Behavior Insights for an Incentive-Based Active Demand Management Platform

Hu, Xianbiao; Chiu, Yi Chang; Zhu, Lei

doi:10.1260/2046-0430.4.2.119

Cited by 68 publications

(47 citation statements)

References 7 publications

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“…The number of tours threshold ranges as [1,2,5,10,15,20,30,40,50,60,70,80,90,100,120,150,180]. For example, a value of 5 indicates that any travelers with less than five tours are disqualified and discarded, while the travelers with 5 or more are qualified and collected.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…Travel demand management, such as ATDM, aims to reduce traffic demand or to redistribute the traffic demand temporally or spatially [10]. There are "hard" and "soft" strategies [11].…”

Section: Introductionmentioning

confidence: 99%

“…An early bird, free-ticket program, applied in Melbourne, Australia, aimed to mitigate the rail overcrowding issue and to shift the demand from peak to nonpeak hours [24]. In 2 Journal of Advanced Transportation 2013, a 10-week pilot study was conducted by Metropia in the Los Angeles area using an incentive-based activity demand management smartphone app [10], and significant travel behavior changes, including departure time choices and route options, were observed.…”

Section: Introductionmentioning

confidence: 99%

“…Some ATDM programs use incentives to influence travelers in specific groups [25,26], like transit riders, while some apply incentives to general autodrivers directly [10]. The limited incentive resources distributed to a significant amount of general travelers may not be efficient for influencing travel behavior.…”

Section: Introductionmentioning

confidence: 99%

“…Fortunately, with the prevalence of location-aware devices, such as a smartphone or GPS-enabled devices, the long-term (months or years) continuous collection of individualized trajectory data offers an unprecedented opportunity to gain insight into the traveler's daily travel pattern. Particularly, the GPS data provided by smartphone apps, such as Uber [28], Google, and Metropia [10], and the instrumented data derived by GPS devices mounted in vehicles, are among the latest sources of a new information collection mechanism. Rich information relevant to one's travel behavior is embedded in such longterm continuous collected raw GPS data.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

Zhu

Gonder

Lin

2017

Journal of Advanced Transportation

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With the development of and advances in smartphones and global positioning system (GPS) devices, travelers' long-term travel behaviors are not impossible to obtain. This study investigates the pattern of individual travel behavior and its correlation with social-demographic features. For different social-demographic groups (e.g., full-time employees and students), the individual travel behavior may have specific temporal-spatial-mobile constraints. The study first extracts the home-based tours, including Home-toHome and Home-to-Non-Home, from long-term raw GPS data. The travel behavior pattern is then delineated by home-based tour features, such as departure time, destination location entropy, travel time, and driving time ratio. The travel behavior variability describes the variances of travelers' activity behavior features for an extended period. After that, the variability pattern of an individual's travel behavior is used for estimating the individual's social-demographic information, such as social-demographic role, by a supervised learning approach, support vector machine. In this study, a long-term (18-month) recorded GPS data set from Puget Sound Regional Council is used. The experiment's result is very promising. The sensitivity analysis shows that as the number of tours thresholds increases, the variability of most travel behavior features converges, while the prediction performance may not change for the fixed test data.

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Section: Sensitivity Analysismentioning

confidence: 99%

“…Travel demand management, such as ATDM, aims to reduce traffic demand or to redistribute the traffic demand temporally or spatially [10]. There are "hard" and "soft" strategies [11].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

Zhu

Gonder

Lin

2017

Journal of Advanced Transportation

Self Cite

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Positioning infrastructure and technologies for low‐carbon urbanization

et al. 2014

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The expected urbanization of the planet in the coming century coupled with aging infrastructure in developed regions, increasing complexity of man-made systems, and pressing climate change impacts have created opportunities for reassessing the role of infrastructure and technologies in cities and how they contribute to greenhouse gas (GHG) emissions. Modern urbanization is predicated on complex, increasingly coupled infrastructure systems, and energy use continues to be largely met from fossil fuels. Until energy infrastructures evolve away from carbon-based fuels, GHG emissions are critically tied to the urbanization process. Further complicating the challenge of decoupling urban growth from GHG emissions are lock-in effects and interdependencies. This paper synthesizes state-of-the-art thinking for transportation, fuels, buildings, water, electricity, and waste systems and finds that GHG emissions assessments tend to view these systems as static and isolated from social and institutional systems. Despite significant understanding of methods and technologies for reducing infrastructure-related GHG emissions, physical, institutional, and cultural constraints continue to work against us, pointing to knowledge gaps that must be addressed. This paper identifies three challenge themes to improve our understanding of the role of infrastructure and technologies in urbanization processes and position these increasingly complex systems for low-carbon growth. The challenges emphasize how we can reimagine the role of infrastructure in the future and how people, institutions, and ecological systems interface with infrastructure.

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Home-work carpooling for social mixing

et al. 2019

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Shared mobility is widely recognized for its contribution in reducing carbon footprint, traffic congestion, parking needs and transportation-related costs in urban and suburban areas. In this context, the use of carpooling in homework commute is particularly appealing for its potential of lessening the number of cars and kilometers traveled, consequently reducing major causes of traffic in cities. Accordingly, most of the carpooling algorithms are optimized for reducing total travel time, cost, and other transportation-related metrics. In this paper, we analyze carpooling from a new perspective, investigating the question of whether it can be used also as a tool to favor social integration, and to what extent social benefits should be traded off with transportation efficiency. By incorporating traveler's social characteristics into a recently introduced network-based approach to model ride-sharing opportunities, we define two social-related carpooling problems: how to maximize the number of rides shared between people belonging to different social groups, and how to maximize the amount of time people spend together along the ride. For each of the problems, we provide corresponding optimal and computationally efficient solutions. We then demonstrate our approach on two datasets collected in the city of Pisa, Italy, and Cambridge, US, and quantify the potential social benefits of carpooling, and how they can be traded off with traditional transportation-related metrics. When collectively considered, the models, algorithms, and results presented in this paper broaden the perspective from which carpooling problems are typically analyzed to encompass multiple disciplines including urban planning, public policy, and social sciences.

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Behavior Insights for an Incentive-Based Active Demand Management Platform

Cited by 68 publications

References 7 publications

Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

Positioning infrastructure and technologies for low‐carbon urbanization

Home-work carpooling for social mixing

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