scholarly journals Understanding the Spatiotemporal Variation of High-Efficiency Ride-Hailing Orders: A Case Study of Haikou, China

2022 ◽  
Vol 11 (1) ◽  
pp. 42
Author(s):  
Mingyang Du ◽  
Xuefeng Li ◽  
Mei-Po Kwan ◽  
Jingzong Yang ◽  
Qiyang Liu
Keyword(s):  
Built Environment ◽  
High Efficiency ◽  
Transportation Network ◽  
Supply And Demand ◽  
Ordinary Least Squares ◽  
Spatiotemporal Variation ◽  
Road Density ◽  
Healthy Development ◽  
Order Management ◽  
Global Regression

Understanding the spatiotemporal variation of high-efficiency ride-hailing orders (HROs) is helpful for transportation network companies (TNCs) to balance the income of drivers through reasonable order dispatch, and to alleviate the imbalance between supply and demand by improving the pricing mechanism, so as to promote the sustainable and healthy development of the ride-hailing industry and urban transportation. From the perspective of TNCs for order management, this study investigates the spatiotemporal variation of HROs and common ride-hailing orders (CROs) for ride-hailing services using the trip data of Didi Chuxing in Haikou, China. Ordinary least squares (OLS) and geographically weighted regression (GWR) models are established to examine the factors that affect the densities of HROs and CROs during different time periods, such as morning, evening, afternoon and night, with considering various built environment variables. The OLS models show that factors including road density, average travel time rate, companies and enterprises and transportation facilities have significant impacts on HROs and CROs for most periods. The results of the GWR models are consistent with the global regression results and show the local effects of the built environment on HROs and CROs in different regions.

scholarly journals Nonlinear Influence Model of Built Environment of Residential Area on Electric Vehicle Miles Traveled

2021 ◽  
Vol 12 (4) ◽  
pp. 247
Author(s):  
Xinghua Hu ◽  
Yanshi Cao ◽  
Tao Peng ◽  
Runze Gao ◽  
Gao Dai
Keyword(s):  
Built Environment ◽  
Electric Vehicle ◽  
Travel Behavior ◽  
Ordinary Least Squares ◽  
Heterogeneous Data ◽  
Gradient Boosting ◽  
Vehicle Miles Traveled ◽  
Road Density ◽  
Charging Time ◽  
Business District

In this study, gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models were constructed to systematically ascertain the influencing factors and electric vehicle (EV) use action laws from the perspective of travelers. The use intensity of EVs was represented by electric vehicle miles traveled (eVMT); variables such as the charging time, travel preference, and annual income were used to describe the travel characteristics. Seven variables, including distance to the nearest business district, road density, public transport service level, and land use mix were extracted from different dimensions to describe the built environment, explore the influence of the travel behavior mode and built environment on EV use. From the eVMT survey data, points of interest (POI) data, urban road network data, and other heterogeneous data from Chongqing, an empirical analysis of EV usage intensity was conducted. The results indicated that the deviation of the GBDT model (9.62%) was 11.72% lower than that of the OLS model (21.34%). The charging time was the most significant factor influencing the service intensity of EVs (18.37%). The charging pile density (15.24%), EV preference (11.52%), and distance to the nearest business district (10.28%) also exerted a significant influence.

scholarly journals Spatiotemporal Varying Effects of Built Environment on Taxi and Ride-Hailing Ridership in New York City

2020 ◽  
Vol 9 (8) ◽  
pp. 475 ◽  
Author(s):  
Xinxin Zhang ◽  
Bo Huang ◽  
Shunzhi Zhu
Keyword(s):  
New York ◽  
New York City ◽  
Built Environment ◽  
Influencing Factors ◽  
Public Transportation ◽  
York City ◽  
Transportation Network ◽  
Ordinary Least Squares ◽  
Vehicle Ownership ◽  
The Difference

