Microtransit Has the Potential to Flip Transit on Its Head

2021 ◽  
pp. 036119812110145
Author(s):  
Jerome Mayaud ◽  
Francesca Ward ◽  
Josh Andrews
Keyword(s):  
Machine Learning ◽  
North America ◽  
Dynamic Routing ◽  
Data Driven ◽  
Routing And Scheduling ◽  
On Demand ◽  
Transit Services ◽  
Planning Processes ◽  
Transit Agencies ◽  
Unique Dataset

Fixed-route transit in the U.S.A. is facing stiff competition from more convenient mobility options. Microtransit—shared transportation that offers dynamic routing and scheduling to efficiently match demand—is emerging as an ally to fixed-route services. However, its positive impacts are too often constrained by the politics and economics imposed by existing transit infrastructure. This paper proposes a solution that “flips transit on its head.” By rapidly prototyping microtransit services across cities and analyzing supply-demand mismatches, it is possible to launch truly data-driven transit services. To illustrate the framework, a unique dataset generated from a year of Dallas Area Rapid Transit’s GoLink service, one of the largest on-demand microtransit services in North America, is used. Mapping and machine learning are combined to empower planners to “join the dots” when (re)designing fixed-route transit lines. It is shown that microtransit should not simply fill in the gaps left by inefficiently scheduled bus routes: by incorporating it fully into their planning processes, cities and transit agencies could dramatically reverse the fortunes of public transit.

Equity Analysis and New Mobility Technologies: Toward Meaningful Interventions

2020 ◽  
pp. 088541222095519
Author(s):  
Matthew Palm ◽  
Steven Farber ◽  
Amer Shalaby ◽  
Mischa Young
Keyword(s):  
Social Equity ◽  
Active Travel ◽  
Transit Systems ◽  
On Demand ◽  
Equity Analysis ◽  
The Social ◽  
Transit Services ◽  
Transit Agencies ◽  
Bike Share

Transit agencies in multiple countries have started piloting the integration of new mobility technologies (NMTs) into their transit services. This article reviews the emerging evidence from NMT-based transit pilots to identify the social equity impacts of NMT–transit integration. We focus on ride-hailing, on-demand transit, microtransit, and active travel NMTs like dockless bike share. We find evidence that three of these technologies have the potential to improve the social equity of transit systems, but we note that most agency pilots do not explicitly measure equity impacts. We offer recommendations on how future pilots should frame equity measurement to yield policy-relevant insights.

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

scholarly journals A Data-Driven Method for the Temporal Estimation of Soil Water Potential and Its Application for Shallow Landslides Prediction

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1208
Author(s):  
Massimiliano Bordoni ◽  
Fabrizio Inzaghi ◽  
Valerio Vivaldi ◽  
Roberto Valentino ◽  
Marco Bittelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Potential ◽  
Soil Water ◽  
Temporal Trends ◽  
Soil Water Potential ◽  
Shallow Landslides ◽  
Water Dynamics ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Physically Based

Soil water potential is a key factor to study water dynamics in soil and for estimating the occurrence of natural hazards, as landslides. This parameter can be measured in field or estimated through physically-based models, limited by the availability of effective input soil properties and preliminary calibrations. Data-driven models, based on machine learning techniques, could overcome these gaps. The aim of this paper is then to develop an innovative machine learning methodology to assess soil water potential trends and to implement them in models to predict shallow landslides. Monitoring data since 2012 from test-sites slopes in Oltrepò Pavese (northern Italy) were used to build the models. Within the tested techniques, Random Forest models allowed an outstanding reconstruction of measured soil water potential temporal trends. Each model is sensitive to meteorological and hydrological characteristics according to soil depths and features. Reliability of the proposed models was confirmed by correct estimation of days when shallow landslides were triggered in the study areas in December 2020, after implementing the modeled trends on a slope stability model, and by the correct choice of physically-based rainfall thresholds. These results confirm the potential application of the developed methodology to estimate hydrological scenarios that could be used for decision-making purposes.

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