Transportation最新文献

The significance of public transit in curbing greenhouse gas (GHG) emissions and reducing vehicle miles traveled (VMT) goes beyond its users. Investments in transit infrastructure, coupled with service enhancements and their consequential impacts on urban development (termed as indirect effects), have the potential to foster location efficiency. This concept encompasses the advantageous proximity of vital destinations such as workplaces and retail establishments to the residences that necessitate access. In this context, investments made in public transit systems exhibit a multiplier effect, commonly quantified as the reduction in VMT per each passenger mile of transit usage. While this topic has gained attention over the past few decades, an agreement regarding the size of the multiplier effect has yet to be reached among researchers. This study employs a multilevel structural equation model and leverages a comprehensive database of household travel survey data from 31 diverse regions. By utilizing trip-level data, this study provides results that possess external validity and generalizability, overcoming limitations identified in earlier research. Additionally, this study aims to present a simplified formula that enables transit agencies nationwide to compute their unique multipliers. The findings suggest that regions with extensive transit systems exhibit higher transit multipliers compared to regions with limited transit access. Furthermore, the impact of transit within a community extends well beyond merely the reduction in private vehicle usage by transit passengers. Rather, the alterations in the built environment in transit-served communities lead to substantial VMT savings, surpassing the effects solely attributed to transit passenger usage.

Public Transportation (PT) is a universal service in most countries, and it is acknowledged for its social and environmental role in enhancing accessibility and promoting a sustainable transport system. However, when disruptions alter the service, the level of service (LoS) can be massively affected. Consequently, the perceived quality can be influenced, and users can be encouraged (or forced) to modify their subsequent modal choice, in accordance with the users’ socioeconomic profile. A survival analysis, namely a Cox proportional hazards model, was tested in Bologna, Italy, using real data provided by TPER S.p.A, specifically Automatic Vehicle Location (AVL) and Automatic Passenger Counter (APC). This analysis aimed to assess the variations in demand over time taking into account variables related to the socioeconomic characteristics of the demand and several service attributes. The results contribute to the literature in several ways. Firstly, they confirm the predominant role of PT in the modal alternative spectrum of disadvantaged users. Secondly, they provide insights into the perception of quality service among different user categories, including commuters and non-frequent users.

Home-to-Work (H2W) commuting travel, together with urbanization and transport networks, have seen significant development since the end of the 19th century in both workers’ lives and transport network traffic. The attraction of metropolitan areas and the ease of transport have even triggered long-distance commuting, sometimes in conjunction with overnighting practices that extend the commuting tours (or cycles) beyond the single day level. Based on the French nationwide household travel survey of 2019, this article gives a comprehensive description of commuting practices across origin-destination distances and over time, as experienced by individual workers at the monthly level. Commuting rhythms are characterized in terms of commuting cycle length in days and monthly frequency. Four typical patterns are identified: namely bi-daily, full daily, mono-daily and overnighting. Their respective shares are measured in the statistical populations of (i) workers, (ii) days of life or just at work, (iii) distances travelled on modal networks. Elementary discrete-choice models of H2W rhythm choice are designed and estimated, revealing the influence of travel impedance in terms of time and distance at the level of one-way trips for short-range H2W commuting and at the level of monthly budgets for long-range H2W commuting. Lastly, the shares of commuting trips in modal traffic on road and rail networks are measured, highlighting the significant impact of long-distance commuting on transport-related GHG emissions.

In ride-sharing, fixing the roles of commuters with vehicles could come at an opportunity of missed balance between riders and drivers. The decisions made by commuters with vehicles about roles are related to both to-work travel and return-home travel. Commuters may choose not to drive when they have a confirmed ride back home. This study designs a stable ride-sharing model with role flexibility for the commuting trip chain. The preferences of commuters over matching partners in trip chains are formulated. A stable matching algorithm based on commuting trip chain is designed. To assess the performance of the proposed model and algorithm, real-life data from a resident trip survey in Dalian are used to design experimental cases. The results indicate that stable matching based on the trip chain is not a consequence of the repetition of successive one-period stable matching. Compared with matching in two phases, the matching rate of commuters increases by an average of 10%. When the number of commuters with role flexibility increases by four times, the matching rate of commuters increases by an average of 5.6%.

Network criticality assessment aims to identify important nodes or links within a network that that are critical to its overall performance. Existing studies often focus on factors such as traffic volume and sociodemographic attributes, giving preference to heavily traveled highways in densely populated areas. Rural highways, particularly those with low traffic volumes, tend to be undervalued, despite their potential significance in sustaining network connectivity and facilitating access to services and opportunities within local communities. This paper addresses this concern by developing a criticality framework grounded in the egalitarian principle. The framework comprises two complimentary measures: normalized betweenness centrality and detour importance. The former measures a road’s relative importance in efficiently linking the local network by quantifying the percentage of origin-destination pairs within the neighborhood that utilize it. The latter, detour importance, reflects the aggregated percentage of trips rerouted to the road during disruptions to other critical roads, thereby highlighting its role in providing the needed redundancy of the network. Given the fundamental role of finding realistic shortest paths in this analysis, a large-scale GPS-based probe vehicle dataset is integrated to provide real-world travel time information. The results from the Kentucky case study validate the effectiveness of the developed framework. The framework utilizes efficient algorithms and readily available data, making it practically feasible for large-scale applications.

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.