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

energies

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

Jinlei Zhang 1, Feng Chen 1,2, Zijia Wang 1,* ID , Rui Wang 1 and Shunwei Shi 1

1 School of Civil and Architectural Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China; [email protected] (J.Z.); [email protected] (F.C.); [email protected] (R.W.); [email protected] (S.S.) 2 Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention, No.3 Shangyuancun, Haidian District, Beijing 100044, China * Correspondence: [email protected]; Tel.: +86-010-5168-8070

Received: 21 December 2017; Accepted: 19 February 2018; Published: 27 February 2018

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.

Keywords: taxi GPS data; carbon emission; dynamic spatiotemporal distribution; kernel density analysis

1. 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 Position System) 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 taxi GPS 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. At present, studies using taxi GPS data include, but are not limited to, the following aspects: route planning and path-finding, traffic operational state identification, identification of origin-destination (OD) and the clustering method, and taxi fuel consumption and emissions estimation. The typical and important current researches are summarized in Table1. Previous studies have extracted experiential driving route sets from taxi trajectories, built experiential road hierarchies according to the driving experiences of taxi drivers, such as travel frequency and speed information for different road segments at different times, and proposed

Energies 2018, 11, 500; doi:10.3390/en11030500 www.mdpi.com/journal/energies Energies 2018, 11, 500 2 of 22 hierarchical path-planning methods to determine the optimal paths to support dynamic route planning or path-finding [1–6]. 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 [14–16]. 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

Energies 2018, 11, x 3 of 22 Then, this methodology is applied to Beijing. We calculate taxi fuel consumption over the whole the dynamic spatiotemporal distribution as maps. To verify the accuracy of the results, we creatively network in Beijing, compute carbon emissions via the carbon emission factor, and then present the convert carbon emissions and fuel consumption into CEPK and FCPOK, respectively, which are more dynamic spatiotemporal distribution as maps. To verify the accuracy of the results, we creatively global, standard, and intuitive factors. The dynamic spatiotemporal distribution of carbon emissions convert carbon emissions and fuel consumption into CEPK and FCPOK, respectively, which are more and taxi travel patterns on weekdays and weekends are then highlighted. Finally, the limitations of global, standard, and intuitive factors. The dynamic spatiotemporal distribution of carbon emissions this research and potential future research areas are proposed. and taxi travel patterns on weekdays and weekends are then highlighted. Finally, the limitations of this research and potential future research areas are proposed. 2. Methodology 2. MethodologyIn this section, we formulate the methodological framework used in this study. First, we process the taxiIn GPS this section, data to we enable formulate its direct the methodological use in the next framework step. Second, used in using this study. the carbon First, we emission process cathelculation taxi GPS model data based to enable on a its taxi direct fuel use consumption in the next step.algorithm Second, and using emission the carbon factors, emission we calculate calculation the carbonmodel emissions based on over a taxi the fuel whole consumption network. algorithm Finally, a and visualization emission factors, method we called calculate kernel the density carbon analysisemissions is applied over the to wholeobtain network.the spatiotemporal Finally, a visualizationdynamic distri methodbution of called carbon kernel emissions. density analysis is applied to obtain the spatiotemporal dynamic distribution of carbon emissions. 2.1. Description and Data Cleaning of Taxi GPS Data 2.1.Currently, Description taxis and in Data many Cleaning cities ofare Taxi equipped GPS Data with vehicle information collection devices such as GPS systems.Currently, There taxis were in many approximately cities are equipped 65,000 taxis with vehiclein Beijing information in 2014, andcollection the proportions devices such of as differentGPS systems. car models There of were the approximatelycurrent taxi are 65,000 shown taxis in Figure in Beijing 1 [27] in 2014,. The anddata the used proportions in this study of different come fromcar modelsGPS devices of the installed current taxiin taxis, are shown which insend Figure information1[ 27]. The such data as used taxi identification, in this study come coordinates, from GPS time,devices driving installed speed, in taxis, and which passenger send stinformationate to the such taxi as control taxi identification, center every coordinates, 30 to 60 s. time, It covers driving approximatelyspeed, and passenger 30,000 to state 40,000 to thevehicles, taxi control including center more every than 30 to30,000 60 s. Ittaxis, covers 5000 approximately to 8000 sightseeing 30,000 to vehicles,40,000 vehicles,and all car including models moreof the than current 30,000 taxi taxis, in Beijing. 5000 to These 8000 sightseeingtaxis account vehicles, for approximatel and all cary models 50% ofof all the taxis current in Beijing taxi inand Beijing. produce These approximately taxis account 40 formillion approximately records every 50% day. of allWe taxis choose in Beijing a typical and workdayproduce and approximately the weekend 40 million to comprehensively records every analyzeday. We choosetemporal a typical variations. workday An example and the weekend of the datasetto comprehensively is shown in Table analyze 2 temporal variations. An example of the dataset is shown in Table2. TheThe data data are are stored stored in Oracle Oracle and, and, thus, thus, are are easily easily dealt dealt with with using using the connection the connection between between Oracle Oracle(Oracle (Oracle 11g, Oracle,11g, Oracle, Redwood Redwood City, City CA,, USA)CA, USA) and ArcGISand ArcGIS (ArcGIS (ArcGIS 10.3, 10.3, Esri, Esri, Redlands, Redlands CA,, CA, USA) USA)software. software. For example,For example, we clean we clean records records with invalidwith invalid locations, locations, those thosewhose whose coordinates coordinates deviate deviatsignificantlye significantly within within a few seconds, a few seconds, and those and whose those speed whose is speed always is at always zero. at We zero. delete We repetitive delete repetitiverecords andrecords convert and convert some fields some tofields enable to enable its direct its direct use in use the in next the next step, step, thus thus reducing reducing the the data datascale, scale, as as well well as as improving improving result result accuracy accuracy andand computingcomputing efficiency. efficiency. We We then then match match data data points points toto spatial spatial coordinates coordinates using using the the geographic geographic map map in inArcGIS, ArcGIS, after after which which the the “Track “Track intervals intervals to toline” line” tooltool is isapplied applied to obtain to obtain taxi taxi trajectories trajectories with with an average an average speed, speed, which which are used are usedlater to later obtain to obtain the fuel the consumptiofuel consumption,n, the distance, the distance, and driving and driving duration duration between between two two adjacent adjacent points points of the of the same same taxi. taxi. ThroughThrough the the reconstructed reconstructed taxi taxi trajectories trajectories shown shown in inFigure Figure 2,2 ,we we obtain obtain the the structure structure of of the the road road networknetwork in in Beijing Beijing (Figure (Figure 3),3), which which corresponds corresponds closely closely to to the the actual actual ro roadad network. network.

