Urban Public Transport Systems Research Articles

Multi-scale risk assessment and mitigations comparison for COVID-19 in urban public transport: A combined field measurement and modeling approach

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused an unparalleled disruption to daily life. Given that COVID-19 primarily spreads in densely populated indoor areas, urban public transport (UPT) systems pose significant risks. This study presents an analysis of the air change rate in buses, subways, and high speed trains based on measured CO2 concentrations and passenger behaviors. The resulting values were used as inputs for an infection risk assessment model, which was used to quantitatively evaluate the effects of various factors, including ventilation rates, respiratory activities, and viral variants, on the infection risk. The findings demonstrate that ventilation has a negligible impact on reducing average risks (less than 10.0%) for short-range scales, but can result in a reduction of average risks by 32.1%–57.4% for room scales. When all passengers wear masks, the average risk reduction ranges from 4.5-folds to 7.5-folds. Based on our analysis, the average total reproduction numbers (R) of subways are 1.4-folds higher than buses, and 2-folds higher than high speed trains. Additionally, it is important to note that the Omicron variant may result in a much higher R value, estimated to be approximately 4.9-folds higher than the Delta variant. To reduce disease transmission, it is important to keep the R value below 1. Thus, two indices have been proposed: time-scale based exposure thresholds and spatial-scale based upper limit warnings. Mask wearing provides the greatest protection against infection in the face of long exposure duration to the omicron epidemic.

Diseconomies of scale and subsidies in urban public transportation

Subsidization of urban public transportation systems is often motivated by economies of scale and second-best considerations such as an underpriced road alternative. We model a public transit line subject to frictions between users (in-vehicle crowding), users and vehicles (boarding and alighting delays), and vehicles (congestion). We derive the profit- and welfare-maximizing provisions of supply. We show that if demand exceeds a first threshold, the system enters a congested regime and service frequency decreases. Beyond a second threshold, the strong deterioration of service quality causes the transit line to operate under diseconomies of scale, calling for a Pigouvian tax instead of a subsidy. This finding, which goes against Mohring’s classical rule (1972), holds with an untolled road alternative, provided that the network structure remains constant. We estimate the model for the London Piccadilly line and find evidence of substantial diseconomies of scale during the morning peak, adding up to −1.49 £/trip for the observed provision of service quality (-0.61 £/trip at optimum). These results question current subsidy policies for the busiest transit lines.

A novel data-driven energy management strategy for fuel cell hybrid electric bus based on improved twin delayed deep deterministic policy gradient algorithm

With the prosperity of artificial intelligence in recent years, energy management strategies (EMSs) based on deep reinforcement learning (DRL) have become the mainstream approach to ensure efficient energy distribution of hybrid electric vehicles (HEVs). Moreover, in terms of developing sustainable urban public transportation systems, zero-emission fuel cell hybrid electric buses (FCHEBs) are more promising than hybrid electric buses (HEBs). Given that, this article proposes a novel data-driven EMS based on DRL to enhance the training efficiency of the proposed EMS while improving the fuel economy of an FCHEB. In this article, to optimize the power allocation of the FCHEB, an improved twin delayed deep deterministic policy gradient (TD3) algorithm combined with prioritized experience replay is innovatively formulated, and then a promising EMS based on it is proposed. Furthermore, to enhance the adaptability and improve the training efficiency of the proposed EMS, a stochastic training environment is established with massive real-world velocity data, and a pre-training method using the global optimal experience is designed. Simulation results show that the proposed EMS improves fuel economy by 5.87% compared with the EMS based on original TD3, reaching 97.15% of the global optimal dynamic programming method.

