European Transport Research Review
ISSN / EISSN : 1867-0717 / 1866-8887
Published by: Springer Science and Business Media LLC (10.1186)
Total articles ≅ 489
Latest articles in this journal
European Transport Research Review, Volume 13, pp 1-11; doi:10.1186/s12544-021-00500-7
Background Travel surveys show that the amount of private car driving in Norway has increased significantly since the mid-1980s. Private car driving has for a long time been the main mode of transport for retail and service trips, and grocery shopping trips represent over 60% of the retail and service travels. Despite the growing number of studies addressing accessibility to daily destinations, to the best of the authors’ knowledge there are no studies examining these issues over time. Methods This paper aims to investigate changes in accessibility to grocery stores over time and use two counties in Norway as examples. Based on GIS data at a detailed level, distances from dwellings to nearest grocery store has been examined. Findings The results from the spatial analyses reveal significant changes from 1980 to 2019: The share of the population living within 500-m from a grocery store has decreased from 55% to 34% in one of the counties examined and from 36% to 19% in the other. This indicates that the share of people living within walking distance to a local grocery store has nearly halved. With such changes in accessibility to grocery stores, increased car driving for grocery shopping should not come as a surprise. Contrary to the frequent statements about sustainable urban development and active transportation, it seems that Norway still is developing as a country that in the future will be more and not less dependent on private cars.
European Transport Research Review, Volume 13, pp 1-12; doi:10.1186/s12544-021-00497-z
Cities throughout the world have increasingly promoted walking and cycling as healthy and sustainable modes of travel. However, collisions between pedestrians and cyclists have remained largely unstudied, and existing accident statistics suffer from underreporting. This study aimed to explore near accidents and collisions between pedestrians and cyclists, assess the frequency of near accidents, and evaluate pedestrians’ and cyclists’ sense of safety in traffic. An online survey was directed to inhabitants of Finnish cities with populations greater than 100,000, and the resulting data included 1046 respondents who walk and/or cycle regularly. The main results show that near accidents between pedestrians and cyclists are around 50 times more frequent than collisions. Only 16 survey respondents had been involved in a collision during the 3-year period, whereas roughly a third had experienced at least one near accident. For both near accidents and collisions, the involved parties were usually travelling in the same direction. Most incidents occurred on pedestrian paths and shared pedestrian and bicycle paths. On shared pedestrian and bicycle paths separated by mode of transport, incidents were much rarer. Furthermore, sense of safety and willingness to walk and cycle were lower in environments where near accidents were more frequent. These findings tentatively suggest that spatially separating modes of transport could improve people’s sense of safety and prevent near accidents and collisions. Prevention of near accidents could increase the willingness to walk and cycle.
European Transport Research Review, Volume 13, pp 1-3; doi:10.1186/s12544-021-00496-0
European Transport Research Review, Volume 13, pp 1-16; doi:10.1186/s12544-021-00495-1
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.
European Transport Research Review, Volume 13, pp 1-13; doi:10.1186/s12544-021-00492-4
Background The car has so far played an important role for transporting goods. However, new services emerging from e-commerce may increasingly reduce its relevance as the transporting of goods might no longer be a reason for car use. As a result, e-commerce or the delivery of goods by third-parties can function as potential supplement for car-free households and support a car-free lifestyle. To assess this potential, appropriate segmentation to subgroups is needed to better understand differences in shopping behavior and the linked role of the car. Methods The presented study from Munich (Germany) provides a comprehensive approach by applying a latent class analysis. The classification revealed six distinct classes with differences in shopping behavior as well as sociodemographic and spatial characteristics. To asses underlying motivations, this approach is complemented through relating the latent classes to attitudes towards shopping and mode choice. Findings Results show that those people who frequently use their cars also have an affinity for frequent online shopping. This relationship should be considered when discussing whether e-commerce can promote a car-free lifestyle.
European Transport Research Review, Volume 13, pp 1-10; doi:10.1186/s12544-021-00493-3
Purpose Ridesourcing services have become popular recently and play a crucial role in Mobility as a Service (MaaS) offers. With their increasing importance, the need arises to integrate them into travel demand models to investigate transport system-related effects. As strong interdependencies between different people’s choices exist, microscopic and agent-based model approaches are especially suitable for their simulation. Method This paper presents the integration of shared and non-shared ridesourcing services (i.e., ride-hailing and ride-pooling) into the agent-based travel demand model mobiTopp. We include a simple vehicle allocation and fleet control component and extend the mode choice by the ridesourcing service. Thus, ridesourcing is integrated into the decision-making processes on an agent’s level, based on the system’s specific current performance, considering current waiting times and detours, among other data. Results and Discussion In this paper, we analyze the results concerning provider-related figures such as the number of bookings, trip times, and occupation rates, as well as effects on other travel modes. We performed simulation runs in an exemplary scenario with several variations with up to 1600 vehicles for the city of Stuttgart, Germany. This extension for mobiTopp provides insights into interdependencies between ridesourcing services and other travel modes and may help design and regulate ridesourcing services.
