Search

Social and economic change in the built environment is increasingly driven by processes of datafication. These often find expression through smart phone apps and private platforms that seek to upset the status quo by mediating consumer and producer interactions, and by monetising the data these produce. This paper uses the practice-oriented concept of ‘disruptive data’ to draw attention away from specific technologies and towards the broader political economic logics that underlie them. In so doing, disruption is reframed as a capitalist strategy for creating and capitalising on uncertainty. The rapid change to Dublin’s taxi industry over the past decade illustrates these dynamics. By following how ride-hailing apps, most notably Hailo, were introduced into and effected the city, the importance of regulatory context but also wider flows of data and capital are stressed. Data disruptions occur not at the level of the app or platform, but at the economic relations in which they are embedded. By paying attention to the historical details of data disruption, the specificities of change processes are revealed without losing track of their broader economic function. Policy relevance This research will be of interest to policymakers for explaining local-level innovation. The dominant narrative of disruption presents innovation as a technology-driven change process, dependent upon individual brilliance and breakthrough. However, what occurred in the Dublin taxi industry does not confirm this narrative. Instead, the Irish government regulated the market of drivers, and the infrastructural limits of the bus and taxi lanes encouraged some ride-hailing apps while discouraging others. This tight coupling between technology and its context is indicative of a change process of continuation rather than disruption, which is more amenable to government steering. Disruption certainly did occur in Dublin, but not as a result of individual innovation. Following the ride-hailing apps past their moment of market entrance to their poorly executed attempts to scale-up reveals the corporate and financial interests that oversee and capitalise upon data disruption.

It has been almost half a century since the first interest in autonomous robots was shown, and research is still continuing to improve their ability to make perfectly conscious decisions from a user safety point of view. These autonomous robots are now at a fairly advanced level, which means that their adoption rate in social environments is also increasing. This article reviews the current state of development of this technology and highlights the evolution of interest in it. We analyze and discuss specific areas of its use, for example, its functionality and current level of development. Finally, challenges related to the current level of research and new methods that are still being developed for the wider adoption of these autonomous robots are highlighted. [ABSTRACT FROM AUTHOR]

This study investigated electric-scooter (e-scooter) rider behaviors and preferences to inform ways to increase safety for e-scooter riders. Data was collected from 329 e-scooter riders via two online and one in-person survey. Survey questions considered rider roadway infrastructure preferences, safety perceptions, and helmet-wearing behavior. Protected bike lanes were more commonly indicated as the safest infrastructure (62.4%) but were less likely to be the most preferred infrastructure (49.7%). Sidewalks were better matched between riders, indicating them as their preferred riding infrastructure (22.7%) and the perceived safest riding infrastructure (24.5%). Riders had low feelings of safety and preference for riding on major/neighborhood streets or on unprotected bike lanes. Riders reported significant concern about being hit by a moving vehicle, running into a pothole/rough roadway, and running into a moving vehicle. In line with the Theory of Planned Behavior, a significant relationship was found between the frequency of riding and helmet-wearing behavior, with more frequent riders being more likely to wear helmets. Findings suggest that existing roadway infrastructure may pose safety challenges and encourage rider-selected workarounds. Public policy may consider emphasizing protected bicycle lane development, rather than helmet mandates, to support e-scooter riding safety for all vulnerable road users. [ABSTRACT FROM AUTHOR]

Social and economic change in the built environment is increasingly driven by processes of datafication. These often find expression through smart phone apps and private platforms that seek to upset the status quo by mediating consumer and producer interactions, and by monetising the data these produce. This paper uses the practice-oriented concept of 'disruptive data' to draw attention away from specific technologies and towards the broader political economic logics that underlie them. In so doing, disruption is reframed as a capitalist strategy for creating and capitalising on uncertainty. The rapid change to Dublin's taxi industry over the past decade illustrates these dynamics. By following how ride-hailing apps, most notably Hailo, were introduced into and effected the city, the importance of regulatory context but also wider flows of data and capital are stressed. Data disruptions occur not at the level of the app or platform, but at the economic relations in which they are embedded. By paying attention to the historical details of data disruption, the specificities of change processes are revealed without losing track of their broader economic function. [ABSTRACT FROM AUTHOR]