The rapid growth of transportation network companies (TNCs) has reshaped the traditional taxi market in many modern cities around the world. This study aims to explore the spatiotemporal variations of built environment on traditional taxis (TTs) and TNC. Considering the heterogeneity of ridership distribution in spatial and temporal aspects, we implemented a geographically and temporally weighted regression (GTWR) model, which was improved by parallel computing technology, to efficiently evaluate the effects of local influencing factors on the monthly ridership distribution for both modes at each taxi zone. A case study was implemented in New York City (NYC) using 659 million pick-up points recorded by TT and TNC from 2015 to 2017. Fourteen influencing factors from four groups, including weather, land use, socioeconomic and transportation, are selected as independent variables. The modeling results show that the improved parallel-based GTWR model can achieve better fitting results than the ordinary least squares (OLS) model, and it is more efficient for big datasets. The coefficients of the influencing variables further indicate that TNC has become more convenient for passengers in snowy weather, while TT is more concentrated at the locations close to public transportation. Moreover, the socioeconomic properties are the most important factors that caused the difference of spatiotemporal patterns. For example, passengers with higher education/income are more inclined to select TT in the western of NYC, while vehicle ownership promotes the utility of TNC in the middle of NYC. These findings can provide scientific insights and a basis for transportation departments and companies to make rational and effective use of existing resources.

scholarly journals Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression

2019 ◽  
Vol 8 (1) ◽  
pp. 23 ◽  
Author(s):  
Xinxin Zhang ◽  
Bo Huang ◽  
Shunzhi Zhu
Keyword(s):  
Urban Environment ◽  
Optimal Allocation ◽  
Ordinary Least Squares ◽  
Weighted Regression ◽  
Road Density ◽  
Transit Systems ◽  
Grid Unit ◽  
Environment Variables ◽  
Point Data ◽  
Conventional Models

Taxicabs play an important role in urban transit systems, and their ridership is significantly influenced by the urban built environment. The intricate relationship between taxi ridership and the urban environment has been explored using either conventional ordinary least squares (OLS) regression or geographically weighted regression (GWR). However, time constitutes a significant dimension, particularly when analyzing spatiotemporal hourly taxi ridership, which is not effectively incorporated into conventional models. In this study, the geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal heterogeneity of hourly taxi ridership, and visualize the spatial and temporal coefficient variations. To test the performance of the GTWR model, an empirical study was implemented for Xiamen city in China using a set of weekday taxi pickup point data. Using point-of-interest (POI) data, hourly taxi ridership was analyzed by incorporating it to various spatially urban environment variables based on a 500 × 500 m grid unit. Compared to the OLS and GWR, the GTWR model obtained the best performance, both in terms of model fit and explanatory accuracy. Moreover, the urban environment was revealed to have a significant impact on taxi ridership. Road density was found to decrease the number of taxi trips in particular places, and the density of bus stops competed with taxi ridership over time. The GTWR modelling provides valuable insights for investigating taxi ridership variation as a function of spatiotemporal urban environment variables, thereby facilitating an optimal allocation of taxi resources and transportation planning.

Study on High Efficiency Technology of Industrial Circulating Water in Yellow River Estuary Area

2015 ◽  
Vol 737 ◽  
pp. 719-722
Author(s):  
Xiao Feng Yang ◽  
Bao Xiang Zhang ◽  
Yu Zhi Shi ◽  
Ming Yuan Fan ◽  
Hai Jiao Liu
Keyword(s):  
Water Resources ◽  
Brackish Water ◽  
High Efficiency ◽  
Yellow River ◽  
Supply And Demand ◽  
Water Discharge ◽  
River Estuary ◽  
Yellow River Estuary ◽  
Recycled Water ◽  
Circulating Water

