Public Transit Network Research Articles

Multimodal Public Transit Network Design Method Based on Hub-and-Spoke Infrastructure

This paper presents a two-phase method for generating a hub-and-spoke network for a multimodal public transit network (MPTN). The first phase addresses the problem of clustering in MPTNs, which is a key operation in solving the hub location problem (HLP) in MPTN design. However, previous work often oversimplifies or neglects the clustering phase, so this study presents a spatial clustering algorithm that can process real urban MPTNs without reducing their size or dividing them into zones. The algorithm is tested on four large city datasets and compared with popular spatial clustering algorithms. In the second phase, a heuristic algorithm is presented that aims to maximize network coverage. The proposed method is tested using the MPTN of Greater London and the resulting hub-and-spoke network is compared with the Journey API provided by Transport for London. The results show that the total travel time is improved by 16.9% with a strict hubbing policy. Transportation planners can adapt the proposed method to their specific needs by changing the size and number of clusters using the cluster scaling factor ([Formula: see text]) parameter and including or excluding any criteria in the decision equations.

The Impacts of Road Traffic on Urban Carbon Emissions and the Corresponding Planning Strategies

Carbon emissions increase the risk of climate change. As one of the primary sources of carbon emissions, road traffic faces a significant challenge in terms of reducing carbon emissions. Many studies have been conducted to examine the impacts of cities on carbon emissions from the perspectives of urbanization, population size, and economics. However, a detailed understanding of the relationship between road traffic and urban carbon emissions is lacking due to the lack of a reasonable set of road traffic metrics. Furthermore, there have been fewer studies that have conducted cluster analyses of the impact factors, which will be supplemented in this research. We established 10 impact metrics, including the highway network system, city road network system, public transit system, and land use system of streets and transportation, using 117 county-level cities in Hebei Province as the study area, which is one of the regions in China with the most acute conflicts between economic development and the environment. We built an ordinary least squares (OLS) model, a spatial lag model (SLM), a spatial error model (SEM), a spatial Durbin model (SDM), and a geographically weighted regression (GWR) model, and performed a cluster analysis on the key metrics. The results are as follows: (1) The difference in spatial distribution of urban land-average carbon emissions is obvious, highly concentrated in the areas surrounding Beijing and Tianjin. (2) The GWR model has a higher R2 and a lower AICc than global models (OLS, SLM, SEM, and SDM) and performs better when analyzing the impact mechanism. (3) Highway network density, city road length, and density of the public transit network have significant effects on urban land-average carbon emissions, whereas the street and transportation land use systems have no significant effect, which indicates that the highway network and public transit systems should be prioritized. (4) The GWR model results show that the impact of the four metrics on the urban land-average carbon emissions exhibits clear spatial heterogeneity with a significant piecewise spatial distribution pattern. The highway network density has a relatively large impact on the northern region. The northwest is more affected by the density of the public transit network. The southwest is most impacted by the length of city roads. (5) The study area is divided into four distinct characteristic areas: the highway network dominant impact area, the public transit dominant impact area, the city road network dominant impact area, and the multi-factor joint impact area. Different traffic optimization strategies are proposed for different areas.

Queueing-based formulation model in a public transit network considering energy storage technology and demand charges

Battery Electric Buses (BEBs) are quickly emerging as a viable option for dramatically lowering transportation-related emissions. Finding optimal locations of charging stations for BEBs can facilitate the development of electric transit networks that provide continuous services. Using fast charging along with Energy Storage (ES) charger technologies has also garnered attention to mitigate the peak power demand. A consensus regarding two types of chargers may result in more investment in the electric transit network, which resulted in considering installing fast chargers in designing an electric transit network. While this study aims to consider two types of fast- and ES-chargers, along with demand charges and waiting times at terminal stops simultaneously, to minimize the total costs of the electric transit network. The queuing model integrated with the Mixed-Integer Non-Linear Programming (MINLP) model is proposed to locate the optimal placements of charging stations in the electric transit network. To validate the model, a benchmark Mumford0 network is applied. Results indicated that the proposed charging location model could efficiently determine optimal locations of fast- and ES-chargers across the electric transit network. Also, the results confirmed that considering ES chargers could significantly save 16.6 % of the total costs of the Mumford0 transit network and reduce the total demand charge imposed on the network by the fast charger. More interestingly, by purchasing more chargers, transit agencies can decide on an acceptable waiting time that would be comfortable for BEBs arriving at terminal stops.

