Public Transport Demand Research Articles

Assessing the evolution of Public Transportation demand over time based on real data through survival analysis in Bologna, Italy

Assessing the evolution of Public Transportation demand over time based on real data through survival analysis in Bologna, Italy

Changes in Fuel Prices and the Use of Public Transport: Insights from the European Union following the Invasion of Ukraine

Rising fuel prices are known to affect public transport (PT) demand. However, the impact of the oil market inflation and subsequent energy crisis following the 2022 Russian invasion of Ukraine on mobility behaviour in Europe has received little attention. This study examines shifts in fuel prices and PT mobility across 25 Europe-an Union countries between February and May 2022. Weekly variations in PT mobility were correlated with diesel and gasoline prices, and a multiple linear regression was conducted to assess the influence of contextual factors in explaining variations in PT usage. The results indicate a significant increase in PT mobility across all countries after February 2022, which was positively associated with rising fuel prices. Approximately 84% of the variance in mobility was explained, with more pronounced increases observed in countries characterised by lower telecommuting prevalence, higher housing costs relative to household income, greater price differentials between gasoline and diesel, lower adoption of alternative fuels, and higher motorisation rates. These findings suggest that the surge in fuel prices, driven by the 2022 energy crisis, may have stimulated an increase in transit ridership.

Understanding the impact of COVID-19 on mobility behavior of public transport passengers: the case of Metropolitan Area of Porto

Public transport systems worldwide experienced significant declines in usage during the COVID-19 pandemic due to lockdowns and work-from-home mandates. While numerous studies have examined these phenomena, there is still a need for empirical evidence that not only documents what occurred but also provides actionable insights for future transport planning. This study aims to enhance understanding of public transport passengers’ mobility behaviors during different stages of the pandemic, using the Metropolitan Area of Porto, Portugal, as a case study. Automated Fare Collection data from 2020 were analyzed and compared with data from the pre-pandemic year of 2019. The analysis included temporal, spatial, spatio-temporal, and sociodemographic dimensions. Key patterns and trends identified include a rapid recovery of ridership post-restriction easing, homogenized daily travel patterns, varied impacts on different transport modes, and significant shifts in demographic travel behaviors. These findings highlight the resilience of public transport demand and suggest that adaptive scheduling, enhanced safety measures, targeted support for vulnerable groups, promotion of off-peak travel, investment in bus infrastructure, and encouragement of multi-modal transport are essential strategies. Implementing these strategies can help improve public transport planning and mitigate the adverse effects of future crises.

Machine Learning for public transportation demand prediction: A Systematic Literature Review

Within the Intelligent Public Transportation Systems (IPTS) field, the prediction of public transportation demand is a key point for enhancing the quality of the services. These predictions can be exploited by transit operators, for service planning and management, as well as by passengers, for planning their trips. Hence, the public transportation demand prediction problem is being more and more investigated by the scientific community, with several different proposals in terms of Machine Learning (ML) techniques, employed sensors, considered external features, and so on. However, there is a lack of comprehensive reviews that summarize and organize the available proposals on this topic. Hence, researchers intending to propose novel ML solutions, and practitioners willing to implement such solutions, may be overwhelmed by the plethora and heterogeneity of scientific work.To support these kinds of profiles, in this paper, we provide a Systematic Literature Review on ML techniques for predicting public transportation demand. This review highlights a growing interest by the scientific community on this topic, but also the lack of open datasets (useful for benchmarking), and in general of reproducible studies. Moreover, while deep learning techniques are frequently favored for their potential in terms of accuracy, associated costs, interpretability, and energy consumption are often overlooked.

Impacts of COVID-19 on the premiums of proximity to railway stations: An in-depth analysis using passenger flows

