Public Transit Network Research Articles

Identifying Critical Components of a Public Transit System for Outbreak Control

Modern public transport networks provide an efficient medium for the spread of infectious diseases within a region. The ability to identify components of the public transit system most likely to be carrying infected individuals during an outbreak is critical for public health authorities to be able to plan for outbreaks, and control their spread. In this study we propose a novel network structure, denoted as the vehicle trip network, to capture the dynamic public transit ridership patterns in a compact form, and illustrate how it can be used for efficient detection of the high risk network components. We evaluate a range of network-based statistics for the vehicle trip network, and validate their ability to identify the routes and individual vehicles most likely to spread infection using simulated epidemic scenarios. A variety of outbreak scenarios are simulated, which vary by their set of initially infected individuals and disease parameters. Results from a case study using the public transit network from Twin Cities, MN are presented. The results indicate that the set of transit vehicle trips at highest risk of infection can be efficiently identified, and are relatively robust to the initial conditions of the outbreak. Furthermore, the methods are illustrated to be robust to two types of data uncertainty, those being passenger infection levels and travel patterns of the passengers.

Light rail transit (LRT) and transit villages in Qatar: A planning strategy to revitalise the built environment of Doha

For the past several decades, the car has been the most popular method for travelling within cities, small towns and rural areas. This has caused increasing traffic congestion, which in turn has become a drain on the economy of cities due to lost productivity. Additionally, the impact of climate change, the consequent need of reducing greenhouse gas emissions and the wide fluctuations in the price of oil are prompting cities around the world to make large investments in urban public transit. The implementation of public transport has become a critical issue for the urban development of cities: more communities are planning light rail transit systems (LRT) and transit villages (TODs) as part of an integrated transport and land use planning approach for revitalising cities. Middle Eastern cities, known for their extreme dependence on cars, are currently investing large sums on public transit systems. In the past two decades, Doha, the capital city of Qatar, has witnessed unforeseen economic growth due to oil and natural gas production and export. Currently the nation is developing a major urban public transit network consisting of the Doha Metro, the Lusail LRT and a bus rapid transit system. This paper explores: (1) how public transit systems and land use fit into the larger challenge of urban transformation and regeneration (2) the progress and implications of the development of the new LRT system for the planning of Doha’s built environment, and (3) the extent to which TODs are strategies for sustainable urbanism and smart growth in Qatar.

A Frequency-Based Assignment Model under Day-to-Day Information Evolution of Oversaturated Conditions on a Feeder Bus Service

Day-to-day information is increasingly being implemented in transit networks worldwide. Feeder bus service (FBS) plays a vital role in a public transit network by providing feeder access to hubs and rails. As a feeder service, a space-time path for frequent passengers is decided by its dynamic strategy procedure, in which a day-to-day information self-learning mechanism is identified and analyzed from our survey data. We formulate a frequency-based assignment model considering day-to-day evolution under oversaturated conditions, which takes into account the residual capacity of bus and the comfort feelings of sitting or standing. The core of our proposed model is to allocate the passengers on each segment belonging to their own paths according to multi-utilities transformed from the time values and parametric demands, such as frequency, bus capacity, seat comfort, and stop layout. The assignment method, albeit general, allows us to formulate an equivalent optimization problem in terms of interaction between the FBS’ operation and frequent passengers’ rational behaviors. Finally, a real application case is generated to test the ability of the modeling framework capturing the theoretical consequents, serving the passengers’ dynamic externalities.

A visual segmentation method for temporal smart card data

ABSTRACTIn many cities, worldwide public transit companies use smart card system to manage fare collection. Analysis of this acquisitive information provides a comprehensive insight of user's influence in the interactive public transit network. In this regard, analysis of temporal data, describing the time of entering to the public transit network is considered as the most substantial component of the data gathered from the smart cards. Classical distance-based techniques are not always suitable to analyze this time series data. A novel projection with intuitive visual map from higher dimension into a three-dimensional clock-like space is suggested to reveal the underlying temporal pattern of public transit users. This projection retains the temporal distance between any arbitrary pair of time-stamped data with meaningful visualization. Consequently, this information is fed into a hierarchical clustering algorithm as a method of data segmentation to discover the pattern of users.