Proportion of the car models 5% 3%

11%

50%

31% Elantra Jetta Sonata Elysee Others

FigureFigure 1. 1.ProportionProportion of ofdifferent different car car models models of ofthe the current current taxi taxi in inBeijing. Beijing.

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Table 1. Summary of current typical research.

Aspects Authors Data Source Methodology and Contents Findings and Applications Effectively extract the driving experience to Build space-time trajectory cube; Shenzhen, China; support path-finding and dynamic path planning; Origin-destination constrained experience Yang et al. (2016) [1] 12,448 taxis; Road segment global frequency is not appropriate extraction methods; 75.94 million trajectory points. for representing driving experience in route Generate space-time constrained graph; planning models. Route planning and Path selection algorithm based on probability; path-finding Improved Prim path selection algorithm; Improved Prim path selection algorithm based on Nanjing, China; Zhang et al. (2016) [2] Improved Prim path selection algorithm based on probability is more optimal in producing an 5 million records. probability; efficient driving route. Shortest Path Faster Algorithm. Beijing, China; Build a hierarchical road network; Experienced routes are selected with considering Lin et al. (2015) [3] more than 3000 taxis. Path planning method based on experience. all factors especially with saving more travel time. Identify traffic congestion; Find that continuous congestion often happens in Shanghai, China; Investigate the relationship between traffic city centre and factors such as road type, bus Zhang et al. (2017) [9] 58,000 taxis; congestion and built environment; station, ramp nearby and commercial land use 114 million records. Fuzzy C-means clustering and spatial have large impacts on congestion formation. autoregressive moving average model. TTI is a traffic congestion indicator with Shenzhen, China; Introduce a travel time index (TTI) to measure the fine-grained spatial and temporal accuracy and Kong et al. (2015) [10] 13,798 taxies; level of congestion; can be used in many other cities to evaluate traffic 20 million records. Compute TTI in different time intervals and roads; conditions. Using taxi GPS data to build traffic flow matrix; Anomalous traffic events can be detected when Harbin, China; Anomaly detection method was proposed based Kuang et al. (2015) [11] corresponding indicators deviate significantly 15,000 taxies; on wavelet transform and principal component Traffic operational state from the expected values. identification analysis; Proposing a link travel time estimation method The average absolute error and relative error of LI et al. (2014) [14] Nanjing, China; without signal timing data. this method is 12 s and 8.67% respectively. Monitoring unexpected behaviors such as vehicle statistic detection models based on likelihood ratio Pang et al. (2013) [13] Beijing, China; breakdowns, or one-time events like large sporting test statistic events, fairs, and conventions. Isolation Based Online Anomaly Trajectory Detection; Hangzhou, China; A real-time method that identifies which parts of a Chen et al. (2012) [12] Detection of fraudulent taxi drivers. 7600 taxis; trajectory are anomalous; Introducing an ongoing anomaly score which can be used to rank the different trajectories. Energies 2018, 11, 500 5 of 22

Table 1. Cont.

Aspects Authors Data Source Methodology and Contents Findings and Applications The hotspots in different time in holiday, weekday, Wuhan, China; Proposing a method of trajectory clustering based and weekend are discovered and visualized in Zhao et al. (2015) [17] 3000 taxis. on decision graph and data field. heat maps; Taxi demand prediction. Using an improved DBSCAN algorithm in hot spot extraction; Providing recommendation to help cruising taxi Nanjing, China; Shen et al. (2015) [20] Defining weighted tree including factors of drivers to find potential passengers with 7600 taxis. distance, driving time, velocity and end point optimal routes. Identification of OD and the attractiveness in route evaluation. clustering method Establishing a travel OD matrix based on traffic analysis zone; Proposing a commute identification model based Identification of commute behavior using taxis, on trajectory data; workplace and residence. Xin et al. (2017) [21] Xi’an, China; Establishing a commute time and distance Optimization of traffic system for computing model; commuter services. Analysis of spatiotemporal characteristics of commute behavior. Fuel consumption and emissions calculation Shanghai, China; The distribution characteristics of emissions model; Luo et al. (2016) [23] 13,675 taxis; including CO, HC, NOx, and fuel consumption in Mapping technique for spatial distribution of 140 million records. Shanghai. emissions. Exploring the fuel consumption pattern and congestion pattern; Probing drivers’ information and vehicles’ parameters; Beijing, China; Using a Back Propagation Neural Network to Du et al. (2017) [24] The spatiotemporal characteristics of average Taxi fuel consumption and 13,000 floating vehicles. establish a fuel consumption forecasting model. emissions estimation speed and average fuel consumption; The difference in fuel consumption distribution within downtown Beijing between weekdays and weekends. A bench test involving three driving cycles: Energy Testing cruising, acceleration and deceleration, and the Center of Motor Transport Industry composite driving cycle including these two; The fuel consumption and emissions can be gotten Weng et al. (2017) [25] of Ministry of Proposing a model to calculate fuel consumption from second-by-second GPS data. Transportation, China. and emissions based on the driving trajectory reconstruction. Energies 2018, 11, 500 6 of 22 Energies 2018, 11, x FOR PEER REVIEW 6 of 22

Table 2.2. ExampleExample ofof taxitaxi GPSGPS data.data.

