Intelligent Carpool Routing for Urban Ridesharing by Mining GPS Trajectories

He, Wen; Hwang, Kai; Li, Deyi

doi:10.1109/tits.2014.2315521

Cited by 83 publications

(42 citation statements)

References 23 publications

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“…Their model suggested a 23-40% reduction in vehicle kilometers depending on the strategies used in selection of the stops. He, Hwang and Li (2014) found that increasing the number of riders to eight per vehicle, by using a mini van, would increase overall travel savings up to 60%. In an investigation for taxi ridesharing in New York City, Santi et al (2014) concluded that, with waiting time not exceeding 5 minutes, ridesharing with two or three passengers could reduce total taxi trips by 50% (reaching full potential in trip reduction) and 60%, respectively.…”

Section: Recent Findings On Ridesharing Potentialmentioning

confidence: 99%

“…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%

“…Santi et al (2014) used a delay time of up to 5 minutes while Ota et al (2015) used extra distance traveled for recognition of nearby potential rides. He et al (2014) showed that excessive detouring (i.e., larger than 4 kilometers) reduces ridesharing efficiency to less than 5%.…”

Section: Data Analysis To Model Ridesharingmentioning

confidence: 99%

“…He et al (2014) and Ota et al (2015) found that as the limit on the number of shared rides increases, shareability potential also increases. Results of simulations by Santi et al (2014), however, showed the number of saved taxi trips is increased from about 50% (maximum theoretical potential) with two shared rides to only about 60% with three (below the 66.7% maximum theoretical potential) suggesting that the benefits of ridesharing do not increase linearly with the number of shared rides.…”

Section: Number Of Users Allowed To Share Ridesmentioning

confidence: 99%

“…The algorithm computes optimal sharing strategies for taxi trips in New York City considering two parameters: the maximum number of trips that can be shared and the minimum time to accommodate all trips. He et al (2014) proposed a carpooling system that generates an efficient route for dynamic ridesharing using a GPS-assisted trajectory mining scheme to identify frequent routes taken by participating riders, including private car, taxi, bus, subway and walking. The routing optimization goal is to minimize the driving distance, commute costs, detour distance, social distance and advance time to start the carpool.…”

Section: Techniques Used In Ridesharing Data Analysismentioning

confidence: 99%

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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: Recent Findings On Ridesharing Potentialmentioning

confidence: 99%

Section: Vehicle Trip Data Setmentioning

confidence: 99%

Section: Data Analysis To Model Ridesharingmentioning

confidence: 99%

Section: Number Of Users Allowed To Share Ridesmentioning

confidence: 99%

Section: Techniques Used In Ridesharing Data Analysismentioning

confidence: 99%

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Investigating the Potential of Ridesharing to Reduce Vehicle Emissions

Jalali

Koohi-Fayegh

El-Khatib

et al. 2017

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A Differentially Private and Truthful Incentive Mechanism for Traffic Offload to Public Transportation

Niu

Clark

2018

Lecture Notes in Computer Science

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Increasingly large trip demands have strained urban transportation capacity, which consequently leads to traffic congestion. In this work, we focus on mitigating traffic congestion by incentivizing passengers to switch from private to public transit services. We address the following challenges. First, the passengers incur inconvenience costs when participating in traffic offload due to delay and discomfort, and thus need to be reimbursed. The inconvenience cost, however, is unknown to the government when choosing the incentives. Furthermore, participating in traffic offload raises privacy concerns from passengers. An adversary could infer personal information, (e.g., daily routine, region of interest, and wealth), by observing the decisions made by the government, which are known to the public. We adopt the concept of differential privacy and propose privacy-preserving incentive designs under two settings, denoted as two-way communication and one-way communication. Under two-way communication, we focus on how the government should reveal passengers' inconvenience costs to properly incentivize them while preserving differential privacy. We formulate the problem as a mixed integer linear program, and propose a polynomial-time approximation algorithm. We show the proposed approach achieves truthfulness, individual rationality, social optimality, and differential privacy. Under one-way communication, we focus on how the government should design the incentives without revealing passengers' inconvenience costs while still preserving differential privacy. We formulate the problem as a convex program, and propose a differentially private and near-optimal solution algorithm. A numerical case study using Caltrans Performance Measurement System (PeMS) data source is presented as evaluation. arXiv:1906.01683v1 [cs.CY] 4 Jun 2019integer linear program. We propose an efficient approximation algorithm to reduce the computation complexity. • We prove that the proposed mechanism design under two-way communication achieves approximate optimal social welfare, truthfulness, individual rationality, and differential privacy. • For the one-way communication, we formulate the problem as an online convex program. We give a polynomialtime algorithm to solve for the mechanism design. We prove that the proposed mechanism is differentially private and Hannan consistent. • We present a numerical case study with real-world trace data as evaluation. The results show that the proposed approach achieves individual rationality and non-negative social welfare, and is privacy preserving. The remainder of this paper is organized as follows. We discuss the related works in Section II. In Section III, we present the problem formulation under two-way and one-way communication settings, respectively. We present the proposed incentive mechanism design in Section IV for the two-way communication setting. Section V gives the proposed solution for one-way communication setting. The proposed approaches are demonstrated using a numerical case study in Sectio...

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