Most of the water in yellow river estuary mixed with irrigation and leaching water, saline and brackish water, recycled water discharge into the sea without reuse except part for agriculture irrigation and aquaculture, a small part for recycling. In order to improve the efficiency and benefits of water resources utilization, this paper proposes a new way to study industrial water recycling method of the mixed water including irrigation and leaching water, saline and brackish water, recycled water. The research could have an important realistic significance to ease the contradiction between supply and demand of water resources, reduce reliance on the Yellow River and improve regional eco-environment.

scholarly journals Transportation with volume discount: a case study of a logistic operator in Ghana

2014 ◽  
Vol 8 (2) ◽  
pp. 7-37
Author(s):  
Abdul-Salam Sibidoo Mubashiru
Keyword(s):  
Integer Programming ◽  
Production Scheduling ◽  
Transportation Problem ◽  
Transportation Network ◽  
Supply And Demand ◽  
Network Models ◽  
Cost Effective ◽  
Capital Budgeting ◽  
Integer Programming Problems ◽  
Optimality Algorithm

Network models and integer programming are well known variety of decision making problems. A very useful and widespread area of application is the management and efficient use of scarce resources to increase productivity. These applications include operational problems such as the distributions of goods, production scheduling and machine sequencing, and planning problems such as capital budgeting facility allocation, portfolio selection, and design problems such as telecommunication and transportation network design. The transportation problem, which is one of network integer programming problems is a problem that deals with distributing any commodity from any group of 'sources' to any group of destinations or 'sinks' in the most cost effective way with a given 'supply' and 'demand' constraints. Depending on the nature of the cost function, the transportation problem can be categorized into linear and nonlinear transportation problem. We applied Karush-Kuhn-Tucker (KKT) optimality algorithm to solve our problem of transportation with volume discount for a logistic operator in Ghana.

scholarly journals Public Transportation Environment and Medical Choice for Chronic Disease: A Case Study of Gaoyou, China

2019 ◽  
Vol 16 (9) ◽  
pp. 1612
Author(s):  
Yang Cao ◽  
Feng Zhen ◽  
Hao Wu
Keyword(s):  
Built Environment ◽  
Low Income ◽  
Patient Preferences ◽  
Public Transportation ◽  
Supply And Demand ◽  
Socioeconomic Background ◽  
Medical Facilities ◽  
Patient Questionnaires ◽  
Medical Choice ◽  
The Impact

Current research on the built environment and medical choice focuses mainly on the construction and optimization of medical service systems from the perspective of supply. There is a lack of in-depth research on medical choice from the perspective of patient demand. Based on the medical choice behaviour of patients with chronic diseases, this article identifies the spatial distribution and heterogeneity characteristics of medical choice and evaluates the balance between medical supply and demand in each block. On this basis, we explored the mechanism of patient preferences for different levels of medical facilities by considering the patient’s socioeconomic background, medical resource evaluation, and other built environment features of the neighbourhood by referring to patient questionnaires. In addition to socioeconomic characteristics, the results show that public transportation convenience, medical accessibility, and medical institution conditions also have significant influences on patient preferences, and the impact on low-income patients is more remarkable. The conclusions of the study provide a reference for the promotion and optimization of the functions of urban medical resources and the guidance of relevant public health policies.

scholarly journals Exploring Spatiotemporal Variation in Hourly Metro Ridership at Station Level: The Influence of Built Environment and Topological Structure

2018 ◽  
Vol 10 (12) ◽  
pp. 4564 ◽  
Author(s):  
Zhuangbin Shi ◽  
Ning Zhang ◽  
Yang Liu ◽  
Wei Xu
Keyword(s):  
Built Environment ◽  
Topological Structure ◽  
Regression Models ◽  
Closeness Centrality ◽  
Spatiotemporal Variation ◽  
Spatial And Temporal Variation ◽  
Structure Factors ◽  
The Relationship ◽  
Best Fit

