Transportation research. part c, emerging technologies

Transportation Research, Part A: Policy and Practice

A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-007155.pdf

Limits of predictability in human mobility

Cities around the world and in the US are implementing bikesharing systems, which allow users to access shared bicycles for short trips, typically in the urban core. Yet few scholars have examined the determinants of bikeshare station usage using a fine-grained approach. The authors estimate a series of Bayesian regression models of trip generation at stations, examining the effects bicycle infrastructure, population and employment, land use mix, and transit access separately by season of the year, weekday/weekend, and user type (subscriber versus casual). The authors find that bikeshare stations located near busy subway stations and bicycle infrastructure see greater utilization, and that greater population and employment generally predict greater usage. The authors' findings are more nuanced, however; for instance, those areas with more residential population are associated with more trips by subscribers and on both weekdays and non-working days; however, the effect is much stronger on non-working days. Additional nuances can be found in how various land use variables affect bikeshare usage. The authors discuss these nuances in detail and elaborate on how these findings may be of use to transportation planners.

Bikeshare Trip Generation in New York City

How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics.

Shared micromobility services (e-scooters, bikes, e-bikes) have rapidly gained popularity in the past few years, yet little is known about their usage. While most previous studies have analysed single modes, only few comparative studies of two modes exist and none so-far have analysed competition or mode choice at a high spatiotemporal resolution for more than two modes. To this end, we develop a generally applicable methodology to model and analyse shared micromobility competition and mode choice using widely accessible vehicle location data. We apply this methodology to estimate the first comprehensive mode choice models between four different micromobility modes using the largest and densest empirical shared micromobility dataset to-date. Our results suggest that mode choice is nested (dockless and docked) and dominated by distance and time of day. Docked modes are preferred for commuting. Hence, docking infrastructure for currently dockless modes could be vital for bolstering micromobility as an attractive alternative to private cars to tackle urban congestion during rush hours. Furthermore, our results reveal a fundamental relationship between fleet density and usage. A “plateau effect” is observed with decreasing marginal utility gains for increasing fleet densities. City authorities and service providers can leverage this quantitative relationship to develop evidence-based micromobility regulation and optimise their fleet deployment, respectively.

Explaining shared micromobility usage, competition and mode choice by modelling empirical data from Zurich, Switzerland

Vehicle availability data is emerging as a potential data source for micro-mobility research and applications. However, there is not yet research that systematically evaluates or validates the processing of this emerging mobility data. To fill this gap, we propose a generally applicable data processing framework and validate its related algorithms. The framework exploits micro-mobility vehicle availability data to identify individual trips and derive aggregate patterns by evaluating a range of temporal, spatial, and statistical mobility descriptors. The impact of data processing is systematically and rigorously investigated by applying the proposed framework with a case study dataset from Zurich, Switzerland. Our results demonstrate that the sampling rate used when collecting vehicle availability data has a significant and intricate impact on the derived micro-mobility patterns. This research calls for more attention to investigate various issues with emerging mobility data processing to ensure its validity for transportation research and practices.

Impact of data processing on deriving micro-mobility patterns from vehicle availability data

Shared micromobility in Zurich, Switzerland: Analysing usage, competition and mode choice

Abstract The inappropriate parking of free-floating shared bikes is a critical issue that needs to be addressed to realize the potential environmental, socioeconomic, and health benefits of this emerging green mode of transport. To address this challenge, this paper developes a Geographic Information Systems (GIS) based Multi-Criteria Decision Analysis (MCDA) framework for geo-fence planning of dockless bike-sharing systems based on openly accessible data. The Analytic Hierarchy Process (AHP) and the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method are applied in the proposed framework to derive optimal geo-fence locations. The proposed framework is validated in a case study using a dataset of dockless bike-sharing trips from February 2020 in the City of Zurich and comparing the selected geo-fence locations with the existing bike-sharing stations. The assessment results show that the calculated geo-fence locations have a smaller average distance of 1395 m than that of 1692 m, and a larger demand coverage of 81% than that of 77% for bike-sharing stations. Overall, the proposed framework and the insights from the case study can help transport planners better implement shared micro-mobility hence facilitating the uptake of this sustainable mode of urban transport. 

/pdf/geo-fence-planning-for-dockless-bike-sharing-systems-a-gis-15whgu39.pdf

He Haitao

Papers

Explaining shared micromobility usage, competition and mode choice by modelling empirical data from Zurich, Switzerland

How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics.

Impact of data processing on deriving micro-mobility patterns from vehicle availability data

Shared micromobility in Zurich, Switzerland: Analysing usage, competition and mode choice

Geo-fence planning for dockless bike-sharing systems: a GIS-based multi-criteria decision analysis framework