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

Jinlei Zhang, Feng Chen, Zijia Wang, Rui Wang, Shunwei Shi
<span title="2018-02-27">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/a2yvk5xhdnhpxjnk6yd33uudqq" style="color: black;">Energies</a> </i> &nbsp;
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
more &raquo; ... he 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. Energies 2018, 11, 500 2 of 22 hierarchical path-planning methods to determine the optimal paths to support dynamic route planning or path-finding [1] [2] [3] [4] [5] [6] . Furthermore, because of their low cost, wide coverage, easy data access, accurate allocation, high continuity, and, most importantly, their feasibility, big taxi GPS data can also identify the traffic state [7, 8] . This includes exhaustive analyses of spatiotemporal congestion patterns on urban roads [9] and measurements of traffic jam indicators [10], allowing for a better understanding of the operational states of road networks. Moreover, by proposing different and effective anomaly detection methods, some studies have used the taxi dataset to detect anomalous traffic events, which occur when the corresponding indicators deviate significantly from the expected values [11] , and to detect anomalous routes such as fraudulent taxi travel patterns or traffic accidents, as well as identifying the parts of the trajectories responsible for the anomalies and ranking them with an ongoing anomaly score [12] . Other studies have monitored unexpected behaviors, such as vehicle breakdowns, or one-time events like large sporting events, fairs, and conventions, which exhibit the largest statistically significant departure from expected behavior [13] . Additional research has estimated the average relationship between travel time and driving speed [14] [15] [16] . Through identification of origin-destination (OD) and the clustering method, some researchers have attempted to detect urban passenger hotspots, i.e., taxi demand prediction [17, 18] , and then provide location recommendation services for empty taxis [19, 20] . By establishing an OD taxi travel matrix, Xin et al. [21] focused on the recognition of commuter behavior, along with employment and residential areas, based on taxi trajectory data. In addition, using OD data, taxi trip characteristics can be evaluated, including their temporal and spatial distribution [22] . To date, research into fuel consumption and taxi emission calculations using big data-mining techniques and taxi trajectories has been relatively rare. For example, by obtaining the average speed of trajectory segments, Luo et al. [23] computed taxi energy consumption and emissions, and analyzed their spatiotemporal distribution in Shanghai via ArcGIS software. However, the study did not consider variations in carbon emissions per kilometer (CEPK), fuel consumption per 100-km (FCPOK), or the average travel speed over the whole network during the calculation, which are the global, standard, and intuitive indicators, nor did it verify the accuracy of the results. Moreover, their data only covered 20% of all taxis in Shanghai. In this study, we analyze more than 50% of all taxis in Beijing, thereby providing more credible insights for urban planning and traffic management authorities. Du et al. [24] applied floating vehicle data to predict vehicle fuel consumption using a Back Propagation Neural Network. They also compared the difference in fuel consumption distribution within downtown Beijing between weekdays and weekends using a heat map. However, this study only involved the total fuel consumption and did not analyze its variation throughout a day, nor did it include carbon emission patterns. Other researchers conducted a bench test to obtain fuel consumption and emissions from taxi GPS trajectory data [25] but did not fully take advantage of big data-mining techniques and employed only one car model of the current taxi, the Hyundai Elantra taxi, which could not reflect the effects of most car models in Beijing. In this study, we include more than five car models of the current taxi. With the availability of more detailed data and big data-mining techniques, taxi carbon emissions can be addressed at a much higher resolution, and more factors can be considered, such as the impact of average speed on emissions, in order to overcome the limitations of previous studies. To this end, this study establishes an analytical framework to calculate and present taxi emissions that can be adapted to big data resources and tools. First, we describe the data sources and the reconstruction of taxi trajectories employing taxi GPS data along with Oracle (Oracle 11g, Oracle, Redwood City, CA, USA) and ArcGIS (ArcGIS 10.3, Esri, Redlands, CA, USA) software. Second, this study introduces a carbon emission calculation model based on the fuel consumption calculation algorithm using the Vehicle Specific Power (VSP) of taxis and carbon emission factors to calculate taxi carbon emissions over the whole network [26] . Third, a visualization method called kernel density analysis is formulated in detail to obtain the dynamic spatiotemporal distribution of carbon emissions. Energies 2018, 11, 500 3 of 22 Then, this methodology is applied to Beijing. We calculate taxi fuel consumption over the whole network in Beijing, compute carbon emissions via the carbon emission factor, and then present the dynamic spatiotemporal distribution as maps. To verify the accuracy of the results, we creatively convert carbon emissions and fuel consumption into CEPK and FCPOK, respectively, which are more global, standard, and intuitive factors. The dynamic spatiotemporal distribution of carbon emissions and taxi travel patterns on weekdays and weekends are then highlighted. Finally, the limitations of this research and potential future research areas are proposed.
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