Quantifying the potential of ride-sharing using call description records

Cici, Blerim; Markopoulou, Athina; Frı́as-Martı́nez, Enrique; Laoutaris, Nikolaos

doi:10.1145/2444776.2444799

Cited by 22 publications

(15 citation statements)

References 18 publications

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“…For example, cell phone activities, commonly know as Call Description Records (CDR), are used for capturing human communication activities [3]. In addition, it is also used for recovering the human mobility trajectory [9], inferring demographics [6], and uncovering urban ecology [2].…”

Section: Related Workmentioning

confidence: 99%

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Wang

et al. 2017

IEEE/ACM Trans. Networking

206

105

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Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis. Categories and Subject Descriptors Modeling techniquesPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. IMC '15, October 28-30, 2015, Tokyo, Japan.

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Section: Related Workmentioning

confidence: 99%

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Wang

et al. 2017

IEEE/ACM Trans. Networking

206

105

View full text Add to dashboard Cite

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“…In [18] authors analyse users' localizations collected by mobile phones to enable ride sharing among people with overlapped mobility routines on a daily base. The total number of circulating cars could be halved at the expense of 1 Km detours, if people that share the same home and work areas would also share their private cars.…”

Section: A Mobility Analysis For Ride Sharingmentioning

confidence: 99%

On Recommending Opportunistic Rides

Bicocchi

Mamei

Sassi

et al. 2017

IEEE Trans. Intell. Transport. Syst.

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Research on social and mobile technologies recently provided tools to collect and mine massive amounts of mobility data. Ride sharing is one of the most prominent applications in this area. While a number of research and commercial initiatives already proposed solutions for long-distance journeys, the opportunities provided by modern pervasive systems can be used to promote local, daily ride sharing within the city. We present a set of algorithms to analyze urban mobility traces and to recognize matching rides along similar routes. These rides are amenable for ride sharing recommendations. We validate the proposed methodology using data provided by a large Italian telecom operator. Assuming the full set of considered users are willing to accept 1-km detours, experimental results on two large cities show that more than 60% of trips could be saved. These results can be used to evaluate the potential of a ride sharing system before its actual deployment and to actually support an opportunistic ride sharing recommender system

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“…Therefore, potential of ridesharing is generally found to be lower in studies that account for this factor. For example, Cici et al (2013Cici et al ( , 2014 showed that a time window of 10-30 minutes results in 10-20% reduction in the number of cars in real-time situations if a delay time of 10 minutes and a detour distance of one kilometer are accepted by users. Without this time restriction, they show a higher ridesharing potential of up to 60%.…”

Section: Dynamic Versus Static Ridesharingmentioning

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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Quantifying the potential of ride-sharing using call description records

Cited by 22 publications

References 18 publications

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

On Recommending Opportunistic Rides

Investigating the Potential of Ridesharing to Reduce Vehicle Emissions

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