Combinatorial Contracts Beyond Gross Substitutes

We study the combinatorial contracting problem of D\"utting et al. [FOCS '21], in which a principal seeks to incentivize an agent to take a set of costly actions. In their model, there is a binary outcome (the agent can succeed or fail), and the success probability and the costs depend on the set of actions taken. The optimal contract is linear, paying the agent an $\alpha$ fraction of the reward. For gross substitutes (GS) rewards and additive costs, they give a poly-time algorithm for finding the optimal contract. They use the properties of GS functions to argue that there are poly-many "critical values" of $\alpha$, and that one can iterate through all of them efficiently in order to find the optimal contract. In this work we study to which extent GS rewards and additive costs constitute a tractability frontier for combinatorial contracts. We present an algorithm that for any rewards and costs, enumerates all critical values, with poly-many demand queries (in the number of critical values). This implies the tractability of the optimal contract for any setting with poly-many critical values and efficient demand oracle. A direct corollary is a poly-time algorithm for the optimal contract in settings with supermodular rewards and submodular costs. We also study a natural class of matching-based instances with XOS rewards and additive costs. While the demand problem for this setting is tractable, we show that it admits an exponential number of critical values. On the positive side, we present (pseudo-) polynomial-time algorithms for two natural special cases of this setting. Our work unveils a profound connection to sensitivity analysis, and designates matching-based instances as a crucial focal point for gaining a deeper understanding of combinatorial contract settings.Comment: 22 pages, 3 figure

Incorporating Systems Engineering Methodologies to Increase the Transferability of Journey Planners

AbstractOne characteristic that is highly desired in transportation-related applications, and particularly journey planners, is transferability – i.e., the capacity to be used with minimal modification in different locations. To achieve transferability, the initial design must take into account all factors that may diverge between locations, including existing modes of transport, the availability of required data, the technological habits of users, etc. In consequence, a highly transferable system is difficult and expensive to develop and maintain. A very flexible initial design, one ensuring low-cost adaptability of the system for different cities, regions, or countries, might not be cost-effective. On the other hand, a rigid design, tailored for a specific location, might act as a barrier to implementing the system elsewhere. This dilemma has motivated researchers to seek a structured process for selecting the most promising design, one that will realize the benefits of transferability while minimizing development costs.One of the fundamental building blocks of structured design in SE is requirements-design exploration. This paper evaluates the use of Multi-Attribute Tradespace Exploration (MATE), a leading design exploration process, for the effective design of journey planners.We examine the process of changeability assessment (e.g., transferability) in light of the goals of journey planning from the point of view of different stakeholders: travelers, private developers, and transport authorities. The analysis demonstrates how tradespace exploration can also be used to identify specific designs that bridge the gap between the public and private sectors and provide value over time to all parties. Moreover, when specific concerns of public authorities are not met, tradespace exploration can reveal measures the public sector can take (financial or others) for making their preferred design attractive to the private sector as well

Using Key Performance Indicators for traffic management and Intelligent Transport Systems as a prediction tool

In recent research work (FP7 CONDUITS) a performance evaluation framework for traffic management and Intelligent Transport Systems was developed. The new framework consists of a set of Key Performance Indicators (KPIs) for the strategic themes of traffic efficiency, safety, pollution reduction and social inclusion, and the last stages of the project saw its validation through its application to four case studies. Following up from this work, this paper presents the extension of the framework for use as a prediction tool enabling urban transport authorities to assess the impacts of relevant policies and technologies before implementing them. Focussing on pollution reduction, a tool (CONDUITS-DST) integrating the respective KPIs with microsimulation modelling is developed. The paper describes the integration process, including the model chosen for calculating the emissions levels of a number of scenarios, presents the results of the application to a case study in the city of Brussels, and outlines future developments targeted at broadening the integration of the KPIs into decision-makin

Using Key Performance Indicators for multi-criteria traffic management strategic decisions

In recent research work (FP7 CONDUITS) a performance evaluation framework for traffic management and Intelligent Transport Systems was developed. The new framework consists of a set of Key Performance Indicators (KPIs) for the strategic themes of traffic efficiency, safety, pollution reduction and social inclusion, and the last stages of the project saw its validation through its application to four case studies. Following up from this work, this paper presents the extension of the framework for use as a prediction tool enabling urban transport authorities to assess the impacts of relevant policies and technologies before implementing them. The first stage of the extension focused on pollution reduction, and a novel decision support tool (CONDUITS_DST) integrating the respective KPI with micro-simulation modelling was developed. Case studies executed in Brussels and Zurich demonstrated the usability and viability of the tool. This paper takes the development one step further and reports on the extension of the approach, which moves from single-criterion to a multi-criteria decision support tool through the inclusion of the KPI on traffic efficiency, again based onmicro-simulation modelling outputs

Development and application of an evaluation framework for urban traffic management and Intelligent Transport Systems

The aim of this paper is to present and apply a new evaluation framework for traffic management and Intelligent Transport Systems, to assist urban transport authorities in assessing relevant policies and technologies as to their performance. The principles behind performance measures and indices are outlined, along with a description of theframework development methodology. Two Key Performance Indicators (KPIs) for the topics of mobility and traffic accidents respectively are formulated and operative definitions are presented. Then, the new KPIs are applied to a case study in the city of Paris, involving the introduction of a scheme granting priority to buses at signalised junctions. The results from the before- and after-analysis are reported and interpreted, not only in terms of the case study itself, but most importantly from the standpoint of the applicability of the evaluation framework

Car-Sharing Subscription Preferences and the Role of Incentives: The Case of Copenhagen, Munich, and Tel Aviv-Yafo