The increasing number of private vehicles has caused severe traffic congestion and air pollution. As an innovative transportation mode, mobility services such as ridesharing have been considered as an alternative. However, it has been evidenced that most private vehicle owners would not switch from driving to using ridesharing because such options only attract those who never intend to own a car. Therefore, it is more meaningful for private vehicle owners to provide ridesharing services, also known as Hitch, which can improve urban traffic efficiency on the service supply side. This study outlines an empirical study which is conducted to assess drivers as a group, to examine the factors which influence their intention of ridesharing. A sample of 597 private vehicle owners in Tianjin, China, completed a questionnaire. Structural equation modeling (SEM) is implemented to analyze the proposed model, specially explore the mediating role of trust and habit. The results indicate that habit is the most important factor, which is mainly influenced by extrinsic motivation (i.e., instrumental motive). Furthermore, habit can increase trust in ridesharing, another important factor, which is mainly affected by intrinsic motivation (i.e., affective motive and responsibility). Therefore, this shows the relationship between intrinsic motivation and extrinsic motivation. The findings in this paper have strong implications for both governments and platform developers, to promote the transition from solo driving to ridesharing to improve traffic condition and environmental pollution. • We test the factors leading private vehicle owners to share a ride, on the service supply side. • This study analyzes trust and habit as mediators bridging motivations. • Motivation theory and material possession theory (MPT) are used. [ABSTRACT FROM AUTHOR]

The state-of-the-art of mobility services among large-scale agent-based travel demand models has focused predominantly on the demand side, whereas complexities of the supply side have been given much less attention. Conventional definitions must be revisited to establish solid foundations for model implementations. First, several mobility services are characterized from abstract attributes, by all agents involved, and by operational tasks involved in service provision. Then, a high-level generic service provision process is developed to allow instantiation of various services. Extensions for conventional travel demand model systems are also developed, including: a mobility services component containing service provider agentsand their operational tasks integrated logically into service provision processes; a fleet component containing vehicle agents of each service and a driver activity model when human-driven; a MaaS component containing mobility provider agents that coordinate and integrate mobility services. Generic modeling issues are also discussed, including treatment of time, data structures, and computational efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Kölner Zeitschrift für Soziologie und Sozialpsychologie ( KZfSS) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The main purpose of this S-LCA case study is to analyse social impacts of the use phase of mobility services is a holistic way, taking into account positive as well as negative impacts. The use phase plays an important role for the assessment of services, which is why this study exclusively focuses on the use phase assessment. That way, this study aims to contribute to answer the question whether mobility services can improve quality of life in cities. Methods: For the analysis, seven different mobility options were chosen in the city of Berlin, Germany, including free-floating car sharing, e-scooter sharing, S-Bahn, subway, tram, bus and the car in private ownership. For the analysis, five stakeholder groups that are outlined in the S-LCA Guidelines (UNEP 2020) were taken into account: Local Community, Consumer, Worker, Value Chain Actors and Society. For a detailed analysis of all relevant aspects, 37 indicators were analysed, out of which 23 are quantitative and 14 are qualitative. For data collection, several different data sources were used, including publicly available data e.g. from statistics as well as own data from interviews. Results and discussion: For comparability, all results are displayed on a 5-point scale from − 2 to + 2, in line with the Handbook (Goedkoop et al. 2018) and the S-LCA Guidelines (UNEP 2020). For some indicators, the results of the case study are as expected, for example regarding impacts on air quality. For other indicators, however, the results are specific for the analysed mobility services in Berlin and therefore give new insights and reveal new aspects, as for example in the case of job creation for the local community. The main challenge of this S-LCA case study was data availability and data quality, which is why assumptions and simplifications had to be made, especially regarding space occupancy and the allocation of common infrastructure. Conclusions: This S-LCA case study provides a holistic assessment of the use phase of mobility services, taking into account five stakeholder categories and their respective social impacts. The study illustrates specific results for the city of Berlin, showing positive as well as negative social impacts of mobility services and outlines a procedure for further studies. That way, this case study contributes to answer the overlying question whether mobility services can improve quality of life in cities. [ABSTRACT FROM AUTHOR]

