From mobility patterns to behavioural change: leveraging travel behaviour and personality profiles to nudge for sustainable transportation

Promoting active transportation through technology: a scoping review of mobile apps for walking and cycling

BACKGROUND

Incorporating active transportation (AT), such as walking and cycling, into daily routines is a promising solution for meeting the World Health Organization's physical activity recommendations and contributes to reducing the risk of many noncommunicable diseases. Smartphone apps offer versatile platforms for embedding health behavior promotion strategies to encourage AT.

OBJECTIVE

This scoping review aimed to provide an overview of how mobile apps are being used to promote AT through reviews of the academic literature and commercial app stores.

METHODS

We searched six academic databases (Embase, Medline, Web of Science, PsychINFO, Transport Database, and Google Scholar) for academic literature. The literature was included if it presented a developed app to promote AT behaviors. AT promotion strategies and theories were extracted and analyzed for their impact on changing behaviors and behavioral intentions toward AT. Commercial apps were searched in two app stores (the Apple App Store and the Google Play Store) across six countries, one per continent. Apps were included if they promoted and encouraged AT behavior. We evaluated the apps on the basis of user engagement and their quality and potential to change behaviors, as assessed via the Mobile App Rating Scale (MARS) and the App Behavior Change Scale (ABACUS).

RESULTS

The academic literature search identified 38 articles, presenting 29 apps. All the studies that evaluated behavioral intentions reported success in raising awareness and changing behavioral intentions. A promising strategy to motivate behavior involves providing multiple relevant feedback (calories burned, money saved, time saved, and CO2/particulate matter emissions) on behavioral impacts alongside action plans (route recommendations and personalized travel plans). Only two apps from the literature search were publicly available. The commercial app search identified 78 apps. Apps with high-quality engagement, functionality, aesthetics, and information presented greater user engagement than those that did not; therefore, they were more likely to succeed.

CONCLUSION

Mobile apps have great potential to motivate changes and be part of a comprehensive system to promote AT. Given the rapid growth of app-based interventions, leveraging mobile apps to encourage AT warrants further exploration. Upon development, these apps should be maintained and made publicly accessible.

Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPS

This study introduces an innovative algorithm for classifying transportation modes. It categorizes modes such as walking, biking, tram, bus, taxi, and private vehicles based on data collected through sensors embedded in smartphones. The data include date, time, latitude, longitude, altitude, and speed, gathered using a mobile application specifically designed for this project. These data were collected through the smartphone's GPS to enhance the accuracy of the analysis. The stopping times of each transport mode, as well as the distance traveled and average speed, are analyzed to identify patterns and distinctive features. Conducted in Cuenca, Ecuador, the study aims to develop and validate an algorithm to enhance urban planning. It extracts significant features from mobility patterns, including speed, acceleration, and over-acceleration, and applies longitudinal dynamics to train the classification model. The classification algorithm relies on a decision tree model, achieving a high accuracy of 94.6% in validation and 94.9% in testing, demonstrating the effectiveness of the proposed approach. Additionally, the precision metric of 0.8938 signifies the model's ability to make correct positive predictions, with nearly 90% of positive instances correctly identified. Furthermore, the recall metric at 0.83084 highlights the model's capability to identify real positive instances within the dataset, capturing over 80% of positive instances. The calculated F1-score of 0.86117 indicates a harmonious balance between precision and recall, showcasing the models robust and well-rounded performance in classifying transport modes effectively. The study discusses the potential applications of this method in urban planning, transport management, public transport route optimization, and urban traffic monitoring. This research represents a preliminary stage in generating an origin-destination (OD) matrix to better understand how people move within the city.

Online flight booking: digital nudging to decrease aviation-related carbon emissions

Purpose

Online flight booking websites compare airfares, convenience and other consumer relevant attributes. Environmental concerns are typically not addressed, even though aviation is the most emission-intensive mode of transportation. This article demonstrates the potential for digital nudges to facilitate more environmentally friendly decision-making on online flight booking websites.

Design/methodology/approach

The authors used the digital nudging design process to implement two nudging interventions in an experimental setting on a fictitious flight booking website. The two nudging interventions are (1) an informational nudge, presented as an emission label, and (2) an understanding mapping nudge, presented as an emission converter.

Findings

This article finds that both digital nudges are useful interventions in online choice environments; however, emission labels more effectively encourage sustainable booking behavior.

Originality/value

The contributions of this article are twofold. In contribution to research, this article builds on existing research in sustainability contexts and successfully evaluates the effectiveness of anchoring and understanding mapping heuristics to influence sustainable decision-making in virtual environments. Furthermore, in contribution to practice, this article contributes knowledge to nudge design and provides hands on examples for designers or website operators on how to put nudge designs to practice in virtual choice environments. Additionally, this article contributes relevant considerations in a high-impact research field with growing importance given the global climate crisis.

