European Transport Research Review最新文献

Background: Last-mile delivery (LMD) is becoming more and more demanding due to an increasing number of users and traffic problems in cities. Besides, medical crises (like the COVID-19 outbreak) and air pollution represent additional motives for the transition from traditional to socially and environmentally sustainable LMD mode. An emerging problem for companies in the postal and logistics industry is how to determine the best LMD mode in a multi-criteria setting under uncertainty.

Method: For the first time, an extension of the Weighted Aggregated Sum Product ASsessment (WASPAS) method under the picture fuzzy environment is presented to solve the LMD mode selection problem. The introduced picture fuzzy set (PFS) based multi-criteria decision-making (MCDM) method can be highly beneficial to managers who are in charge of LMD since it can take into account the neutral/refusal information and efficiently deal with high levels of imprecise, vague, and uncertain information. The comparative analysis with the existing state-of-the-art PFS-based MCDM methods approved the high reliability of the proposed picture fuzzy WASPAS method. Its high robustness and consistency are also confirmed. The presented method can be used to improve LMD in urban areas worldwide. Besides, it can be applied to solve other emerging MCDM problems in an uncertain environment.

Findings: A real-life case study of Belgrade is presented to fully illustrate the potentials and applicability of the picture fuzzy WASPAS method. The results show that postomates are the best mode for LMD in Belgrade, followed by cargo bicycles, drones, traditional delivery, autonomous vehicles, and tube transport.

Supplementary information: The online version contains supplementary material available at 10.1186/s12544-021-00501-6.

Background: A significant mode shift will be required in order to meet the ambitious greenhouse gas emissions reduction targets in Germany and elsewhere. Such a mode shift can only be achieved by a combination of drastic push and pull measures. Getting commuters to switch modes might be particularly difficult and have a negative impact on their access to employment and welfare.

Methodology: We investigate the potential for a mode shift from car to public transport for German commuters using a data-driven approach based mainly on open data sources that avoids complex transport model runs. Different datasets on the home and workplace location of all employees in Germany are consolidated to create an origin-destination commuter matrix at traffic analysis zone level. The commuter matrix is merged with travel time data for car and public transport to calculate a spatially disaggregated and mode-specific measure of accessibility. The comparison of accessibility by car and public transport is used to derive the potential for a mode shift and identify potential challenges and barriers.

Results: Public transport accessibility to workplaces is poorer across the country compared to access by car. On average, public transport travel times are almost three times higher than the corresponding car travel times. The differences in accessibility are largely independent of the region type. Results are validated by an independent dataset from a household travel survey. Based on these results, the potential for a mode shift appears to be very low.

The aim of this paper is to detect port maritime communities sharing similar international trade patterns, by a modelisation of maritime traffic using a bipartite weighted network, providing decision-makers the tools to search for alliances or identify their competitors. Our bipartite weighted network considers two different types of nodes: one represents the ports, while the other represents the countries where there are major import/export activity from each port. The freight traffic among both types of nodes is modeled by weighting the volume of product transported. To illustrate the model, the Spanish case is considered, with the data segmented by each type of traffic for a fine tuning. A sort of link prediction is possible, finding for those communities with two or more ports, countries that are part of the same community but with which some ports do not have yet significant traffic. The evolution of the traffics is analyzed by comparing the communities in 2009 and 2019. The set of communities formed by the ports of the Spanish port system can be used to identify global similarities between them, comparing the membership of the different ports in communities for both periods and each type of traffic in particular.

Background: The purpose of this study is to analyse what factors that explain individual differences in walking and cycling when commuting in different parts of Sweden. Walking and cycling is potentially accessible all over the country, while well developed public transport is mainly a viable option in densely populated areas.

Methodology: The importance of differences in local characteristics for the choice of transport mode will be scrutinised, together with individual differences in attitudes andpreferences. Data is collected through a survey sent to people living in five Swedish municipalities with different demographic, socio-economic ,infrastructural and geographical characteristics.

Results: The results for the pooled sample indicate that the choice to walk/cycle when commuting is related to health considerations and environmental concerns. Distance to work/school is also an important factor. Men tend to be more prone to choose active transport, and so do respondents with lower income. The results further reveal that availability of safe routes for walking and cycling are important for the choice to walk/cycle when commuting. As health considerations are important, we suggest policy makers to stress health motives when they promote walking and cycling in the future. Our results further suggest that it is important to consider availability and accessibility in community planning, and to prioritize safety and comfort of walking and cycling, not least in parts of the country where public transport is not an economically viable option.

Background: The COVID-19 pandemic is a new phenomenon and has affected the population's lifestyle in many ways, such as panic buying (the so-called "hamster shopping"), adoption of home-office, and decline in retail shopping. For transportation planners and operators, it is interesting to analyze the spatial factors' role in the demand patterns at a POI (Point of Interest) during the COVID-19 lockdown viz-a-viz before lockdown.