TaxiTaxi IDPositioning Positioning Time Latitude LongitudeSpeed SpeedPassenger (m/s) Passenger State Description Latitude Longitude Description ID Time (m/s) State closed door, effective 1379 2014/6/18 11:50:00 39.91004 116.30812 11.83 268435456 2014/6/18 closed door, effectivelocation, occupied.location, 1379 39.91004 116.30812 11.83 268435456 11:50:00 occupied.closed door, effective 1379 2014/6/18 11:51:01 39.9099 116.31544 9.26 268435456 2014/6/18 closed door, effectivelocation, occupied.location, 1379 39.9099 116.31544 9.26 268435456 11:51:01 occupied.closed door, effective 1379 2014/6/18 11:52:00 39.9097 116.32033 0 268435456 2014/6/18 closed door, effectivelocation, occupied.location, 1379 39.9097 116.32033 0 268435456 11:52:00 occupied.closed door, effective 1379 2014/6/18 11:53:00 39.90942 116.32373 9.77 268435456 2014/6/18 closed door, effectivelocation, occupied.location, 1379 39.90942 116.32373 9.77 268435456 11:53:00 occupied.closed door, effective 1379 2014/6/18 11:54:00 39.9098 116.3285 3.08 268435456 2014/6/18 closed door, effectivelocation, occupied.location, 1379 39.9098 116.3285 3.08 268435456 closed door, effective 138011:54:00 2014/6/18 5:38:20 39.91548 116.18801 4.11 0 occupied. 2014/6/18 closed door, effectivelocation, location, vacant. 1380 39.91548 116.18801 4.11 0 closed door, effective 13805:38:20 2014/6/18 5:39:21 39.91453 116.18817 2.05 0 vacant. 2014/6/18 closed door, effectivelocation, location, vacant. 1380 39.91453 116.18817 2.05 0 closed door, effective 13805:39:21 2014/6/18 5:40:20 39.9143 116.19248 4.63 0 vacant. location, vacant. 2014/6/18 closed door, effective location, 1380 39.9143 116.19248 4.63 0 closed door, effective 13805:40:20 2014/6/18 5:41:19 39.91246 116.19693 8.74 0 vacant. location, vacant. 2014/6/18 closed door, effective location, 1380 39.91246 116.19693 8.74 0 In the passenger5:41:19 state column, “268435456” means there is at least one passenger in the taxi and “0”vacant. means the taxi is vacant. In the passenger state column, “268435456” means there is at least one passenger in the taxi and “0” means the taxi is vacant.

Figure 2. An example of taxi trajectories in Beijing. Figure 2. An example of taxi trajectories in Beijing.

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Figure 3. Reconstructed structure of the road network in Beijing from taxi trajectories. Figure 3. Reconstructed structure of the road network in Beijing from taxi trajectories. 2.2. Carbon Emission Calculation Model 2.2. CarbonVehicle Emission Specific Calculation Power (VSP), Model which indicates the output power of the engine when it moves a Vehicleton of weight, Speciﬁc including Power its (VSP), own, was which first indicates proposed the by Jiménez output- powerPalacios of in the his enginedoctoral when dissertation it moves a ton ofin weight, 1999 [28] including. We obtain its the own, VSP was from ﬁrst vehicle proposed speed and by Jim acceleénez-Palaciosration. in his doctoral dissertation in 1999 [28]. We obtain the VSPVSP from = v∙( vehicle1. 1∙a + 9. speed 81∙grade and(% acceleration.) + 0. 132) + 0. 000302∙v3 (1) where v and a are the speed (m/s) and acceleration (m/s2), respectively, and grade represents the road = v·( ·a + ·grade( ) + ) + ·v3 grade, which is assumedVSP to be 1.zero 1 in urban9. 81 road netwo% rks.0.132 0. 000302 (1) Compared with speed and acceleration, fuel consumption has a stronger correlation with VSP 2 where[29v–and31]. Ina are practice, the speed however, (m/s) we and cannot acceleration easily obtain (m/s the ),exact respectively, running state and ofgrade a vehicle,represents especially the road grade,the which speedis per assumed second, to required be zero to in compute urban road the VSP,networks. because it will cause high costs for the taxi Comparedoperational company with speed if it and tries acceleration,to obtain the exact fuel running consumption state of has a vehicle. a stronger Therefore, correlation a more with VSP accurate[29–31]. fuel In consumption practice, however, estimation we is less cannot likely. easily To overcome obtain this the shortcoming, exact running Song state and Tu of [26,32 a vehicle,] especiallyperformed the speed experiments per second, to explore required the relationship to compute between the VSP, VSP because and fuel it consumption will cause high and costshow to for the taxi operationalmore meaningfully company obtai ifn itthe tries VSP to and obtain fuel consumption the exact running from the state average of a speed, vehicle. which Therefore, is easier ato more accurateacquire. fuel Theconsumption relationship estimation between isthe less average likely. speed To overcome and the thisnormalized shortcoming, fuel consumption Song and Tu rate [26 ,32] (NFCR) [26] is shown in Table 3. performed experiments to explore the relationship between VSP and fuel consumption and how to more meaningfullyTable 3. obtain Normalized the VSPfuel consumption and fuel consumptionrate (NFCR) accord froming to the average average speed speed, interval. which is easier to acquire. The relationship between the average speed and the normalized fuel consumption rate Index Average Speed Interval (km/h) NFCR Index Average Speed Interval (km/h) NFCR (NFCR)1 [ 26] is shown in Table0–2 3. 1.085137 18 34–36 2.338148 2 2–4 1.258708 19 36–38 2.361389 3 Table 3. Normalized4–6 fuel consumption1.311138 rate (NFCR)20 according to average38–40 speed interval.2.395369 4 6–8 1.477515 21 40–42 2.441831 5 8–10 1.573123 22 42–44 2.470396 Index Average Speed Interval (km/h) NFCR Index Average Speed Interval (km/h) NFCR 6 10–12 1.64575 23 44–46 2.538255 17 0–212–14 1.0851371.729985 24 1846–48 34–36 2.566097 2.338148 28 2–414–16 1.2587081.807417 25 1948–50 36–38 2.581801 2.361389 3 4–6 1.311138 20 38–40 2.395369 9 16–18 1.841056 26 50–52 2.595985 4 6–8 1.477515 21 40–42 2.441831 10 18–20 1.922954 27 52–54 2.6796 5 8–10 1.573123 22 42–44 2.470396 6 10–12 1.64575 23 44–46 2.538255 7 12–14 1.729985 24 46–48 2.566097 8 14–16 1.807417 25 48–50 2.581801 Energies 2018, 11, 500 8 of 22

Table 3. Cont.