Optimal design method of public transit network considering transfer efficiency

AbstractImproving the sharing rate of public transport and reducing operation costs are key factors affecting the sustainable development of the public transport system. After quantifying the impact of transfer, this study proposes a multi‐objective optimization model of bus networks considering the integral transfer efficiency and operation costs, whose optimal solution is hard to find. Since the heuristic algorithm has great global searchability, a non‐dominated sorted genetic algorithm (NSGA‐II) is adopted to solve this NP‐hard problem. Also, an adaptive mutation mode selection algorithm is proposed to enhance the solution efficiency. The optimization framework is applied to the benchmark case of the Swiss public transit network. The results show that the method significantly improves the transfer efficiency of the designed network when compared to existing studies. After calculation, it is found that enhancing the direct rate can improve transfer efficiency, but with high costs. Moreover, the proposed method can design an efficient and economic network to balance the benefits of passengers and operators in practice.

Building a socially-aware solution to the urban transit routing problem

Public transit ridership in the United States has been decreasing, which is especially true for bus ridership. Some of the factors that drive this decrease include fuel prices, housing density, and employment levels, but these are factors well beyond a transit agency’s control. A common reason riders cite for not taking public transit is a concern for personal well-being because the physical separation provided by a vehicle is not available in public transit, and with perceptions of safety contributing to declining ridership, agencies have limited resources available to address such concerns. So, where can public transit agencies make impacts? One potential solution is to optimize a public transit network to increase rider comfort. In this work, two new socially-aware objectives, the Familiar Stranger (FS) metric and the encounter network clustering (ENC) metric, are introduced for use in an urban transit routing problem (UTRP). The UTRP is solved using an evolutionary heuristic optimization. Empirically sourced origin-destination pairs from a university campus were used to represent potential transit users. The improved routes showed a 242% and a 119% increase for the FS and ENC metrics, respectively, compared to the existing transit network during the busiest week of the university campus’s term. The results of this work are a starting point to introduce additional socially-aware objectives in formulations used to solve the UTRP. Such objectives could serve to improve service without the funding demands of other service improvement efforts.

Optimal Placement of Battery Electric Bus Charging Stations Considering Energy Storage Technology: Queuing Modeling Approach

In recent years, there has been growing attention on the electrification of the public transit network. Battery electric buses (BEBs) are among the promising alternatives to replace diesel-powered buses. However, the possible driving range from a full charge has proved a matter of concern, as has the waiting times of BEBs returning to terminal stops after completing their journeys. This study aimed to design an efficient electric transit network considering waiting times at terminal stops and two configurations of charger to avoid BEBs running out of charge: a fast charger with energy storage (ES) technology and one without. A queuing-based mathematical model was proposed. To validate the proposed model, we tested it on two sizes of network: the Mumford0 (small) and the Mumford2 (large). By conducting a sensitivity analysis, certain model parameters, including the power of fast chargers, duration of service interval, BEB energy consumption, and maximum allowable waiting time were found to have substantial impacts on the electric public transit network. ES chargers were found to have the potential to save 15.35% of total costs. Other analyses confirmed that altering the capacity of fast- and ES chargers could affect the number of chargers required in the transit network and the total cost. Policies are suggested for transit agencies to plan to optimize their electric transit networks.

Carbonaceous electrode materials for supercapacitor: Preparation and surface functionalization

Supercapacitors became more and more important recently in the area of energy storage and conversion. Their large power deliveries abilities, high stability and environmental friendliness characteristics draw tremendous attention in high-power applications such as public transit networks. Carbonaceous materials with unique surface and electrochemical properties were widely used in supercapacitors as electrode materials. This review focuses on the developments in supercapacitor electrodes made from carbonaceous materials recently, their working principle and evaluation parameters were summarized briefly. The preparation methods and electrochemical properties of different carbonaceous materials were compared and classified. It was found that the surface situation (e.g., porous structure, hydrophilic) of carbonaceous materials strongly affect the electrochemical performances of supercapacitor. So far, active carbons would be the most applicable carbonaceous electrode materials owing to their good chemical stability and conductivity, extensive accessibility inexpensiveness. But their energy densities still fall behind practical demands. Both theoretical calculations and experimental studies show that surface modification and doping of carbonaceous materials can not only optimize their pore size, structure, conductivity and surface properties, but also can introduce extra pseudocapacitance into these materials. Considering global environmental pollution and energy shortage problems nowadays, we sincerely suggested that future work should focus on domestic, medical and industrial wastes residues derived carbonaceous materials and scaled production process such as reactors and exhaust gas treatment.

