Tram or Bus? A Stated-Preference Analysis of Road User Mode Choice in Larissa, Greece

This paper presents the first representative estimates of the marginal willingness to pay for a reduction in travel time (VTTS) for Austria, being of great importance for transport policy appraisals.The main focus is to investigate mode and user-type effects using a pooled RP/SP modeling approach for mode, route and destination choice data, revealing average VTTS estimates for car (9.90 Euro/h), public transport (3.90 Euro/h), bike (7.30Euro/h) and walk (11.40 Euro/h).The only user characteristic being able to decompose this large difference in average VTTS between car and public transport into a smaller part, that can be purely attributed to the mode-specific valuation of in-vehicle travel time, is urban residential location area:When controlling for it, the VTTS difference becomes 5.5 Euro/h, which, compared to the total average VTTS difference of about 6 Euro/h, is still relatively high.As our results indicate that in the case of Austria, characteristics of the mode are more important than characteristics of the users, and that the conditions of travel time spent in public transport are perceived as more pleasant than in a car, the investigation of the value of time assigned to travel (VTAT) is a fundamental next research step.

What value do travelers put on connectivity to mobile phone and Internet networks in public transport? Empirical evidence from the Paris region

Public transport and cycling often are combined in one trip. However, this combination has not attracted much research attention. Existing research has identified several hard factors that may explain the combined use of public transport and bicycle: station accessibility, distance to the station, and bicycle facilities at stations. Even though the effect of attitudes toward mode choice is widely acknowledged, the authors are not aware of any study that analyzes this effect on the combined use of bicycle and public transport. The effect of attitudes on the decision to commute by both public transport and bicycle was investigated. Results indicated that public transport–bicycle commuters differed significantly from those who commuted by only car, public transport, or bicycle. Nevertheless, public transport–bicycle commuters shared similarities with public transport commuters (who did not cycle to or from the station) and bicycle commuters. Public transport commuters had a more positive attitude toward car commuting and a less favorable attitude toward cycling, and bicycle commuters had a more positive attitude toward cycling and a less favorable attitude toward public transport than did public transport–bicycle commuters. Public transport–bicycle commuters also shared most beliefs about public transport with public transport commuters and shared beliefs about cycling with bicycle commuters and public transport commuters but differed on several characteristics. Nevertheless, differences between the groups were significant and indicated that commuters who used both public transport and a bicycle in one trip were different from single-mode commuters.

Securing public transportation ridership is critical for developing a sustainable urban future. However, many modern and growing cities are facing declines in public transport usage. Existing systems for analyzing and identifying weaknesses in public transport connections face major limitations. In cities, origin-destination (OD) matrices--which measure the flow of people between different geographical regions--are often generated using household surveys, which are time consuming and lack spatial and temporal accuracy. Focus in more recent research has been drawn towards using cellphones to overcome these limitations. In this article, we show that urban mobility patterns and transport mode choices can be derived from cellphone call detail records coupled with public transport data recorded from smart cards. Specifically, we present new methods to determine the spatial and temporal variability of public and private transportation usage and transport mode preferences across the dense, metropolitan city-state of Singapore.The cellphone dataset consists of location data of 3.4 million anonymized users of SingTel, Singapore's largest telecommunications company with a market share of 45.3%. The data were recorded during a two-month period from mid-March to mid-May 2011. A call detail record (CDR) includes the location (spatial resolution of 400 m) of the cell tower each cellphone connects to and was created in the case of following network events:• a phone call was initiated or received (at the beginning and at the end of the call).• a short message was sent or received.• the cellphone user accessed the data network (for example, to open a website or retrieve emails).By applying a clustering detection algorithm to these call detail records, we detect individual trips and extrapolate the overall mobility of people between the 55 administrative districts of Singapore (taking into account the market share of SingTel and the cellphone penetration of 144 %). The mode share of private transport usage is then derived by computing the difference between the estimated overall mobility and the traces of 4.4 million public transportation smart card users during the same time period.We validate out data mining approach using the results from Singapore's Household Interview Travel Survey (HITS): Our results revealed that there are 3.5 million (HITS: 3.5 million) inter-district trips by public transport and 4.3 million (HITS: 4.4 million) inter-district trips by private transport (including taxis). Private transport usage dominates in regions without access to a subway line (see Figure 1). Along with classifying which transportation connections are weak or underserved---where people prefer to take private rather than public transport---the analysis shows that the mode share of public transport increases from 38% in the morning to 44% around mid-day and 52% in the evening.The value of deriving such patterns using cellphone call detail records have important implications not only for urban and transportation planning, but also for other domains such as disease control in cities. As humans serve as the primary and secondary vectors of many infectious diseases, understanding from where people arrive and depart and by which transportation modes people are traveling, we have the potential to model how and where diseases might be spreading and from where they might originate.