Index Average Speed Interval (km/h) NFCR Index Average Speed Interval (km/h) NFCR 9 16–18 1.841056 26 50–52 2.595985 10 18–20 1.922954 27 52–54 2.6796 11 20–22 1.996735 28 54–56 2.715854 12 22–24 2.045498 29 56–58 2.755036 13 24–26 2.092286 30 58–60 2.809524 14 26–28 2.163184 31 60–62 2.864735 15 28–30 2.186922 32 62–66 2.956168 16 30–32 2.25144 33 66–70 3.049095 17 32–34 2.328849 34 70–80 3.289347 35 Above 80 3.550955

Based on Table3 and the average speed and driving duration obtained from taxi trajectories in ArcGIS, the carbon emissions can be calculated through Equation (2).

35 Ek,j= EFk· ∑ (ER0· ∑ NFCRi·Ti,j) (2) s i=1 From Equation (2), the fuel consumption can be computed through Equation (3).

35 Qs, j= ER0· ∑ NFCRi·Ti, j (3) i=1 where Qi, j (g) is the actual fuel consumption in each road segment s of taxi j. ER0 (g/s), which is calculated as 0.274 g/s using the average value of three car models of the current taxi in [26], is the average fuel consumption rate when VSP is equal to 0. NFCRi is the non-dimensional normalized fuel consumption rate in the average speed interval i. Ti, j is the driving duration in the average speed interval i of taxi j. After obtaining the fuel consumption of each segment s, the total fuel consumption and the corresponding carbon emissions are obtained according to Equation (2).

Qj = ∑ Qs, j (4) s

Ek, j= Qj·EFk (5) where Qj is the total fuel consumption of taxi j, Ek, j is the emissions of exhaust k, and EFk is the emission factor of exhaust k. The IPCC provides the emission factors (693,000 kg/TJ) of carbon dioxide for motor gasoline [33]. According to the caloriﬁc value (43,040 KJ/kg) and density (0.73 kg/L) of gasoline, which is the 93# gasoline at 20 ◦C, obtained from the China Statistical Yearbook [34], the carbon dioxide emission factors (693,000 kg/TJ) are transformed into 2.18 kg/L. This value is used in this study.

2.3. Taxi Carbon Emission Visualization Method To clearly present the distribution of carbon emissions, set as an attribute of the reconstructed taxi trajectory segments in ArcGIS, we calculate the density of trajectory segments weighted by their carbon emission attribute, applying the kernel density tool. Kernel density can create a smooth curved surface over each line, whose value is greatest exactly on the line, and which decreases to zero as the distance increases to the search radius. The search radius is set as 400 m in this study to derive the standard density available for comparison between different results at different times. The volume under the surface is equal to the product of the line length and the weighted ﬁeld, namely carbon emissions. Globally, each output raster cell is set as 100 × 100, and its density is the sum of all kernel surface values above it. Additionally, the length unit is meters, so the default area unit of the density is km/km2, which contains the impact of the carbon emission attribute. Finally, different density intervals are represented with speciﬁc colors to demonstrate the emission intensity. An illustration of Energies 2018, 11, 500 9 of 22

the kernel surface over a line segment is shown in Figure4, and a diagram of the kernel analysis result is displayed in Figure5, in which the darker colors represent greater emissions. Energies 2018, 11, x FOR PEER REVIEW 9 of 22 Energies 2018, 11, x FOR PEER REVIEW 9 of 22

Figure 4. Illustration of the kernel surface over a line segment. Figure 4. Illustration of the kernel surface over a line segment. Figure 4. Illustration of the kernel surface over a line segment.