Digitalisation in transport services offers many benefits for travellers. However, not everyone is willing or able to follow the new, more or less formal requirements digitalisation has brought along. Existing reviews on the intersection between Information and Communication Technologies (ICTs) and mobility cover a range of vantage points, but the perspective of how various levels of engagement with digital technologies affect access and navigation of transport services has not been addressed yet. In communication science, studying disparities in terms of ICT appropriation and their consequences is known as digital inequality research. This review paper aims at shedding light on what digital inequality in the context of transport services consists of and what its consequences are. To do so, we define and use a conceptual framework for the analysis of digital inequality in transport services. The review of the twenty-five papers, as selected in our systematic literature search, shows that there is a burgeoning interest in this topic. Vulnerability to digitalisation in transport services exists along dimensions of age, income, education, ethnicity, gender and geographical region. We find that motivations and material access get more attention than digital skills and effective usage. Nevertheless, literature acknowledges that having material access to technology does not mean that people benefit from what technology has to offer. Furthermore, the characteristics of ICTs impact one's possibilities to access digital technologies, such as how user-friendly a technology is. Data-driven and algorithm-based decision-making present a particularly pernicious form of digital exclusion from transport services. As digital technologies are progressively becoming indispensable to navigate the world of transport services, low levels of digital engagement may create a new layer of transport disadvantage, possibly on top of existing ones. Although digitalisation can be part of the solution to transport disadvantage, it can also be part of the problem. With network effects at play, what might start as a relative disadvantage may turn into an absolute disadvantage. Given the nascent state of research on digital inequality in transport services, much remains to be understood. Suggested research avenues include mechanisms of digital exclusion from transport services, the contribution of digital inequality to transport disadvantage, and importantly, solutions to mitigate its impacts. [ABSTRACT FROM AUTHOR]

This paper proposes a holistic, within-day ridehailing service provision modelling approach, focusing on large-scale services with human-driven fleets. The model is powered by a full, two-year record of ridehailing trips in Toronto. Canada. Despite the abundant data, operations and fleet-related data still lacks, and uncertainty still exists concerning actual service provision deployment. Hence, a novel approach dealing with limited data availability is developed, which extracts implicit supply levels from trip-based demand data. The model is time-step driven, agent-based, and uses list-based data structures. It focuses on three key components: matching mechanisms, time intervals, and (several features of) driver activity. After testing various component combinations, a Hungarian matching algorithm, a normal distribution of driver work hours, and 5-minute time intervals allow close replication of observed wait time distributions and unique drivers per hour. Emergent model outputs also include VKT by vehicle states (idling, en-route, in-service), vehicle shifts and trip chains. [ABSTRACT FROM AUTHOR]

Providing mobility services effectively to residents and visitors is a complex socio-technical system task to city public managers. Smart mobility systems aim to support the efficient exploitation of city transport facilities and sustainable mobility within the urban environment. People need to travel quickly and conveniently between locations at different scales, ranging from a few blocks within a city to a journey across cities. At the same time, goods need to be timely delivered, considering both the users and the businesses’ needs. Several cities indicated an interest in using Autonomous Vehicles (AV) for the “last-mile” mobility services in the last few years. With them, it seems to be easier to get people and goods around using fewer vehicles. In this context, Autonomous Shuttles (AS) are beginning to be thought of as a new mobility/delivery service into the city center where narrow streets are not easily served by traditional buses. They allow them to perform critical areas with minimal new infrastructure and reduce noise and pollution. The article analyses the state-of-art on autonomous shuttles by proposing four application scenarios targeting the last-mile delivery of goods, the tourist experiences, and the shared and integrated mobility. Furthermore, we contribute with the proposition of the Autonomous Shuttles-as-a service (ASaaS) concept as the key pillar for the realization of innovative and sustainable proximity mobility. Our research proposed new research challenges for ASaaS, and we discuss social implications and governance challenges that consider user engagement and sustainability. It also recommended extending new research to focus on simulation and machine learning techniques for last-mile mobility planning and explore the journeys tracking certification via artificial intelligence and blockchain-based techniques. [ABSTRACT FROM AUTHOR]

This study aims to evaluate and quantify the impact of demand forecast spatial resolution on the operational performance of a shared-use automated vehicle (AV) mobility service (SAMS) fleet. To perform the evaluation, this study employs an agent-based modeling framework that includes user requests, AVs, and an SAMS fleet controller. In the simulation, an SAMS fleet controller dynamically assigns AVs to on-demand user requests and repositions empty AVs throughout the service region to serve expected future demand requests. The fleet controller uses an offline demand forecast model and an online optimization model that jointly assigns AVs to users and repositioning trips. Results indicate that despite demand forecast quality decreasing at higher spatial resolutions, the operational efficiency of the SAMS fleet increases with higher spatial resolution forecasts (i.e. smaller subareas). Results also indicate that there is a significant operational value associated with improving short-term demand forecasts at high spatial resolutions. [ABSTRACT FROM AUTHOR]