The Role of DRT in European Urban Public Transport Systems—A Comparison between Tampere, Braunschweig and Genoa

Demand-Responsive Transport (DRT) is one of the most valid solutions to tackle the problems affecting public transport today, both in urban and rural areas. Despite its undoubted advantages, it still remains underdeveloped compared to its great potential. The purpose of this paper is to understand the role that on-demand transport plays in the strategic choices of public transport authorities (PTAs): to this end, this study examined the DRT services of three geographically distant European cities, in order to test different social, cultural and regulatory backgrounds, examining their main characteristics. Tampere, Braunschweig and Genoa were selected for the purposes of this work; data and information were collected by viewing the official websites of public transport companies and by direct contact via mail/telephone with the managers responsible for on-call transport. The data collected were then analyzed based on specific Key Performance Indicators (KPIs) identified in academic literature. The results of this paper show that the role of on-call service in the strategic decisions of PTAs depends on the cultural context of reference; some cities focus more on urban services, others on rural transport. In all three case studies examined, on-demand transport is an important aspect of local mobility and with wide room for growth.

Dynamic model of respiratory infectious disease transmission in urban public transportation systems

During the epidemics of respiratory infectious diseases, the use of public transportation increases the risk of disease transmission. Therefore, we established a dynamic model to provide an in-depth understanding of the mechanism of epidemic spread via this route. We designed a computer program to model a rail transit system including four transit lines in a small town in which assumed 70% of the residents commute via these trams in weekdays and the remaining residents take the tram at random. The model could identify the best travel route for each passenger and the specific passengers onboard when the tram passed through each station, and simulate the dynamic spread of a respiratory pathogen as the passengers used the rail transit system. Based on the program operating, we estimated that all residents in the town were ultimately infected, including 86.6% who were infected due to the public transportation system. The remaining individuals were infected at home. As the infection rate increased, the number of infected individuals increased more rapidly. Reducing the frequency of trams, driving private cars or riding bicycles, showing nucleic acid certificates and wearing masks for passengers, etc., are effective measures for the prevention of the spread of epidemic diseases.

Covid-19 and Public Transport: two years later. Investigating the transport demand trend in the City of Brescia

Public Transport (PT) extremely suffered from the Covid-19 pandemic. While trying to guarantee the service supply as regularly as possible, great efforts have been made by Transport Companies to deal with constantly changing emergency prescriptions. Moreover, overall uncertainty led to a sharp shift in travel behaviors and habits, resulting in a general increase in private (especially motorized) modes. Over the past months, a lot of research has tried to investigate the several perspectives for the PT sector, during and beyond the Covid-19, by accounting for e.g., new technologies, innovative service supply or transport modes, etc. which have been implemented overtime. However, after two-years from the Covid-19 outbreak, the transport demand is still not back to the pre-pandemic situation. Considering the trends of the past two-years, this paper aims at exploring how the transport demand has varied, with respect to the Covid-19 impact, the safety prescriptions, and the PT service supply, to shed light over still unobserved factors that may be useful to move forward. By considering the area of Brescia as a case study, the analysis compares the transport demand from the different urban PT systems (i.e., buses, metro light rail and bike sharing) of Brescia with the related service supply (i.e., kms-travelled, service availability, etc.), the Covid-19 infections, the service limitations (i.e., lockdowns, vehicle capacity reductions, etc.), to understand whether relationships exist among the several variables included. It emerged that the progressive easing of restrictions, the enhancements of intermodal transport system and the improvement of service's quality could have contributed to recovery transport demand. Future developments of the research could be addressed to extend the available database to confirm or improve the research findings.

Exploring the Multi-Layer Structural Properties of the Bus-Subway Transportation Network of Shanghai

Buses and subways are essential to urban public transportation systems and an important engine for activating high-quality urban development. Traditional multi-modal transportation networks focus on the structural feature mining of single-layer networks or each layer, ignoring the structural association of multi-layer networks. In this paper, we examined the multi-layer structural property of the bus-subway network of Shanghai at both global and nodal scales. A dual-layer model of the city’s bus and subway system was built. Single-layer complex network indicators were also extended. The paper also explored the spatial coupling properties of the city’s bus and subway system and identified its primary traffic nodes. It was found that 1) the dual-layer network increased the network’s connectivity to a certain extent and broke through the spatial limitation in terms of physical structure, making the connection between any two locations more direct. 2) The dual-layer network changed the topological characteristics of the transit network, increasing the centrality value and bit order in degree centrality, betweenness centrality, and closeness centrality to different degrees, and making each centrality tend to converge to the city center in spatial distribution. Enhancing the management of critical network nodes would help the integrated public transportation system operate more effectively and provide higher-quality services.