Figure 5. Diagram of the kernel analysis result. Figure 5. Diagram of the kernel analysis result. 3. Case Study Figure 5. Diagram of the kernel analysis result. 3. Case Study Beijing is a developed and vibrant mega city with a total city area and central city area in 2014 3. 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The Science multipleHigh -andtech centers Technology Park refer(ZHP), to Innovation theOlympic core function CenterCenter District(HSTIC),regions (OCD), of Shunyi the city, Central Modern including Bus Manufacturinginess the Zhongguancun District (CBD), Base High-tech (SMMB),Haidian Science ParkTongzhou (ZHP), and ComprehensiveTechnology Olympic Center Innovation Service District CenterCenter (OCD), (HSTIC),(TCSC),Central Business YizhuangShunyi DistrictModern High- (CBD),tech Manufacturing Industry Haidian CenterScience Base (YHIC),(SMMB), and Technology Shijingshan Tongzhou Innovation Comprehensive Comprehensive Center (HSTIC), Service Service Center ShunyiCenter (TCSC),(SCSCModern), and Yizhuang Manufacturing Wang HighJing (WJ),- Basetech called Industry(SMMB), “the CenterTongzhou first community (YHIC), Comprehensive Shijingshan of the capital” Service Comprehensive (Figure Center 6). (TCSC), Service Yizhuang Center (SCSCHigh-techDue), and to Industry Wangincreasingly Jing Center (WJ), rapid (YHIC), called economic Shijingshan“the first and community technological Comprehensive of thedevelopment Servicecapital” Center (Figure in China, (SCSC), 6). traffic and Wangdemands Jing have(WJ),Due surged called to increasingly “therapidly. first For community rapidexample, economic of the the motor capital” and vehicletechnological (Figure and6). private development car population in China, had traffic approached demands 5.6 haveand 4.2Duesurged million, to increasinglyrapidly. respectively, For example, rapid by economic 2014 the motorin Beijing and vehicle technological (Figure and 7a),private developmentand car the population number in China, of had cars trafficapp perroached household demands 5.6 andandhave 4.2per surged million, square rapidly. kilometerrespectively, For example,are byillustrated 2014 the in motor Beijingin [35] vehicle. (FigureThe number and 7a), private and of cars the car has populationnumber continued of cars had to approachedper grow, household and car 5.6 anddensityand per 4.2 decreasessquare million, kilometer respectively, with distance are illustrated by from 2014 the in incen Beijing [35]ter. ofThe (Figure Beijing number7 a),(Figure andof cars the7b) hasexcept number continued for of the cars areatoper grow, in household 2nd and to car3rd densityRingand perRoads, decreases square where kilometer with car distancedensity are illustrated increasesfrom the cento in 55,000/km [ter35 ].of The Beijing number2 [35] (Figure. As of a carsconsequence,7b) except has continued for trafficthe area to congestion grow,in 2nd and to and3rd car Ringairdensity pollution Roads, decreases where become with car increasingly density distance increases from severe, the to center and 55,000/km the of Beijingnumber2 [35] (Figure. ofAs motor a consequence,7b) vehicles except for and traffic the amount area congestion in 2ndof exhau to and 3rdst airemissionsRing pollution Roads, must wherebecome be car regulated increasingly density to increases relievesevere, to trafficand 55,000/km the jams number and2 [35 improve].of Asmotor a consequence, airvehicles quality and in traffic amount Beijing. congestion of Therefore, exhau andst emissionsdeterminingair pollution must the become becarbon regulated increasingly emission to relieve patterns severe, traffic andof taxis the jams numberis criticaland improve of for motor authorities air vehicles quality to and makein Beijing. amount more reasonable Therefore, of exhaust determiningmanagement the policies, carbon andemission for taxi patterns companies of taxis to is promo criticalte for their authorities operation to andmake management, more reasonable save managementoperating costs, policies, and most and importantly, for taxi companies provide tomore promo favorablete their services operation for passengers. and management, save operating costs, and most importantly, provide more favorable services for passengers.

Energies 2018, 11, 500 10 of 22 emissions must be regulated to relieve trafﬁc jams and improve air quality in Beijing. Therefore, determining the carbon emission patterns of taxis is critical for authorities to make more reasonable Energiesmanagement 2018, 11, x policies, FOR PEER and REVIEW for taxi companies to promote their operation and management,10save of 22 operating costs, and most importantly, provide more favorable services for passengers. Energies 2018, 11, x FOR PEER REVIEW 10 of 22

Figure 6. Research area and core function regions of Beijing. Figure 6. Research area and core function regions of Beijing. Figure 6. Research area and core function regions of Beijing. Thus, we analyze the spatiotemporal carbon emissions and taxi travel patterns in Beijing and compareThus, we their analyze differences the spatiotemporal between weekdays carbon and weekends. emissions At anda temporal taxi travel level, patternswe analyze in the Beijing laws and Thus, we analyze the spatiotemporal carbon emissions and taxi travel patterns in Beijing and compareof carbon their emissions, differences fuel between consumption, weekdays average and weekends.travel speed, At and a temporaltotal travel level, distance we analyzeat different the laws compare their differences between weekdays and weekends. At a temporal level, we analyze the laws of carbontime intervals. emissions, From fuel the consumption, spatial perspective, average we travelanalyze speed, the dynamic and total distribution travel distance of carbon at emissions different time of carbon emissions, fuel consumption, average travel speed, and total travel distance at different intervals.at different From time the spatialintervals. perspective, All the differences we analyze in these the indicators dynamic are distribution compared between of carbon weekdays emissions at time andintervals. weekends, From in the the spmorningatial perspective, and evening we rush analyze hour periods. the dynamic distribution of carbon emissions different time intervals. All the differences in these indicators are compared between weekdays and at different time intervals. All the differences in these indicators are compared between weekdays weekends, in the morning private car and evening rush hour periods. average car number per household and weekends,6 in the morning and evening rush hour periods. ten thousand cars per square kilometer motor vehicle 5.591 0.6

5 4.809 0.5 average car number per household 6 private car 4.197 ten thousand cars per square kilometer motor vehicle 5.591 0.6 4 3.566 0.4 5 4.809 0.5 3 0.3

2.583 4.197 Car density 4 0.4 2 3.566 0.2

Number of cars (Million) 1.507 1.343 3 0.10.3

1 0.7972.583 Car density

0.00.2 2 0

Number of cars (Million) 1.507 2000 2005 2010 2014 2nd-3rd Ring 3rd-4th Ring 4th-5th Ring 5th-6th Ring 1.343 within 2dn Ring Year 0.1 Area between different Ring Roads 1 0.797 (a) (b) 0.0 0 Figure2000 7. (a) Annual2005 variation2010 of cars and2014 (b) spatial variation of cars with distance from ring roads 2nd-3rd Ring 3rd-4th Ring 4th-5th Ring 5th-6th Ring in 2014. within 2dn Ring Year Area between different Ring Roads (a) (b)

Figure 7. (a) Annual variation of cars and (b) spatial variation of cars with distance from ring roads Figure 7. (a) Annual variation of cars and (b) spatial variation of cars with distance from ring roads in 2014. in 2014.