Public transport (PT) usage was severely impacted during the COVID-19 pandemic, resulting in up to a 90% reduction in many cities in 2020. Numerous studies have been conducted since then to determine the relationship between individual-level factors (such as gender, attitudes, etc.) and the decrease in PT usage during the pandemic. Despite the evidence provided, findings are dispersed, and for several factors contradictory, making it challenging to reach any generalised conclusion. Furthermore, a comprehensive comparison of the effect sizes among travellers’ factors affecting PT use during this period is yet to be compiled. This paper aims to address these gaps by systematically reviewing the existing evidence and synthesising the effect sizes of travellers’ factors through a meta-analysis. We first identified 36 studies that statistically assessed the contribution of 15 individual-level factors on PT usage during the COVID-19 pandemic. By merging the empirical evidence of those studies, the direction of the association between those factors and PT usage was analysed. Then, after selecting comparable studies, meta-analyses were conducted for each factor to estimate the corresponding pooled effect sizes. The meta-analysis established that car availability, teleworking opportunities and high educational level contributed the most to reducing PT use during the pandemic. These factors increased the odds of reducing PT usage compared with the pre-pandemic by about three times. Factors such as COVID-19 risk perception, gender, high income and health had a moderate effect on the decision to stop using PT. PT habits, travel distance and physical accessibility also influenced PT use during the pandemic. Geographical location and the pandemic period explained part of the heterogeneity found. The findings provided in this study can help policy-makers understand the impacts of travellers’ factors on the decision to reduce PT usage during future pandemics/epidemics and guide public policies accordingly.

Public transport users versus private vehicle users: Differences about quality of service, satisfaction and attitudes toward public transport in Madrid (Spain)

Mediation effects of social isolation on pathways connecting public transport use with subjective wellbeing among older people

Increasing sustainable travel patterns necessitates a considerable amount of research aimed at the detail measurement and understanding of travel mode choice. Most Public transport (PT) service quality improvements are expected to have positive effects on a shift from car to PT. The effects of improvements such as integrated ticketing is often overlooked in mode choice analysis. Considering the widespread implementation of integrated ticketing schemes globally and some evidence confirming the positive substitution effects between car and season ticket ownerships, the objective of this study was to examine the correlation between mode choice for commuting and multi-regional integrated ticketing. A stated preference (SP) survey was conducted along the E4 motorway between Stockholm and Uppsala, Sweden. 84 out of the 96 respondents answered the SP questions, resulting in 756 SP observations. Subsequently, binary and mixed logit models were estimated. The results suggested that integrated ticketing has an overall positive effect on promoting greater public transport use; in particular, male car commuters compared to females are more likely to switch to PT for commuting. The methodological and policy implication of this positive association is that the effects of integrated ticketing should be included in demand modeling to improve both the accuracy of the estimates and the policy decisions that are based on these estimates.

Transport strategy in European Union supports public transport against individual transport. The reason is using of public transport is possible to fulfil all the goals of the EU strategy in the field of road safety. The paper deals with the issue of supporting public passenger transport and it is integration with the aim of ensuring the sustainable mobility of population. The aim of this paper is to identify the importance of public passenger transport and define method for creating effective integrated transport systems. The paper points to the importance of public passenger transport and the reasons why the population prefers cars. Based on the analysis, it is arguable that public passenger transport without mutual integration is not capable enough to compete with individual motoring. Contribution proposes the process integration of public passenger transport as a key elements in increasing road safety. Contribution confirms the hypothesis that the integration of public passenger transport and achieving a higher use of public passenger transport of population can contribute to improving of road safety.

Does emphasis change in transportation mode choice affect workers’ actual mode choice? Implications from Japan in the COVID-19 era