DFTrans: Dual Frequency Temporal Attention Mechanism-Based Transportation Mode Detection

In recent years, with the diversification of people's modes of transportation, a large amount of traffic data is generated when people travel every day, and this data can help transportation mode detection to be of great use in a variety of applications. Although transportation mode detection has been investigated, there are still challenges in terms of accuracy and robustness. This paper presents a novel transportation mode detection algorithm, DFTrans, which is based on Temporal Block and Attention Block. Low- and high-frequency components of traffic sequences are obtained using discrete wavelet transforms. A two-channel encoder is carefully designed to accurately capture the temporal and spatial correlation between low- and high-frequency components in both long- and short-term patterns. With the Temporal Block, the inductive bias of the CNN is introduced at high frequencies to improve generalization performance. At the same time, the network is generated with the same length as the input, ensuring a long effective history. Low frequencies are passed through Attention Block, which has fewer parameters to capture the global focus and solves the problem that RNNs cannot be computed in parallel. After fusing the output of the feature by Temporal Block and Attention Block, the classification results are output by MLP. Extensive experimental results show that the DFTrans algorithm achieves macro F1 scores of 86.34% on the real-world SHL dataset and 87.64% on the HTC dataset. Our model can better identify eight modes of transportation, including stationary, walking, running, cycling, bus, car, underground, and train, and has better performance in transportation mode detection than other baseline algorithms.

Transportation Mode Detection Using Temporal Convolutional Networks Based on Sensors Integrated into Smartphones

In recent years, with the development of science and technology, people have more and more choices for daily travel. However, assisting with various mobile intelligent services by transportation mode detection has become more urgent for the refinement of human activity identification. Although much work has been done on transportation mode detection, accurate and reliable transportation mode detection remains challenging. In this paper, we propose a novel transportation mode detection algorithm, namely T2Trans, based on a temporal convolutional network (i.e., TCN), which employs multiple lightweight sensors integrated into a phone. The feature representation learning of multiple preprocessed sensor data using temporal convolutional networks can improve transportation mode detection accuracy and enhance learning efficiency. Extensive experimental results demonstrated that our algorithm attains a macro F1-score of 86.42% on the real-world SHL dataset and 88.37% on the HTC dataset, with an average accuracy of 86.37% on the SHL dataset and 89.13% on the HTC dataset. Our model can better identify eight transportation modes, including stationary, walking, running, cycling, car, bus, subway, and train, with better transportation mode detection accuracy, and outperform other benchmark algorithms.

What Actually Works for Activity Recognition in Scenarios with Significant Domain Shift: Lessons Learned from the 2019 and 2020 Sussex-Huawei Challenges

From 2018 to 2021, the Sussex-Huawei Locomotion-Transportation Recognition Challenge presented different scenarios in which participants were tasked with recognizing eight different modes of locomotion and transportation using sensor data from smartphones. In 2019, the main challenge was using sensor data from one location to recognize activities with sensors in another location, while in the following year, the main challenge was using the sensor data of one person to recognize the activities of other persons. We use these two challenge scenarios as a framework in which to analyze the effectiveness of different components of a machine-learning pipeline for activity recognition. We show that: (i) selecting an appropriate (location-specific) portion of the available data for training can improve the F1 score by up to 10 percentage points (p. p.) compared to a more naive approach, (ii) separate models for human locomotion and for transportation in vehicles can yield an increase of roughly 1 p. p., (iii) using semi-supervised learning can, again, yield an increase of roughly 1 p. p., and (iv) temporal smoothing of predictions with Hidden Markov models, when applicable, can bring an improvement of almost 10 p. p. Our experiments also indicate that the usefulness of advanced feature selection techniques and clustering to create person-specific models is inconclusive and should be explored separately in each use-case.

Can Personalization Persuade? Study of Notification Adaptation in Mobile Behavior Change Intervention Application

The growing ubiquity of smartphones and the ease of creating and distributing applications render the mobile platform an attractive means for facilitating positive behavior change at scale. Within the smartphone as a behavior change support system, mobile notifications play a critical role as they enable timely and relevant information distribution. In this paper we describe our preliminary investigation of the persuasiveness of mobile notifications delivered within a real-world behavior change intervention mobile app, which enabled users to set goals and define tasks related to those goals. The application aimed to motivate the users with notifications belonging to one of two groups—tailored and non-tailored, seeing them as sparks in the Fogg Behavior Model and personalizing them according to the users’ Big Five personality traits. Results indicate that customized messages may work for some individuals while working poorly for others. When analyzing users as a single group, no significant differences were observed, but when proceeding with the analysis on the individual level we found seven users whose personality traits notifications interact with in interesting ways. Our results offer two general insights: (1) Using personality-tailored messaging in a dynamic mobile domain as opposed to a static domain leads to different outcomes, and it seems that there is no one-to-one mapping between domains; (2) A major reason for most of our hypotheses being false may be that messages that are deemed as persuasive on their own are not what persuades people to perform an action. Unlike the clear-cut findings observed in other domains, we discover a rather nuanced relationship between the personalization and persuasiveness that calls for further exploration at the individual participant level.