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4.4. Discussion 4.1. Temporal Carbon Emissions and Taxi Travel Patterns 4.1. Temporal Carbon Emissions and Taxi Travel Patterns The number of taxis and records, as summarized in Figure8, are the most straightforward The number of taxis and records, as summarized in Figure 8, are the most straightforward indicators for representing the global travel patterns of taxis. We can clearly see the travel regularity of indicators for representing the global travel patterns of taxis. We can clearly see the travel regularity taxi drivers in Figure8, which aligns well with people’s expected daily activities. of taxi drivers in Figure 8, which aligns well with people’s expected daily activities. The number of taxis and records are related throughout the day, with a minimum at 03:00–04:00 The number of taxis and records are related throughout the day, with a minimum at 03:00–04:00 when most people are sleeping. The gap between car number and car usage is greatest from 04:00–06:00. when most people are sleeping. The gap between car number and car usage is greatest from 04:00– Interviews with taxi drivers reveal that most taxi drivers take a shift in this time interval; therefore, 06:00. Interviews with taxi drivers reveal that most taxi drivers take a shift in this time interval; not all of them are on full duty. After 06:00, the number of cars on duty climbs to 25,000 cars at 10:00, therefore, not all of them are on full duty. After 06:00, the number of cars on duty climbs to 25,000 signifying that many drivers start work during this time interval. Numbers remain stable until 18:00 cars at 10:00, signifying that many drivers start work during this time interval. Numbers remain and then decrease steadily to the minimum. Several speciﬁc and typical time intervals are chosen to stable until 18:00 and then decrease steadily to the minimum. Several specific and typical time analyze the spatiotemporal carbon emission patterns in Section 4.3. intervals are chosen to analyze the spatiotemporal carbon emission patterns in Section 4.3.

1.6x106 2.8x104 6 1.4x10 2.4x104 1.2x106 2.0x104 1.0x106 1.6x104 8.0x105 1.2x104 6.0x105 3 5

4.0x10 Number of records 8.0x10 Number of taxis Number of records Number of taxis 3 2.0x105 4.0x10 0.0 0.0 -- -- 0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-1010-1111-1212-1313-1414-1515-1616-1717-1818-1919-2020-2121-2222-2323-24 Time

Figure 8. Variation of taxi and record numbers duringduring oneone day.day.

To describe carbon emissions in a more standard way, we introduce an indicator named carbon 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). emissions per kilometer (CEPK, kg), calculated according to Equation (6). Total Carbon Emissions CEPK = (6) TotalTotal Carbon travel Emissionsdistance CEPK = (6) Total travel distance The CEPK, which is a global and standard indicator, can evaluate carbon emission levels more objectively,The CEPK, without which considering is a global the and influence standard of indicator,the individual can evaluate characteristics carbon and emission driving levels conditions more objectively,of taxis. The without total carbon considering emissions the per influence hour varies of the substantially individual characteristics during the day and (Figure driving 9), conditions while the ofCEPK taxis. exhibits The total a slight carbon fluctuation. emissions per hour varies substantially during the day (Figure9), while the CEPKTotal exhibits carbon a slight emissions, fluctuation. in line with the number of taxis, reaches a minimum of 7.43 t from 03:00– 04:00.Total However, carbon they emissions, increase in by line more with than the seven number times of during taxis, the reaches morning a minimum and evening of 7.43 rush t fromhour 03:00–04:00.periods, to 57.53 However, t and they 55.15 increase t, respectively. by more thanCEPK seven remains times relatively during the stable morning at approximately and evening rush0.20 hourkg/km periods, throughout to 57.53 the tday, and yet 55.15 still t, increases respectively. during CEPK rush remains hour, peaking relatively at stable0.26 kg/km at approximately from 17:00– 0.2018:00. kg/km Both total throughout carbon emissions the day, yetand still CEPK increases then begin during to gradually rush hour, decli peakingne. M atoreover, 0.26 kg/km both values from 17:00–18:00.decrease slightly Both at total 12:00 carbon–13:00, emissions when most and drivers CEPK thenare on begin their to lunch gradually break. decline. Generally, Moreover, total carbon both valuesemissions decrease in a day slightly reach at965 12:00–13:00, t, which is whena substantial most drivers figure. are on their lunch break. Generally, total carbonFrom emissions this analytical in a day result, reach 965it is t, obvious which is that a substantial the carbon figure. emissions from taxis are huge. Policy makersFrom must this analyticalissue corresponding result, it is obvious regulations that to the reduce carbon carbon emissions emissions. from taxis For are example, huge. Policy taxi makersrestrictions must in issue the morning corresponding and evening regulations rush tohour reduce periods carbon could emissions. reduce carbon For example, emissions, taxi restrictionsease traffic injam the, and morning encourage and eveningpeople to rush use hourpublic periods transits. could What reduce is more, carbon increasing emissions, taxi rental ease trafficcharges jam, during and encouragethe morning people and evening to use rush public hour transits. periods What may isalso more, be a good increasing policy taxi to ease rental traffic charges jams duringand reduce the morningcarbon emissions. and evening rush hour periods may also be a good policy to ease traffic jams and reduce carbon emissions.

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0.30 70 0.25 60

50 0.20

40 0.15 30 0.10 Energies 2018, 11, x FOR20 PEER REVIEW Total Carbon Emission 12 of 22 0.05

10 Carbon Emission Per Kilometer Total CarbonTotal Emission(t) 0.30

0 0.00 Per Kilometer(kg) Carbon Emission 70 -- -- 0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24 0.25 60 Time 50 0.20

40Figure 9. Carbon emissions throughout one day. 0.15 30Figure 9. Carbon emissions throughout one day. 0.10 The average fuel consumption20 per 100 km (FCPOK), also a global Total andCarbon well Emission-known indicator, can 0.05 10 Carbon Emission Per Kilometer be calculated according to CarbonTotal Emission(t) Equation (7). The average fuel consumption per 100 km (FCPOK), also a global and well-known indicator, can

