Transportation最新文献

Mobility as a Service (MaaS) is an innovative solution for improving transport systems and has gained significant attention in recent years. With the development of MaaS, the additional utility provided by offering packaged services is considered a key factor in attracting users, meaning that evaluating the added value of MaaS bundles becomes a critical issue for MaaS providers. Motivated by this, we proposed a novel approach for evaluating the added value of MaaS bundles considering heterogeneous subscription willingness. First, we develop an Integrated Choice and Latent Variable (ICLV) model to capture the factors influencing user subscription to MaaS bundles and to estimate the subscription willingness of different users. Building upon the estimations, we identify the user groups with different subscription willingness to MaaS bundles and further evaluate the added value of MaaS bundles considering their heterogeneous subscription willingness. The proposed approach is tested using collected data from a stated preference survey conducted in Beijing, China. The results estimated by the ICLV model offer some insights from Beijing. Furthermore, the identification of target users of these four designed MaaS bundles shows that the target users of public-transportation-oriented (PT-oriented) bundles have higher subscription willingness. As for the estimated added values, the estimated added value of the metro-oriented bundle is highest, followed by that of the bus-oriented bundle. Furthermore, the stability analysis of the added value is also conducted and verifies the robustness of the proposed approach. These findings help formulate the pricing scheme of the entire MaaS bundle and suggest that formulating MaaS bundles based on the PT-oriented philosophy may help increase the penetration rate of MaaS and the profitability of MaaS providers.

Metropolitan traffic networks are becoming increasingly complex due to the growing population and diverse range of travel modes. However, the limited installation of continuous count stations leads to partially observable networks, posing a significant challenge for effective highway planning and design practices at various scales. Travel demand models have been developed and calibrated using sparse traffic counts at the metropolitan level. Nevertheless, these models are cumbersome to recalibrate and rerun whenever network changes occur. To overcome this challenge, we propose a flexible learning-based approach that extracts embedded knowledge from large-scale activity-based travel demand models to estimate Annual Average Daily Traffic (AADT). The approach offers two primary advantages: (1) directly learning network flow patterns based on segment attributes and network topology that can be transferred across regions, and (2) enabling efficient and reliable AADT estimation for projects of various scales. Our study explores a wide range of machine learning techniques, including novel graph neural networks that explicitly account for network topology, as well as modern and traditional regression and regression kriging models, which either disregard or implicitly consider network topology. We conducted extensive experiments using the loaded network data from the activity-based travel demand model for the Atlanta metropolitan area. Our findings underscore the importance of network topology in AADT estimation, with the diffusion graph convolutional network model demonstrating the best performance in both transductive and inductive settings. Additionally, modern tree ensemble models such as random forest regressor and CatBoost, despite their ignorance of network topology, show the second-best inductive performance with relatively lightweight structures.

The relation between price and transport demand is one of the main aspects of transport mode choice. While price elasticities are well known for conventional transport modes like driving or public transport, only few studies exist dealing with fares and prices for rather new (shared) modes like bike sharing. In particular, hardly no evidence is available on the impact of the usage fee on trip demand for urban bike share systems. Therefore, the present paper develops an empirical approach to estimate the impact of a temporarily introduced 30-day free bike share rides promotion in Boston, MA in summer 2022. Based on daily trip data of bike sharing systems in Boston, MA and Washington, D.C., a difference-in-differences model is estimated to analyze the impact of the free fare. Results show that trip demand rise by up to 55% due to the waived usage fee during the time of the promotion. Furthermore, model results reveal that trip demand stays at a 20% increased level even three months after the end of the fare free program.

Household travel surveys have been used for decades to collect individuals and households’ travel behavior. However, self-reported surveys are subject to recall bias, as respondents might struggle to recall and report their activities accurately. This study examines the time reporting error of public transit users in a nationwide household travel survey by matching, at the individual level, five consecutive years of data from two sources, namely the Danish national travel survey (TU) and the Danish smart card system (Rejsekort). Survey respondents are matched with travel cards from the Rejsekort data solely based on the respondents’ declared spatiotemporal travel behavior. Approximately, 70% of the respondents were successfully matched with Rejsekort travel cards. The findings reveal a median time reporting error of 11.34 min, with an Interquartile Range of 28.14 min. Furthermore, a statistical analysis was performed to explore the relationships between the survey respondents’ reporting error and their socio-economic and demographic characteristics. The results indicate that females and respondents with a fixed schedule are in general more accurate than males and respondents with a flexible schedule in reporting their times of travel. Moreover, trips reported during weekdays or via the internet displayed higher accuracies compared to trips reported during weekends and holidays or via telephone interviews. This disaggregated analysis provides valuable insights that could help in improving the design and analysis of travel surveys, as well accounting for reporting errors/biases in travel survey-based applications. Furthermore, it offers valuable insights underlying the psychology of travel recall by survey respondents.

The dynamics of urban transportation can be captured using activity-based models, which rely on travel demand data to get a comprehensive understanding of urban mobility. This data is usually derived from population samples and Household Travel Surveys (HTSs), which can be expensive and as a result, are conducted only every 5 to 10 years. Moreover, due to their limited reach, they are not adapted to represent the spatio-temporal structure of the flows of the total population. This calls for complementary data sources that could be used to update old surveys to cut costs and to estimate the global spatial mobility behavior of the population. In this paper, we propose steps in the state-of-the-art pipeline for travel demand synthesis with an approach for the temporal calibration and the location attribution based on time-dependent origin–destination (OD) matrices. These matrices describe the flows between zones of a city. This methodology is illustrated on the city of Lyon, France, with OD matrices estimated from the mobile phone activity of the subscribers of French telecom operator Orange. We explore how the spatialization can be performed using various probabilistic graph models whose parameters are evaluated via the OD matrices. The structure of the models enforces the consistency of the locations with the chains of activities, such as the fact that two “home” activities must have the same location. Multiple models are proposed, corresponding to different compromises between the two potentially incompatible sources that are HTS and mobile data. We show that while a very naive spatialization approach allows the generation of synthetic travel demand that perfectly fits the flows described by the OD matrices without respecting the consistency of the locations, the other proposed approaches offer much more realistic agendas at the expense of only small discrepancies with the mobile data.

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.