Transportation最新文献

Urban areas have been dramatically impacted by the sudden and fast spread of the COVID-19 pandemic. As one of the most noticeable consequences of the pandemic, people have quickly reconsidered their travel options to minimize infection risk. Many studies on the Bike Sharing System (BSS) of several towns have shown that, in this context, cycling appears as a resilient, safe, and very reliable mobility option. Differences and similarities exist about how people reacted depending on the place being considered, and it is paramount to identify and understand such reactions in the aftermath of an event in order to successfully foster permanent changes. In this paper, we carry out two analyses, both from a geographical and temporal point of view: on the one hand, we compare the short-term effects of the pandemic on BSS usage in two French towns (Toulouse and Lyon), and on the other, hand we analyze its mid-term effects in Toulouse. We used Origin/Destination data for 4 years: 2019 (pre-pandemic), 2020 (pandemic before massive vaccination campaigns), 2021 (pandemic after massive vaccination campaigns), and 2022 (year after the pandemic peak). We consider two complementary quantitative approaches. Our results confirm that cycling increased during the pandemic, more significantly in Lyon than in Toulouse, with rush times remaining exactly the same for the 4 years, even during the lockdowns. The year 2021 shows a transitional profile between 2020 and 2022 that could be attributed to adaptation to living with COVID and perhaps also to the increased sense of safety brought by the vaccination campaign. We also found that trip duration during the pandemic situation was longer both on working days and weekends. Comparing BSS traffic with road traffic and public-transport validations shows that cycling is a resilient mode of transport in a pandemic. Among several general observations, we note that peripheral/city center BSS flow is more noticeable in Toulouse than in Lyon and that student BSS usage is more specific in Lyon.

Synthetic populations are increasingly required in transportation demand modelling practice to feed the large-scale agent-based microsimulation platforms gaining in popularity. The quality of the synthetic population, i.e., its representativeness of the sociodemographic and the spatial distribution of the real population, is a determinant factor of the reliability of the microsimulation it feeds. While many research works focused on improving the sociodemographic accuracy of synthetic populations, the quality of their spatial distribution remained less covered. This paper suggests a new explicitly spatialized population synthesis framework. It leverages the performant Clustering Large Applications (CLARA) and Random Forest algorithms as well as rich spatial information collected as part of surveys to make accurate predictions of synthetic households’ locations at the building scale directly. In addition to preserving optimal sociodemographic accuracy and achieving realistic explicit spatialization, the new framework shows acceptable transferability thanks to CLARA’s efficiency. An explicitly spatialized synthetic population for Montreal Island is generated using the proposed clustering + classification framework. The four components of the proposed framework have generated satisfactory results with the zonal synthetic population established showing a 2.85% average relative error, the building clustering selected having a 0.48 average silhouette width, the classification model achieving a 0.79 macro-average F1 score, and 78.9% of the synthetic households being assigned to their preferred building cluster.

Micromobility, which includes bicycle-sharing systems, e-scooters, and shared moped-style scooters, has emerged as a popular alternative to traditional transport modes in urban environments, thus expanding the number of transportation options available to urban travellers. Previous research has primarily relied on trip-based data to explore the multimodal character of micromobility. However, existing evidence has failed to understand the ways in which urban travellers have reshaped their mobility patterns as a consequence of the introduction of micromobility. Using a travel survey (N = 902) set in Barcelona, Spain, cluster techniques are used to group micromobility users according to their frequency of use of three different micromobility modes (bicycle-sharing systems, private e-scooter, and moped-style scooter-sharing services). Then, a multinomial logistic regression was used, in order to explore each cluster’s usage of traditional modes of transport, along with all potential weekly combinations between modes. Results show that most micromobility users rely on a single type of micromobility mode on a weekly basis. The model further indicates that private e-scooter, shared bicycle, and shared moped-style scooter users develop different weekly mobility combination patterns. While personal micromobility options (private e-scooter) are associated with monomodal tendencies, sharing services (bicycle sharing and moped-style scooter sharing) encourage multimodal behaviours. These findings contribute to the limited knowledge concerning the role of some micromobility alternatives in creating more rational and less habit-dependent travel behaviour choices.

