Urban Public Transport Systems Research Articles

Spatial coverage analysis of public transport bus stops through a citizen perception study. Case study: Manizales – Colombia

The development of urban public transport systems today has focused on increasing their operability, trying to minimize travel times and mobilize the greatest number of users by increasing operational speed, renewing the vehicle fleet, and increasing frequencies, among others. However, in addition to restructuring the operating distance and the location of the bus stops, this process of increasing operational capacity has paid little attention to the people's perception of these stop systems’ location and coverage. This causes, largely, users’ apathy to take advantage of the system, promoting the use of other means of transport, or in the least cases, to make stops within the system in unauthorized locations.Considering the above, the aim of this research is to analyze the overage variation of bus-stop sets, based on the perception of users in normal and eager conditions, through the application of an analysis of population and zonal coverage, in which physical and operational variables of interest are considered, complemented with geostatistical models and digital tools that allow implementing a set of bus stops creating an inclusive environment with a broader vision of the ideal conditions for the definition of bus-stop sets. Within the research process, it is necessary to structure a statistical sampling, which allows for characterizing the population of public transport systems. Therefore, Manizales, the capital of the department of Caldas − Colombia, through its 12 district clusters and 378,000 inhabitants, is taken as a focus of study. However, due to the conurbation agreement with the municipality of Villamaría, the inclusion of its urban framework is carried out, resulting in a total population of 424,300 inhabitants.

Bus Drivers’ Behavioral Intention to Comply with Real-Time Control Instructions: An Empirical Study from China

Developing intelligent bus control systems is crucial for fostering the sustainability of urban transportation. Control instructions are produced in real time by the bus control system; these are important technical commands to stabilize the order in which buses operate and improve service reliability. Understanding the behavioral intention of bus drivers to comply with these instructions will help improve the effectiveness of intelligent bus control system implementation. We have developed a psychological model that incorporates decomposed variables of the theory of planned behavior (TPB) and other influencing variables to explain the micromechanisms that determine bus drivers’ behavioral intention to comply with real-time control instructions during both peak and off-peak-hour scenarios. A total of 258 responses were obtained and verified for analysis. The results showed that the influential factors in the peak- and off-peak-hour scenarios were not identical. Female drivers had greater off-peak-hour behavior intention to comply than male drivers, and there were significant differences in peak-hour behavior intention among drivers of different ages. In both peak and off-peak-hour scenarios, perceived benefit positively and perceived risk negatively affected behavioral intention. Perceived controllability positively affected behavioral intention only during peak hours. Self-efficacy only negatively affected behavioral intention during off-peak hours. Three antecedent variables (i.e., trust, mental workload, and line infrastructure support) influenced drivers’ behavioral intentions indirectly via the decomposed variables of TPB. These results provide profound insights for the improvement and implementation of real-time control technology for bus services, thereby facilitating the development of smart and sustainable urban public transport systems.

The financial impacts of the electronic bus transition

This article delves into the financial impact of five factors, including the procurement cost of electric bus (e-bus), charging equipment, installation cost of charging equipment, maintenance cost of e-bus, electricity cost, and electricity cost, on the transformation of e-bus. A linear programming model for the transformation cost of e-commerce is established. Research has found that although the initial purchase cost of an e-bus is higher than that of traditional diesel buses, their operating and maintenance costs are lower and more economically competitive in the long run. Meanwhile, introducing external funds such as government subsidies can further reduce transformation costs and accelerate the popularization of e-bus in urban transportation systems. In addition, reasonable planning and layout of charging facilities can effectively reduce costs while improving the efficiency of charging facilities. Overall, this article reveals the key factors and optimization strategies in the process of electronic bus transformation through in-depth research on the cost model of electronic bus transformation. The research results have important theoretical and practical guidance for promoting the green transformation of urban public transportation systems worldwide.

