Due to the potential of automated vehicles to offer a multitude of advantages to the travelers and therefore influence their daily routines, it is essential to monitor the public’s opinion on this ...particular technological development. The goal of a number of surveys in recent years was therefore not only to elicit the general acceptance of the technology but to additionally explore when, how and why respondents were inclined to make use of it. This is the first literature review on surveys regarding automated vehicles with the intention to investigate the various methods currently being applied and the conclusions they lead to. In addition to comparing the general results in terms of the distributions of the response variables, the surveyed explanatory variables are categorized and analyzed according to their influence in different experiments. Based on these investigations, this review identifies research gaps that can be addressed in future experiments.
Free-floating car-sharing schemes operate without fixed car-sharing stations, ahead reservations or return-trip requirements. Providing fast and convenient motorization, they attract both public ...transportation users and (former) car-owners. However, given their highly flexible nature and different pricing structures, previous findings on user groups and environmental impact of station-based car-sharing may not be easily transferable. Therefore, this research uses survey data to compare user groups and usage patterns of a free-floating and station-based car-sharing service both operating in the city of Basel, Switzerland. The findings suggest, that the schemes indeed attract different user groups and are also used differently. Moreover, we see, that car-sharing membership is governed by other factors than car-sharing activity.
Mobility as a Service (MaaS) is an attempt to overcome market segmentation by offering transport services tailored to the individual traveler's needs. An alternative to prior investment into single ...mobility tools, it may allow less biased mode choice decisions. Such a setting favors shared modes, where fixed costs can be apportioned among a large number of users. In turn, car-sharing, bike-sharing or ride-hailing may themselves become efficient alternatives to public transport. Although early field studies confirm the expected changes away from private car use and towards public or shared modes, impacts are yet to be studied for larger transport systems. This research conducts a first joint simulation of car-sharing, bike-sharing and ride-hailing for a city-scale transport system using MATSim. Results show that in Zurich, through less biased mode choice decisions alone, transport-related energy consumption can be reduced by 25%. In addition, introduction of car-sharing and bike-sharing schemes may increase transport system energy efficiency by up to 7%, whereas the impact of ride-hailing appears less positive. Efficiency gains may be higher if shared modes were used as a substitute for public transport in lower-density areas. In summary, a MaaS scheme with shared mobility may allow to slightly increase system efficiency (travel times & cost), while substantially reducing energy consumption.
•Survey among a randomly selected sample of 17,500 inhabitants of Zurich.•Shared micro-mobility adoption rate is highest for e-scooters (28%).•On average, ~50% of shared micro-mobility members are ...inactive / ‘dormant’.•Shared micro-mobility users tend to be young, well-educated, affluent males.•Shared e-scooter users are the most representative of the larger population.
Shared micro-mobility services have rapidly gained popularity yet challenged city administrations to develop adequate policies while scientific insight is largely missing. From a transportation equity perspective, it is particularly important to understand user correlates, as they are the beneficiaries from public investment and reallocation of public space. This paper provides an up-to-date account of shared micro-mobility adoption and user characteristics in Zurich, Switzerland. Our results suggest that shared micro-mobility users tend to be young, university-educated males with full-time employment living in affluent households without children or cars. Shared e-scooter users, in particular, are younger, yet more representative of the general population in terms of education, full-time employment, income and gender than bike-sharing users. This suggests that shared e-scooters may contribute to transportation equity, yet their promotion should be handled with care as life-cycle emissions exceed those of bike-sharing and equity contributions might be skewed as many users are students.
•Studying impact free-floating car-sharing (FFCS) using quantitative data.•Early application of smartphone-based GPS tracking as travel diary.•Two-wave survey approach allowing to measure the FFCS ...impact.•FFCS found to lower private vehicle ownership and use.•FFCS mainly used for non-regular, discretionary trips.
Free-floating car-sharing schemes operate without fixed car-sharing stations, ahead reservations or return-trip requirements. Providing fast and convenient motorization, they attract both public transport users and (former) car-owners. Thus, their impact on individual travel behavior depends on the user type. Estimating the travel behavior impact of these systems therefore requires quantitative data. Using a two-wave survey approach (shortly after launch of the scheme plus one year later) including travel diaries, this research indicates that (due to their membership) 6% of the free-floating car-sharing customers reduce their private vehicle ownership. Moreover, the results suggest that free-floating car-sharing both complements and competes with station-based car-sharing.
•A probabilistic factorization framework is introduced to deal with high-dimensional mobility data.•The framework is based on tensor decomposition and probabilistic latent semantic analysis.•We apply ...the model on 14 million transit journeys extracted from smart card data.
