Urban Transit System Research Articles

Design a robust joint service of integrating demand responsive connectors (DRC) and shared bikes

ABSTRACT The integration of shared bikes and flexible transit services offers significant potential and benefits for urban transit systems. This paper presents a novel robust optimization framework for designing demand responsive connectors (DRC) that are fed by shared bikes. A two-stage model is formulated, with the first stage focusing on prereserved requests and the second stage addressing en-route random requests. The findings demonstrate that incorporating shared bikes can enhance the efficiency of serving en route late requests and reduce the required DRC vehicle fleet size. Additionally, it is observed that e-bikes are particularly suitable for this joint service due to their adaptability.

Control system for automatic adjustment of departure intervals of public transport vehicles

In numerous urban areas, fine-tuning bus schedules to align with fluctuating passenger numbers poses a sizable challenge, compounded by diverse factors such as weather and unexpected events. This paper conducts a comprehensive examination of existing optimization methods aimed at enhancing the departure intervals of public transport systems, particularly focusing on the Bidirectional Coordination Optimization Algorithm (BCOA) and the genetic algorithm. Through systematic analysis, these methodologies are meticulously explored for their effectiveness in addressing the scheduling challenges faced by urban transit systems. Additionally, it delves into specific instances where the Proportional-Integral-Derivative (PID) control mechanism has been employed to adeptly adjust the timing of bus departures based on real-time demand. The PID system's proven reliability and efficacy spotlight its potential as a transformative tool for advancing public transit systems. By leveraging such technology, there's tangible optimism for achieving a more responsive, efficient, and passenger-friendly public transportation network in the years to come.

SPATIAL PARAMETERS FOR MULTIMODAL INTEGRATION AT METRO STATIONS: A CONDUCIVE CASE STUDY OF METRO STATIONS IN DELHI

Urban public transit contributes to the multisector growth of a developing economy and is an essential component of sustainable urban development. Public transit has seen considerable upsurge in ridership over the past decade. In order to make public transportation more appealing to regular commuters than private automobiles, the current infrastructure in the majority of India's metropolitan cities must be modernized in a sustainable way. The Delhi Metro was designed with an identical objective in consideration, anticipating the increasing concern over traffic in and around the Delhi region, and after being implemented, it significantly reduced the number of individual vehicles on the road. Delhi Metro operates within the framework of the citywide multi-modal integrated transportation system, reinforced by other modes such as buses and IPT. It is the lifeline of Delhi's urban transit system. This research paper aims to study numerous aspects of multimodal integration at metro stations in order to increase the passengers' comfort and convenience. Convenient transitions in a multimodal transportation system are greatly aided by station amenities like the physical infrastructure, which includes the parking space, transfer area, metro ridership data, pedestrian network, cycling facilities, feeder shelter, taxi stands (IPT Stands), etc. Furthermore, it intends to promote walkability by establishing a high-quality public realm that is both welcoming and secure for anyone to use. This research is centered around Delhi Metro stations on the Yellow line. An extensive inventory of the station area and a commuter travel time questionnaire were used to evaluate the current state of affairs. Several standards of design were analyzed to optimize the output of research. The inferences yield specific recommendations and modifications for the present system, in addition to identifying several best practices that could be reiterated across all stations.

Geo-spatial analysis of transit planning for sustainable tourism development in Bangkok, Thailand

ABSTRACT In the contemporary urban landscape, the rail mass transit system assumes a crucial role in shaping city tourism, primarily attributable to its commendable efficiency in ensuring safety and optimizing travel times. A comprehensive understanding of this system becomes imperative for formulating strategies aimed at augmenting the tourism potential it facilitates. Consequently, this study employs rigorous spatial analysis, concentrating on tourist attractions and their accessibility levels within the Transit-Oriented Development (TOD) center and an area outside the TOD center (non-TOD). The empirical results of this investigation unveil that the distribution characteristics of a substantial proportion of tourist attractions manifest clustering tendencies (Z-score exceeding 1.65), particularly in areas synonymous with historical and recreational activities. In contrast, specific activities such as cultural and agricultural pursuits exhibit a random distribution characteristic (Z-score between −1.65 and 1.65). Notably, it becomes apparent that activities characterized by distinct distribution patterns also harbour varying potentials for accessibility. In essence, this scholarly endeavour contributes nuanced insights into the intersection of urban rail mass transit systems and tourism dynamics, shedding light on the intricate relationships between spatial distribution, activity characteristics, and accessibility levels within diverse urban contexts.