Encounter Network and Its Application to Studying Epidemic Spread through Public Transportation Networks

Epidemic diseases have always been a challenging issue in public health. Densely populated urban public transportation systems could be the perfect candidate for transmitting epidemics by direct contact with the infected population and contaminated surfaces. In network analysis, betweenness centrality (BC), as the primary metric, has been used to measure an individual’s communication in the network. This research uses this concept to measure the risk of transmitting disease in public transportation links. For each network node, the set of shortest paths from that node to all other nodes compose a shortest paths sub-graph (tree). Source node total betweenness centrality (TBC) is defined as the sum of BC of all edges of this tree. Similarly, the shortest path sub-graph consisting of the set of the shortest paths from all nodes to a single target node is considered, and the target node TBC is defined as the sum of BC of all edges of this tree. The node encounter matrix is also defined to measure each node pair encounter in the network. The source node TBC of a node depicts the risk of transmitting the disease from that node to other nodes. Conversely, the target node TBC of a node is related to the risk of transmitting the disease from other nodes to that node. The susceptible-infected model is used to evaluate the proposed method by measuring the number of infected nodes.

A data-driven framework for natural feature profile of public transport ridership: Insights from Suzhou and Lianyungang, China

Urban public transport systems, characterised by their complexity, generate vast data sets that pose challenges to traditional analytical methods. To address this issue, our research introduces an innovative natural feature profile framework, leveraging a comprehensive, data-driven approach that incorporates big data, data mining, machine learning, and correlation analysis. This approach provides detailed insights essential for transport planning and policy development. The framework's core is its three-layered structure: the data layer, the feature layer, and the application layer, complemented by a unique four-level feature tagging system. This system investigates correlations, significance, and sensitivities amongst feature tags. It facilitates the extraction of natural feature profiles from voluminous data sets, rendering the framework highly applicable in practical scenarios. The implementation of this framework in Suzhou and Lianyungang demonstrated its adaptability and effectiveness. The findings underscored distinct city-specific transport patterns, highlighting the necessity for customised transport strategies. Furthermore, our framework excels at capturing spatial–temporal dynamics, offering essential insights grounded in evidence. Overall, this paper introduces a methodical, adaptable, and data-oriented framework, signalling a promising future for the development of intelligent and sustainable urban public transport systems.

Resilience assessment model for urban public transportation systems based on structure and function

Urbanization and the increasing frequency of extreme climates affect the sustainability of urban public transportation systems, and improving resilience is one of the primary directions for sustainable development. To scientifically assess the resilience of urban public transportation systems, a resilience assessment model based on structure and function is established in this study. This model mathematically quantifies and simulates the structural and functional changes in public transportation systems under disruption scenarios and provides a comprehensive assessment of six abilities: 1) structural resistance, 2) structural recoverability, 3) functional resistance, 4) functional recoverability, 5) passenger adaptability, and 6) management adaptability. Depending on the initial failure stations, this model can simulate the resilience of a public transportation system under various scenarios. This model is applied to assess the resilience of public transportation systems in a provincial capital city under an equipment failure scenario. The results show that the impact of equipment failure on resilience varies according to the metro lines, and improvement strategies for functional recoverability and management adaptability are proposed. The weaknesses in the resilience of urban public transportation systems can be identified using the proposed model, which helps provide strategies for improving the capacity to face perturbations.

A multiscale spatial analysis of taxi ridership

Taxi plays a supplement role in sustainable development of urban public transport systems. However, the extent to which the built environment affects taxi ridership at various spatial scales deserves further exploration because understanding the true spatial heterogeneity across a varying scale could be valuable for both global and localized policy decision-makings. In this study, we attempt to analyze and discuss the spatial predictors of taxi ridership by utilizing an multiscale geographically weighted regression (MGWR) model and comparing the model's performance to that of ordinary least square (OLS) and geographically weighted regression (GWR) models. Using the taxi data of Xi'an city, we found that the MGWR model could explain 81.8% of the total taxi ridership fluctuations and allows localized and targeted policy makings to help taxi drivers search for passengers and to improve passengers' taxi-hailing experiences in specific districts.