The rapid developments of ubiquitous mobile computing provide planners and researchers with new opportunities to understand and build smart cities by mining the massive spatial-temporal mobility data. However, given the increasing complexity and volume of the emerging mobility datasets, it also becomes challenging to build novel analytical framework that is capable of understanding the structural properties and critical features. In this paper, we introduce an analytical framework to deal with high-dimensional human mobility data. To this end, we formulate mobility data in a probabilistic setting and consider each record a multivariate observation sampled from an underlying distribution. In order to characterize this distribution, we use a multi-way probabilistic factorization model based on the concept of tensor decomposition and probabilistic latent semantic analysis (PLSA). The model provides us with a flexible approach to understand multi-way mobility involving higher-order interactions—which are difficult to characterize with conventional approaches—using simple latent structures. The model can be efficiently estimated using the expectation maximization (EM) algorithm. As a numerical example, this model is applied on a four-way dataset recording 14 million public transport journeys extracted from smart card transactions in Singapore. This framework can shed light on the modeling of urban structure by understanding mobility flows in both spatial and temporal dimensions.
Understanding of the mechanisms driving our daily face-to-face encounters is still limited; the field lacks large-scale datasets describing both individual behaviors and their collective ...interactions. However, here, with the help of travel smart card data, we uncover such encounter mechanisms and structures by constructing a time-resolved in-vehicle social encounter network on public buses in a city (about 5 million residents). Using a population scale dataset, we find physical encounters display reproducible temporal patterns, indicating that repeated encounters are regular and identical. On an individual scale, we find that collective regularities dominate distinct encounters’ bounded nature. An individual’s encounter capability is rooted in his/her daily behavioral regularity, explaining the emergence of “familiar strangers” in daily life. Strikingly, we find individuals with repeated encounters are not grouped into small communities, but become strongly connected over time, resulting in a large, but imperceptible, small-world contact network or “structure of co-presence” across the whole metropolitan area. Revealing the encounter pattern and identifying this large-scale contact network are crucial to understanding the dynamics in patterns of social acquaintances, collective human behaviors, and—particularly—disclosing the impact of human behavior on various diffusion/spreading processes.
Characteristics of users and usage of station-based car-sharing services have been discussed in various studies. First analyses of the free-floating car-sharing model
DriveNow
have shown that member ...composition and patterns of use are not very different from those of station-based car-sharing schemes. Nevertheless, free-floating car-sharing members were drawn from a new pool of travellers, they were not attracted by existing station-based car-sharing schemes. This paper goes beyond these analyses and looks not only at the usage of car-sharing services but at the overall travel behaviour of free-floating car-sharing members (FFCS). To the best of our knowledge, this is the first time that the specifics of this travel behaviour have been analysed based on substantial data that was collected specifically for this purpose with an innovative survey design based on a GPS tracking smartphone application. The goal of this study is to contrast the core group of members of the free-floating car-sharing model
DriveNow
(male, 25-45 years old) with people who do not use car-sharing. Key travel indicators are compared for FFCS and non-car-sharers (NCS) with a special emphasis on type and extend of multimodal travel behaviour within those two groups. The results show higher trip frequency for FFCS and differences in mode choice pattern. FFCS are more intermodal and multimodal in their behaviour. Shares of cycling are significantly higher, shares of private car trips are significantly lower for FFCS compared to NCS. The insights gained in this study can help cities and car-sharing operators to develop framework conditions and services that optimally integrate free-floating car-sharing services into the overall urban transport systems.
•Spatial regression analysis of free-floating car-sharing (FFCS) demand.•Mode choice model based on car-sharing transactions and travel diary data.•FFCS mainly used for discretionary trips.•Access ...walk to FFCS vehicles only perceived as a low burden.•FFCS mainly used for tangential trips bridging gaps in the public transport network.
Free-floating car-sharing has been one of the latest innovations in the car-sharing market. It allows its customers to locate available vehicles via a smartphone app and reserve them for a short time prior to their rental. Because it is available for point-to-point trips, free-floating car-sharing is not only an alternative to private cars, but also to public transportation. Using spatial regression and conditional logit analysis of original transaction data of a free-floating car-sharing scheme in Switzerland, this research shows that free-floating car-sharing is mainly used for discretionary trips, for which only substantially inferior public transportation alternatives are available. In contrast to station-based car-sharing, it does not rely on high-quality local public transportation access, but bridges gaps in the existing public transportation network.
Traffic in an urban network becomes congested once there is a critical number of vehicles in the network. To improve traffic operations, develop new congestion mitigation strategies, and reduce ...negative traffic externalities, understanding the basic laws governing the network's critical number of vehicles and the network's traffic capacity is necessary. However, until now, a holistic understanding of this critical point and an empirical quantification of its driving factors has been missing. Here we show with billions of vehicle observations from more than 40 cities, how road and bus network topology explains around 90% of the empirically observed critical point variation, making it therefore predictable. Importantly, we find a sublinear relationship between network size and critical accumulation emphasizing decreasing marginal returns of infrastructure investment. As transportation networks are the lifeline of our cities, our findings have profound implications on how to build and operate our cities more efficiently.
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