Mining mobility user profiles for car pooling

Trasarti, Roberto; Pinelli, Fabio; Nanni, Mirco; Giannotti, Fosca

doi:10.1145/2020408.2020591

Cited by 116 publications

(91 citation statements)

References 10 publications

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“…This often depends on the tools used to collect data (e.g., cellphone [Alexander & González, 2015;Cici et al, 2014], GPS systems [He, Hwang, & Li, 2014;Tachet et al, 2017;Trasarti, Pinelli, Nanni, & Giannotti, 2011], surveys [Ghoseiri, Haghani, & Hamedi, 2011] and social networking tools [Cici et al, 2014]). In general, cellphone datasets often in the form of Call Detail Records (CDRs) have less granular information in terms of user trajectories since they often record user information when users make calls or send text messages.…”

Section: Vehicle Trip Data Setmentioning

confidence: 99%

Investigating the Potential of Ridesharing to Reduce Vehicle Emissions

Jalali

Koohi-Fayegh

El-Khatib

et al. 2017

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As urban populations grow, cities need new strategies to maintain a good standard of living while enhancing services and infrastructure development. A key area for improving city operations and spatial layout is the transportation of people and goods. While conventional transportation systems (i.e., fossil fuel based) are struggling to serve mobility needs for growing populations, they also represent serious environmental threats. Alternative-fuel vehicles can reduce emissions that contribute to local air pollution and greenhouse gases as mobility needs grow. However, even if alternative-powered vehicles were widely employed, road congestion would still increase. This paper investigates ridesharing as a mobility option to reduce emissions (carbon, particulates and ozone) while accommodating growing transportation needs and reducing overall congestion. The potential of ridesharing to reduce carbon emissions from personal vehicles in Changsha, China, is examined by reviewing mobility patterns of approximately 8,900 privately-owned vehicles over two months. Big data analytics identify ridesharing potential among these drivers by grouping vehicles by their trajectory similarity. The approach includes five steps: data preprocessing, trip recognition, feature vector creation, similarity measurement and clustering. Potential reductions in vehicle emissions through ridesharing among a specific group of drivers are calculated and discussed. While the quantitative results of this analysis are specific to the population of Changsha, they provide useful insights for the potential of ridesharing to reduce vehicle emissions and the congestion expected to grow with mobility needs. Within the study area, ridesharing has the potential to reduce total kilometers driven by about 24% assuming a maximum distance between trips less than 10 kilometers, and schedule time less than 60 minutes. For a more conservative maximum trip distance of 2 kilometers and passenger schedule time of less than 40 minutes, the reductions in traveled kilometers could translate to the equivalent of approximately 4.0 tons CO 2 emission reductions daily.

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Section: Vehicle Trip Data Setmentioning

confidence: 99%

Investigating the Potential of Ridesharing to Reduce Vehicle Emissions

Jalali

Koohi-Fayegh

El-Khatib

et al. 2017

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“…For instance, [2] defines a set of representative trips performed by the object in his/her historical movement, and a profile is the spatiotemporal trajectory which is frequent in the object's movement history. Profiles in this work are computed for car pooling.…”

Section: Related Workmentioning

confidence: 99%

“…The similarity of negative rules (line 24) is calculated according to Equation (2). These values are used to compute the matching between a moving object history model and a profile name (line 33).…”

Section: T-profiles: An Algorithm For Discovering Trajectory Profmentioning

confidence: 99%

“…Only a few works address the discovery of user profiles, but from a different perspective and for different mobility data. For GPS trajectories, which is the focus of this paper, [2] defines as object profile the representative trajectory of a set of similar trips, for car pooling. [3] defines as profiles the users that visit similar places at similar times.…”

Section: Introductionmentioning

confidence: 99%

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A Rule-based Method for Discovering Trajectory Profiles

Alencar

Älvares

Renso

et al. 2015

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-The discovery of people profiles such as workers, students, families with kids, etc, is of interest for several application domains. For decades, such information has been extracted using census data, and more recently, from social networks, where people's profile is clearly defined. A new type of data that has not been explored for discovering profiles, but which stores the real movement of people, are trajectories of moving objects. In this paper we propose a rule-based method to represent socio-demographic profiles, a moving object history model to summarize the daily movement of individuals, and define similarity functions for matching the profile model and the history model. We evaluate the method for single and multiple profile discovery.

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“…The adversary has the motivation to obtain users' travel history (i.e., traces), because extracting traces is an essential preliminary step towards further data inference, e.g., trajectory pattern mining [3], location-based recommendation, car pooling and friend finder [16,17]. In this paper, we assume that toll servers are malicious and collude with the adversary, which makes users' location records and toll payment information part of the adversary's knowledge.…”

Section: Adversary Modelmentioning

confidence: 99%

Post-hoc User Traceability Analysis in Electronic Toll Pricing Systems

Chen

Fonkwe

Pang

2013

Data Privacy Management and Autonomous Spontaneous Security

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Abstract. Electronic Toll Pricing (ETP), a location-based vehicular service, allows users to pay tolls without stopping or even slowing down their cars. User location records are collected so as to calculate their payments. However, users have privacy concerns as locations are considered as private information. In this paper, we focus on user traceability in ETP systems where anonymous location records are stored by the service providers. Based on user toll payment information, we propose a post-hoc analysis of user traceability, which aims at computing a user's all possible traces. Moreover, we propose several methods to improve the effectiveness of the analysis by combining other contextual information and propose a number of optimisations to improve its efficiency as well. We develop a prototype and evaluate the effectiveness of the analysis by conducting extensive experiments on a number of simulated datasets.

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Mining mobility user profiles for car pooling

Cited by 116 publications

References 10 publications

Investigating the Potential of Ridesharing to Reduce Vehicle Emissions

Investigating the Potential of Ridesharing to Reduce Vehicle Emissions

A Rule-based Method for Discovering Trajectory Profiles

Post-hoc User Traceability Analysis in Electronic Toll Pricing Systems

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