European Transport Research Review, Volume 13, pp 1-14; doi:10.1186/s12544-021-00491-5
Background The paper presents a simulation model for freight. In the paper, this model is applied to understand the impacts of electric vans and cargo bikes for the last-mile delivery of parcels. Cargo bikes are electrically assisted vehicles that distribute parcels from micro depots located close to the final customers by means of short tours. The parcels are sent from the major distribution center to micro depots in vans (called feeders). Materials and methods An agent-based model is used for the purpose of the paper. The model is based on the disaggregation of commodity flows to represent trucks (for all commodities) and individual shipments (for parcel deliveries). The model represents microscopically every freight vehicle in the study area. Results The simulation of various scenarios with different shares of cargo bikes and electric vans assesses the impacts of electrification and cargo bikes. The use of cargo bikes to deliver parcels allows to reduce the number of motorized vehicles, although the presence of large parcels requires that at least half of deliveries by vans are still required. The shift to cargo bikes represents a slight increase in the total operating time to deliver the parcel demand. With low shares of cargo bikes, the total distance traveled increases, since the reduction of van tours cannot compensate the additional feeder trips from distribution centers to micro depots. The cargo bikes also do not reduce the number of vehicles for the served area, but modify the composition of vehicle types. Low noise, smaller, low emission vehicles increase, while delivery vans are reduced. Conclusion Both cargo bikes and electric vans are able to reduce CO2 emissions, even after accounting for the emissions related to electricity production.
European Transport Research Review, Volume 13, pp 1-21; doi:10.1186/s12544-021-00484-4
Background This paper provides insight into the opportunity offered by shared autonomous vehicles (SAVs) to improve urban populations’ spatial equity in accessibility. It provides a concrete implementation model for SAVs set to improve equity in accessibility and highlights the need of regulation in order for SAVs to help overcome identified spatial mismatches. Methodology Through the formulation of linear regression models, the relationship between land-use and transportation accessibility (by car and public transport) and socio-economic well-being indicators is tested on district-level in four European cities: Paris, Berlin, London and Vienna. Accessibility data is used to analyse access to points of interest within given timespans by both car and public transport. To measure equity in socio-economic well-being, three district-level proxies are introduced: yearly income, unemployment rate and educational attainment. Results In the cities of Paris, London and Vienna, as well as partially in Berlin, positive effects of educational attainment on accessibility are evidenced. Further, positive effects on accessibility by yearly income are found in Paris and London. Additionally, negative effects of an increased unemployment rate on accessibility are observed in Paris and Vienna. Through the comparison between accessibility by car and public transportation in the districts of the four cities, the potential for SAVs is evidenced. Lastly, on the basis of the findings a ‘SAV identification matrix’ is created, visualizing the underserved districts in each of the four cities and the need of equity enhancing policy for the introduction of SAVs is emphasized.
European Transport Research Review, Volume 13, pp 1-13; doi:10.1186/s12544-021-00488-0
Introduction The COVID-19 pandemic has changed travel behaviour and reduced the use of public transport throughout the world, but the reduction has not been uniform. In this study we analyse the propensity to stop travelling by public transport during COVID-19 for the holders of 1.8 million smart cards in Stockholm, Sweden, for the spring and autumn of 2020. We suggest two binomial logit models for explaining the change in travel pattern, linking socioeconomic data per area and travel data with the probability to stop travelling. Modelled variables The first model investigates the impact of the socioeconomic factors: age; income; education level; gender; housing type; population density; country of origin; and employment level. The results show that decreases in public transport use are linked to all these factors. The second model groups the investigated areas into five distinct clusters based on the socioeconomic data, showing the impacts for different socioeconomic groups. During the autumn the differences between the groups diminished, and especially Cluster 1 (with the lowest education levels, lowest income and highest share of immigrants) reduced their public transport use to a similar level as the more affluent clusters. Results The results show that socioeconomic status affect the change in behaviour during the pandemic and that exposure to the virus is determined by citizens’ socioeconomic class. Furthermore, the results can guide policy into tailoring public transport supply to where the need is, instead of assuming that e.g. crowding is equally distributed within the public transport system in the event of a pandemic.
European Transport Research Review, Volume 13, pp 1-14; doi:10.1186/s12544-021-00490-6
Background Urban safety performance functions are used to predict crash frequencies, mostly based on Negative Binomial (NB) count models. They could be differentiated for considering homogeneous subsets of segments/intersections and different predictors. Materials and methods The main research questions concerned: a) finding the best possible subsets for segments and intersections for safety modelling, by discussing the related problems and inquiring into the variability of predictors within the subsets; b) comparing the modelling results with the existing literature to highlight common trends and/or main differences; c) assessing the importance of additional crash predictors, besides traditional variables. In the context of a National research project, traffic volumes, geometric, control and additional variables were collected for road segments and intersections in the City of Bari, Italy, with 1500 fatal+injury related crashes (2012–2016). Six NB models were developed for: one/two-way homogeneous segments, three/four-legged, signalized/unsignalized intersections. Results Crash predictors greatly vary within the different subsets considered. The effect of vertical signs on minor roads/driveways, critical sight distance, cycle crossings, pavement/markings maintenance was specifically discussed. Some common trends but also differences in both types and effect of crash predictors were found by comparing results with literature. Conclusion The disaggregation of urban crash prediction models by considering different subsets of segments and intersections helps in revealing the specific influence of some predictors. Local characteristics may influence the relationships between well-established crash predictors and crash frequencies. A significant part of the urban crash frequency variability remains unexplained, thus encouraging research on this topic.