Car telematics is a large and growing business sector aiming to collect mobility-related data (mainly private and commercial vehicles) and to develop services of various nature both for individual citizens and other companies. Such services and applications include information systems to support car insurances, info-mobility services, ad hoc studies for planning purposes, etc. In this work we report and discuss some of the key challenges that a car telematics pilot application is facing within the EU project "Track and Know". The paper introduces the overall context, the main business goals identified as potentially beneficial of big data solutions and the type of data sources that such applications can rely on (in particular, those available within the project for experimental studies), then discusses initial results of the solutions developed so far and ongoing lines of research. In particular, the discussion will focus on the most relevant applications identified for the project purposes, namely new services for car insurance, electric vehicles mobility and car- and ride-sharing. [ABSTRACT FROM AUTHOR]

Purpose: The main purpose of this review is to describe the state of the art of social impact assessment with a focus on mobility services. Whereas the use phase plays an important role for the assessment of services in general, the evaluation of the use phase has been underrepresented in previous social life cycle assessment studies. For that reason, particular attention has been paid to indicators, which allow the assessment of social impacts during the use phase of mobility services. Methods: Continuous efforts to mitigate climate change and to improve quality of life in cities result in new mobility solutions based on collective use. This will have a huge impact on our society transforming the use of vehicles. In order to better understand the implications for cities, society and the automotive industry, it is essential to evaluate the social impact generated along a product life cycle with particular attention to the use phase. To reach the goal, a systematic literature review was carried out with a focus on social indicators that allow assessing use phase impacts of mobility services. The indicators were analysed and allocated to stakeholder groups. Based on the analysis, a core set of indicators is proposed under consideration of data availability. Results and discussion: Based on the selected search strings, 51 publications were selected for the literature review, including 579 social indicators. The analysis revealed a wide variety and diversity of indicators that are trying to measure the same aspect. The allocation to the respective stakeholder groups showed that most of the indicators (36%) evaluate impacts regarding the stakeholder group local community. The majority of analysed indicators are of quantitative nature (63%). Nevertheless, a clear assessment method was often missing in the respective publications. Therefore, for the core set of indicators, an assessment method is proposed for every indicator. Conclusions: The results from this study can help practitioners as well as researchers in the field of urban mobility assessment as it systematically analyses social sustainability aspects. The presented data gives an overview of various indicators that are suggested in other publications, and the proposed core set of indicators can be used to evaluate different mobility services in further research. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive literature review focused on the supply side of mobility services, providing relevant insights at the conceptual, operational, and modelling levels. Definitions are first drawn from the Mobility as a Service paradigm due to its predominance in the literature. This is followed by an assessment of the operational features of a range of mobility services, including carsharing, bikesharing, ridehailing, and demand responsive transit. To conclude the review, the state-of-the-art in modelling approaches for mobility services is reported, at different levels of complexity and integration. Three of the most important findings and arguments from this paper suggest that a high degree of generality exists for operational features of mobility services; that it is essential to make a distinction between Mobility as a Service and a mobility service in isolation; along with the argument that human agency should be carefully considered in modelling efforts, both for user agent and driver agent decision-making processes. Finally, key considerations are proposed for the future development of a conceptual framework for modelling the supply side of mobility services, which would have a generic service provider model as its core component. [ABSTRACT FROM AUTHOR]

The advent of automated vehicles could be transformative to the existing transportation system; however, the ways in which changes could happen are uncertain. This research was aimed at contributing to the literature of acceptance of self-driving vehicles and the role of ride-hailing in this decision-making process. An online survey was implemented to collect information for two distinct segments of the population: ride-hailing users and non-ride-hailing users. The data were examined through bivariate descriptive analysis and multivariate models. The results provided several insights. The overall intent to use was found to be higher among users of ride-hailing services than among non-users of such services. The results highlighted significant association of several variables with intent to use, including demographics, residential characteristics, technology and shared mobility use factors, and attitudes and perceptions. The results of this research could inform several important policy issues at the intersection of emerging technology and mobility options. [ABSTRACT FROM AUTHOR]