As a result of the coronavirus (COVID-19) pandemic, work and leisure patterns have changed significantly, as have commuting and travel behavior. This has led to changes in the degree of dependence and demand for mass transportation. This paper proposes that these changes decrease people's demand to live near public transportation services and cause the decline of the positive externality created by public transit as measured by house prices. This paper explores the train stations of Taiwan's two largest cities (Taipei and Kaohsiung) by adopting various hedonic price models to estimate the COVID-19 effect on the premiums of proximity to rail stops. We then analyze whether the changes in premiums are affected by the passenger flow at the station. This paper uses four variables to measure passenger flows: the number of total passengers on services, the net number of passengers (the number leaving the local area minus the number of arriving at the local area), and the expected numbers of commuters and non-commuters. The empirical results of this paper show that after the COVID-19 outbreak, the monetary value of the proximity of railway stations decreased significantly. However, the COVID-19 effect on different stations is heterogeneous. The post-COVID-19 decrease in the positive externality associated with proximity to a rail station is significantly affected by passenger flows. The changes in the positive externality of stations found in this study suggest the public has reduced their dependence on stations due to changes in commuting behavior. We urge the government to pay attention to whether the commuting flows and the public's demand for public transportation have shifted and the use of vehicles and road transportation has risen.

Investigating the Nonlinear Effect of Land Use and Built Environment on Public Transportation Choice Using a Machine Learning Approach

Understanding the relationship between the demand for public transportation and land use is critical to promoting public-transportation-oriented urban development. Taking Beijing as an example, we took the Public Transportation Index (PTI) during the working day’s early peak hours as the dependent variable. And 15 land use and built environment variables were selected as the independent variables according to the “7D” built environment dimensions. According to the Modifiable Areal Unit Problem (MAUP), the size and shape of the spatial units will affect the aggregation results of the dependent variable and the independent variables. To find the ideal spatial unit division method, we assess how well the nonlinear model fits several spatial units. Extreme Gradient Boosting (XGBoost) was utilized to investigate the nonlinear effects of the built environment on PTI and threshold effects based on the ideal spatial unit. The results show that (1) the best spatial unit division method is based on traffic analysis zones (TAZs); (2) the top four explanatory variables affecting PTI are, in order: mean travel distance, residential density, subway station density, and public services density; (3) there are nonlinear relationships and significant threshold effects between the land use variables and PTI. The priority regeneration TAZs were identified according to the intersection analysis of the low PTI TAZs set and the PTI-sensitive TAZs set based on different land use variables. Prioritized urban regeneration TAZs require targeted strategies, and the results of the study may provide a scientific basis for proposing strategies to renew land use to increase PTI.

Problems of Development of Transport and Road Transport Infrastructure of Andijan City

As a result of the acceleration of the urbanization process, the increase of megalopolis cities, and the growth of the population, the demand and need for public transport is increasing every year. This, in turn, requires improving the quality of public transport service, improving its culture, adapting it to today's times and conditions. The President of the Republic of Uzbekistan told the officials of the field to fundamentally reform public transport, to develop public transport in the districts and cities of our country, to meet the needs of the regions, to organize transport infrastructure, to take a scientific approach to transport and logistics issues, in particular, to study foreign experience, to hire modern personnel for the field. set the tasks of training and recruitment. Public transport is essential for ensuring equitable access to transportation, reducing traffic congestion, protecting the environment, promoting social interactions, shaping urban development, and providing economic benefits. It is crucial to invest in and prioritize the development of high-quality public transport systems to address our transportation needs sustainably and improve the quality of life for individuals and communities. This article presents tasks on improving the public transport system in Andijan agro-development are presented.

The movement pattern changes of population following a disaster: Example of the Aegean Sea earthquake of October 2020

Unexpected seismic events can have a significant impact on our daily lives. Examining post-disaster population movement patterns contributes to disaster response and recovery operations. While most studies analyze population movement or displacement data, only a few examine multi-level movement patterns and consider public transportation demand. This paper aims to analyze changes in movement patterns following a major earthquake that severely affected İzmir in Türkiye, mainly using movement data from the Data for Good platform of Meta. According to the findings, on the first day following the earthquake, those traveling within İzmir considered the areas least damaged to be safer, despite the higher seismic intensity. The timeframe alignment between aftershock activity and the number of people traveling within İzmir suggests the routine movement pattern within the city was regained as the aftershocks decreased day by day. Unlike intra-city travel routines, there was an increase in the number of people moving to summer houses, located far from the center of İzmir. By November 2, 2020, three days after the earthquake, intra-city movements reached a new normal, as confirmed by public transportation use. However, intra-city travel patterns were impacted for approximately one month. This study reveals key findings from the evaluation of post-earthquake movements to improve disaster management decision making and aid crisis response and recovery operations.