Data-Driven Flexible Vehicle Scheduling and Route Optimization

The flexible transit service reflects a trend of demand on the flexibility and convenience in urban public transport systems, within which the vehicle scheduling and passenger insertion are two challenging issues. Especially, finding the optimal solution for a flexible transit system can be viewed as an extension of the traveling salesman problem which is NP-complete. Yet most of the existing research mainly focuses on one aspect, i.e. route planning, stop selection or vehicle scheduling, where a combined integration and optimization of the whole system is largely neglected. In this paper, we propose a data-driven flexible transit system that integrates the origin-destination insertion algorithm and the milp-based (mixed-integer linear programming) scheduling scheme. Specifically, stops are mined from the historical datasets and some stops act as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$backbone$ </tex-math></inline-formula> stops that should be visited by the vehicles; and a heuristic backbone-based origin-destination insertion algorithm is proposed to schedule the routing path of vehicles, where the time loss caused by the optimal insertion positions is calculated for the vehicles to decide whether to accept the requests or not when constructing a path for the flexible routes. Moreover, a vehicle scheduling model based on milp is proposed to minimise the gap between the passenger flow and available seats. The proposed flexible transit systems are simulated in real-world taxi datasets, and experimental results show that the proposed flexible transit system can effectively increase the delivery ratio and decrease the passengers’ waiting time compared with existing methods.

Influence of TOD Modes on Passenger Travel Behavior in Urban Rail Transit Systems

Transit-oriented development (TOD) mode refers to the integrated development of high-density and multi-functional land in the vicinity of core public transportation stations to increase public transportation rates and address problems such as traffic congestion and land shortages. Therefore, it is crucial to comprehend the relationship between TOD areas and the travel behavior of rail transit residents. However, the income level and occupation of residents have a significant impact on their travel behavior, as people tend to choose their residential, work, and entertainment areas based on their economic characteristics. This paper focuses primarily on two aspects: how to distinguish TOD areas from non-TOD areas (specifically, rail stations) and the variations in the travel behavior of the people residing in these areas. Rail stations were first classified via cluster analysis according to the selected TOD indexes; then, a propensity score matching method was applied to control the influence of self-selection behavior. Based on this, the matched results were analyzed to study the difference in the travel behavior characteristics of residents in TOD and non-TOD areas. The results indicate that residents in TOD areas are more likely to travel by public transportation than those from non-TOD areas. The findings of this study promote a people-oriented urban planning concept and would have practical implications for applications of TOD modes on urban public transportation systems.

Beyond fear and abandonment: Public transport resilience during the COVID-19 pandemic

The outbreak of the COVID-19 pandemic challenged urban public transport systems to maintain accessibility and service for daily users while adapting to local health and safety regulations. Developing sustainable and resilient urban policies under such crisis conditions requires understanding the different feelings, experiences and expectations of passengers and how these relate to socio-economic inequalities. Drawing on a mixed-method study in Berlin (Germany), Brussels (Belgium), Stockholm (Sweden) and Tallinn (Estonia), we show how the pandemic outbreak has changed both who uses public transport and how users experience their journeys. Challenging the narratives that portray public transport as a place of fear, we find that remaining passengers assess the risk of contagion lower than those who avoided it completely. We argue that promoting resilient public transport requires policies that address the needs of passengers relying on public transport services. Therefore, we question the current policies under the sustainable mobility paradigm for not taking sufficient account of the feelings, experiences and emotions and particularly of transport-dependent users.