What determines modal substitution between bike-sharing and public transit? Evidence from Columbus, Ohio during the COVID-19 pandemic

This study explores the relationship between bike-sharing and public transit trips before and after COVID-19 lockdowns, focusing on Columbus, Ohio. Using CoGo Bike Share trip data together with Automatic Passenger Counter (APC) data from the Central Ohio Transit Authority (COTA), we first identify bike-share trips and public transit ridership patterns during this period and classify bike-share trips as substitutive and complementary to public transit trips. We then employ binary logit models to analyze the determinants of substitutive trips. Some key findings are as follows. First, the COVID-19 outbreak caused decreases in bus ridership while increases in bike-sharing trips. Second, bike-sharing may compete with public transit for short distance trips. Third, bike-sharing trips are likely to substitute public transit within more intensive and congested public transit networks after controlling for various other factors. Fourth, trips made in different neighborhoods and land use reveal significant relationships with modal types. Lastly, lockdowns due to the COVID-19 pandemic led to substitutive trips substantially, particularly on weekdays.

Between research, practice and governmental policies in the global south: understanding the Egyptian urban planning scene

PurposeUrban planning in the Global South is insufficiently discussed in research. In addition to the global lack of utilization of research in practice, this lack of discussion resulted in an apparent gap between research, practice, governmental policies and education particularly in the Global South. The paper explores the inconsistency of interest between policymakers, practitioners and researchers by reviewing their themes of interest comparatively. The paper investigates the case study of Egypt, which recently faces a new regime with optimistic urban agendas and examines the research publication that coincided with it.Design/methodology/approachA scientometric analysis is conducted on all urban planning publications in Egypt between 2016 and 2022 using the software ‘CiteSpace’. The analysis illustrates the dominating research interests using the publications keyword.FindingsThe analysis revealed a distinct interest between researchers, who were more invested in themes like ‘planning policies’, ‘informal settings’, ‘urban expansion and growth’ and ‘urban governance and management’, while policymakers were more devoted to themes related ‘satellite cities or new cities’, ‘public transit and transit networks’, ‘informal settings’ and ‘urban expansion and growth’. The themes of interest are displayed in network analysis for a comparative analysis with other aspects like practices, governmental policies and education.Originality/valueThe research utilizes all research publications in the field (in a selected period) to develop a study of the research interest in the field of urban planning in Egypt during the recent years. In addition, it uses novel scientometric software to assist in analysing existing publications to produce new findings and trends.

Improving the Prediction of Passenger Numbers in Public Transit Networks by Combining Short-Term Forecasts With Real-Time Occupancy Data

Passengers of public transportation nowadays expect reliable and accurate travel information. The need for occupancy information is becoming more prevalent in intelligent public transport systems as people started avoiding overcrowded vehicles during the COVID-19 pandemic. Furthermore, public transportation companies require accurate occupancy forecasts to improve service quality. We present a novel approach to improve the prediction of passenger numbers that enhances a day-ahead prediction with real-time data. We first train a baseline predictor on historical automatic passenger counting data. Next, we train a realtime model on the deviations between baseline prediction and observed values, thus capturing events not addressed by the baseline. For the forecast, we attempt to detect emerging patterns in real time and adjust the baseline prediction with deviations from the patterns. Our experiments with data from Germany show that the proposed model improves the forecast of the baseline model and is only outperformed by artificial neural networks in some instances. If the training sets only cover a limited period of up to four months, our approach outperforms competing methods. For larger training sets, there are mixed results in the sense that for some test cases, certain types of neural networks yield slightly better results, but our method still performs well with less training effort, is explainable along the whole prediction process and can be applied to existing prediction methods.

