Nudging drivers : sensing, smartphone apps and incentives
Abstract/Contents
- Abstract
- Societal networks, such as electricity, healthcare and transportation networks, are constructs built around the resources that we consume on a daily basis. Problems surrounding societal networks come from the scarcity and wastage of these resources. These problems can be difficult to solve due to their distributed nature: while the cost of the problem to each network participant is small, the aggregate cost to society can be enormous. Peak demand further complicates the problems: the cost of a resource can be non-linear, and increases dramatically as the resource load increases, affecting all network participants. Two categories of solutions for these problems are capacity addition and demand management. Neither solution is perfect -- capacity addition can be ineffective as demand often increases to fill extra capacity; demand management schemes, often implemented using using charges, can run into public and political reresistance. Incentive programs present a complementary approach for demand management. Instead of charging, incentives are rewarded for behavior change. Incentive programs allow for positive public perception, incremental deployment and personalization, all of which are difficult with charging schemes. Concepts from nudges can be applied to the design of incentive programs; the use of accurate sensing, random rewards, interactive games, social nudging and personalized incentives can synergize to create an effective incentive program. Steptacular and INSINC are programs previously deployed by the Stanford Center for Societal Networks which demonstrate the effectiveness of nudges. Capri, the main thrust of the thesis, is an incentive program for commuters at Stanford University, seeking to reduce peak hour automobile congestion on the Stanford University campus. By incentivizing off-peak and alternative mode commutes, Capri is an effective complement to Stanford's existing transporation demand management programs. By using low-cost, high-accuracy sensing methods of RFID and smartphone geolocationing, and incorporating elements of nudging, personalization and trendjacking, Capri has been effective in shifting the commute times of a large set of users out of peak hours. The rich data generated by Capri participants allow us to quantify the effects of various program features on user performance. More generally, Capri shows that concepts of nudging are widely applicable for incentive programs in societal networks. The architecture espoused by Capri can be used to deploy incentive programs in other settings such as health networks.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2014 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Yue, Jia Shuo |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Prabhakar, Balaji, 1967- |
| Thesis advisor | Prabhakar, Balaji, 1967- |
| Thesis advisor | Rajagopal, Ram |
| Thesis advisor | Rosenblum, Mendel |
| Advisor | Rajagopal, Ram |
| Advisor | Rosenblum, Mendel |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Jia Shuo Yue. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2014. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2014 by Jia Shuo Yue
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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