While the impacts of shared micromobility (SMM) on the environment and transport systems are being extensively researched, its societal implications and the influence of the social environment on the use of SMM remain largely unexplored. In this research, we investigate the interrelationships between the use of SMM, perceived overall accessibility, and social capital. We focus on two types of SMM – shared bikes and shared e-scooters – in three European countries: the Netherlands, England, and Sweden. We measure perceived overall accessibility through a multicriteria subjective evaluation of individuals’ ability to reach regular destinations, services, and activities. We consider multidimensional social capital measures: social trust, cooperativeness, reciprocity, network bonding, and network bridging. We use multivariate models to investigate the associations between perceived overall accessibility, SMM use, and social capital, and examine the dominant direction of these associations using the direct linear non-Gaussian acyclic model (DirectLiNGAM) and direction dependence analysis (DDA). We find that lower levels of perceived overall accessibility may contribute to lower levels of social trust, reciprocity, and cooperativeness. However, individuals with a lower level of perceived overall accessibility tend to use shared bikes more frequently, which in turn, may increase their social trust and cooperativeness. We also find that increased shared e-scooter use may contribute to increased network bonding, yet the frequency of use has no relation with perceived overall accessibility. Our research suggests that the introduction of shared bikes alone, independent of other measures aimed at encouraging their use, may help mitigate individual differences in social capital. We argue that the applied DirectLiNGAM and DDA help gain deeper insights into the likely causal relationship between transport and social capital in non-intervention studies.

Carsharing services have a significant potential for improving urban mobility by increasing the independence and freedom of travel and reducing traffic externalities. Although carsharing has been used for over a decade, several aspects need further investigation, such as the impact of user’s psychological factors on service use, as well as the factors impacting users’ choices between different carsharing operators, in particular their preferences for different payment schemes, and their perceptions of the operators’ application rating. Accordingly, four hybrid choice models (HCM) were estimated to investigate factors impacting (i) the knowledge about carsharing services, (ii) carsharing adoption, (iii) the shift from other modes to carsharing, (iv) the choice between carsharing operators with different payment schemes, using a large survey sample (N = 1044 responses 9469 SP observation) from Munich, Germany. The models showed the significance of sociodemographics, such as income level, education level, household size, employment status, ownership of a bike, access to a car, the availability of a driving license, and public transport subscription-based tickets on the carsharing use directly and indirectly, and four psychological factors encompassing different personality traits (i.e., adventurous), travel behavior, and attitudes were found to be significant in the various models; the latter covered service-related attitudes (perceived carsharing app importance) and travel behavior attitudes or profiles (frequent public transport user and frequent shared micromobility user). This research raises questions regarding the inequitable use of carsharing, the impacts of mobile applications on using the service, and the potential of integrating carsharing in mobility as a Service platforms to increase the potential for multimodality.

This meta-analysis aims to analyse how the activities of rail passengers have changed in Japan as a result of rapid technological developments. To be eligible for inclusion in this analysis, source studies must have reported the number of passengers performing specific activities, and the number must have been directly counted by surveyors who actually ride on trains. Databases searched included CiNii, J-STAGE, Web of Science, and Google Scholar. References in selected studies were trialled using a snowballing method. In addition, past onboard activities were retrospectively identified by content analysis of YouTube videos in which the surveyors hypothetically travelled on a train and observed the passengers. The use of YouTube videos for meta-analysis of rail passengers’ activities is a novel contribution of this study. The search for the YouTube video was entirely manual. In total, 23 independent studies with 332,355 passengers were included in the analysis. Data were collected from 1983 to 2019. The effect sizes were the proportion of each of the following activities: ‘(a) mobile phones’, ‘(b) sleeping’, ‘(c) reading’, ‘(d) music’, and ‘(e) other’. Meta-regressions were performed with the year of data collection as a moderator. Demonstrating historical changes in activities through statistical analysis is another novel contribution: ‘(a) mobile phones’ and ‘(d) music’ had a significantly increasing trend, ‘(c) reading’ had a significantly decreasing trend, and ‘(b) sleeping’ and ‘(e) other’ did not change. Studies with and without YouTube videos did not affect the conclusions, which supports the use of YouTube videos for the purposes of this study. Ideas are presented for research methods that use directly observed data to explain the possible social factors behind longitudinal variation in travel-based multitasking.