Classifying station areas in greater Manchester using the node-place-design model: A comparative analysis with system centrality and green space coverage

Transit-oriented development (TOD) is receiving increasing attention from planners and policymakers as an essential strategy for addressing urban travel inequalities and air pollution. As an analytical method based on the conceptual construct of TOD, the node-place-design (NPD) model is utilized by researchers to assess TOD in urban public transport systems. However, current research prioritizes local perspectives of station development and fails to identify the functions of stations at the system level. In addition, few TOD-related analyses have considered the ecological aspects of cities. In the present study, we used an NPD model to evaluate TOD development at rail stations in Greater Manchester. We incorporated extended indicators of systemic importance and green space coverage in our analysis to provide additional insight into the model. First, we found a low level of TOD integration around stations in Greater Manchester, and some differences in development levels around train stations and Metrolink stations. Second, a comparison with the extended system importance indicators revealed the potential for significant differences in the NPD index and the system importance indicators of stations. This identified some stations that have the potential to take on new routes and some areas that are worthy of transit-oriented development. Finally, we found a specific negative correlation between the NPD index and the green space indicator. This indicated that the TOD model sacrifices green space to a certain extent and demonstrated the importance of including ecological indicators in the TOD assessment. The present study provides additional insight into the NPD model by introducing extended systemic and ecological indicators.

The vehicle routing problem with underground logistics: Formulation and algorithm

Recognizing the pressure on urban logistics and the overcapacity of urban public transportation systems during off-peak hours, this study investigates a subway-assisted delivery model. This is a system in which part of the goods to be delivered into a city can be transferred to specific subway stations in advance (e.g., during the night) using underground logistics. During the day, vehicles can then be replenished at these subway stations. In our study, we investigate how a system of this kind affects the decisions to be made by a logistics service provider. We introduce the vehicle routing problem with underground logistics to model how to find the best vehicle routes and goods transfer plan in this system. First, we formulate this problem as a mixed integer linear model. Then, we propose a problem-customized adaptive large neighborhood search heuristic algorithm to solve it. Numerical experiments demonstrate that our methodology performs well in terms of effectiveness and efficiency. Additionally, we discuss the resulting schedules and include a sensitivity analysis of the transfer prices to provide information that can be used in strategic and tactical decision making in a subway-assisted delivery system.

Spatio-temporal analysis of carbon footprints for urban public transport systems based on smart card data

The increasing severity of global climate change has made reductions in carbon emissions an urgent global issue. The relative lack of carbon footprint analyses of urban public transportation systems (UPTS) is therefore surprising, given that UPTS is an important component of urban transportation and one that may play a crucial role in carbon emission reduction. This study conducts a spatio-temporal analysis of carbon footprints for UPTS during the COVID-19 pandemic based on smart card data in Beijing. Since the core of carbon footprint calculation is to estimate travellers' trip trajectories and the ridership of urban rail transit (URT) and buses, we construct a novel multi-layer urban rail network model to calculate passenger volume and travellers' trajectories through a traffic assignment model. Furthermore, we utilize the Generalized Additive Model (GAM) to analyse the correlation relationship between the carbon footprints of buses and URT. Additionally, we conduct statistical analysis of the carbon footprint of UPTS. The results of the spatio-temporal analysis of carbon footprints for UPTS show significantly lower carbon emissions during holidays compared to those on working days, and emissions during peak hours contribute approximately half of the total daily UPTS emissions, while there are notable variations in the distribution of the carbon footprint among different districts. Moreover, our analysis reveals a positive correlation between the carbon footprints of buses and URT. The statistical analysis reflects different patterns of carbon footprint distribution on different dates during the pandemic, but the carbon footprint distributions on selected dates all follow a power-law distribution. This study may facilitate the understanding to the impacts of UPTS on the environment during the COVID-19 pandemic, and also provide important guidance and reference for the development of carbon emission reduction strategies.