Expresiones pictóricas urbanas: paisaje urbano y configuración espacial en Iztacalco

<p>Este artículo se enfoca en las prácticas pictóricas localizadas en el paisaje urbano de Iztacalco. A través de sistemas de información geográficas se pretende encontrar patrones de localización o relaciones de proximidad entre los edificios educativos, el espacio público o la red de transporte público, y la presencia de prácticas pictóricas en los muros de las edificaciones, sin perder de vista la aceptación, familiaridad y tolerancia de los habitantes hacia este tipo de prácticas.</p>

Multi-objective integration of timetables, vehicle schedules and user routings in a transit network

The Transit Network Timetabling and Scheduling Problem (TNTSP) aims at determining an optimal timetable for each line of a transit network by establishing departure and arrival times at each station and allocating a vehicle to each timetable. The current models for the planning of timetables and vehicle schedules use the a priori knowledge of users’ routings. However, the actual route choice of a user depends on the timetable. This paper solves the TNTSP in a public transit network by integrating users’ routings in the model. The proposed formulation guarantees that each user is allocated to the best possible timetable, while satisfying capacity constraints. In addition, we perform a trade-off analysis by means of a multi-objective formulation which jointly optimizes the operator’s and the users’ criteria.

Mapping bus transit services in Hyderabad – an illustrative example of the use of open geospatial data

Abstract Most public transit agencies in India do a poor job of making even basic route information available to the public. The transit mapping exercise reported here demonstrates that crowd-sourcing can be used to generate useful data at very low cost. Bus routes, including route frequency information, are identified and mapped, and the maps used to identify areas of the city which are not well served by the transit system. Available information about the socio-economic status of the underserved areas is used to fulfill a secondary objective of showing that mapping of public transit and paratransit networks can offer insights into how considerations of equity, access and safety can be integrated into transit planning in urban areas where spatial data is hard to come by, especially in cities of the global south

A multimodal transport network model and efficient algorithms for building advanced traveler information systems

Abstract: Route planning in urban public transport systems constitutes a common decision problem faced by travelers. Therefore, building Advanced Traveler Information Systems (ATIS) that provide passengers with pre-trip information on navigating through the network has become a certain need. Since passengers do not only seek a short-time travel, but they endeavor to optimize other criteria such as cost and effort, an efficient routing system should incorporate a multiobjective analysis for both routes and transport modes. We propose in this paper a new formulation that adequately allows representing a public transit network, as well as, yielding correct results when applying routing algorithms. Based on this formulation, we develop a multicriteria routing algorithm to determine the entire set of nondominated solutions to solve an itinerary planning problem. We introduce also several enhancement strategies to accelerate the algorithm's search process. As transportation modes, we focus on Railway, bus, tram and pedestrian. As optimization criteria, we use travel time, number of transfers and the total walking time. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that test problems were solved within reasonable amount of time and the new approach is efficient enough to be integrated within a real world journey-planning system.

Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

The increasing availability of public transit smart card data has enabled several studies to focus on identifying passengers with similar spatial and/or temporal trip characteristics. However, this paper goes one step further by investigating the relationship between passengers’ spatial and temporal characteristics. For the first time, this paper investigates the correlation of the spatial similarity with the temporal similarity between public transit passengers by developing spatial similarity and temporal similarity measures for the public transit network with a novel passenger-based perspective. The perspective considers the passengers as agents who can make multiple trips in the network. The spatial similarity measure takes into account direction as well as the distance between the trips of the passengers. The temporal similarity measure considers both the boarding and alighting time in a continuous linear space. The spatial-temporal similarity correlation between passengers is analysed using histograms, Pearson correlation coefficients, and hexagonal binning. Also, relations between the spatial and temporal similarity values with the trip time and length are examined. The proposed methodology is implemented for four-day smart card data including 80,000 passengers in Brisbane, Australia. The results show a nonlinear spatial-temporal similarity correlation among the passengers.