Delayed effects of air pollution on public bike-sharing system use in Seoul, South Korea: A time series analysis

Background:As a complementary means to urban public transit systems, public bike-sharing provides a green and active mode of sustainable mobility, while reducing carbon-dioxide emissions and promoting health. There has been increasing interest in factors affecting bike-sharing usage, but little is known about the effect of ambient air pollution. Method:To assess the short-term impact of daily exposure to multiple air pollutants (PM2.5, PM10, NO2, and O3) on the public bike-sharing system (PBS) usage in Seoul, South Korea (2018–2021), we applied a quasi-Poisson generalized linear model combined with a distributed lag nonlinear model (DLNM). The model was adjusted for day of the week, holiday, temperature, relative humidity, and long-term trend. We also conducted stratification analyses to examine the potential effect modification by age group, seasonality, and COVID-19. Results:We found that there was a negative association between daily ambient air pollution and the PBS usage level at a single lag day 1 (i.e., air quality a day before the event) across all four pollutants. Our results suggest that days with high levels of air pollutants (at 95th percentile) are associated with a 0.91% (0.86% to 0.96%) for PM2.5, 0.89% (0.85% to 0.94%) for PM10, 0.87% (0.82% to 0.91%) for O3, and 0.92% (0.87% to 0.98%) for NO2, reduction in cycling behavior in the next day compared to days with low levels of pollutants (at 25th percentile). No evidence of effect modification was found by seasonality, age nor the COVID-19 pandemic for any of the four pollutants. Conclusions:Our findings suggest that high concentrations of ambient air pollution are associated with decreased rates of PBS usage on the subsequent day regardless of the type of air pollutant measured.

Data-Driven Approach to State of Good Repair: Predicting Rolling Stock Service Life with Machine Learning for State of Good Repair Backlog Reduction and Long-Range Replacement Cost Estimation in Small Urban and Rural Transit Systems

This paper presents a data-driven approach to address the state of good repair (SGR) in small urban and rural transit systems in the U.S. by predicting the service life of rolling stock vehicles. Achieving and maintaining public transportation rolling stock in SGR is crucial to providing safe and reliable services to riders, particularly for transit agencies utilizing federal grants that mandate asset maintenance at a full level of performance. In this context, an intelligent predictive model is proposed to analyze the transportation rolling stock, determine their backlog and current condition, predict their replacement or rehabilitation needs, and estimate the funding required for future replacements, ensuring SGR. The model utilizes historical data from retired revenue vehicles in the National Transit Database and employs machine learning techniques, specifically random forest regression and gradient boosting regression, to develop the predictive tool. By addressing the backlog of vehicles beyond their service lives and estimating replacement costs, the model provides valuable decision support for effective transit asset management. Transit agencies in small urban and rural transit systems, lacking analytical tools for service life prediction, can greatly benefit from this simple yet powerful predictive model. The approach offers a practical resource to address SGR needs, optimize investment prioritization, and enhance operational efficiency in public transit systems.

Electric bus arrival and charging station placement assessment using machine learning techniques

ABSTRACT The goal of this study is to create a dynamic model that can accurately forecast the estimated time of arrival of a bus at a specific bus stop using data obtained from the global positioning system (GPS) in Addis Ababa, Ethiopia. Both operators and customers use accurate and timely bus arrival information. In turn, it helps determine the correct placement of charging stations when green-energy vehicles are introduced in the urban mass transit system. A plethora of machine learning models have been used for prediction. Additionally, we tailored an ensemble method based on the average prediction. The performances of these machine learning methods were estimated and compared using conventional measures, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) values. The results showed that the performance of the Random Forest technique used in this study was impressive, with MAE of 0.137, MSE of 0.054, RMSE of 0.233, and R2 value of 0. 999. The findings demonstrate that the proposed method is reliable for predicting the arrival times of urban and rural buses. Next, in order to position the charging points for Electric Vehicles (EV) along specific bus routes, self-organising map (SOM) is employed to optimise EV charging point placement locations, and the result signify better performance than the kNN and DBSCAN clustering approaches.

Analysis of Multi-Modal Public Transit Competing Relationships and Evolutionary Mechanisms in Cities in Cold Regions

In order to reveal the characteristics of the competition and cooperation of multi-modal public transportation in the cities of cold areas and to study the competitive relationship of multi-modal public transportation in cold areas, the urban public transportation system in cold areas is divided into three sub-systems, namely, conventional buses, urban mass transit, and cabs, to analyze the competitive relationship and evolution mechanism of multi-modal public transportation in cities of cold areas. We construct a multi-modal public transit Lotka–Volterra competitiveness model, considering that conventional buses and urban mass transit are mainly dominant in urban public transit systems in cold regions, and introduce the competitive influence effect coefficients and cooperative influence effect coefficients to analyze the mechanism of the evolution of the competitiveness between conventional buses and urban mass transit. The data on traffic supply and demand and travel of the Harbin bus system, a typical city in China, are collected, and the simulation analysis of multi-modal competing relationship and evolution mechanism of conventional bus and urban mass transit competition is carried out, respectively, with Harbin as the research object. The results show that the constructed model can better describe the multi-modal bus competition relationship and evolution mechanism.