Exploring the robustness of public transportation system on augmented network: A case from Nanjing China

A highly robust urban public transportation network is the foundation for the stable and sustainable development of a city. Managers may benefit from a reasonable investigation into the robustness of the public transportation system to recognize and deliver improved public transportation services. This paper developed a robustness analysis framework for public transportation systems on augmented network (AN). Specifically, an AN was first built to treat the public transit network from a multi-layer perspective. The AN carefully considers the boarding, alighting, riding and transfer behaviors, and develops cost functions under the congestion effect, thus sufficiently simulating the real passengers’ behaviors. Then, the user equilibrium (UE) assignment model, a model widely used for passenger flow forecasting, is applied to guide the robustness analysis under cascading failures. Finally, a real large-scale bus–rail hybrid network in Nanjing is used as a case study. The results show that the initial station capacities are significant to the impact of the cascading failure, from which we conclude that a larger initial capacity of stations can prevent the cascading failures. By investigating the relationship between passenger flow and cascade size, the comparative results further explain the mechanisms of cascading failures. Thanks to the AN and UE, three types of travel times can be compared to reflect the quality of public passenger trips, which should provide traffic managers with pertinent advice for improving service.

Innovative GTFS Data Application for Transit Network Analysis Using a Graph-Oriented Method

Public transit networks are constantly evolving in the face of frequent economic and social challenges. There exists a large knowledge base on travel demand; however, there is a shortage of information on travel supply and networks. To our knowledge, no analysis tool can, at this point, systematically characterize a network and observe changes over time in a structured and automated manner. This paper addresses this issue and proposes a graph-oriented method for developing an analysis tool that will characterize a single network and then provide the necessary means to compare two distinct networks. A time-expanded model was applied to import General Transit Feed Specification (GTFS) data into a graph database. With built-in algorithms, shortest paths were computed and indicators were derived from these paths. A small case study demonstrates the applicability of the method. This approach still needs to be optimized to process networks that are more complex.

Charging Infrastructure and Schedule Planning for a Public Transit Network with a Mixed Fleet of Electric and Diesel Buses

Promoting battery electric buses (BEBs) can reduce fuel consumption and air pollution from the transit system. A complete transition from the current diesel fleet to BEBs is costly and time-consuming. Thus, the intermediate solution is a combination of diesel, hybrid, and BEBs. Therefore, a planning framework is required that simultaneously tackles three contiguous aspects of transit electrification and their interconnections, namely charging infrastructure, fleet configuration, and scheduling. Accordingly, this study considers a mixed fleet of diesel and BEBs. It aims to concurrently find (i) the optimal location and capacity of charging infrastructure, considering micro-grid specifications, the impact of distributed energy resources, and time-of-use electricity rates and (ii) optimum operation and refueling strategies. Another objective of this study is to capture the impacts of adverse weather conditions on transit electrification. A mixed-integer problem is proposed and solved using a metaheuristic algorithm based on simulated annealing to minimize system costs, including infrastructure, fleet, and operation costs. A subnetwork of transit in Worcester, Massachusetts, is selected as a case study, including three routes, five candidate charging locations, and three bus types. Findings suggest that BEBs can operate and serve the passenger demand with sufficient charging infrastructure. Sensitivity analyses show that even though high-power chargers are more expensive per piece, they reduce the overall cost as fewer chargers are required. The cost rises for chargers with power of 350 kW or more. It is worth noting that the benefits of BEBs are more significant in smaller buses and are heavily affected by adverse weather conditions.

Socially Connected Neighborhoods and the Spread of Sexually Transmitted Infections.

Sexually transmitted infections (STIs) in the United States have been increasing at record levels and exhibit unequal spatial patterning across urban populations and neighborhoods. Research on the effects of residential and nearby neighborhoods on STI proliferation has largely ignored the role of socially connected contexts, even though neighborhoods are routinely linked by individuals' movements across space for work and other social activities. We showcase how commuting and public transit networks contribute to the social spillover of STIs in Chicago. Examining data on all employee-employer location links recorded yearly by the Census Bureau for more than a decade, we assess network spillover effects of local community STI rates on interconnected communities. Spatial and network autoregressive models show that exposure to STIs in geographically proximate and socially proximate communities contributes to increases in local STI levels, even net of socioeconomic and demographic factors and prior STIs. These findings suggest that geographically proximate and socially connected communities influence one another's infection rates through social spillover effects.

An incentive subsidy mechanism for bus lines based on service level

Reasonable financial subsidies can promote public transport enterprises to raise service level, attract public transport passengers and improve the efficiency of fiscal funds. There are various bus lines that make up the urban public transit network. In this study, the single bus line is taken as the research object to establish the close relationship between the service level and the subsidy. An incentive bus subsidy method including measurement and effect evaluation is proposed. The measurement method is a dual-objective model that considers public welfare and market characteristics, and aims at maximizing the expected welfare of passengers and enterprises. The effect evaluation method of the incentive subsidy is designed to manage bus enterprises by the calculation and comparison of service level indicators. A practical example is presented to verify the effectiveness of the bus subsidy method, and shows how to regulate the bus enterprises when the bus service level declines. This study provides a method for the scientific design of incentive bus subsidies, which can encourage bus enterprises to improve their level of service, and help the government to optimize the decision-making on public transport subsidy policy.

