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.

Travel and the Built Environment: Insights Using Activity Densities, the Sprawl Index, and Neighborhood Types

2017 ◽  
Vol 2653 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Kelcie Ralph ◽  
Carole Turley Voulgaris ◽  
Anne Brown
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Individual Characteristics ◽  
Vehicle Miles Traveled ◽  
Combined Model ◽  
Composite Measures ◽  
Activity Density ◽  
Composite Variables ◽  
Best Fit ◽  
Neighborhood Type

There are many ways to evaluate the built environment, including measures of observable individual characteristics (such as activity density), continuous composite measures (such as the sprawl index), and categorically measured variables (such as neighborhood types). However, a systematic comparison of how well each of these three measurement types captures the influence of the built environment on travel behavior has not yet been undertaken. This lack presents a quandary for both researchers and practitioners who seek to quantify and describe the effects of the built environment on travel behavior. This paper assesses whether continuous, composite, or categorical measures provide more information and better-fitting models compared with measures of observable individual characteristics across four travel behaviors: vehicle miles traveled, walk trips, transit trips, and trip length. For each travel variable, four multivariate regression models were estimated with various measures of the built environment: activity density, sprawl index, neighborhood type, and combined sprawl index and neighborhood type. Both the sprawl index and the neighborhood-type models outperformed the activity density model. Moreover, a combined model with both the sprawl index and neighborhood types provided the best fit for all four travel behavior variables. These results suggest that both continuous and categorical composite variables provide unique and complementary information about how the built environment influences travel behavior. These findings underscore the importance of researchers’ decisions on how to represent the built environment quantitatively in models, because measurement decisions influence the understanding of how the built environment affects travel behavior.

Impact of Built Environment on Mode Choice to Major Destinations in Dhaka

2020 ◽  
pp. 036119812097841
Author(s):  
Paromita Nakshi ◽  
Anindya Kishore Debnath
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Travel Behavior ◽  
Mode Choice ◽  
Multinomial Logit Model ◽  
Dissimilarity Index ◽  
Road Density ◽  
Environment Factors ◽  
Environment Variables ◽  
Study Results

In recent years, there has been a growing interest in investigating the impacts of built environment on mode choice decisions. There is a consensus that built environment factors influence travel behavior, although this influence is far from being homogenous. Compared with the North American and some European countries, there has been comparatively limited research in this field in the context of the Global South, especially South Asia. In this context, this paper aims to explore the extent to which built environment influences mode choice behavior to major destinations in Dhaka, the capital of Bangladesh. “Major destinations” refers to the statistically significant trip-attracting clusters in the city. Dhaka is a city with heterogeneous motorized and non-motorized modes. Investigating mode choice decisions, in such a setting, is vital for the planners and policymakers to realize the goals of sustainable development with measured insights. A multinomial logit model was used to estimate the effects of built environment factors on mode choice to work and non-work trips in Dhaka. The study results showed that inclusion of built environment variables had significantly improved the models. Several built environment variables, including dissimilarity index, distance to the nearest bus stop, road density, and so forth, were found to be strong predictors of mode choice, and their elasticities were higher than the elasticities of several personal and household characteristics. Down that line, the findings provided support in favor of considering land use policies intended to increase accessibility, mixed land use, density, and so forth.

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.

The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

2019 ◽  
Vol 11 (1) ◽  
pp. 108-129
Author(s):  
Andrew G. Mueller ◽  
Daniel J. Trujillo
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Discrete Choice ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Land Use Regulation ◽  
Policy Action ◽  
Development Goals ◽  
The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

Using Deep Learning to Understand Travel Demands in Different Urban Districts

2021 ◽  
pp. 036119812199458
Author(s):  
Shunhua Bai ◽  
Junfeng Jiao
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Short Term Memory ◽  
Future Research ◽  
Demand Forecast ◽  
Time And Space ◽  
The University ◽  
The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

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.

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