{"id":784450,"date":"2021-10-12T13:24:15","date_gmt":"2021-10-12T20:24:15","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=784450"},"modified":"2022-01-28T11:36:47","modified_gmt":"2022-01-28T19:36:47","slug":"understanding-driver-passenger-interactions-in-vehicular-crowdsensing","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/understanding-driver-passenger-interactions-in-vehicular-crowdsensing\/","title":{"rendered":"Understanding Driver-Passenger Interactions in Vehicular Crowdsensing"},"content":{"rendered":"
Smart city projects collect data on urban environments to identify problems, inform policymaking, and boost citizen engagement. Typically, this data is collected by static sensors placed around the city, which is not ideal for spatiotemporal needs of certain sensing applications such as air quality monitoring. Vehicular crowdsensing is an upcoming approach that addresses this problem by utilizing vehicles\u2019 mobility to collect fine-grained city-scale data. Prior work has mainly focused on designing vehicular crowdsensing systems and related components, including incentive schemes, vehicle selection, and application-specific sensing, without understanding the motivations and challenges faced by drivers and passengers, the two key stakeholders of any vehicular crowdsensing solution. Our work aims to fill this gap. To understand drivers\u2019 and passengers\u2019 perspectives, we developed Turn2Earn, a generic vehicular crowdsensing system that incentivizes drivers to take specific routes for data collection. Turn2Earn system was deployed with 13 auto-rickshaw drivers for two weeks in Bangalore, India. Our drivers took 709 trips using Turn2Earn covering 79.2% of the city\u2019s grid cells.\u00a0Interviews with 13 drivers and 15 passengers revealed innovative information-based strategies adopted by the drivers to convince passengers in taking alternative routes, and passengers\u2019 altruism in supporting the drivers. We uncovered novel insights, including viability of offered routes due to road closure, issues with electric vehicles, and selection bias among the drivers. We conclude with design recommendations to inform the future of vehicular crowdsensing, including engaging and incentivizing passengers, and criticality-based reward structure.<\/p>\n","protected":false},"excerpt":{"rendered":"
Smart city projects collect data on urban environments to identify problems, inform policymaking, and boost citizen engagement. Typically, this data is collected by static sensors placed around the city, which is not ideal for spatiotemporal needs of certain sensing applications such as air quality monitoring. Vehicular crowdsensing is an upcoming approach that addresses this problem […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[246574],"research-area":[13554,13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[255415],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[261670],"msr-pillar":[],"class_list":["post-784450","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-highlight-award","msr-research-area-human-computer-interaction","msr-research-area-systems-and-networking","msr-locale-en_us","msr-field-of-study-human-centered-computing"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2021-10-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"Methods Recognition","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/10\/Turn2Earn_CSCW2021.pdf","id":"784453","title":"turn2earn_cscw2021","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":784453,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/10\/Turn2Earn_CSCW2021.pdf"}],"msr-author-ordering":[{"type":"guest","value":"dhruv-agarwal-2","user_id":791693,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=dhruv-agarwal-2"},{"type":"text","value":"Srishti Agarwal","user_id":0,"rest_url":false},{"type":"text","value":"Vidur Singh","user_id":0,"rest_url":false},{"type":"text","value":"Rohita Kochupillai","user_id":0,"rest_url":false},{"type":"text","value":"Rosemary Pierce-Messick","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Srinivasan Iyengar","user_id":41221,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Srinivasan Iyengar"},{"type":"user_nicename","value":"Mohit Jain","user_id":38769,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Mohit Jain"}],"msr_impact_theme":["Resilience"],"msr_research_lab":[199562],"msr_event":[781570],"msr_group":[144784,602169],"msr_project":[757810],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":757810,"post_title":"Air Pollution Sensing and Causal Modelling","post_name":"air-pollution-sensing-and-causal-modelling","post_type":"msr-project","post_date":"2021-09-13 13:43:25","post_modified":"2022-02-08 09:59:20","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/air-pollution-sensing-and-causal-modelling\/","post_excerpt":"Through our drive-by air pollution sensing research project, we are enabling cost-efficient collection of granular spatiotemporal pollution data through drive-by sensing.","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/757810"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/784450"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/784450\/revisions"}],"predecessor-version":[{"id":784456,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/784450\/revisions\/784456"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=784450"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=784450"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=784450"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=784450"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=784450"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=784450"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=784450"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=784450"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=784450"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=784450"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=784450"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=784450"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=784450"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=784450"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=784450"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=784450"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}