MDLF: A Multi-View-Based Deep Learning Framework for Individual Trip Destination Prediction in Public Transportation Systems

Understanding and predicting each individual’s real-time travel destination given the origin information in urban public transportation systems is crucial for personalized traveler recommendation, targeted demand management, dynamic traffic operations and so on. Existing methods are often based on modeling the regular travel patterns through analyzing the long-term personal travel information. They are suitable for destination prediction of individual regular trips with regular travel patterns, but may not work well for occasional trips with strong randomness and uncertainty, especially for the individuals with a few historical travel data. In this paper, we focus on more challenging issue about destination prediction of occasional trips. We design a general Multi-View Deep Learning Framework (MDLF) based on the data-driven insight that a location where a user will destine to is not only related to the user’s own travel preference to the location, but also influenced by crowd’s travel preference and the region’s characteristics of the location under certain spatiotemporal contexts. The destination of an individual’s occasional trip can be predicted by combining all these complementary influencing factors. The novelty of MDLF is mainly reflected in two aspects. The first is the effective feature extraction from multiple and complementary views. The second is that a CNN (Recurrent Neural Network) based deep learning component for predicting each occasional trip’s destination by calculating a moving trend score for each possible destination. We evaluate the MDLF based on two real-world smart card datasets collected by AFC (Automatic Fare Collection) Systems. The experimental results demonstrate the superiority of MDLF against other competitors.

Trambüs Sisteminin Seçimi ve Uygulama Koşullarının Değerlendirilmesi: Malatya Örneği

Today, due to the increase in the demand for urban public transportation due to the population growth in cities, there is a need to use economic, sustainable and innovative technologies. Trambus, as one of the urban public transportation systems, is a sustainable and environmentally friendly public transportation vehicle of the modern age, powered by electricity, due to its high capacity in urban passenger transportation and being economical in terms of energy consumption. Trambus stands out with its low initial investment cost when compared to other public transportation vehicles. In addition, it provides advantages in terms of needing periodic maintenance for a longer period of time, low maintenance costs, uninterrupted service and operating costs compared to other fossil fuel public transportation vehicles. In this study, it was aimed to investigate the suitability of the trambus system, which became operational in 2015, by examining the data and selection criteria between 2013 and 2015, in terms of being an example for other cities for Malatya urban transportation. As a result of the selection criteria of the trambus system and the first 5 months of use, it has been concluded that it is a suitable choice for Malatya city.

Detection of SARS-CoV-2 in the Indoor and Outdoor Areas of Urban Public Transport Systems of Three Major Cities of Portugal in 2021.

Airborne transmission is mainly associated with poorly ventilated and crowded indoor environments where people stay for long periods of time. As such, public transport is often perceived as having a high risk for the transmission of SARS-CoV-2. Considering that data on the detection of SARS-CoV-2 in public transport systems are scarce, we performed air sampling for SARS-CoV-2 in indoor and outdoor spaces of public transport systems in Portugal. Air (n = 31) and surface (n = 70) samples were collected using a Coriolis® Compact microbial air sampler and sterile flocked plastic swabs, respectively. Samples were extracted and analyzed through RT-qPCR. Only two air samples from an outdoor and a partially open space were found to be positive for SARS-CoV-2 RNA. No positive surface samples were detected. These results indicate that the viral concentration in ambient air in public transport systems is linked to the number of people present in that environment and whether they are wearing properly fitting masks. Considering the current lifting of COVID-19 restrictions around the world, it is essential that people continue to wear masks in both indoor and outdoor environments, especially in crowded spaces. More studies on this topic are needed to fully elucidate the real risk of infection in outdoor spaces.

Network-based Assessment of Metro Infrastructure with a Spatial–temporal Resilience Cycle Framework

Urban public transportation systems tend to cripple when faced with challenges, such as natural hazards and social unrest. It is imperative to engineer resilient public transportation systems to provide urban commuters with a reliable alternative to private vehicles. Current network-based approaches for resilience quantification focused on the network topology but seldom considered the impacts of temporal variation of flow pattern and system’s spatial distribution, which provide unique people-centric insights into resilience. This paper applies a resilience cycle framework consisting of four life-cycle stages associated with any disruptive event – preparedness, robustness, recoverability, and adaptation. The proposed flow-weighted and spatial analysis captures the resilience of both the system and users. Additionally, the temporal trends are compared for different resilience indicators associated with the topology and flow patterns. A case study of the Hong Kong metro system shows the utility of the framework. The study found that the average travel distance of flows has a strong negative effect on the network’s robustness to random failures. The vulnerability of the network to random failures can also be explained by the node homogeneity results from the preparedness stage. In the recovery stage, densely-built metro stations are found to provide significant benefit in response to disruptions, provided that the shared risks for the nearby stations are minimal. The resilience cycle framework provides actionable insights for all the relevant stakeholders.

