigns in nine public transport networks: Open timetable data approach

Redesigns of public transport networks are complex undertakings requiring a careful balancing of several conflicting perspectives (e.g., user requirements vs system performance) and aims (e.g., increasing spatial coverage, increasing frequency). Current assessment tools omit an explicit focus on navigability, often identified as a key aspect of the user perspective. For understanding the multidimensional perspective of navigability, this research introduces an assessment framework with both system and journey-level measures. The system-level measures provide an overview of redesigns based on static network representations. The journey-level measures are based on journey trajectories generated with a customized routing algorithm, assessing the distributive effects of the redesign. The framework is applied to public transport networks from nine cities with recently implemented redesigns, namely Amsterdam, Auckland, Austin, Baltimore, Columbus, Helsinki, Houston, Indianapolis, and Wellington. Results indicate that the redesigns have improved navigability both from a system-level and user perspective in general. However, in some cases, improvements in navigability come at the cost of increased travel time and number of transfers. Furthermore, the results suggest that the redesigns have differing emphasis within the regions, for different times of day, and for different aspects of network structure. The results are discussed both from the perspective of the case findings and for drawing more general planning and policy recommendations. Finally, this research provides a basis for further transdisciplinary approaches, encouraging connections between transport modeling and complex networks approaches.

Implementation of connection scan algorithm in tourism intermodal transportation journey planner: a case study

Accessibility to tourist destinations is an important component in a tourism system, especially for natural tourist destinations located in suburban areas. Good linkage of travel information and physical connections with local transportation services for intercity travel can facilitate more people to travel and promote national tourism destinations. This research takes the popular national tourism destinations and their public transportation service in Taiwan as a research object due to the unavailability of integrated public transport information service. Free Independent Travelers (FIT) demand is growing. This research aims to integrate intermodal public transportation information to support FIT by proposing a seamless way journey planner. In this scenario, the journey planner requires timetable data as input. The Connection Scan Algorithm is used to find the earliest arrival time routes at their destinations. This journey planner is built in PHP language and can complement the official tourism travel information website by Tourism Bureau, MOTC. Hence, the FIT could get the quickest routes to reach the destinations without compiling the public transportation information provided independently.

Properties of Chinese railway network: Multilayer structures based on timetable data

Railway is a main transportation mode for medium-distance and long-distance travel in China, which provides various services to satisfy different type of traffic demand. Recent studies exhibit that railway network is a complex network that has hierarchical structures. In this paper, we employ a network-based method to evaluate the structure of railway network as a multilayer network according to railway routes with different prefix letter. A node failure process is proposed to identify important stations. Results show that the degree of nodes follows a shifted power law distribution and a few nodes have large degree. The railway network is an assortative network with obvious community structures. Moreover, the connection among layers is heterogeneous. Dynamic node failure process shows that nodes with large degree play key roles in the railway network. Finally, we analyze the railway network performance from 2014 to 2018. The results imply that the efficiency increases year by year, which mainly because more and more conventional stations have been built to comprehensive stations that also serve high speed railway lines.

Does China's high-speed rail development lead to regional disparities? A network perspective.

This research examines whether cities are getting more equally accessible and connected via high-speed rail (HSR) in China over the period from 2010 to 2015. Existing studies mainly use network centralities to describe the spatial pattern of HSR network without measuring the spatial disparity of these centralities, and most of them rely on the infrastructure network and thus fail to incorporate HSR service quality in the centrality measures. Using HSR timetable data, we incorporate both scheduled travel time and daily frequency of each origin-destination city pair into three centrality measures and further quantify their inequalities using Theil's T index. We find that as the HSR network expands, cities appear to be more equal in terms of accessibility, but their disparities in connectivity and transitivity depend on the dimensions of comparison. In general, although the difference between economic regions or between megalopolises has reduced, small/medium-sized cities not belonging to any major city cluster are further lagged behind in HSR development. The difference between core and non-core cities in the same megalopolises has decreased despite that non-core cities are increasingly relying on core cities to access other regions.

Generating Travel Plan Sets in a High-Speed Railway Network With Complex Timetables and Transfers

Developments in high-speed railways have seen them become the mainstay of passenger transport systems in China, satisfying the increasing demand for rapid access to numerous locations. In practice, the operation of the network is a vital feature of the Chinese High-speed Railway (CHR), enabling the provision of through services while allowing indirect services via major hubs. The generation of travel plan sets in a large-scale, complex high-speed railway network is an important issue, as it is the foundation and key to those travel service systems where people can inquiry about their travel plans. This paper describes a method of generating all alternative travel plans that are potentially attractive to passengers. The inherent problem is first modeled for any journey between a given origin and destination pair, and then a spatiotemporal network is constructed. For each journey, a direct set of travel plans without transfers is extracted from the train timetable data. The construction of indirect travel plans is more complicated, and a two-stage generation method is proposed: (i) compute the $K$ -shortest paths using the improved Yen* algorithm to identify transfer node(s), and (ii) connect train services according to the train timetables. Furthermore, the reliability of a specific travel plan with transfers is determined by mapping the buffer time as a reliability measure. Finally, the proposed method is tested on the CHR network using the 2019 train timetable.

Inter-city connections in China: High-speed train vs. inter-city coach

High-speed train (HST) and inter-city coach (ICC) have been two important ground transportation modes for travelling between cities in China. They influence inter-city connections significantly. This study uses HST's and ICC's timetable data to construct networks; evaluates city centrality and city-pair connectivity to compare the hierarchical structures. The results show that the HST network shows linear distribution characteristics while ICC network presents regional “core-periphery” structure. Provincial administrative boundaries have an obvious constraint on the ICC network, while the HST community structure follows the railway lines' distribution. Finally, this study illustrates the spatial organization model and gives implications for regional transportation planning.

Effects from usage of pre-trip information and passenger scheduling strategies on waiting times in public transport: an empirical survey based on a dedicated smartphone application

Waiting times are important indicators of the degree of travel time optimisation and other behavioural traits among public transport (PT) passengers. As previous studies have shown, the level and usage of pre-trip information regarding schedule or real-time departures are important factors that influence the potential to realise travel time savings by enabling PT passengers to optimise waiting times. Most empirical evidence regarding the revealed PT travel behaviour concerning information levels is based on manual interviews or traditional travel surveys, in which there is a risk that the actual context of where and when the choice of departure time was made is not taken into account. This paper reports the results of a travel survey based on a dedicated smartphone application applied in a field study in a Swedish mid-size urban and regional context. Context-aware notification prompting was used to allow respondents to state their use of pre-trip information as well as whether they had pre-planned their trip and how contingent planning aids were used for time optimisation. The implications on passenger waiting times of the use of information regarding departure times by passengers were emphasised during analyses of the resulting data, along with personal characteristics, in which auxiliary sources such as timetable data and Automatic Vehicle Location were utilised to determine ground truth trip trajectories and trip-contextual factors. The results indicate the significance of having access to pre-trip information, especially for long trips above one hour’s duration, in order to pre-plan and thereby optimise waiting times. In addition, the use and source of pre-trip information differ among age and gender groups. Trip purpose and time of day to some extent determine waiting times and choice of trip optimisation strategy (arrival or departure time).