The article describes the historical and geographical aspects of the development of urban passenger transport in Ukraine. Urban transport in Ukraine has long historical traditions. The first electric transport networks in the cities of Ukraine were among the oldest in Eastern Europe. Historical and geographical studies of the development of public transport systems in Ukraine were conducted by many scientists, the results of which are reflected in reference books, monographs, and articles. In the development of urban spaces and transport in Ukraine, the authors distinguish the following stages: intensive industrialization in imperial times, the First World War and national liberation struggles, the interwar period (mass industrialization of eastern Ukraine), the Second World War, post-war reconstruction of cities, mass housing and industrial construction, the economic crisis and the decline of industrial giants, the revival of urban spaces and transport, the Russian-Ukrainian war. The prototype of modern urban transport in Ukraine was the horse-drawn tram and omnibuses, which appeared on the streets of cities at the end of the 19th century. In the 1890s, the first electric tram networks were launched in Kyiv, Lviv, Chernivtsi, and Dnipro. By 1914, trams were operating in 13 cities of Ukraine. During the First World War and the national liberation struggles (1914-1922), trams and horse-drawn trams often stopped, and in Kremenchuk, traffic was suspended. Many tram and city bus networks began operating during the mass industrialization of the cities of Central and Eastern Ukraine in the 1920s and 1930s. In 1935, the first trolleybus system in Ukraine was launched in Kyiv, and later such systems were launched in Kharkiv, Donetsk, and Chernivtsi. During World War II, the urban transport networks in Ukraine suffered significant damage and destruction. Due to the destruction of the infrastructure, the tram systems in Kropyvnytskyi, Sevastopol and Kerch were dismantled. In the post-war years, the public transport networks in the cities of Ukraine were gradually restored, but there was a shortage of the necessary equipment and rolling stock. During this period, tram systems were opened in Druzhkivka, Konotop and trolleybuses in Odessa, Dnipro, Lviv, Sevastopol, Simferopol, Alchevsk, Zaporizhia. The world’s longest trolleybus line Simferopol - Yalta, 80 km long, was built in Crimea. Bus transport began to operate in most large cities. During the mass residential and industrial development of Ukrainian cities in the 1960s-1980s, more than 30 new trolleybus systems were implemented. Large-capacity buses and trolleybuses, mostly imported, began to operate in the cities. During this period, the first metros in Ukraine began to operate in Kyiv (1960) and Kharkiv (1976). After the collapse of the USSR and Ukraine gained independence, a deep economic crisis began. There was a decline in passenger transportation volume in cities, and transport operated intermittently due to a shortage of fuel, rolling stock, and spare parts. The market for private motor transportation by minibuses and small buses began to develop, leading to street congestion and increased environmental pollution. Tram (Kyiv, Kharkiv, Makiivka) and trolleybus (mainly cities in Donbas) lines were dismantled in many cities. In 1995, the third metro in Ukraine, in Dnipro, was put into operation. Since 2006-2007, due to the balancing of local budgets and the implementation of state programs, investments in the field of urban passenger transport have increased. New lines were built, and rolling stock was purchased. A significant upgrade of infrastructure and transport took place as part of the preparation and holding of the Euro 2012 Football Championship in the cities of Ukraine. However, in several cities of Donbas, the movement of urban electric transport was suspended (Makiivka, Kadiivka, Kostiantynivka, Dobropillya). In 2014, the Russian-Ukrainian war began due to the occupation of Crimea and part of Donbas, and in 2022, a full-scale invasion of Russian troops into the territory of Ukraine took place. As a result of hostilities and shelling, the urban transport infrastructure in many cities was damaged or destroyed. In many cities of Donbas near the front line, urban electric transport stopped working (Alchevsk, Bakhmut, Lysychansk, Luhansk, Avdiivka, and others). Many cities in Ukraine received assistance from partners in EU countries through rolling stock. In the process of modern transformation of urban transport systems in Ukraine, significant challenges arise: reorientation and reduction of passenger flows, changes in the functions of cities and their districts, changes in the number and employment of the population, and the introduction of innovative technologies. Keywords: bus transport, electric transport, city, urban transport, transport, transport network, Ukraine.

The attractiveness of public transport is not high and therefore it is obvious that private transport is the preferred mode of transport. The interest in public transport has declined even more since the outbreak of the COVID-19 pandemic, during which passengers feared the rapid spread of the disease in public vehicles. As a result, the habit of traveling individually has increased. This paper highlights the massive increase in private transport in the V4 Countries by the use of private vehicles and the related decrease in use of public transport. It also presents some of the relevant tools of the Smart City concept that could improve the use of public transport in cities. This paper is aimed at highlighting the current use of public bus transport (including public transport) in the V4 countries vis-a-vis the ongoing COVID-19 pandemic and highlighting the information and communication technology (ICT) - related means of incentivization.