Reliable and accurate estimates of metro demand can provide metro authorities with insightful information for the planning of route alignment and station locations. Many existing studies focus on metro demand from daily or annual ridership profiles, but only a few concern the variation in hourly ridership. In this paper, a geographically and temporally weighted regression (GTWR) model was used to examine the spatial and temporal variation in the relationship between hourly ridership and factors related to the built environment and topological structure. Taking Nanjing, China as a case study, an empirical study was conducted with automatic fare collection (AFC) data in three weeks. With an analysis of variance (ANOVA), it was found that the GTWR model produced the best fit for hourly ridership data compared with traditional regression models. Four built-environment factors, namely residence, commerce, scenery, and parking, and two topological-structure factors, namely degree centrality and closeness centrality, were proven to be significantly related to station-level ridership. The spatial distribution pattern and temporal nonstationarity of these six variables were further analyzed. The result of this study confirmed that the GTWR model can provide more realistic and useful information by capturing spatiotemporal heterogeneity.

Informing Prescribing Allocations at District Level in England

1997 ◽  
Vol 2 (3) ◽  
pp. 154-159 ◽  
Author(s):  
Nigel Rice ◽  
Roy Carr-Hill ◽  
David Roberts ◽  
David Lloyd
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Least Squares ◽  
General Practitioners ◽  
Supply And Demand ◽  
Ordinary Least Squares ◽  
Reciprocal Relationship ◽  
District Level ◽  
Least Squares Regression ◽  
Adult Males

Objectives: To derive a predictive model based on the morbidity, demographic and socio-economic characteristics of district populations to explain variations in prescribing costs in England. Method: Inter-relations between morbidity, demographic, socio-economic, general practice supply characteristics and net ingredient cost per age, sex and temporary resident originated prescribing unit (ASTRO-PU) were explored statistically for 90 districts in England using 1994 cost data. The possibility of mutual inter-relationship between ‘supply’ and ‘demand’ was examined; then the associations between a range of factors and prescribing costs were estimated using ordinary least squares regression and the predictive power of the possible models was systematically examined. Results: Whilst there was a relatively weak relationship between the supply factors that were measured, there did not appear to be any reciprocal relationship. Three parsimonious models estimated using ordinary least squares multiple regression techniques based on combinations of permanent sickness, low birth weight and the proportion of general practitioners registered for postgraduate certificate of education were identified. The models explained up to 61% of variation between districts in prescribing costs. Conclusions: ‘Need’ and ‘supply’ characteristics are independently associated with variations in prescribing costs at district level. The negative association between the proportion of general practitioners eligible for postgraduate education allowance and prescribing costs may reflect ‘better’ prescribing but could not be introduced into a resource allocation formula without introducing perverse incentives. The combination of permanent sickness and low birth weight complement each other by providing a proxy measure of morbidity mostly applicable to adult males (permanent sickness) and mothers (low birth weight being a measure of maternal health). These variables should be considered further for use in the process of allocating resources for prescribing to districts.

scholarly journals Jaringan Transportasi dan Pengembangan Destinasi Pariwisata di Kota Cirebon

2019 ◽  
Author(s):  
Millary Agung Widiawaty ◽  
G P Pramulatsih ◽  
V Pebriani
Keyword(s):  
Road Network ◽  
Transportation Network ◽  
Tourism Development ◽  
Network System ◽  
Road Density ◽  
Accessible Region ◽  
System Components ◽  
Network Spatial Analysis ◽  
Alpha Index

Tourism development needs a good accessibility to support regional connectivity. This study aims to analyse the role of transportation network for tourism destination development in the Cirebon City. This research uses GIS-based network spatial analysis to obtain road network system components in each sub-district, thus transportation indices value which includes alpha index, beta index, Gama index, eta index and road density network. The results shows The Cirebon City has low-medium connectivity and accessibility with alpha index 0.1323, beta index 1.2608, Gama index, 0.4221, eta index 0.1576 and road network density reach 20.869 km / km2. On sub-district level, Pekalipan is the most accessible region based on all parameters of road network and create a suitable place for tourism development in the Cirebon City.

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