Prototype business models for Mobility-as-a-Service

Mobility as a Service (MaaS) is a promising concept which aims at offering seamless mobility to end users and providing economic, societal, transport-related and environmental benefits to the cities of the future. To achieve a successful future market take-up of MaaS it is important to develop prototype business models to offer high-value bundled mobility services to customers, as well as enable the MaaS operator and the involved actors to capture value. This paper aims at investigating the business perspective of MaaS by collecting qualitative data from workshops and in-depth interviews in three European metropolitan areas: Budapest, Greater Manchester and the city of Luxembourg. The analysis of the collected data contributed to the in-depth analysis of the MaaS business ecosystem and the identification of the champions of MaaS in the three areas. Prototype business models for MaaS are developed based on the Osterwalder's canvas, to describe how MaaS operators may create, deliver, and capture value. Our findings indicate that the MaaS ecosystem comprises of public and private actors who need to cooperate and compete in order to capture value. Although noticeable deviations among the study areas are observed, mobility service providers, public transport authorities and regional authorities were commonly indicated as the key actors in a MaaS partnership. In addition, viewed as a system, enablers and barriers to MaaS are identified based on the systems’ of innovation approach. The analysis indicates that the regulatory framework of the cities, the lack of standardization and openness of the application programming interfaces and the need for transport-related investments constitute risks for the successful implementation of MaaS in the study areas. Trust between MaaS actors and cooperation in e-ticketing are key enablers in some of the study areas

Incorporating social interaction into hybrid choice models

The aim of this paper is to develop a methodological framework for the incorporation of social interaction effects into choice models. The developed method provides insights for modeling the effect of social interaction on the formation of psychological factors (latent variables) and on the decision-making process. The assumption is based on the fact that the way the decision maker anticipates and processes the information regarding the behavior and the choices exhibited in her/his social environment, affects her/his attitudes and perceptions, which in turn affect her/his choices. The proposed method integrates choice models with decision makers’ psychological factors and latent social interaction. The model structure is simultaneously estimated providing an improvement over sequential methods as it provides consistent and efficient estimates of the parameters. The methodology is tested within the context of a household aiming to identify the social interaction effects between teenagers and their parents regarding walking-loving behavior and then the effect of this on mode to school choice behavior. The sample consists of 9,714 participants aged from 12 to 18 years old, representing 21 % of the adolescent population of Cyprus. The findings from the case study indicate that if the teenagers anticipate that their parents are walking lovers, then this increases the probability of teenagers to be walking-lovers too and in turn to choose walking to school. Generally, the findings from the application result in: (a) improvements in the explanatory power of choice models, (b) latent variables that are statistically significant, and (c) a real-world behavioral representation that includes the social interaction effect

Modeling cross-national differences in automated vehicle acceptance

The technology that allows fully automated driving already exists and it may gradually enter the market over the forthcoming decades. Technology assimilation and automated vehicle acceptance in different countries is of high interest to many scholars, manufacturers, and policymakers worldwide. We model the mode choice between automated vehicles and conventional cars using a mixed multinomial logit heteroskedastic error component type model. Specifically, we capture preference heterogeneity assuming a continuous distribution across individuals. Different choice scenarios, based on respondents’ reported trip, were presented to respondents from six European countries: Cyprus, Hungary, Iceland, Montenegro, Slovenia, and the UK. We found that large reservations towards automated vehicles exist in all countries with 70% conventional private car choices, and 30% automated vehicles choices. We found that men, under the age of 60, with a high income who currently use private car, are more likely to be early adopters of automated vehicles. We found significant differences in automated vehicles acceptance in different countries. Individuals from Slovenia and Cyprus show higher automated vehicles acceptance while individuals from wealthier countries, UK, and Iceland, show more reservations towards them. Nontrading mode choice behaviors, value of travel time, and differences in model parameters among the different countries are discussed

Latent variables and route choice behavior

In the last decade, a broad array of disciplines has shown a general interest in enhancing discrete choice models by considering the incorporation of psychological factors affecting decision making. This paper provides insight into the comprehension of the determinants of route choice behavior by proposing and estimating a hybrid model that integrates latent variable and route choice models. Data contain information about latent variable indicators and chosen routes of travelers driving regularly from home to work in an urban network. Choice sets include alternative routes generated with a branch and bound algorithm. A hybrid model consists of measurement equations, which relate latent variables to measurement indicators and utilities to choice indicators, and structural equations, which link travelers' observable characteristics to latent variables and explanatory variables to utilities. Estimation results illustrate that considering latent variables (i.e., memory, habit, familiarity, spatial ability, time saving skills) alongside traditional variables (e.g., travel time, distance, congestion level) enriches the comprehension of route choice behavior