Exploring the effects of public transport subsidies on satisfaction and ridership

The determinants of public transport demand are multifaceted, necessitating consideration of various factors that extend beyond the objective aspects of operation. In addition to travel time and costs, it is imperative to comprehend psychosocial factors influencing public transport usage to enhance policies geared toward fostering ridership and sustainable mobility. Empirical evidence suggests that subjective attributes, such as satisfaction and attitudes, exert a discernible influence on travel behavior and the utilization of public transport. Nevertheless, these linkages remain largely unexplored, especially within the context of large public transport networks in cities of the Global South. This paper aims to address this gap by evaluating the impact of demand-side subsidies in the form of travel vouchers on public transport ridership to understand the mediating effect of satisfaction levels according to the users’ socioeconomic characteristics. These satisfaction levels are related to overall satisfaction with transport, employment and educational opportunities, and life. To achieve this, a large-scale randomized controlled experiment was conducted in Bogotá, Colombia, involving a sample of frequent public transport users (N = 1,607). The participants were divided into a treatment group, which received the travel vouchers loaded onto their smart travel cards for four months, and a control group. Hybrid-ordered logit models were employed to estimate the average number of weekly trips undertaken before, during, and after the intervention. The result shows an increase in ridership by the treatment group, which does not solely depend on improved affordability; but is influenced by a complex interplay of socioeconomic factors and shifts in satisfaction levels. This confirmed that travel satisfaction can contribute to increased public transport demand.

Investigating urban mobility through multi-source public transportation data: A multiplex network perspective

The integration of multi-source and diverse spatio-temporal travel data provides a comprehensive insight into urban mobility. Using data from Shenzhen's public transportation system, this study presents an analytical framework based on multiplex networks to examine variations in multi-mode public transportation usage (metro, bus, taxi, and shared bike) and their correlation with the built environment. This framework encompasses the analysis of network topological characteristics, centrality, and communities. The examination of network topological characteristics reveals that the multiplex transportation network exhibits high global accessibility and local connectivity. Network centrality analysis, focusing on weighted outdegree centrality, captures the patterns of public transportation ridership. Centrality modeling, employing the light gradient boosting machine, demonstrates a nonlinear relationship between ridership and the built environment. Factors including population density, residential land use percentage, entertainment service density, restaurant density, and metro station density consistently exhibit positive correlations with ridership across different times of the day. The community structure analysis, using consensus community detection, indicates that distinct urban areas exhibit clustering behavior based on public transportation demand patterns, forming distinct communities that closely align with the functional zoning of urban planning. These findings could provide valuable insights for the strategic planning of transportation services and the built environment.

Public transport provision and social sustainability in Sweden

ABSTRACT The provision of public transport is important for a sustainable society and is vital for many groups. Children, older people, and people from carless households are examples of such groups: they rely on public transport and risk social exclusion without it. The aim of this study is to identify areas in Sweden where an improved public transport system might enhance social sustainability. To this end, an index of public transport needs at a sub-municipality level was calculated along with an index of public transport provision using data from public transport authorities in Sweden. The results indicate that areas with a mismatch between the provision of and estimated demand for public transport are relatively heterogenous. High gaps exist both in rural and peripheral regions of Sweden as well as the suburbs of urban regions.

Ride-hailing Service and The Shape of Urban Mobility (Case Study: The City of Makassar, Indonesia)

The significant gap between supply and demand of flexible and comfortable public transport within cities in the lower-income countries encourages the opportunity for the private sector to provide a ride-hailing service. Since its introduction in 2010, the ride-hailing service has continued to grow and has shaped urban mobility in the past twenty years. Due to the growth of ride-hailing services, some concerns arise regarding their contribution to the increasing traffic volume and the increased dependency on smaller-capacity vehicles in citizen mobility. This study aims to: 1) analyze the travel patterns of ride-hailing users based on their mobility needs; 2) analyze the relationship between travel characteristics and users’ socio-economic background and travel characteristics; 3) analyze the implication of ride-hailing users in shaping Makassar’s urban mobility. This study focused on the city of Makassar as the case study. The data in this study was obtained from an online survey of ride-hailing users with 270 samples. Several approaches are used for the analysis, i.e., descriptive statistics, spatial analysis, and network analysis. The result shows that 1) Most of the ride-hailing users are young generation (Gen Z). They perform a short-distance trip daily (internal trip within the district). 2) Regardless of the users’ age, income, and private vehicle ownership, ease of service is the foremost preference of the users since it is application-based and can be accessed using a smartphone. Door-to-door service is also one of the advantages provided by ride-hailing services.