A courier’s choice for delivery gigs in a crowdshipping service is not well understood in the literature. Thus the objective of this study is to empirically investigate the crowdshipping (CS) couriers’ bidding preferences for delivery gigs, and how the gig features impact the gig delivery status of a real-world CS service in the United States. The delivery records were made available between 2015 and 2018. A descriptive analysis reveals that there exist significant preference discrepancies between the senders and the couriers in terms of package size, delivery time window, delivery distance, and delivery fee. Therefore, four features to capture the above discrepancy are specifically created from the data in predicting the bidding level and the delivery status. The bidding level which is measured by the number of bids received per gig is classified into low, medium, and high bidding levels to reflect the couriers’ preferences for the delivery gigs. The delivery status, labeled as delivered or undelivered, is affected by the couriers’ eventual choice of the delivery gigs. Five popular machine learning (ML) methods, namely Random Forest Decision Tree, Artificial Neural Network, eXtreme Gradient Boosting (XGBoost), Support Vector Machine, and Bayesian Network are applied to the predictions. Among them, the XGBoost is found to perform the best. Furthermore, the Shapley Additive exPlanations (SHAP) values are introduced to explain and visualize how each feature influences the dependent variable (prediction target). The SHAP values provide an effective visualization and interpretability of the feature impact values and importance rankings, much like the coefficients of the traditional econometric based logit model. The paper further demonstrates that the ML models and the logit models produce consistent feature influences. Overall, the couriers are generally interested in the delivery gigs of extra-large and huge package sizes, medium to long delivery distance, insured packages, and flexible delivery time window. Discrepancy related features significantly influence couriers’ bidding behavior as expected. The study also reveals that gigs that receive a high number of bids do not translate into their eventual successful deliveries. Finally, policy and practical implications for improving the CS service particularly through pricing strategies are discussed.

Understanding the factors that will influence people’s preferences for Electric Vehicles (EVs) over Internal Combustion Engine Vehicles (ICEVs) is crucial. A discrete choice experiment was designed and administered as an online survey resulting in 1077 completed questionnaires. This study examined the influence of over 83 variables on preferences for EVs. As well, previous studies have used tailpipe emissions only to present GHG information, but in this study lifecycle GHG emissions of vehicles are presented. Five ensemble learning techniques and two interpretation techniques were employed to investigate individual decisions regarding selecting between EVs and ICEVs. The results demonstrate that when lifecycle emissions are presented, financial impacts are the principal influences on predicting preference for an EV over ICEV. Following the financial impacts are existing preferences for EVs and attitudes related to climate change. How the emissions are presented was the 12th and 9th most influential factor for BEVs and PHEVs respectively.

The study investigates car purchase intention among students. We advance existing literature by proposing a model integrating parent influence, attitude toward car ownership and psychological predictors in the scope of family or household decision-making. We collected 514 multi-actor sample data consisting of fathers, mothers, and their young adult children from the Jakarta metropolitan area. The results show that parents’ car attitudes are determinants of their influence on their children’s car purchases. The influence is further indirectly related to the child’s perception of their parent’s expectations which in turn depends on the closeness of family relationships. Moreover, children’s car attitude toward the prestige value of a car and their father’s influence significantly affects their car purchase intention. Based on this, we suggest that it is important to target families instead of only young adults in mobility intervention policies.

Ride-pooling remains a promising emerging mode with a potential to contribute towards urban sustainability and emission reductions. Recent studies revealed complexity and diversity among travellers’ ride-pooling attitudes. So far, ride-poling analyses assumed homogeneity of ride-pooling travellers. This, as we demonstrate, leads to a false assessment of ride-pooling system performance. We experiment with an actual NYC demand from 2016 and classify travellers into four groups of various ride-pooling behaviours (value of time and penalty for sharing), as reported in the recent SP study from Netherlands. We replicate their behavioural characteristics, according to the population distribution, to obtain meaningful performance estimations. Results vary significantly from the homogeneous benchmark: mileage savings were lower, while the utility gains for travellers were greater. Observing performance of heterogeneous travellers, we find that those with a low value of time are most beneficial travellers in the pooling system, while those with an average penalty for sharing benefit the most. Notably, despite the highly variable travellers’ behaviour, the confidence intervals for the key performance indicators are reasonably narrow and system-wide performance remains predictable. Our results show that the incorrect assumption of homogeneous traits leads to a high dissatisfaction of 18.5% and a cancellation rate of 36%. Such findings shed a new light on the expected performance of large scale ride-pooling systems.