EAGER/Collaborative Research: Towards Dynamic Social Ride-sharing: An Essential Component in Envisioned Smart Communities

EAGER/协作研究：迈向动态社交乘车共享：设想的智能社区的重要组成部分

• 批准号: 1637604
• 负责人: Qing He
• 金额: $ 7.5万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637604&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Towards; Dynamic

Prior work has demonstrated the potential economic and environmental benefits of ride-sharing; however, ride-sharing has not been adopted by the majority of travelers, since previous efforts aimed to maximize the number of participants but failed to take travelers social attributes into account. One of the major challenges in advocating ride-sharing is how to reduce social barriers. Taking advantage of advances in information technology and social media for building smart communities, and with emerging social attitudes regarding travel behavior, ride-sharing along with its social experience should be considered as a viable and possibly dominant mode of transportation in smart communities. This EArly-concept Grant for Exploratory Research (EAGER) project will address the current social barriers to ride-sharing and will provide more appealing ride matches leading to increased user participation. While a slight compromise in travel time may be tolerated by users in exchange of better social experience, the economy of scale in social ride-sharing would result in better performance of transportation systems in smart cities and communities. The project will support the realization of asocial ride-sharing system that can serve in validating the long-term research goals of deploying Social Transportation. The goal of this research is to develop theoretical foundations for supporting social ride-sharing. Users social attributes and preferences will be jointly determined by two innovative patterns: the spatial-activity pattern and the social-activity pattern. A probabilistic location clustering model will be built to identify abnormal non-commuting travels for the spatial-activity pattern. This project leverages a key word-search approach and topic recognition to estimate travelers social activities and travel purposes for the social-activity pattern. Novel optimization models for estimation of travel pattern from social media and ride-sharing data will be developed, to include using state-of-the-art matrix estimation techniques to infer various aspects of travel pattern, such as regular and special-pattern (anomalous) trips by type, time of day, day-of-week, etc. This step is particularly innovative in that these estimation methods are new to the transportation field, and their successful application would be beneficial to the research community. New theory will be developed for social ride-sharing in maximizing both riders satisfaction and trip efficiency. A living lab will be used in collaboration with local entities to demonstrate the pilot implementation of the research product.

之前的工作已经证明了拼车的潜在经济和环境效益;然而，拼车并没有被大多数旅行者采用，因为之前的努力旨在最大化参与者的数量，但没有考虑旅行者的社会属性。倡导拼车的主要挑战之一是如何减少社会障碍。利用信息技术和社交媒体的进步来建设智能社区，以及对出行行为的新兴社会态度，拼车沿着社会体验应被视为智能社区中可行且可能占主导地位的交通方式。EARLY概念探索性研究补助金（EAGER）项目将解决目前共享乘车的社会障碍，并将提供更具吸引力的乘车比赛，从而提高用户参与度。虽然用户可以容忍出行时间的轻微妥协，以换取更好的社交体验，但社交共乘的规模经济将使智能城市和社区的交通系统性能更好。该项目将支持社会乘车共享系统的实现，该系统可以用于验证部署社会交通的长期研究目标。这项研究的目标是为支持社会乘车共享开发理论基础。用户的社会属性和偏好将由两种创新模式共同决定：空间活动模式和社会活动模式。一个概率位置聚类模型将建立识别异常的非通勤出行的空间活动模式。本研究利用关键字搜寻与主题辨识的方法，来评估旅游者的社会活动与旅游目的。将开发用于从社交媒体和乘车共享数据估计出行模式的新型优化模型，包括使用最先进的矩阵估计技术来推断出行模式的各个方面，例如规则和特殊模式该步骤特别具有创新性，因为这些估计方法对于运输领域是新的，它们的成功应用将有益于研究界。新的理论将发展为社会共乘，最大限度地提高乘客的满意度和旅行效率。将与当地实体合作使用一个生活实验室，以展示研究产品的试点实施。

项目成果

Travel Behavior Classification: An Approach with Social Network and Deep Learning

• DOI: 10.1177/0361198118772723
• 发表时间: 2018-06
• 期刊: Transportation Research Record
• 影响因子: 1.7
• 作者: Yu Cui;Qing He;A. Khani

Forecasting current and next trip purpose with social media data and Google Places

• DOI: 10.1016/j.trc.2018.10.017
• 发表时间: 2018-12-01
• 期刊: TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
• 影响因子: 8.3
• 作者: Cui, Yu;Meng, Chuishi;Gao, Jing
• 通讯作者: Gao, Jing

A robust method for estimating transit passenger trajectories using automated data

• DOI: 10.1016/j.trc.2018.08.006
• 发表时间: 2018-10-01
• 期刊: TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
• 影响因子: 8.3
• 作者: Kumar, Pramesh;Khani, Alireza;He, Qing
• 通讯作者: He, Qing

Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method

• DOI: 10.1016/j.trc.2017.10.005
• 发表时间: 2017-12
• 期刊: Transportation Research Part C-emerging Technologies
• 影响因子: 8.3
• 作者: Zhenhua Zhang;Qing He;Shanjiang Zhu

Travel purpose inference with GPS trajectories, POIs, and geo-tagged social media data

• DOI: 10.1109/bigdata.2017.8258062
• 发表时间: 2017-12
• 期刊: 2017 IEEE International Conference on Big Data (Big Data)
• 作者: Chuishi Meng;Yu Cui;Qi He;Lu Su;Jing Gao