Determinants of irregular demand for regional rail passenger services – case study of High Tatras in Slovakia

AbstractThe demand for public transport by tourists increases significantly in tourist-attractive destinations. This is in addition to regular passengers commuting to school and work. The level of irregular demand is influenced by several factors related to the characteristics of the day of the week, the period of the year, and the current weather. The main goal of the paper is to verify which factors most influence the irregular demand for transport in a tourist-attractive area to ensure operational planning of public passenger transport. Thanks to this, it is possible to ensure sufficient capacity and, at the same time, the efficiency of the operation of public passenger transport. The paper analyzes the main determinants of the irregular demand for regional public rail passenger transport in the High Tatras region of Slovakia. Multiple linear regressions were used to model the number of irregular passengers. The variables representing the day of the week, the attractiveness of the period, and the holiday were found to be the most significant. The variables describing the weather such as maximum daily temperature, precipitation, clouds, and wind had less influence. The obtained mathematical models for forecasting the irregular demand for public passenger transport can help optimize the timetable’s operational setting and the train sets’ size.

Twitter, Instagram, Youtube Speak: Understanding Sentiments on LRT Jabodebek Services via Inset Lexicon, IndoBERT and BERTopic Approaches

Rapid urbanization in the Jabodetabek region has led to an increased demand for public transportation. Responding to this need, the government has initiated the development of a new public transportation mode, namely the LRT Jabodebek. However, as a new public transportation mode, the LRT Jabodebek has both strengths and weaknesses in serving the community. Various public comments are expressed through social media platforms. To enhance service quality, it is crucial to pay attention to public comments. Therefore, a sentiment analysis is required to identify and delve into both positive and negative sentiments regarding the LRT Jabodebek service through comments on Twitter, Instagram, and Youtube. The methodology involves a combination of Lexicon-based, IndoBERT model, and BERTopic approaches to gain a deeper understanding of the Jabodebek LRT service trends. The study reveals that 55.9% of the 8,523 comments carry a negative sentiment, and the IndoBERT model achieves an accuracy of 85.97% in sentiment classification.

Compensating energy demand of public transport and yielding green hydrogen with floating photovoltaic power plant

The last three decades have seen a dramatic increase in the renewable energy sector as a result of increased human energy consumption and environmental concerns about fossil fuels. Offshore renewable energy sources are the most alluring and promising technologies because of more energy potential, less space, and visual restrictions than onshore ones. Among those, floating solar photovoltaic (FPV) has a remarkable reputation. The present study focuses on a viable way to replace energy resources derived from fossil fuels with renewable solar energy. In this regard, electrical energy demand is investigated where a floating photovoltaic system and integrated hydrogen production unit are employed on water surface of Yamula Dam. Energy demand of public trams would be compensated with electricity generated by FPV and rest of energy would be utilized for hydrogen production. Key results illustrated that in various scenarios, the energy generation amounts were around 31×106 kW, 32×106 kW, and 39×106 kW, while the energy consumption amounts were approximately 24×106 kW. It was evident that the energy created more than offset the amount consumed. It was also note that the total costs of entire system were $94.1 M, $78.5 M and $71.2 M according to the different cases. It was also observed that in October and November, the remaining energy from the Bozankaya tram produced the most hydrogen with 125 kg, whereas in September and October, the remaining energy from the Sirio tram produced approximately 70 kg.

Exploring the relationship between public transport use and COVID-19 infection: A survey data analysis in Madrid Region

The COVID-19 pandemic has changed people's mobility patterns, increasing the preference for private modes and reducing the public transportation demand. Most scientific contributions have studied the role of mobility levels in the spread of the virus and the influence of public transport on COVID-19 infections, but ignoring the importance of individual-level variables potentially affecting COVID-19 infection, such as daily habits. This paper analyses the relationship between the probability of being infected by COVID-19 and using public transport through a survey data analysis, taking Madrid (Spain) as the case study. This research uses a survey campaign with more than 15,000 responses, capturing socio-demographic aspects, COVID-19 infections, daily habits, and mobility patterns with high risk of COVID-19 infection. Through a multilevel probit model, this paper explores the extent to which a higher use of public transport is related to a greater likelihood of COVID-19 infection. The results suggest a relationship, although not very strong, between the probability of infection and the conjunction of higher frequency of use of metro services and level of crowding during the trip, whereas the use of bus services and travel time within the vehicle do not appear to affect.

