Spatiotemporal Patterns of Carbon Emissions and Taxi Travel Using GPS Data in Beijing

Jinlei Zhang,Zijia Wang,Feng Chen,Rui Wang,Shunwei Shi

doi:10.3390/en11030500

Abstract

Taxis are significant contributors to carbon dioxide emissions due to their frequent usage, yet current research into taxi carbon emissions is insufficient. Emerging data sources and big data–mining techniques enable analysis of carbon emissions, which contributes to their reduction and the promotion of low-carbon societies. This study uses taxi GPS data to reconstruct taxi trajectories in Beijing. We then use the carbon emission calculation model based on a taxi fuel consumption algorithm and the carbon dioxide emission factor to calculate emissions and apply a visualization method called kernel density analysis to obtain the dynamic spatiotemporal distribution of carbon emissions. Total carbon emissions show substantial temporal variations during the day, with maximum values from 10:00–11:00 (57.53 t), which is seven times the minimum value of 7.43 t (from 03:00–04:00). Carbon emissions per kilometer at the network level are steady throughout the day (0.2 kg/km). The Airport Expressway, Ring Roads, and large intersections within the 5th Ring Road maintain higher carbon emissions than other areas. Spatiotemporal carbon emissions and travel patterns differ between weekdays and weekends, especially during morning rush hours. This research provides critical insights for taxi companies, authorities, and future studies.

Highlights

With improved information and communication technologies, as well as location-based services (LBS) such as mobile phone communications, social software, vehicle-carried GPS (Global PositionSystem) positioning terminals, etc., large-scale, high-quality, and consecutive spatiotemporal trajectory data on urban mobility has become an increasingly popular dataset and principal resource.Many researchers employ advanced data mining techniques and big geospatial data, among which taxiGPS data is one of the prevailing resources, to analyze individual travel patterns, the organization and planning of urban public spaces, construction of smart cities, and so forth
We process calculation model based on a taxi fuel consumption algorithm and emission factors, we calculate the the taxi GPS data to enable its direct use in the step
To describe carbon emissions in a more standard way, we introduce an indicator named carbon emissions per kilometer (CEPK, kg), calculated according to Equation (6)

Summary

Introduction

With improved information and communication technologies, as well as location-based services (LBS) such as mobile phone communications, social software, vehicle-carried GPS (Global PositionSystem) positioning terminals, etc., large-scale, high-quality, and consecutive spatiotemporal trajectory data on urban mobility has become an increasingly popular dataset and principal resource.Many researchers employ advanced data mining techniques and big geospatial data, among which taxiGPS data is one of the prevailing resources, to analyze individual travel patterns, the organization and planning of urban public spaces, construction of smart cities, and so forth. Energies 2018, 11, 500 hierarchical path-planning methods to determine the optimal paths to support dynamic route planning or path-finding [1,2,3,4,5,6] Because of their low cost, wide coverage, easy data access, accurate allocation, high continuity, and, most importantly, their feasibility, big taxi GPS data can identify the traffic state [7,8]. This includes exhaustive analyses of spatiotemporal congestion patterns on urban roads [9]

Methods

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Discussion

Conclusion