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.

先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所采用，因为之前的努力旨在最大限度地增加参与者数量，但未能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慧社区，并且随着社会对出行行为的态度不断兴起，拼车及其社会体验应被视为智慧社区中可行且可能占主导地位的交通方式。这个早期概念探索性研究资助（EAGER）项目将解决当前拼车的社会障碍，并将提供更具吸引力的拼车比赛，从而提高用户参与度。虽然用户可能会容忍出行时间上的轻微妥协，以换取更好的社交体验，但社交乘车共享的规模经济将导致智慧城市和社区的交通系统表现更好。该项目将支持实现社会化乘车共享系统，该系统可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奠定理论基础。用户的社交属性和偏好将由两种创新模式共同决定：空间活动模式和社交活动模式。将建立概率位置聚类模型来识别空间活动模式的异常非通勤旅行。该项目利用关键词搜索方法和主题识别来估计旅行者的社交活动和社交活动模式的旅行目的。将开发用于根据社交媒体和乘车共享数据估计出行模式的新颖优化模型，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，例如常规和特殊模式（异常） ）按类型、一天中的时间、一周中的某天等划分的出行。这一步骤特别具有创新性，因为这些估计方法对于交通领域来说是新的，它们的成功应用将有利于研究界。将为社会乘车共享开发新的理论，以最大限度地提高乘客满意度和出行效率。将与当地实体合作使用一个生活实验室来演示研究产品的试点实施。

项目成果

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

利用 GPS 轨迹、POI 和地理标记的社交媒体数据进行旅行目的推断

• DOI: 10.1109/bigdata.2017.8258062
• 发表时间: 2017-12-01
• 期刊: 2017 IEEE International Conference on Big Data (Big Data)
• 作者: Chuishi Meng;Yu Cui;Qi He;Lu Su;Jing Gao
• 通讯作者: Jing Gao

A robust method for estimating transit passenger trajectories using automated data

使用自动化数据估计过境旅客轨迹的稳健方法

• DOI: 10.1016/j.trc.2018.08.006
• 发表时间: 2018-10-01
• 期刊: Transportation Research Part C: Emerging Technologies
• 作者: Pramesh Kumar;A. Khani;Qing He
• 通讯作者: Qing He

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

出行行为分类：社交网络和深度学习的方法

• DOI: 10.1177/0361198118772723
• 发表时间: 2018-06-08
• 期刊: Transportation Research Record
• 影响因子: 1.7
• 作者: Yu Cui;Qing He;A. Khani
• 通讯作者: A. Khani

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

使用社交媒体推断纵向旅行行为的潜力：基于序列模型的聚类方法

• DOI: 10.1016/j.trc.2017.10.005
• 发表时间: 2017-12-01
• 期刊: Transportation Research Part C-emerging Technologies
• 影响因子: 8.3
• 作者: Zhenhua Zhang;Qing He;Shanjiang Zhu
• 通讯作者: Shanjiang Zhu

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
• 作者: Yu Cui;Chuishi Meng;Qing He;Jing Gao
• 通讯作者: Jing Gao