Identification of key performance indicators using hybrid COPRAS-TOPSIS for urban public transit systems by evaluating with AHP and FAHP

Identification of key performance indicators using hybrid COPRAS-TOPSIS for urban public transit systems by evaluating with AHP and FAHP

Unsupervised Learning-Based Exploration of Urban Rail Transit Passenger Flow and Travel Pattern Mining

This study delves into the realm of urban rail transit systems, leveraging unsupervised learning techniques to analyze passenger flow characteristics and unearth travel patterns. Focused on the dynamic and complex nature of urban rail networks, the research utilizes extensive datasets, primarily derived from Automated Fare Collection (AFC) systems, to provide a comprehensive analysis of passenger behaviors and movement trends. Employing advanced algorithms like DBSCAN, the study categorizes passengers into distinct groups, including tourists, shoppers, thieves, commuters, and station staff. These classifications reveal intricate patterns in travel behaviors, significantly contributing to a deeper understanding of urban transit dynamics. The findings offer valuable insights into peak travel times, popular routes, and station congestion, highlighting potential areas for operational improvements and infrastructure development. The study’s application of unsupervised learning in analyzing vast, unstructured data sets a precedent in urban transportation research, showcasing the potential of artificial intelligence in enhancing the efficiency and sustainability of urban transit systems. The insights garnered are pivotal not only for optimizing current operations but also for shaping future expansion and adaptation strategies, ensuring urban rail systems continue to meet the evolving needs of growing urban populations.

Will gated community affect public transit service coverage?

The proliferation of gated communities (GC) has prompted researchers worldwide to investigate its causes and consequences. The phenomenon has been extensively discussed as a significant residential form in the world, but most of the studies are primarily concerned with non-technical issues such as inequality and segregation, while technical issues such as urban transit (PT) systems have not been fully discussed. The significance of accessibility-focused PT planning to establish a sustainable urban transportation system makes this issue a crucial topic to explore. PT is a public good expected to be accessible to everyone, and this means the distance between people and the system needs to be minimized to maximize the service coverage, but the GC design is observed to be increasing the distance of its residents willing to use this service. Therefore, this study analyzed the impact of GC on the PT system using Bogor Municipality as a case study through GIS (Geographical Information System) methodologies and spatial data. The quantitative evidence showed that GC has a relatively strong negative correlation with PT service coverage (r (66) = from -0.611 to -0.631, p <.001). This means PT service coverage tends to be low in areas with a high GC ratio and vice versa. It was also discovered that an increase in GC ratio is likely to cause a reduction in PT service coverage in the study area (Beta = -0.48, t(67) = -5.17, p =.000). This article concludes with a discussion of a few potential ideas based on the observations to alleviate the negative impact of GC.

Unleashing the Potential of Boosting Techniques to Optimize Station-Pairs Passenger Flow Forecasting

Station-pair passenger flow forecast modeling is crucial for public transportation to address emerging needs. The accurate prediction and estimation will provide backbone support for various features of transport, viz. (a) Demand forecasting for resource allocation during peak travel periods; (b) Service planning to determine transportation frequency on specific routes; (c) Resource allocation for vehicles and capacity adjustments, including planning for new fleet size; (d) Infrastructure planning to identify areas requiring additional development; (e) Enhancing passenger experience with ample seating and amenities; and (f) Emergency planning for crowd control and passenger safety. Recent advancements in transit information station-pair estimation methods can be implemented with Data Science. Over the years, there has been significant progress in developing innovative modeling approaches to estimate travel behaviour, taking into account various factors such as timeslots, holidays, and other relevant features. In this study, we have synthesized ticket data from Thane Municipal Transport (TMT), collected from e-ticket machines, spanning from January 1 to June 23, 2023, covering all routes and comprising more than 2.80 crore tickets. This paper presents a comprehensive station-pair short-term passenger flow forecast by utilizing three boosting models: extreme Gradient Boosting (XGBoost), Light Gradient Boosting model (LightGBM), and Categorical Boosting model (CatBoost). Their implementation is well-suited based on statistical parameters. The study concludes by comparing the results of the models, considering computation time and statistical parameters, to validate their superiority. By looking into day to day requirements, the LightGBM model is most appropriate for implementation with an accurate result. The research findings contribute to providing valuable data support and a robust scientific foundation for managing passenger flow through route planning and dispatching in urban transit systems.

