| 1. |
- Gao, Kun, 1993, et al.
(author)
-
Quantifying economic benefits from free-floating bike-sharing systems: A trip-level inference approach and city-scale analysis
- 2021
-
In: Transportation Research Part A: Policy and Practice. - : Elsevier BV. - 0965-8564. ; 144, s. 89-103
-
Journal article (peer-reviewed)abstract
- Despite many qualitative discussions about the benefits of free-floating bike-sharing systems (FFBS), high-resolution and quantitative assessments about the economic benefits of FFBS for users are absent. This study proposes an innovative trip-level inference approach for quantifying the economic benefits of FFBS, leveraging massive FFBS transaction data, the emerging multimodal routing Application Programming Interface from online navigators and travel choice modeling. The proposed approach is able to analyze the economic benefit for every single bike-sharing trip and investigate the spatiotemporal heterogeneity in the economic benefits from FFBS. An empirical analysis in Shanghai is conducted using the proposed approach. The estimated saved travel time, cost, and economic benefit due to using FFBS per trip are estimated to be 9.95 min, 3.64 CNY, and 8.68 CNY-eq, respectively. The annual saved travel time, cost, and economic benefits from FFBS in Shanghai are estimated to be 17.665 billion min, 6.463 billion CNY, and 15.410 billion CNY-eq, respectively. The relationships between economic benefits from FFBS and built environment factors in different urban contexts are quantitatively examined using Multiple Linear Regression to explain the spatial heterogeneity in the economic benefits of FFBS. The outcomes provide a useful tool for evaluating the benefits of shared mobility systems, insights into the users’ economic benefit from using FFBS from per-trip, aggregated and spatial perspective, as well as its influencing factors. The results could efficiently support the scientific planning, operation and policy making concerning FFBS in different urban contexts.
|
|
| 2. |
- Gao, Kun, 1993, et al.
(author)
-
Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory
- 2021
-
In: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051. ; 218
-
Journal article (peer-reviewed)abstract
- Modeling individuals’ travel decision making in terms of choosing transport modes, route and departure time for daily activities is an indispensable component for transport system optimization and management. Conventional approaches of modeling travel decision making suffer from presumed model structures and parametric specifications. Emerging machine learning algorithms offer data-driven and non-parametric solutions for modeling travel decision making but encounter extrapolation issues (i.e., disability to predict scenarios beyond training samples) due to neglecting behavioral mechanisms in the framework. This study proposes an extrapolation-enhanced approach for modeling travel decision making, leveraging the complementary merits of ensemble machine learning algorithms (Random Forest in our study) and knowledge-based decision-making theory to enhance both predictive accuracy and model extrapolation. The proposed approach is examined using three datasets about travel decision making, including one estimation dataset (for cross-validation) and two test datasets (for model extrapolation tests). Especially, we use two test datasets containing extrapolated choice scenarios with features that exceed the ranges of training samples, to examine the predictive ability of proposed models in extrapolated choice scenarios, which have hardly been investigated by relevant literature. The results show that both proposed models and the direct application of Random Forest (RF) can give quite good predictive accuracy (around 80%) in the estimation dataset. However, RF has a deficient predictive ability in two test datasets with extrapolated choice scenarios. In contrast, the proposed models provide substantially superior predictive performances in the two test datasets, indicating much stronger extrapolation capacity. The model based on the proposed framework could improve the precision score by 274.93% than the direct application of RF in the first test dataset and by 21.9% in the second test dataset. The results indicate the merits of the proposed approach in terms of prediction power and extrapolation ability as compared to existing methods.
|
|
| 3. |
- Gao, Kun, 1993, et al.
(author)
-
Spatial heterogeneity in distance decay of using bike sharing: An empirical large-scale analysis in Shanghai
- 2021
-
In: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 94
-
Journal article (peer-reviewed)abstract
- Distance decay is a vital aspect for modeling spatial interactions of human movements and an indispensable input for land use planning and travel demand prediction models. Although many studies have investigated the usage demand of bike-sharing systems in an area, research investigating the distance decay patterns of using dockless bike-sharing systems (DLBS) from a spatially heterogeneous perspective based on large-scale datasets is lacking. This study firstly utilizes massive transaction record data from DLBS in Shanghai of China and online map navigator Application Programming Interface to empirically estimate the distance decay patterns of using DLBS and reveal the spatial heterogeneity in distance decay of using DLBS across different urban contexts. Afterward, this study examines the mechanism of spatial heterogeneity in distance decay, leveraging multiple data resources including Point of Interest (POI) data, demographic data, and road network data. The associations among the distance decay of using DLBS with built environment factors are investigated by multiple linear regression. Results indicate that factors such as population density, land use entropy, branch road density, and metro station density are significantly related to larger distance decay of using DLBS, while factors such as commercial land use ratio, industrial land use ratio, and motorway density are significantly linked to smaller distance decay in Shanghai. Lastly, we further employ an adaptative geographically weighted regression to investigate the spatial divergences of the influences of built environment factors on distance decay. Results reveal notably distinct and even inverse influences of a built environment factor on the distance decay of using DLBS in different urban contexts. The findings provide insights into the distance decay patterns of using DLBS in different urban contexts and their interactions with the built environment, which can support accurate planning and management of sustainable DLBS as per specific urban characteristics.
|
|
| 4. |
- Li, Aoyong, 1993, et al.
(author)
-
High-resolution assessment of environmental benefits of dockless bike-sharing systems based on transaction data
- 2021
-
In: Journal of Cleaner Production. - : Elsevier BV. - 0959-6526. ; 296
-
Journal article (peer-reviewed)abstract
- Dockless bike-sharing systems (DLBS) have gained much popularity due to their environmentally friendly features. This study puts forward a distinctive framework for assessing the environmental influences of DLBS in high resolution based on DLBS transaction data. The proposed framework firstly estimates the transport mode substituted by DLBS for each recorded bike-sharing trip by utilizing the route planning techniques of online maps and a well-calibrated discrete choice model. Afterward, greenhouse gases (GHG) emission reductions in every recorded DLBS trip are quantified using Life Cycle Analysis. The proposed framework is applied to an empirical dataset from Shanghai, China. The empirical results reveal that the substitution rates of DLBS to different transport modes have substantial spatiotemporal variances and depend strongly on travel contexts, highlighting the necessity of analyzing the environmental impacts of DLBS at the trip level. Moreover, each DLBS trip is estimated to save an average 80.77 g CO2-eq GHG emissions versus than the situations without DLBS in Shanghai. The annual reduced GHG emissions from DLBS are estimated to be 117 kt CO2-eq, which is substantial and equals to the yearly GHG emissions of over 25,000 typical gasoline passenger vehicles. Additionally, the associations among built environments and GHG emission reductions from DLBS are quantitatively investigated to shed light on the spatial variances in the environmental impacts of DLBS. The results can efficiently support the benefit-cost analysis, planning, and management of DLBS.
|
|