Promoting Sustainable Mobility: To What Extent Is “Health” Considered by Mobility App Studies? A Review and a Conceptual Framework

Promoting cycling and walking in cities improves individual health and wellbeing and, together with public transport, promotes societal sustainability patterns. Recently, smartphone apps informing and motivating sustainable mobility usage have increased. Current research has applied and investigated these apps; however, none have specifically considered mobility-related health components within mobility apps. The aim of this study is to examine the (potential) role of health-related information provided in mobility apps to influence mobility behavior. Following a systematic literature review of empirical studies applying mobility apps, this paper (1) investigates the studies and mobility apps regarding communicated information, strategies, and effects on mobility behavior and (2) explores how, and to what extent, health and its components are addressed. The reviewed studies focus on environmental information, especially CO2-emissions. Health is represented by physical activity or calories burned. The self-exposure to air pollution, noise, heat, traffic injuries or green spaces is rarely addressed. We propose a conceptual framework based on protection motivation theory to include health in mobility apps for sustainable mobility behavior change. Addressing people’s self-protective motivation could empower mobility app users. It might be a possible trigger for behavior change, leading towards healthy and sustainable mobility and thus, have individual and societal benefits.

Three-Year Review of the 2018–2020 SHL Challenge on Transportation and Locomotion Mode Recognition From Mobile Sensors

The Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenges aim to advance and capture the state-of-the-art in locomotion and transportation mode recognition from smartphone motion (inertial) sensors. The goal of this series of machine learning and data science challenges was to recognize eight locomotion and transportation activities (Still, Walk, Run, Bus, Car, Train, Subway). The three challenges focused on time-independent (SHL 2018), position-independent (SHL 2019) and user-independent (SHL 2020) evaluations, respectively. Overall, we received 48 submissions (out of 93 teams who registered interest) involving 201 scientists over the three years. The survey captures the state-of-the-art through a meta-analysis of the contributions to the three challenges, including approaches, recognition performance, computational requirements, software tools and frameworks used. It was shown that state-of-the-art methods can distinguish with relative ease most modes of transportation, although the differentiating between subtly distinct activities, such as rail transport (Train and Subway) and road transport (Bus and Car) still remains challenging. We summarize insightful methods from participants that could be employed to address practical challenges of transportation mode recognition, for instance, to tackle over-fitting, to employ robust representations, to exploit data augmentation, and to exploit smart post-processing techniques to improve performance. Finally, we present baseline results to compare the three challenges with a unified recognition pipeline and decision window length.

Gamification to prevent climate change: a review of games and apps for sustainability

Gamification, the application of game design principles to a nongaming context, has been used to promote pro-environmental behaviors. Such principles have been implemented in board games, team competitions, electronic games, smartphone apps, and in apps that researchers developed primarily to collect data. We review the games and apps that have been evaluated in empirical research in the last 5 years and provide a list of apps and games that have yet to be tested. Gamification has been used for sustainability education, energy reduction, transportation, air quality, waste management, and water conservation. Although we do not know yet why certain games and apps are more effective than others, gamification appears to be a promising avenue for preventing climate change.

Heuristic-Based Journey Planner for Mobility as a Service (MaaS)

The continuing growth of urbanisation poses a real threat to the operation of transportation services in large metropolitan areas around the world. As a response, several initiatives that promote public transport and active travelling have emerged in the last few years. Mobility as a Service (MaaS) is one such initiative with the main goal being the provision of a holistic urban mobility solution through a single interface, the MaaS operator. The successful implementation of MaaS requires the support of a technology platform for travellers to fully benefit from the offered transport services. A central component of such a platform is a journey planner with the ability to provide trip options that efficiently integrate the different modes included in a MaaS scheme. This paper presents a heuristic that implements a scenario-based journey planner for users of MaaS. The proposed heuristic provides routes composed of different modes including private cars, public transport, bike-sharing, car-sharing and ride-hailing. The methodological approach for the generation of journeys is explained and its implementation using a microservices architecture is presented. The implemented system was trialled in two European cities and the analysis of user satisfaction results reveal good overall performance.