Understanding commuting patterns using transit smart card data

Commuting reflects the long-term travel behavior of people and significantly impacts urban traffic congestion and emission. Recent advances in data availability provide new opportunities to understand commuting patterns efficiently and effectively. This study develops a series of data mining methods to identify the spatiotemporal commuting patterns of Beijing public transit riders. Using one-month transit smart card data, we measure spatiotemporal regularity of individual commuters, including residence, workplace, and departure time. This data could be used to identify transit commuters by leveraging spatial clustering and multi-criteria decision analysis approaches. A disaggregated-level survey is performed to demonstrate the effectiveness of the proposed methods with a commuter identification accuracy that reaches as high as 94.1%. By visualizing the spatial distribution of the homes and workplaces of transit commuters, we determine a clear disparity between commuters and noncommuters and confirm the existence of job–house imbalance in Beijing. The findings provide useful insights for policymakers to shape a more balanced job–housing relationship by adjusting the monocentric urban structure of Beijing. In addition, the extracted individual-level commuting patterns can be used as valuable information for public transit network design and optimization. These strategies are expected to reduce car dependency, shorten excess commute, and alleviate traffic congestion.

An advanced GA–VNS combination for multicriteria route planning in public transit networks

Nowadays, passengers in urban public transport systems do not only seek a short-time travel, but they also ask for optimizing other criteria such as cost and effort. Therefore, an efficient routing system should incorporate a multiobjective analysis into its search process. Several algorithms have been proposed to optimally compute the set of nondominated journeys while going from one place to another such as the generalization of the algorithm of Dijkstra. However, such approaches become less performant or even inapplicable when the size of the network becomes very large or when the number of criteria considered is very important. Therefore, we propose in this paper an advanced heuristic approach whereby a Genetic Algorithm (GA) is combined with a Variable Neighborhood Search (VNS) to solve the Multicriteria Shortest Path Problem (MSPP) in multimodal networks. As transportation modes, we focus on railway, bus, tram and pedestrian. As optimization criteria, we consider travel time, monetary cost, number of transfers and the total walking time. The proposed approach is compared with the exact algorithm of Dijkstra, as well as, with a standard GA and a pure VNS. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that the proposed combination GA–VNS represents the best approach in terms of computational time and solutions quality for a real world routing system.

Cascading failures coupled model of interdependent double layered public transit network

Taking urban public transit network as research perspective, this work introduces the influences of adjacent stations on definition of station initial load, the connected edge transit capacity, and the coupled capacity to modify traditional load-capacity cascading failures (CFs) model. Furthermore, we consider the coupled effect of lower layered public transit network on the CFs of upper layered public transit network, and construct CFs coupled model of double layered public transit network with “interdependent relationship”. Finally, taking Jinan city’s public transit network as example, we give the dynamics simulation analysis of CFs under different control parameters based on measurement indicator of station cascading failures ratio (abbreviated as CF) and the scale of time-step cascading failures (abbreviated as [Formula: see text]), get the influencing characteristics of various control parameters, and verify the feasibility of CFs coupled model of double layered public transit network.

An Analysis of Public Transit Connectivity Index in Tehran. The Case Study: Tehran Multi-Modal Transit Network

Public transit is a major priority in modern management of large cities and metropolitan areas in particular. Public transit systems in such cities consist of a large number of nodes and lines which are represented by a complex network. A station for public transit is a bridge between the people and public transit network, based on which, the concept of input and output power for each station can be defined. The objective of this study is the application of the transit connectivity indices to the multimodal transit network in the city of Tehran. The public transit network data employed in this study is taken from Tehran Traffic Control Company, and Tehran Urban and Suburban Railway Operation Company. The methodology for measuring transit connectivity consists of three measures: Node connectivity, Line connectivity and Regional connectivity, where activity density is applied to these measures. The results of node connectivity analysis shows that most of the node connectivity in concentrated in the city center with many nodes in the center along routes going north and south. The line connectivity analysis shows that there is a concentration of highly connected lines that are near Tehran municipality region 12 and 16. Finally, we find that areas with more metro and bus facilities with respect to the other areas, have a better regional connectivity. One of these areas includes Sadeghiyeh Metro Station which is the junction of Tehran Metro Line 2 and Tehran Metro Line 5 which have a high connectivity power. Results of this study can be used to suggest some ideas on how future investments in rail and bus should be prioritized. Particularly in Transit Oriented Development (TOD) and sustainble development projects, urban planners can design transit stations with high performance to access the crucial services in poor areas.