An improved A-ECMS energy management for plug-in hybrid electric vehicles considering transient characteristics of engine

The plug-in hybrid electric vehicles (PHEVs) are playing an increasingly important role in urban public transportation systems for their unique potential for energy saving and emission reduction. However, as an enabling technology for the cost-efficient operation of PHEVs, the current energy management strategies (EMSs) rarely consider the transient characteristics of the engine, especially the limit of engine transient performance and the extra fuel consumption due to engine state changes. To improve the energy-saving effect of EMSs, an optimization study on energy management considering engine transient characteristics for PHEV is carried out in this paper. Firstly, a high-precision transient fuel consumption model is established based on artificial neural network (ANN) to accurately evaluate the real fuel consumption of engine under steady and unsteady states. Secondly, an adaptive equivalent consumption minimization strategy (A-ECMS) is constructed for PHEV, and the engine transient performance boundary is defined in the strategy to avoid unreasonable engine power surge decision. Thirdly, the transient fuel consumption model is incorporated into the equivalent fuel consumption cost function of A-ECMS to fully consider the impact of engine transient fuel consumption on the real-time power allocation of PHEV. The results show that the improved strategy weakens the state fluctuation of the engine, and makes the engine run more smoothly, resulting in a 99.16% reduction in the extra fuel consumption due to engine state changes. Finally, the fuel economy of the PHEV under the combined driving cycle based on the C-WTVC improved by 3.37%.

Exploring the Influence of Parking Penalties on Bike-Sharing System with Willingness Constraints: A Case Study of Beijing, China

Dockless bike-sharing has experienced explosive growth, establishing itself as an integral component of urban public transport systems. Challenges such as parking violations have spurred operators and users to pursue standardized management. While electronic parking spots are employed to promote standard parking, suboptimal parking layouts can lead to illegal parking. Inadequate post-violation penalties fail to achieve standard parking, while excessive punishment diminishes user engagement. This study combines parking spot density and penalties to incentivize standard parking, and Beijing, China, was selected as the research object. Using an SP questionnaire survey, a binary logistic model analyzes bike-sharing users’ standard parking behavior and willingness to adhere to different rules. Findings reveal that optimal walking distances range from 300 to 450 m for service levels and exceed 400 m for service efficiency. Influential factors include gender, age, occupation, usage behavior, and travel preferences. Users with high-frequency, low-convenience expectations, low travel costs, and flexible travel exhibit strong adherence. Additionally, user acceptance of the maximum distances without penalties follows an exponential distribution, with 80% accepting 400 m and 40% accepting 800 m. Enforcement has a visible effect within 300 m, but diminishes with longer distances. Excessive penalties result in significant user loss.

News of Transport Industry in November–December 2022

Our Journal publishes a selection of materials prepared based on news reports of the press centre of the Ministry of Transport of the Russian Federation and dedicated to several significant events of the past two months and some results of the entire 2022. The information refers to implementation of the national project «Safe High-Quality Roads», supply of new rolling stock for urban public passenger transport systems, modernisation, and safety of roads in general and on tourist routes, the impact of implementation of the national project on assessment of quality and accessibility of roads by public opinions. The information also covers issues of introducing new intelligent transport systems in cities, new legislative regulation of taxi services, the work of the CIS Transport Coordination Meeting, and reconstruction of the border crossing between Russia and Azerbaijan. In terms of the activities of the transport education system, attention is drawn to the past competitions of transport universities.

Understanding the mobility of public transport systems based on weighted multiplex networks

The structure and properties of urban public transport networks are important for understanding the mobility of public transport systems. However, most studies have ignored the impact of shared mobility and lacked the support of large-scale empirical data when exploring the mobility of urban public transport networks. Using multi-mode travel data, this study constructs a three-layer multiplex network and assigns weights to reflect the transport capacity of different modes (bus, metro, and shared bike) based on ridership and travel distances. The case study was conducted on the public transport system of Shenzhen, China. The results show that the degree distribution and edge weight distribution of the multiplex network exhibit distinct scale-free characteristics. The effect of the multiplex network model on single-layer networks suggests that cooperation between networks significantly improves the capacity and efficiency of urban public transport systems. Regression analysis reveals the effects of the urban population and land use on the demand for public transport services. The results show an intrinsic association between the public transport system and urban land use, which will facilitate the prediction of public transport demand and serve the sustainable development of cities.

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.