Promoting the integrated use of bikeshare and metro: A focus on the nonlinearity of built environment effects

Bikeshare offers a flexible feeder mode to metro and improves the overall connectivity of urban public transport systems. Although bikeshare has received much research attention, the relationship between the built environment and bikeshare-metro integrated use (i.e. the use of bikeshare for the first/last mile) remains underexplored. Using a one-month dataset of bikeshare trip records in Nanjing, China, this research scrutinizes the built environment correlates of the integrated use. A generalized additive mixed model is employed to capture the temporal autocorrelation attributable to repeated observations over time and the spatial autocorrelation resulting from geographical proximity between individual metro stations. The results show that built environment variables do impose salient nonlinear effects on the bikeshare-metro integrated use. For example, population density only increases the integrated use at certain intervals, and interestingly, extremely high density leads to a decline in bikeshare-metro integrated use. The proportion of commercial and greenspace land uses within metro station catchment areas should not be too high or too low. There exists an optimal land use setting that maximizes the utility of land-use interventions. These findings provide useful policy implications for developing an environment that facilitates the integration of bikeshare and urban metro systems.

A capacity matching model in a collaborative urban public transport system: integrating passenger and freight transportation

Working from home becomes the norm; this trend has put added pressure on urban logistics, as large volumes of goods and services are required for domestic use. Meanwhile, public transport operators face a big challenge and trade-off due to higher labour and frequent cleaning costs, with lower passenger revenue over a longer period. Considering the collaborative urban public transport services achieve a seamless movement for both passengers and goods, and could reduce the adverse effects of the existing urban public transport systems. Therefore, a mixed-integer linear programming model introducing the concept of capacity matching is proposed to assist this collaborative urban freight service network in minimising total freight transport time at station hubs and not affecting passenger transportation in this paper. Moreover, an efficient improved optimisation algorithm based on the Artificial Bee Colony Algorithm (ABC) is designed, and the numerical examples and real cases are illustrated to demonstrate the feasibility and effectiveness of the proposed model and algorithm. The performance evaluations suggest that the coordinated operating strategy of the collaborative freight transportation system supports increasing mobility demands for freight, resulting in declining congestion levels and reducing transport emissions, while no influence in passenger transport, notably in urban areas.

Challenges and prospects for unmanned urban transport

The article deals with current problems and prospects of development of urban unmanned transport. The rapid development of autonomous transport, artificial intelligence, and other information technologies makes it possible to introduce unmanned vehicles in urban public transport systems, primarily buses. The technological factors and obstacles for the development of unmanned public transport systems are summarised. Despite the fact that the capacity of such buses in current use is still small, a maximum of about 15 people, the routes are relatively short, and the use is mainly in test mode, the use of these vehicles, especially in large urban agglomerations, seems undoubtedly promising. The article presents an analysis of the main features and incentives for the development of unmanned public transport, gives a brief overview of pilot systems of autonomous public transport in European cities, considers obstacles to the development of these systems and the experience of development of unmanned public transport in Russia, and formulates assumptions about the future development of this transport segment. The first steps towards full autonomy and widespread use of unmanned urban public transport, however, this path will not be taken quickly.