Geographic access, including mode of transportation, to health care facilities remains understudied. To identify sociodemographic factors associated with public vs private transportation use to access health care and identify the respondent, trip, and community factors associated with longer distance and time traveled for health care visits. This cross-sectional study used data from the 2017 National Household Travel Survey, including 16 760 trips or a nationally weighted estimate of 5 550 527 364 trips to seek care in the United States. Households that completed the recruitment and retrieval survey for all members aged 5 years and older were included. Data were analyzed between June and August 2022. Mode of transportation (private vs public transportation) used to seek care. Survey-weighted multivariable logistic regression models were used to identify factors associated with public vs private transportation and self-reported distance and travel time. Then, for each income category, an interaction term of race and ethnicity with type of transportation was used to estimate the specific increase in travel burden associated with using public transportation compared a private vehicle for each race category. The sample included 12 092 households and 15 063 respondents (8500 respondents [56.4%] aged 51-75 years; 8930 [59.3%] females) who had trips for medical care, of whom 1028 respondents (6.9%) were Hispanic, 1164 respondents (7.8%) were non-Hispanic Black, and 11 957 respondents (79.7%) were non-Hispanic White. Factors associated with public transportation use included non-Hispanic Black race (compared with non-Hispanic White: adjusted odds ratio [aOR], 3.54 [95% CI, 1.90-6.61]; P < .001) and household income less than $25 000 (compared with ≥$100 000: aOR, 7.16 [95% CI, 3.50-14.68]; P < .001). The additional travel time associated with use of public transportation compared with private vehicle use varied by race and household income, with non-Hispanic Black respondents with income of $25 000 to $49 999 experiencing higher burden associated with public transportation (mean difference, 81.9 [95% CI, 48.5-115.3] minutes) than non-Hispanic White respondents with similar income (mean difference, 25.5 [95% CI, 17.5-33.5] minutes; P < .001). These findings suggest that certain racial, ethnic, and socioeconomically disadvantaged populations rely on public transportation to seek health care and that reducing delays associated with public transportation could improve care for these patients.

The impacts of ICT-based mobility services vary in different cities, depending on socioeconomic, urban form, and cultural parameters. The impacts of car-sharing and ridesourcing on public transport have not been investigated appropriately in post-Soviet Union cities. This study presents exploratory evidence on how ridesourcing and car-sharing affect public transport usage in Moscow. Additionally, it studies how demographics, spatial parameters, attitudes, and travel preferences influence the frequency of use of ridesourcing and car-sharing in Moscow. An online mobility survey was conducted at the beginning of 2020 among respondents (sample size is 777) in the Moscow agglomeration. Overall, 66% of ridesourcing users shifted from public transport to these mobility services, which shows the substitutional impact of ridesourcing on public transport. Additionally, the logit model indicates that the regular use of ridesourcing negatively correlates with the regular use of buses/trams/trolleybuses in Moscow. The impact of car-sharing on public transport seems less substitutional and more complementary than the impact of ridesourcing. Overall, 40% of car-sharing users would replace their last car-sharing trip with public transport if car-sharing was unavailable. Moreover, the logit model indicates a positive association between the regular use of car-sharing and the use of buses/trams/trolleybuses. Moreover, the modal split analysis shows a bigger share of public transport use and walking than car use among citizens’ urban journeys in Moscow.

BackgroundActive transportation is a crucial sort of physical activity for developing sustainable environments and provides essential health benefits. This is particularly important in Latin American countries because they present the highest burden of non-communicable diseases relative to other worldwide regions. This study aimed to examine the patterns of active transportation and its association with sociodemographic inequities in Latin American countries.MethodsThis cross-sectional study was conducted in eight countries. Participants (n = 8547, 18–65 years) self-reported their active transportation (walking, cycling, and total) using the International Physical Activity Questionnaire. Sex, age, ethnicity, socioeconomic level, education level, public and private transport use, and transport mode were used as sociodemographic inequities.ResultsParticipants spent a total of 19.9, 3.1, and 23.3 min/day with walking, cycling, and total active transportation, respectively. Mixed and other ethnicity (Asian, Indigenous, Gypsy, and other), high socioeconomic level as well as middle and high education level presented higher walking than Caucasian, low socioeconomic and education level. Private transport mode and use of ≥ 6 days/week of private transport showed lower walking than public transport mode and ≤ 2 days/week of private transport. Use of ≥ 3 days/week of public transport use presented higher walking than ≤ 2 days/week of public transport. Men had higher cycling for active transportation than women. Use of ≥ 3 days/week of public transport use presented higher cycling than ≤ 2 days/week of public transport. ≥6 days/week showed lower cycling than ≤ 2 days/week of private transport use. Men (b: 5.57: 95 %CI: 3.89;7.26), black (3.77: 0.23;7.31), mixed (3.20: 1.39;5.00) and other ethnicity (7.30: 2.55;12.04), had higher total active transportation than women and Caucasian. Private transport mode (-7.03: -11.65;-2.41) and ≥ 6 days/week of private transport use (-4.80: -6.91;-0.31) showed lower total active transportation than public transport mode and ≤ 2 days/week of private transport use. Use of 3–5 (5.10: 1.35;8.85) and ≥ 6 days/week (8.90: 3.07;14.73) of public transport use presented higher total active transportation than ≤ 2 days/week of public transport use. Differences among countries were observed.ConclusionsSociodemographic inequities are associated differently with active transportation across Latin American countries. Interventions and policies that target the promotion of active policies transportation essential to consider sociodemographic inequities.Trial registrationClinicalTrials.Gov NCT02226627. Retrospectively registered on August 27, 2014.