0 0.00 Per Kilometer(kg) Carbon Emission -- -- 0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9FC ∙ 100 be calculated according to Equation (7). FCPOK = 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24 (7) 퐿 Time FC·100 where FC is taxi’s actual fuel consumptionFigure 9. Carbon (L)FCPOK and emissions L is= taxi’s throughout actual driving one day. distance (km) and can be (7) obtained by 푣 ∙ 푡. Therefore, the FCPOK can be obtained accordingL to Equation (8). where FC is taxi’s actual fuel consumption (L) and L is taxi’s actual driving distance (km) and can be The average fuel consumption per 100 km FC(FCPOK),∙ 100 also a global and well-known indicator, can FCPOK = (8) obtained bybe vcalculated·t. Therefore, according the to FCPOK Equation can(7). be obtained푣 ∙ 푡 according to Equation (8). FC·100 where v is travel speed (km/h) and t is travelFCPOK time (h).= FC ∙ 100 (8) FCPOK = · (7) FCPOK varies substantially from 8.43 L from 05:00–06:00퐿v tot 11.97 L from 17:00–18:00 (Figure 10). where Afterv iswhere travel18:00, FCthe speed is averag taxi’s (km/h) eactual FCPOK fuel and slowly consumptiont is decreases travel (L) time with and (h).theL is gradualtaxi’s actual increase driving of average distance travel (km) speed,and can be FCPOKas shownobtained varies in Figure by substantially푣 ∙11,푡. Therefore, which denotes fromthe FCPOK th 8.43at the can L travel from be obtained speed 05:00–06:00 and according FCPOK to to 11.97are Equation negatively L from (8). correlated 17:00–18:00 in (Figure 10). a certain range of speed. The average fuel consumptionFC for∙ 100 the entire day is 10.02 L. To verify the After 18:00, the average FCPOK slowly decreasesFCPOK = with the gradual increase of average( travel8) speed, analytical result of FCPOK, we sampled 120 taxi drivers푣 randomly∙ 푡 , considering the different car as shownmodels in Figureproportions 11, shown which in denotesTable 4. Each that vehicle the travelcorresponds speed to an and FCPOK. FCPOK The weighted are negatively average correlated in where v is travel speed (km/h) and t is travel time (h). a certainFCPOK range derived of speed. from all Thequestionnaires average is fuel 8.23consumption L. The analytical forresult the (10.02 entire L) is day 21.7% is more 10.02 than L. To verify the FCPOK varies substantially from 8.43 L from 05:00–06:00 to 11.97 L from 17:00–18:00 (Figure 10). the figure we investigated (8.23 L). analytical resultAfter 18:00, of FCPOK, the averag wee FCPOK sampled slowly 120 decreases taxi drivers with the randomly, gradual increase considering of average the travel different speed, car models These results could help policy makers issue stricter regulations related to carbon emission proportionsas shown in in Figure Table 11,4 .which Each denotes vehicle th correspondsat the travel speed to anand FCPOK. FCPOK are The negatively weighted correlated average in FCPOK reductions. For example, increasing oil prices may force people to drive less and encourage people to a certain range of speed. The average fuel consumption for the entire day is 10.02 L. To verify the deriveduse from public all transportation questionnaires or new is 8.23 energy L. The automobiles analytical such result as e (10.02lectrical L) vehicles. is 21.7% To more achieve than the ﬁgure analytical result of FCPOK, we sampled 120 taxi drivers randomly, considering the different car we investigatedsustainable development,(8.23 L). the governments could also command taxi companies to transform the models proportions shown in Table 4. Each vehicle corresponds to an FCPOK. The weighted average traffic mode of fuel taxis to electrical vehicles or natural gas vehicles so that the carbon emissions can TheseFCPOK results derived could from help all questionnaires policy makers is 8.23 issue L. The stricter analytical regulations result (10.02 relatedL) is 21.7% to more carbon than emission be reduces and living conditions, especially air quality, can be improved. Besides, a restriction policy reductions.the For figure example, we investigated increasing (8.23 L). oil prices may force people to drive less and encourage people to on private car consumption can reduce fuel consumption and carbon emissions. use public transportationThese results or could new help energy policy automobiles makers issue strictersuch as regulations electrical related vehicles. to carbon To achieve emission sustainable development,reductions. the governments For12 example, increasing could also oil prices command may force taxi people companies to drive less to and transform encourage the people trafﬁc to mode of use public 11 transportation or new energy automobiles such as electrical vehicles. To achieve fuel taxis to electrical vehicles or natural gas vehicles so that the carbon emissions can be reduces and sustainable 10 development, the governments could also command taxi companies to transform the living conditions,traffic mode especially 9of fuel taxis air to electrical quality, vehicles can be or improved. natural gas vehicles Besides, so that a restriction the carbon emissions policy on can private car consumptionbe reduces can reduce and8 living fuel cond consumptionitions, especially and air carbonquality, can emissions. be improved. Besides, a restriction policy on private car7 consumption can reduce fuel consumption and carbon emissions. Average Fuel Consumption Per 100 kilometers 6

Average Fuel Consumption(L) 5 12 -- -- 0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 11 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24 Time 10 9 Figure 10. Average fuel consumption throughout one day. 8 Table 4. Investigated7 fuel consumption per 100-km (FCPOK) results (L/100 km). Average Fuel Consumption Per 100 kilometers 6

Car Type Average Fuel Consumption(L) Elantra Zastava Santana Sonata Citroen Jetta Weighted Average 5 number of investigated cars 40 10 10 10 20 30 120-- -- 0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 average FCPOK 7. 8 9. 2 8. 510-11 11-12 12-138.13-14 7 14-15 15-16 16-178. 417-18 18-19 19-208. 120-21 21-22 22-23 23-24 8. 23 Time

Figure 10. Average fuel consumption throughout one day. Figure 10. Average fuel consumption throughout one day. Table 4. Investigated fuel consumption per 100-km (FCPOK) results (L/100 km).

