Joint Analysis of the Commuting Departure Time and Travel Mode Choice: Role of the Built Environment

Abstract

This paper aims to conduct an empirical study to evaluate the influence of built environment features and socioeconomic factors on commuters’ simultaneous choice of departure time and travel mode. Using Kunming, China, as the study region, the 2015 Regional Household Travel Survey and 2016 Point of Interest data are used in the analysis. The results show that, in addition to socioeconomic factors, built environment, such as the density of residential building, employment, and service facility are correlated with joint choice behavior. Moreover, there exist differences regarding the influence of built environment and socioeconomic factors on departure time and travel mode choice. The dissimilarity parameters show that commuters prefer to shift travel mode than departure time generally when travel condition alters. In order to examine the policy measures’ potential performance, the paper conducts simulation tests based on the Monte Carlo method. The simulation results show that congestion pricing of car travel during peak hours can reduce the number of commuting trips, and reducing travel time of public transit would be a better strategy to attract more passengers during peak hours. Moreover, reasonable land use planning, such as building more bus stops around commuters’ home location, would be a long term and fundamental approach to reduce mobile-source emissions and attract more public transit passengers.

Spatial Heterogeneity in the Nonlinear Impact of Built Environment on Commuting Time of Active Users: A Gradient Boosting Regression Tree Approach

“…Density, as a key component of built environment, has a paradoxical efect on active travel choice [20]. Density appeared to have a signifcant impact on choosing active travel modes in some studies, such as reference [21], but not in other studies (e.g., see [22]).…”

Effects of Individual and Built Environmental Features on Commuting Mode Shifts before and after the COVID-19 Outbreak in Guangzhou, China

“…Te second part of the dataset used in this study is the point-of-interest (POI), which refects the functions and distribution of various facilities in Guangzhou in 2020. Literature has shown that the density of residential facilities, commercial facilities, leisure and entertainment facilities, service facilities, bus stops, and roads and parking lots has had a signifcant impact on travel behavior [63,64]. In addition, because medical facilities are related to the epidemic situation, this study added the factor of medical facilities to the candidate variables.…”

“…After preliminary analysis and screening, we determined the following fve built environment variables: density of healthcare facilities, density of bus stops, density of residential facilities, density of entertainment facilities, and density of government institutions. Te calculation method for these variables is to count the number of specifc facilities in the 800-m bufer zone and then calculate the density of facilities [64,65].…”

How Does Perceived Neighborhood Environment Affect Commuting Mode Choice and Commuting CO2 Emissions? An Empirical Study of Nanjing, China

“…Socio-demographic characteristics are crucial to understanding travel behavior and travel CO 2 emissions. The existing relevant literature has proven that gender, age, income, education, occupation, household size and hukou not only affected travel behaviors [62,63], but also affected travel CO 2 emissions [5,11,22,24,60]. In this study, the above socio-demographic characteristics were considered as exogenous variables introduced into the model.…”