Moving freight on public transit: Best practices, challenges, and opportunities

ABSTRACTFreight on Transit (FOT) refers to an operational strategy where public transit vehicles and/or infrastructure are used to move freight. Examples include moving goods alongside passengers on buses, attaching cargo trailers to transit vehicles, and operating freight vehicles between trains on subway lines. This paper describes the methods and results of a three-round Delphi study engaging 34 transportation experts to explore challenges and opportunities of FOT and to conceptualize and evaluate potential FOT operations in Toronto. Traditional Delphi methods were used for the exploration of FOT challenges and opportunities, and a modified approach was formulated to integrate experts' opinions and develop new FOT concepts for Toronto. The results support previous claims that technical challenges of FOT may be easier to overcome than institutional barriers. Evaluation of potential FOT operating strategies in Toronto suggests that while the current public transit network does not have capacity to support additional movements, there may be realistic opportunities to include freight service in future projects as a means of offsetting operating costs and reducing the impacts of goods movements.

Evolution of weighted complex bus transit networks with flow

Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.

A utility-based travel impedance measure for public transit network accessibility

A utility-based travel impedance measure is developed for public transit modes that is capable of capturing the passengers’ behaviour and their subjective perceptions of impedance when travelling in the transit networks. The proposed measure is time-dependent and it estimates the realisation of the travel impedance by the community of passengers for travelling between an origin–destination (OD) pair.The main advantage of the developed measure, as compared to the existing transit impedance measures, relates to its capability in capturing the diversity benefit that the transit systems may offer the society of travellers with different traveling preferences. To clarify the necessity of such capability, we demonstrate the randomness (subjectivity) of travel impedance perceived by transit passengers, through evidence from the observed path choices made in the transit network of the greater Brisbane metropolitan region in Australia.The proposed impedance measure is basically a nested logit “logsum” composition over a generated set of reasonable path options whose systematic utilities are evaluated based on a discrete choice model previously developed and calibrated for the greater Brisbane transit passengers. As a case study, the proposed impedance measure is calculated for all the origin blocks in the Brisbane area, during the morning commutes to the Central Business District (CBD). The results are presented and discussed, and intuitive and important advantages are demonstrated for the proposed measure.

An Evaluating Method of Public Transit Accessibility for Urban Areas Based on GIS

To calculate the public transit accessibility value and its differences in a particular area to surrounding regions, this paper raised a new concept-- Area Public Transit Accessibility (APTA). This concept is based on the passenger travel behavior, travel psychology hypothesis and the service range of transit network and road network. By using the software –ArcGIS, this paper established the formula and showed the calculation steps. This method was applied to evaluate the accessibility values of every traffic zone in Beijing Chaoyang district. The results showed that the APTA can give us a clear description of the accessibility value level in a particular area to surrounding regions, which provides quantitative information for location analysis and public transit network optimization

Cascading Failure of Urban Weighted Public Transit Network under Single Station Happening Emergency

In order to construct weighted public transit network, this work introduces bus routes density and departure frequency relative value to modify the traditional space L method. Then, the station initial load and capacity are defined based on station intensity, and the cascading failure model of weighted public transit network under single station deliberate attacks is given. Finally, Jinan city's weighted public transit network is taken as sample to analyze the station intensity distribution, which can get that the intensity average value and variance of stations are both divergent, the network follows power-low distribution with exponent λ = 1.66 and has the characteristics of scale-free network; by case simulation experiment, we give the cascading failure simulation analysis under three kinds of station attack strategies, get the intensity value size of failure station, the station load tolerance parameter α and the station load distribution control parameter β having important influences on cascading survivability, and verify the feasibility of cascading failure model.

Development of Urban Public Transit Network Structure Integrating Multi-Class Public Transit Lines and Transfer Hubs

Development of Urban Public Transit Network Structure Integrating Multi-Class Public Transit Lines and Transfer Hubs

Multiobjective Optimization Method of Public Transit Networks Based on Travel Behavior

AbstractThe optimization of transit network can improve transit efficiency and traffic conditions as well as reduce environmental pollution. Transit network optimization that considers the travel behavior of bus passengers comprises four steps. First, a bypass strategy is employed to optimize the bus lines according to the situation of urban traffic congestion. Second, direct ratio is calculated to determine the optimal network. Third, minimum transfer is sought by using the space P method to construct and analyze a transit network model. In building a network adjacency matrix, the Floyd algorithm is used to obtain the matrix of minimum transfers between two stops. The Breadth-First Search algorithm is used to search the bus routes with the shortest transfer time. Fourth, all the bus lines of the same origin-destination are optimized to obtain the shortest travel time. Numerical experiments show that the proposed method can reasonably and efficiently achieve network optimization within a given area.