Car-sharing services provide short-term car access, contributing to sustainable urban mobility and generating positive societal and environmental impacts. Attraction and retention of members are essential for the profitability and survival of these services in cities. Yet, the relevance of a variety of possible business models’ features for car-sharing subscriptions is still under-explored. This study examines individuals’ preferences for subscribing to different car-sharing business models, focusing on the attractiveness of car-sharing-related features and incentives in different contexts. We designed a stated preference experiment and collected data from three different urban car-sharing settings: Copenhagen, Munich, and Tel Aviv-Yafo. A mixed logit model was estimated to uncover the determinants of each city’s car-sharing plan subscription. The achieved insights pave the road for the actual design of car-sharing business models and attractive incentives by car-sharing companies in the studied or similar cities. Our findings reveal that although some car-sharing intrinsic features are likely to be relevant everywhere (e.g., pricing, parking conditions), the local context affects the preferences of others. In Munich, respondents prefer car-sharing services with fleets composed of electric vehicles and value high accessibility to shared cars, so marketing campaigns focusing on the positive environmental impacts of car-sharing and strategic distribution of shared cars (e.g., hubs) are expected to be very appealing there. As for Copenhagen, a high probability of finding a car, the opportunity to book a shared car in advance, and having plans including other modes are more appreciated, making hubs in high-demand areas and Mobility-as-a-Service (MaaS) plans very attractive. Finally, in Tel Aviv, our findings highlight the advantages of exploring different pricing schemes and offering dynamic incentives to users for fleet rebalancing to positively contribute to car-sharing subscriptions and ridership

Automating a framework to extract and analyse transport related social media content: The potential and the challenges

Harnessing the potential of new generation transport data and increasing public participation are high on the agenda for transport stakeholders and the broader community. The initial phase in the program of research reported here proposed a framework for mining transport-related information from social media, demonstrated and evaluated it using transport-related tweets associated with three football matches as case studies. The goal of this paper is to extend and complement the previous published studies. It reports an extended analysis of the research results, highlighting and elaborating the challenges that need to be addressed before a large-scale application of the framework can take place. The focus is specifically on the automatic harvesting of relevant, valuable information from Twitter. The results from automatically mining transport related messages in two scenarios are presented i.e. with a small-scale labelled dataset and with a large-scale dataset of 3.7 m tweets. Tweets authored by individuals that mention a need for transport, express an opinion about transport services or report an event, with respect to different transport modes, were mined. The challenges faced in automatically analysing Twitter messages, written in Twitter’s specific language, are illustrated. The results presented show a strong degree of success in the identification of transport related tweets, with similar success in identifying tweets that expressed an opinion about transport services. The identification of tweets that expressed a need for transport services or reported an event was more challenging, a finding mirrored during the human based message annotation process. Overall, the results demonstrate the potential of automatic extraction of valuable information from tweets while pointing to areas where challenges were encountered and additional research is needed. The impact of a successful solution to these challenges (thereby creating efficient harvesting systems) would be to enable travellers to participate more effectively in the improvement of transport services

Sexual Addiction, Compulsivity, and Impulsivity Among a Predominantly Female Sample of Adults Who Use the Internet for Sex

BACKGROUND AND AIMS: Compulsive sexual behavior is characterized by extensive sexual behavior and unsuccessful efforts to control excessive sexual behavior. The aim of the studies was to investigate compulsivity, anxiety and depression and impulsivity and problematic online sexual activities among adult males and females who use the Internet for finding sexual partners and using online pornography. METHODS: Study 1- 177 participants including 143 women M = 32.79 years (SD = 9.52), and 32 men M = 30.18 years (SD = 10.79). The Sexual Addiction Screening Test (SAST), the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), Spielberger Trait-State Anxiety Inventory (STAI-T STAI-S) and Beck Depression Inventory (BDI). Study 2- 139 participants including 98 women M = 24 years (SD = 5) and 41 men M = 25 years (SD = 4). The impulsivity questionnaire (BIS/BAS), Problematic online sexual activities (s-IAT-sex) and Sexual Addiction Screening Test (SAST). RESULTS: Study 1- Multiple regression analysis has indicated that a model which included BDI, Y-BOCS, and STAI scores contributed to the variance of sexual addiction rates, and explained 33.3% of the variance. Study 2- Multiple regression analysis indicated that BIS/BAS and s-IAT scores contributed to the variance of sexual addiction rates, and explained 33% of the variance. DISCUSSION AND CONCLUSIONS: Obsessive-compulsive symptoms contributed to sexual addiction among individuals who use the Internet for finding sexual partners. Impulsivity and problematic online sexual activity contributed to ratings of sex addiction. These studies support the argument that sex addiction lies on the impulsive-compulsive scale and could be classified as a behavioral addiction

The Impact of Social Media Usage on Transport Policy: Issues, Challenges and Recommendations

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Identifying attributes of public transport services for urban tourists: A data-mining method

The current work focuses on Quality of Service (QoS) of Public Transport (PT) attributes in urban tourist destinations. In particular, we aim to reveal which attributes are most significant for tourists prior to their arrival at their destination, as reflected in questions posted in TripAdvisor Question and Answer forums, a widely used social media platform. We used a data-mining method to classify questions into categories relevant to QoS, using a sample of 8905 items posted between 2005 and 2018 in TripAdvisor forums for seven urban destinations in the United States and Western Europe. We found four PT-QoS attributes: Pricing and ticketing, Accessibility, Trip duration, and Service availability (hours of operation and frequency). These attributes have similar relative significance for all destinations, origins, seasons, and years we checked. Hence, they can help service operators and policymakers to understand tourists\u27 preferences and to adjust PT services accordingly