Challenge and Prospect of High-speed Rail Radar Communication Integration Technologies

Radar plays an important role in urban public transit system, so it is of great significance to ensure the safe operation of High-speed rail. Radar communication integration technology has the characteristics of high efficiency and low energy consumption, it can improve communication quality, speed up transmission efficiency and improve detection accuracy. Firstly, this paper introduces the running scene of High-speed rail, and puts forward that radar communication integration technology has a broad application space in High-speed rail. Then it introduces radar communication integration technology, and the research status, advantages and development prospects of radar communication integration technology based on linear frequency modulation（LFM）. Finally, it is proposed that obstacle detection technology is one of the difficulties to ensure the safe operation of High-speed rail. Radar integration technology can be effectively applied to train obstacle detection, which is an important technical support for improving service quality and speeding up the intelligent construction of High-speed rail in the future.

Coordinated Control Strategy of Railway Multisource Traction System With Energy Storage and Renewable Energy

Influenced by the growing daily travel demand of citizens and extension of urban land, the construction of the urban railway transit (URT) system is gradually increasing nowadays. This situation implies more electricity consumption in URT and more challenges to URT operation stability. Therefore, based on the existing traction substation, this research proposes a configuration of multi-source traction system (MSTS) for URT with a coordinated control strategy according to power profile of the system. The proposed MSTS, including conventional traction system (CTS), renewable energy source (RES), and energy storage system (ESS), have been modelled first. After that, considering the system dynamic performance and ESS capacity, a coordinated control strategy is designed to manage the MSTS energy flow. The control strategy considers the substation voltage as control signal which is optimized by proposed performance index. The case study is conducted on a three-station railway route with two substations. The proposed MSTS with a coordinated control strategy is compared with normal CTS considering multi-train timetables with different departure intervals. The simulation result illustrates that the proposed MSTS can maintain the state of charge (SOC) of ESS and reduce the substation peak power and voltage fluctuation, which is verified both in short-term and long-term simulation. Meanwhile, it can improve the substation capacity by decreasing the energy consumption of CTS to deal with the increasing demand for URT. the case study denotes that the energy-saving rate can be up to 36.25%, and the peak power is reduced up to 46.32%.

Investigating the use of privately-owned micromobility modes for commuting in four European countries

Micromobility modes such as scooters, e-scooters, skateboards, or hoverboards has recently emerged as part of the urban landscape. In this paper, we analyze the use of modes of micromobility for commuting. We distinguish between monomodality (commuters using one mode of micromobility only) and multimodality (commuters using micromobility as a complement or substitute to other modes of transport). We apply non-parametric ordered methods to a survey that was conducted in 2018 on mobility users in four European countries. The survey gathered 4873 observations from commuters in France, Germany, Spain, and the United Kingdom (UK). Micromobility commuting is marginal in all four European countries. The sociodemographic characteristics of micromobility commuters are homogeneous and concern mainly male, young, and urban commuters. We find that travel habits account for a large share of the variability explained by the model. Germany has a low level of multimodality, whereas the UK practices complementarity-oriented multimodal commuting. Overall, our results bring new insights showing that micromobility is used as a (partial) substitute to urban transit systems for short distances and as a complement for longer commuting trips made by train. These emerging patterns of commuting require better modal integration between micromobility and public transport, and a more sophisticated design of transport infrastructures.

DAILY FLOWS, URBAN POLYCENTRICITY, AND TRANSIT COMMUTING PATTERNS: A STUDY OF SHANGHAI, CHINA

ABSTRACT Employment center locations, the jobs-housing relationship, and commuting patterns are inextricably connected in a megacity that heavily relies on the urban transit system to shuttle commuters. Using a transit smartcard dataset, a machine learning method is employed as a preliminary filter to sift through commuter flows and isolate key data, then kernel density and topological analysis methods are implemented to delve into the primary research questions, providing a detailed look at how these patterns unfold. The results show that transit commuting behavior in Shanghai is a dispersal activity based on multi-level employment centers characterized as hierarchical, boundary-clear, and functionally oriented. Further analysis illustrates that the intensity of commuting linkages correlates with the employment center level, shaping a hybrid pattern that couples a core-periphery pattern with a spoke-hub pattern. The commuting network connections present corridor, neighborhood, capture, and replacement features, highlighting the importance of employment centers in shaping commuting patterns. From a daily flow perspective, these findings echo the central place theory and verify that the employment center distribution in Shanghai is also multi-layered and well-nested, forming the basis for commuting links. Policy implications are provided for polycentric megacities with progressively sophisticated urban transit systems.