Orientations and matrix function-based centralities in multiplex network analysis of urban public transport

We study urban public transport systems by means of multiplex networks in which stops are represented as nodes and each line is represented by a layer. We determine and visualize public transport network orientations and compare them with street network orientations of the 36 largest German as well as 18 selected major European cities. We find that German urban public transport networks are mainly oriented in a direction close to the cardinal east-west axis, which usually coincides with one of two orthogonal preferential directions of the corresponding street network. While this behavior is present in only a subset of the considered European cities it remains true that none but one considered public transport network has a distinct north-south-like preferential orientation. Furthermore, we study the applicability of the class of matrix function-based centrality measures, which has recently been generalized from single-layer networks to layer-coupled multiplex networks, to our more general urban multiplex framework. Numerical experiments based on highly efficient and scalable methods from numerical linear algebra show promising results, which are in line with previous studies. The centrality measures allow detailed insights into geometrical properties of urban systems such as the spatial distribution of major transport axes, which can not be inferred from orientation plots. We comment on advantages over existing methodology, elaborate on the comparison of different measures and weight models, and present detailed hyper-parameter studies. All results are illustrated by demonstrative graphical representations.

The Second Generation Electromobility in Polish Urban Public Transport: The Factors and Mechanisms of Spatial Development

One of the key challenges on the road to sustainable mobility is the development of low/zero emission urban public transport (UPT). This is crucial in order to meet environmental requirements aiming at reducing greenhouse gas (GHG) emission. In some countries (e.g., Poland) reduction of air pollution is also an important reason behind the implementation of low/zero emission UPT. The aim of this study is to investigate the factors and mechanisms influencing the development of modern electromobility in Polish UPT. We have examined all 242 UPT systems in the country in terms of the characteristics of the relevant urban municipalities, such as size, economic prosperity, level of human and social capital, development paths of urban public transport in the long term as well as the institutional context and proximity and connections to other cities with experience in electromobility. Classification and statistical methods are used based on a variety of approaches, as assigning a score to various preliminarily identified indicators or applying correlation between quantities to verify the formulated hypotheses. Our analysis demonstrates that electromobility adoption is the result of a combination of favourable economic, urban, social and technological characteristic features of a given city. Zero or low emission buses are more common in large cities which are highly positioned in urban hierarchy, economically sound and which are characterized by a well-developed tertiary economy as well as by high human capital. An additional factor that positively influences the implementation of electromobility—in particular at the very first stage—is proximity to the location of low emission bus producers. The leadership in modern electromobility can be understood as part of a broader, proactive development policy of the cities aimed at improving the quality of life of their residents. This is especially important in medium-sized towns where utilizing electric vehicles can be an instrument to maintain or even develop their role and status. The results of the article may provide a basis for creating sustainable urban policies, especially sustainable mobility and improving environmental quality.

TOD Typology Based on Urban Renewal: A Classification of Metro Stations for Ningbo City

In recent decades, the transit-oriented development (TOD) concept has been widely used all over the world, especially in China, for the massive construction of urban public transportation systems with rail transit as the backbone. However, it is not easy to make significant changes in a city while building a transportation system, and the transit-guided urban development expected by the TOD concept has not been completely realized. The transformation of nearby areas with the guidance of transit is also becoming the choice of many Chinese cities, especially for cities that have only had subways for a few years. Unlike other international metropolitan cities, with metro systems of considerable scale, the modernization process of most of the small and medium-sized cities in China is being carried out simultaneously with metro-based public transportation guidance. For cities which are still in their initial stage of the backbone public transportation system, there is not enough previous experience and evidence to support the suitability of TOD typological analysis based on the node-place model. More research based on the node-place model has also shown practical applications of the TOD in developed cities. However, there are very few studies that analyse cities in which rail transit and urban development are both in a period of rapid growth. The goal of this research is to identify which metro stations in these cities are suitable for TOD improvement and optimization. This article attempts to expand the willingness of residents on the basis of the traditional node-place model as one of the judgment indicators for evaluating whether existing stations and surrounding areas are suitable for TOD improvement. At the same time, traditional statistical analysis is combined with GIS and machine learning technology. Using this method, we propose the TOD improvement-oriented station area classification and identification method based on TOD typology theory. The results show that Ningbo's subway stations can be divided into four categories according to the suitability for TOD improvement, and we selected seven stations suitable for TOD improvement according to the characteristics of the node-place model. The practice in Ningbo has proved that this method is effective for identifying sites suitable for TOD improvement, especially for cities that have recently built subways.