Tram-Pedestrian Collision Modeling Using Experimental Data—Its Validation, Repeatability, and Challenges: A Pilot Study

To satisfy the needs of growing urban populations, mitigate the impacts of climate change, and improve the environmental conditions and quality of life in cities, there has been an increasing demand for public transport, especially effective and economically beneficial trams. As a consequence, the issue of pedestrians’ safety has become more important because of a higher risk of tram-pedestrian collisions. The article is focused on the development of a computational model that can accurately simulate such collisions, and its challenges and limitations. Experiments were conducted using various types of trams, different impact speeds, and an anthropomorphic test device (dummy). Computational models of the tram front ends were created based on CAD geometry supplied by VÚKV a.s. The finite-element mesh itself used a combination of 1D, 2D, and 3D elements. Depending on the shape of the individual components, TRIA, QUAD, HEX, PENTA, and TETRA elements were used for modeling. The results showed that the consequences of these collisions are sensitive to small differences in the posture of the dummies. Therefore, it is difficult to identify a generally more pedestrian-friendly shape for trams using the dummy-tram crash test method.

Specifics of Creating a Public Transport Demand Model for Low-Density Regions: Lithuanian Case

A transport model usually consists of a demand model and an available transport network model. The purpose of this article is to identify the key specifics for the development of a regional public transport (PT) demand model and to point out the differences from the urban PT demand model. The traditional four-step transport planning demand model consists of trip generation, trip distribution, modal split, and assignment. This article consists of PT model development, calibration, and validation. A PTV VISUM macroscopic modeling program is used for this research. As a result, this article presents basic suggestions for how a PT demand model should be developed in regions. The presented suggestions for developing a PT demand model can be applied to any low-density region. The rest of the article is structured as follows: (1) Background: presents a literature analysis of the four-step model, modal splits, and the features of the PTV VISUM program; (2) Methods: describes the considered region of Lithuania and the data of the developed model; describes the four-step model, which is adapted to the Lithuanian region; (3) Results: presents the results and main suggestions for creating a PT demand model; and (4) Conclusions: presents the main conclusions of the study.

Impact of Non Scheduled Public Mini Bus Stops on Transit Trip Time: A Case Study Amman-Jordan

Public transport services face many challenges around the world. Due to the rise in demand for public transport, authorities desperately need to develop the systems, and it provides a service to be the same level for private cars in terms of service reliability measures. This research aims to study the influence of the non-scheduled random public mini bus stops on travel time for a specific mini bus route. These non-scheduled stops became a habitual behavior for Public mini buses, which affects the daily users of this transportation service in Jordan. In this study, a comprehensive plan was designed to collect data regarding the mini bus behavior along a selected bus route, using a transit on- board survey, GPS and questionnaires. The data collection involved locations of non-scheduled stops, number of stops, distance and speed. The data was then used to develop a trip time model. The developed model detects the delay time due to the non- scheduled mini bus stops. The analysis of the data indicated that passengers mainly depend on random mini bus stops because there aren’t scheduled stops, thus a large impact on traffic jam. Furthermore, it was found that the main variables that affect the trip travel time are the total delay time due to stopping randomly to board and alight passengers. The study concluded that eliminating the non-scheduled mini bus stops will reduce the trip time, thus get improve he service reliability.

Determination of the Long-Haul Routes Attractiveness in Railway Passenger Transport in Europe

A crucial component and system of public passenger transportation that must serve as the foundation for the national and global comprehensive integrated system of passenger transport is high-quality, appealing, and efficient long-distance railway passenger transport. The demand for public passenger transport has drastically dropped from March 2020 in numerous European countries as a result of various COVID-19 limitations. The performance of passenger transportation has been impacted by the reduction in mobility. Even though the situation has already improved, finding fresh, workable solutions is still essential for a well-functioning rail passenger transport system. The contribution proposes a method for attractiveness determining of long-haul routes for railway passenger transport in Europe. Three key indicators that have a substantial impact on this issue are suggested based on specific scientific methodologies. The paper also includes real examples and calculations for the practical use of indicators determination of chosen long-distance transport routes.