Expanding the research area of behavior change support systems

The First International Workshop on Behavior Change Support Systems attracted a great research interest. The selected papers focused on abstraction, implementation and evaluation of Behavior Change Support Systems. The workshop is an evidence of how researchers from around the globe have their own perspective of behavior change interventions. In this abstract, we have attempted to outline core issues that can enhance persuasiveness of such support systems. Finally, we highlight important research questions relating to the development of effective Behavior Change Support System

Effectiveness of incentives offered by mobile phone app to encourage cycling: A long‐term study

Reduction of car use is one of the most effective ways to tackle congestion-related problems. Using positive incentives to stimulate bicycle use is one possibility to reduce car use. Cycling is a sustainable transport mode that uses little space and is healthy. There is evidence that positive incentives may be more effective than punishing travellers for undesirable behaviour, and the emergence of mobile applications for delivering interventions has opened up new opportunities for influencing travellers. So far, few studies have focused on exploring the effectiveness of positive incentives on long-term behavioural change. We used the SMART app to deliver positive incentives to more than 6000 travellers in the Dutch region of Twente. The app automatically tracks users and provides incentives such as challenges with rewards, feedback, and messages. This study covers the period from March 2017 to June 2018, in which more than 1000 SMART users participated in monthly challenges. We evaluated the effects of the challenges and rewards and found that the challenges did encourage cycling and reduced car use in the short term. There is also some evidence for behavioural change over a longer time period

Optimising the Loading Diversity of Rail Passenger Crowding using On-Board Occupancy Data

Crowded conditions on trains can lead to lower passenger satisfaction, discourage rail travel, result in negative economic impacts and are a factor in a number of health and safety hazards. In the UK there is an annual survey of rail passenger crowding, although the measures used do not reflect coach-by-coach variations, nor do they reflect variations across the peak period. In this MPhil thesis I investigated the application of weight-based automatic passenger counting data to deliver more even loadings on trains through the provision of new real-time and static solutions. In addition I investigated the potential benefits of such solutions in terms of reduced dwell times and reduced crowding. The overall concept proposed was to make the most of the existing available capacity; for example, so that no-one is standing when seats are available. Through analysing a large sample of air suspension data, I identified station-specific trends where some coaches were over capacity while others had spare capacity. I also conducted a critical review of academic research into on-train crowding and solutions that seek to optimise ‘loading diversity’. This study contributes to this emerging subject area in several ways: I propose two new metrics to describe inter-coach loading diversity that, unlike existing metrics, contain information relative to the capacity; I have revealed a link between the inter-coach loading diversity metrics and estimated boarding times, with trains classified as ‘very uneven’ on departure typically having dwell times of approximately five to ten seconds greater than services that were classified as being ‘even’ with a similar total number of passengers on board; and finally I have applied classification supervised learning techniques to predict the load factor for a given service and these predictors were an improvement over taking the historical average

MOBILITY ANALYSIS AND PROFILING FOR SMART MOBILITY SERVICES: A BIG DATA DRIVEN APPROACH. An Integration of Data Science and Travel Behaviour Analytics

Smart mobility proved to be an important but challenging component of the smart cities paradigm. The increased urbanization and the advent of sharing economy require a complete digitalisation of the way travellers interact with the mobility services. New sharing mobility services and smart transportation models are emerging as partial solutions for solving some tra c problems, improve the resource e ciency and reduce the environmental impact. The high connectivity between travellers and the sharing services generates enormous quantity of data which can reveal valuable knowledge and help understanding complex travel behaviour. Advances in data science, embedded computing, sensing systems, and arti cial intelligence technologies make the development of a new generation of intelligent recommendation systems possible. These systems have the potential to act as intelligent transportation advisors that can o er recommendations for an e cient usage of the sharing services and in uence the travel behaviour towards a more sustainable mobility. However, their methodological and technological requirements will far exceed the capabilities of today's smart mobility systems. This dissertation presents a new data-driven approach for mobility analysis and travel behaviour pro ling for smart mobility services. The main objective of this thesis is to investigate how the latest technologies from data science can contribute to the development of the next generation of mobility recommendation systems. Therefore, the main contribution of this thesis is the development of new methodologies and tools for mobility analysis that aim at combining the domain of transportation engineering with the domain of data science. The addressed challenges are derived from speci c open issues and problems in the current state of the art from the smart mobility domain. First, an intelligent recommendation system for sharing services needs a general metric which can assess if a group of users are compatible for speci c sharing solutions. For this problem, this thesis presents a data driven indicator for collaborative mobility that can give an indication whether it is economically bene cial for a group of users to share the ride, a vehicle or a parking space. Secondly, the complex sharing mobility scenarios involve a high number of users and big data that must be handled by capable modelling frameworks and data analytic platforms. To tackle this problem, a suitable meta model for the transportation domain is created, using the state of the art multi-dimensional graph data models, technologies and analytic frameworks. Thirdly, the sharing mobility paradigm needs an user-centric approach for dynamic extraction of travel habits and mobility patterns. To address this challenge, this dissertation proposes a method capable of dynamically pro ling users and the visited locations in order to extract knowledge (mobility patterns and habits) from raw data that can be used for the implementation of shared mobility solutions. Fourthly, the entire process of data collection and extraction of the knowledge should be done with near no interaction from user side. To tackle this issue, this thesis presents practical applications such as classi cation of visited locations and learning of users' travel habits and mobility patterns using historical and external contextual data