Once the shovel hits the ground : Evaluating the management of complex implementation processes of public-private partnership infrastructure projects with qualitative comparative analysis

Much attention is being paid to the planning of public-private partnership (PPP) infrastructure projects. The subsequent implementation phase – when the contract has been signed and the project ‘starts rolling’ – has received less attention. However, sound agreements and good intentions in project planning can easily fail in project implementation. Implementing PPP infrastructure projects is complex, but what does this complexity entail? How are projects managed, and how do public and private partners cooperate in implementation? What are effective management strategies to achieve satisfactory outcomes? This is the fi rst set of questions addressed in this thesis. Importantly, the complexity of PPP infrastructure development imposes requirements on the evaluation methods that can be applied for studying these questions. Evaluation methods that ignore complexity do not create a realistic understanding of PPP implementation processes, with the consequence that evaluations tell us little about what works and what does not, in which contexts, and why. This hampers learning from evaluations. What are the requirements for a complexity-informed evaluation method? And how does qualitative comparative analysis (QCA) meet these requirements? This is the second set of questions addressed in this thesis

Does Social Networking Substitute for or Stimulate Teenagers’ Travel? Findings from a Latent Class Model.

The aim of this paper is to investigate and quantify the influence of various social networking (SN) usage styles on adolescents’ travel behavior. For this purpose a latent class model is developed, which incorporates SN usage styles as higher-level individual orientations influencing the number of trips made for social purposes. The latent class model consists of two parts: 1. The class membership model, which links the latent SN usage styles to socio-demographic variables; and 2. the classspecific choice model, which is a Poisson regression and shows the influence of an SN usage style and socio-economic variables on the number of trips made for social purposes. The methodology is tested with data from a survey conducted in Cyprus in 2012 and refers only to adolescents. The survey provides data on 15,693 social trips of 9,735 participants (20% of the total high-school population). The class membership model indicates that there are four latent SN usage styles, while the results of the class-specific model indicate that the rational SN usage style (Class 1) and the SN addiction (Class 3) increases the number of social trips, while the indifference in SN usage (Class 2) and non-SN-users (Class 4) affects negatively the number of social trips. The results of the study provide insights into how SN usage affects Net Generations travel behavior, and especially trip substitution vs complementarity, while the class specific model is rich in interpretation, and serves as a harbinger for policy-makers

Shopping-Related Travel in Rich ICT Era: Case Study on Impact of E-shopping on Travel Demand

This paper presents a case study on individuals’ e-shopping behaviour and its effects on transport demand. The developed Stated Preferences (SP) data collection and modelling methodology aimed at predicting how e-shopping affects the number of trips individuals make for shopping, as well as the trips for non-work-related activities in the future. An innovative web-based survey called Information Accelerator was developed to collect the stated preferences data. The respondents were presented with realistic scenarios in year 2010 and stated the number of e-shopping trips they would conduct in the future. These SP scenarios included attributes related to the future level of service of transport system, as well as internet-related characteristics such as connection speed, connection fee, security of transactions payment and product delivery issues. A total of 319 completed surveys were collected over the Internet from 5 different European countries. Based on the results, the highest percentage increases in e-shopping were observed for the purchase of electronic goods and computer software product categories respectively, while the smallest increase found corresponds to leisure products. Substitution of in-store shopping trips with e-shopping was found prominent for specific product categories (especially grocery products). However, the number of trips for other than shopping and work trip purposes (such as leisure trips) was not influenced by e-shopping. A regression model was estimated to forecast the difference in the number of daily trips for shopping as a function of both scenario variables (e.g. problems with credit cards solved, internet connection free or faster, etc.), journey conditions, and individual-specific variables (age, gender, occupation, etc.). Estimation results show that some consumer categories (such as young individuals, workers familiar with the internet), are more likely to perform e-shopping. Estimation results also demonstrate that the technological improvements having the greatest effects on the increase of e-shopping (and on the consequent decrease of shopping trips), are those related to overcoming delivery and security problem