Shared Clean Mobility Operations for First-Mile and Last-Mile Public Transit Connections: A Case Study of Doha, Qatar

With the aid of recent technological advancements, seamless integration of shared mobility services and public transit may offer efficient and affordable connectivity to the transit stations in urban settings, thereby enhancing residents’ mobility. A previous research mainly focused on car-sharing services as a self-standing mode of transportation. However, due to rapid urbanization acceleration and regions’ extension, commuters often combine the fixed-route/fixed schedules public transportation and car-sharing service in one journey. To this end, we study a one-way, station-based electric car-sharing service interaction with public transportation. We propose an integrated route choice and EV assignment model to address the potential of car-sharing services as a feeder to the public transit network. The integrated model consists of two components, operations of the car-sharing service and the commuter’s route choice and the associated mode choice. The service provider decides on the resource levels, allocations, and relocation strategy in the first component. In the second component, the travel options for the commuters are modeled. The two-component model was simulated in an agent-based simulation based on a case study from the state of Qatar. We further extend the integrated model to include the carpooling option, in which multiple passengers sharing the same route can share the same vehicle. Extensive simulation analyses show that the integration can considerably enhance urban mobility and increase public transportation accessibility through enhanced first and last miles linkages. Moreover, the influence of transportation supply and spatial characteristics on the individual mode choice was estimated. Results indicate that public transit ridership can increase up to 17%. Moreover, adding the carpooling option can significantly decrease the number of relocations operations at a minimal impact on the commuters’ trip performance.

Transfer time optimisation in public transit networks: assessment of alternative models

In transfer-based transit networks, it is critical to synchronise timetables of intersecting routes to reduce passenger transfer times. To this end, this study compares different transfer time optimisation approaches and investigates their solutions comprehensively at the network level as well as for each transfer node individually. Additionally, it assesses the assumption of available bus capacity, which has not received adequate attention in previous studies. Four models are tested through a numerical analysis of a network consisting of three nodes under different headway policies to provide agencies and researchers with critical insights into improving transfer coordination. The results show that the incorporation of demand into the objective function and the inclusion of bus capacity constraints have notable effects on model outcomes. Also, the study highlights important transfer node characteristics which should be considered in the choice of appropriate transfer optimisation models.

Art in transit: Mobility, aesthetics and urban development.

High-profile architecture and design, alongside integrated arts and cultural programming are now ubiquitous features of public transit networks. This article considers how and why transit-based arts and cultural programmes are proliferating globally as well as the impact of these programmes on transit and urban dynamics. Through critically analysing the discourses surrounding different transit art initiatives and the institutional structures which support them, this article shows how transit art is used today for varied - and often contradictory - ends. Based on this, it argues that we should not uncritically celebrate the rise of transit art as an unmitigated civic good. Rather, we must situate the rise of transit art within a political and aesthetic economy in which art has become 'expedient', and contend with the way transit art is implicated in elite, exclusionary and unsustainable processes of urbanisation.

Evaluating the dynamic resilience of the multi-mode public transit network for sustainable transport

Improving multi-mode public transit resilience becomes crucial as urban traffic infrastructure gradually completes. This paper aims to build a dynamic resilience evaluation method and explore the resilience evolution process for multi-mode public transit. For establishing the resilience evaluation method, we considered both structural and functional indicators of the network. Then, we built a nonlinear load-capacity model and a cascading failure model to reflect the dynamic feature of resilience. Next, attack scenarios were designed according to the actual characteristics of the multi-mode public transit. Using the passenger flow data from Xi'an, we tested the impact of station capacity on the dynamic resilience results and analyzed the resilience evolution process. The results indicate that the network's resilience performance was the worst when the subway station was destroyed. Through adjusting the capacity control parameters, we found the best station capacity control parameter is α=0.5,β=0.4. Moreover, the sharply reducing intervals found in the results could help traffic managers improve transport efficiency while controlling costs.

Targeted Advertising in the Public Transit Network Using Smart Card Data

Targeted Advertising in the Public Transit Network Using Smart Card Data