Data and methods: This study illustrates a use-case of the POI visitation rate or popularity data and other publicly available data to analyze demand patterns and spatial factors during a highly dynamic and disruptive event like COVID-19. We develop regression models to analyze the correlation of the spatial and non-spatial attributes with the POI popularity before and during COVID-19 lockdown in Munich by using lockdown (treatment) as a dummy variable, with main and interaction effects.

Results: In our case-study for Munich, we find consistent behavior of features like stop distance and day-of-the-week in explaining the popularity. The parking area is found to be correlated only in the non-linear models. Interactions of lockdown with POI type, stop-distance, and day-of-the-week are found to be strongly significant. The results might not be transferable to other cities due to the presence of different city-specific factors.

Conclusion: The findings from our case-study provide evidence of the impact of the restrictions on POIs and show the significant correlation of POI-type and stop distance with POI popularity. These results suggest local and temporal variability in the impact due to the restrictions, which can impact how cities adapt their transport services to the distinct demand and resulting mobility patterns during future disruptive events.

Background: COVID-19 pandemic is a challenge that the world had never encountered in the last 100 years. In order to mitigate its negative effects, governments worldwide took action by prohibiting at first certain activities and in some cases by a countrywide lockdown. Greece was among the countries that were struck by the pandemic. Governmental authorities took action in limiting the spread of the pandemic through a series of countermeasures, which built up to a countrywide lockdown that lasted 42 days.

Methodology: This research aims at identifying the effect of certain socioeconomic factors on the travel behaviour of Greek citizens and at investigating whether any social groups were comparatively less privileged or suffered more from the lockdown. To this end, a dynamic online questionnaire survey on mobility characteristics was designed and distributed to Greek citizens during the lockdown period, which resulted in 1,259 valid responses. Collected data were analysed through descriptive and inferential statistical tests, in order to identify mobility patterns and correlations with certain socioeconomic characteristics. Additionally, a Generalised Linear Model (GLM) was developed in order to examine the potential influence of socioeconomic characteristics to trip frequency before and during the lockdown period.

Results: Outcomes indicate a decisive decrease in trip frequencies due to the lockdown. Furthermore, the model's results indicate significant correlations between gender, income and trip frequencies during the lockdown, something that is not evident in the pre-pandemic era.

Introduction: The global Coronavirus (COVID-19) pandemic is having a great impact on all areas of the everyday life, including travel behaviour. Various measures that focus on restricting social contacts have been implemented in order to reduce the spread of the virus. Understanding how daily activities and travel behaviour change during such global crisis and the reasons behind is crucial for developing suitable strategies for similar future events and analysing potential mid- and long-term impacts.

Methods: In order to provide empirical insights into changes in travel behaviour during the first Coronavirus-related lockdown in 2020 for Germany, an online survey with a relative representative sample for the German population was conducted a week after the start of the nationwide contact ban. The data was analysed performing descriptive and inferential statistical analyses.

Results and discussion: The results suggest in general an increase in car use and decrease in public transport use as well as more negative perception of public transport as a transport alternative during the pandemic. Regarding activity-related travel patterns, the findings show firstly, that the majority of people go less frequent shopping; simultaneously, an increase in online shopping can be seen and characteristics of this group were analysed. Secondly, half of the adult population still left their home for leisure or to run errands; young adults were more active than all other age groups. Thirdly, the majority of the working population still went to work; one out of four people worked in home-office. Lastly, potential implications for travel behaviour and activity patterns as well as policy measures are discussed.

Cars have a high share of global transport-related CO₂ emissions. To model the market diffusion of new energy carriers and powertrains like electric vehicles, fleet turnover models are commonly used. A decisive influence factor for the substitution dynamics of such transformations is the survival rate of the national car fleet of a country. It represents the likelihood of a car reaching a certain lifespan. Due to a lack of data, current methods to estimate such survival probabilities neglect the imports and exports of used cars. Existing studies are limited to countries with a predominant market of new cars, compared to low numbers of imported and exported used cars. In this study, we resolve this marked simplification and propose a new method to estimate survival probabilities for countries with a high number of imported and exported used cars. Empirical data on the car stock, on inflows of new and used cars, and on outflows of exported and scrapped cars are gathered from 71 national statistics offices. Survival rates of the car fleets of 31 European countries are derived, for which we find a pronounced regional variability. Average lifespans of cars vary from 8.0 to 35.1 years, with a mean of 18.1 years in Western and 28.4 years in Eastern European countries, revealing the high impact of cross-border flows of cars. The study also shows that survival rate estimates can be improved significantly even in the absence of reliable data if a combination of a Weibull and a Gaussian distribution is used. It is likely that the predictive power of existing models (regarding the future environmental impact of car fleets) could be improved significantly if these findings were considered accordingly. The findings of this study can directly be included in fleet turnover and policy assessment models. They also enable the analysis of economic and environmental spillover effects from the imports and exports of used cars between countries.

Supplementary information: The online version contains supplementary material available at (10.1186/s12544-020-00464-0).