Revealing Senior Mobility Patterns and Activities in Urban Transit Systems

Urban mobility plays a crucial role in maintaining seniors’ well-being and quality of life, with urban transit systems serving as their primary mode of travel. This study focuses on revealing seniors’ travel patterns and activities and predicting their demands in transit systems. We propose a spatio-temporal travel pattern model that extends the structural topic model from text mining, to detect senior mobility patterns from trip sequences at the rail station level and discover their activities hidden in passively collected data from a probabilistic generative procedure. The proposed model can incorporate a wide range of spatio-temporal covariates that help estimate the relative importance of travel patterns across stations and over time. Numerical experiments are conducted using transit smart card data collected in Nanjing, China. The results demonstrate that our proposed methodology successfully transforms massive mobility records into informative travel patterns, each of which characterizes a distinct distribution of trip attributes. We combine land use characteristics of station areas with detected patterns to explore seniors’ behavioral strategies and activities at multiple spatial and temporal scales. Moreover, by applying prior detected patterns to the station-level ridership prediction, we significantly improve the predictive performance of direct ridership models. This study contributes to detecting seniors’ travel patterns, discovering latent activities, predicting station-level travel demands, and creating senior-friendly mobility systems.

Energy consumption characteristics and rooftop photovoltaic potential assessment of elevated metro station

The current trend of urbanization has spurred the growth of the urban railway transportation sector, leading to increased carbon emission and environmental challenges. Applying renewable energy assisted power supply systems into metro stations is a feasible method for achieving decarbonization in urban transit systems. This study comprehensively reveals the real energy profile of a metro station on an hourly scale and establishes a multi-objective model to investigate the energy flexibility of the metro station with integrated battery energy storage for on-site renewable electricity consumption maximization. The results show that the elevated station has a photovoltaic potential of 20 %–25 %, in terms of self-sufficiency rate, when the rooftop of the station is fully utilized for photovoltaic array installation. Moreover, a photovoltaic-battery system in addition to a multi-objective mixed inter linear programming model is presented for the circumstance, there existing idle land around the station, to achieve a near-zero carbon station. It is indicated that the near-zero carbon station can be achieved with a minimum payback period of 11.5 years by optimizing the photovoltaic array size and battery storage capacity, leading to the annual carbon emission reduced from 457.1 to 91.4 tons. It is anticipated this study could provide reference for clean electrification and decarbonization in urban metro system.

Benchmarking Travel Time and Demand Prediction Methods Using Large-scale Metro Smart Card Data

Urban mass transit systems generate large volumes of data via automated systems established for ticketing, signalling, and other operational processes. This study is motivated by the observation that despite the availability of sophisticated quantitative methods, most public transport operators are constrained in exploiting the information their datasets contain. This paper intends to address this gap in the context of real-time demand and travel time prediction with smart card data. We comparatively benchmark the predictive performance of four quantitative prediction methods: multivariate linear regression (MVLR) and semiparametric regression (SPR) widely used in the econometric literature, and random forest regression (RFR) and support vector machine regression (SVMR) from machine learning. We find that the SVMR and RFR methods are the most accurate in travel flow and travel time prediction, respectively. However, we also find that the SPR technique offers lower computation time at the expense of minor inefficiency in predictive power in comparison with the two machine learning methods.

Methodology to Detect Bus Stop Influence Zones Utilizing Facebook Prophet Changepoint Detection Method

Travel time of buses, a major part of the urban public transit system, is affected by various factors and foremost are the bus stops. In addition to the dwell time at the stop, the deceleration and acceleration zones reduce average speed, particularly in mixed traffic, and increase travel time. Several approaches are used in estimating the link-based travel time of public transit systems in transportation planning, but Bus Stop Influence Zones (BSIZs) are ignored. Moreover, fuel consumption and pollutant emissions increase in BSIZs, and this demonstrates the importance of BSIZs in measuring the performance of public transit systems as well as planning. Data obtained from the Automatic Vehicle Location system was used and visualized on Geographic Information System and data preprocessing steps were performed. Finally, changepoint detection method of Facebook Prophet (FBP-CDM) was exploited to identify changepoints in location-speed data on the selected route. Results were validated with real-life data and expert opinion, and then compared with the findings acquired by the K-means clustering. Based on the conclusions, FBP-CDM was found quite effective in detecting and predicting BSIZs accurately and the proposed methodology is useful for studies in transportation planning and operations.