Spatial-temporal Patterns And Determinants Of Taxis’ Emissions Based On Big Data

Urban air pollution has become a global concern,and taxis,as an irreplaceable and important mode of urban public transport,have become one of the major sources of pollutants.There are few studies on the spatial and temporal evolution patterns of taxi emissions,especially on the spatial and temporal relationships between taxi emissions and external urban-related drivers.To fill this research gap,this study uses massive GPS data combined with the COPERT emission model to estimate the spatio-temporal distribution patterns of taxi trips and emissions in Dalian city on weekdays and weekends,and visualize the emissions in time and space;taxi emissions are strongly correlated with their travel patterns,while the urban built environment and social population are the main drivers of travel patterns.Therefore,this study establishes OLS/GWR/GTWR models to explain the relationship between relevant variables such as land use and socio-demographics and emissions,respectively.To better explain the relationship between the emissions generated by taxis and the relevant drivers,the emissions from taxi generated,passing,and absorbed emissions miles in each traffic analysis cell are defined as generated emissions,passing emissions and absorbed emissions and compares the model results for total emissions with the three types of emissions after classification.The main findings of this study are as follows:In terms of emissions,There are similar temporal characteristics between the total emissions and generated emissions,passing emissions,absorbed emissions,all of which show significant crests,and the main spatial hotspots of emissions are concentrated in the areas of city centers and transportation hubs;In terms of explanatory models,the explanatory power of the OLS,GWR,and GTWR models differs for the relationships between the total emissions,generated emissions,passing emissions,absorbed emissions and urban land use and socio-demographic characteristics,and the results show that GTWR has the strongest explanatory power,which indicates that the explanatory and explained variables have spatial and temporal heterogeneity,and the corresponding model results for the total,generated,passing,and absorbed emissions are better than those for total emissions;In terms of the impact of drivers on emissions,the generated emissions and absorbed emissions are strongly correlated with the movement of residential and occupational attributes in time and space,especially in residential areas and downtown commercial areas during the morning and evening peak periods of weekdays;traffic and road facility density have a strong positive effect on passing emissions during the daytime,and are spatially concentrated in the central part of the dense road network;the contribution of elderly people to total emissions is smaller,with a negative overall effect and a smaller spatial and the spatial extent is also small.Using taxis as the research object,this study provides a research framework for studies related to urban traffic pollution and its associated drivers,with theoretical and practical implications for understanding urban traffic emissions and developing environmentally friendly urban transport policies.