Table 4.CarInvestigated Type fuelElantra consumption Zastava Santana per 100-km Sonata (FCPOK)Citroen Jetta results Weighted (L/100 Average km). number of investigated cars 40 10 10 10 20 30 120 Car Typeaverage Elantra FCPOK Zastava7. 8 9. Santana 2 8. 5 Sonata8. 7 8. Citroen 4 8. 1 Jetta8. 23Weighted Average number of 40 10 10 10 20 30 120 investigated cars average FCPOK 7. 8 9. 2 8. 5 8. 7 8. 4 8. 1 8. 23 Energies 2018, 11, 500 13 of 22 Energies 2018, 11, x FOR PEER REVIEW 13 of 22

The average travel speed and total travel distance are highlighted in Figure 1111,, which exhibit a roughly negative correlation andand considerableconsiderable fluctuations.fluctuations. The maximum maximum and and minimum minimum average average speed speed is reached is reached from from 05:00 05:00–06:00–06:00 (36.21 (36.21 km/h) km/h) and 17:00 and– 17:00–19:0019:00 (21 km/h), (21 km/h), respectively. respectively. In addition, In addition, the travel the travel speed speed during during the themorning morning peak peak hour hour period period is isapproximately approximately 25 25 km/h, km/h, slightly slightly higher higher than than during during evening evening peak peak hours. hours. The The average average travel travel speed speed of ofthe the entire entire day day over over the the whole whole network network is is 28.54 28.54 km/h. km/h. The The figure figure fr fromom the the Beijing Transportation Development Annual Report is 24.60 km/h, which which means means that that the the traffic traffic situation is more serious than previously thought. In contrast, the total travel distance maintains a moderate level during rush hour periods. The maximum t travelravel distance is 67,800 km from 16:00–17:00,16:00–17:00, which is nearly ten times the minimum minimum of of 7400 7400 km km from from 03:00 03:00–04:00.–04:00. In Ingeneral, general, the the travel travel distance distance in a in day a dayreaches reaches up to up 4.27 to 4.27million million km. km. The average travel speed by hour can be used to monitor traffic traffic condit conditionsions throughout an entire urban network.network. TheThe results results indicate indicate that that higher higher resolution resolution travel travel speed speed data, data, such such as the as average the average travel speedtravel inspeed one in minute, one minute, can be can obtained be obtained to instantly to instantly monitor monitor traffic conditions,traffic conditions, as long as as long such as a such task isa feasibletask is feasible for the for hardware. the hardware.

3.0x105 36

2.5x105 32

5 28 2.0x10 24 1.5x105 20 1.0x105 Total Travel Distance 16 5.0x104 Average Travel Speed Average Travel Speed(km/h)

Total Travel Distance(km) Travel Total 12 0.0 -- -- 0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24 Time Figure 11. Average travel speed and total travel distance during one day. Figure 11. Average travel speed and total travel distance during one day.

4.2. Temporal Comparison of Carbon Emissions and Travel Patterns between Weekdays and Weekends 4.2. Temporal Comparison of Carbon Emissions and Travel Patterns between Weekdays and Weekends In terms of CEPK, average travel speed, average FCPOK, and total carbon emissions, we In terms of CEPK, average travel speed, average FCPOK, and total carbon emissions, we compare compare the difference between weekdays and weekends in morning and evening rush hour periods the difference between weekdays and weekends in morning and evening rush hour periods as shown as shown in Figure 12. Overall, carbon emissions and travel patterns on the weekend are improved in Figure 12. Overall, carbon emissions and travel patterns on the weekend are improved compared to compared to weekdays, regardless of morning or evening rush hour periods. weekdays, regardless of morning or evening rush hour periods. The average CEPK decrease over all time intervals is approximately 0.03 kg/km, a substantial The average CEPK decrease over all time intervals is approximately 0.03 kg/km, a substantial improvement because it is a relatively microscopic indicator. The average travel speed increases the improvement because it is a relatively microscopic indicator. The average travel speed increases the most on weekend mornings, by 6.3 km/h, and the average FCPOK and total carbon emissions also most on weekend mornings, by 6.3 km/h, and the average FCPOK and total carbon emissions also show show the greatest decrease at this time of the weekends, of 1.47 L and 14.5 t, respectively. Values of the greatest decrease at this time of the weekends, of 1.47 L and 14.5 t, respectively. Values of the average the average travel speed, average FCPOK, and total carbon emissions at 08:00–09:00 and 17:00–19:00 travel speed, average FCPOK, and total carbon emissions at 08:00–09:00 and 17:00–19:00 show relatively show relatively fewer variations compared to 07:00–08:00 but also have great improvements between fewer variations compared to 07:00–08:00 but also have great improvements between weekdays and weekdays and weekends. The average improvement across all time intervals is 4.2 km/h, 1.18 L/100 weekends. The average improvement across all time intervals is 4.2 km/h, 1.18 L/100 km, and km, and 9.4 t, respectively. 9.4 t, respectively. The primary reason for the difference between weekdays and weekends in the morning and The primary reason for the difference between weekdays and weekends in the morning and evening rush hour periods is that taxi numbers decrease a lot in every time interval—from 24,830 to evening rush hour periods is that taxi numbers decrease a lot in every time interval—from 24,830 21,007, an average decrease of 3823 taxis. Beijing is a megacity, and many people live in the city to 21,007, an average decrease of 3823 taxis. Beijing is a megacity, and many people live in the city suburbs. Thus, on the weekdays, most people need to commute between the suburbs and downtown suburbs. Thus, on the weekdays, most people need to commute between the suburbs and downtown by taxi, which may save more time on the road. On the weekend, however, most people do not need by taxi, which may save more time on the road. On the weekend, however, most people do not need to to commute. If they take a taxi on the weekend for a short trip, rather than for their commute into commute. If they take a taxi on the weekend for a short trip, rather than for their commute into work, work, they do not necessarily need to get up early, and they can go home at any time. they do not necessarily need to get up early, and they can go home at any time.

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Weekday Weekday 35 0.30 Weekend Weekend 31.3 0.26 0.26 30 28.1 0.25 0.24 0.24 0.24 0.23 0.22 25.0 24.6 25.1 0.20 25 24.0 0.20 21.5 21.7 20 0.15 15 0.10 10

0.05 Average Travel Speed(km/h) 5 Carbon Emission Per Kilometer(kg) 0.00 0 7-8 8-9 17-18 18-19 7-8 8-9 17-18 18-19 Time Time (a) (b) Weekday Weekday Weekend 60 Weekend 12 11.97 11.86 51.6 50.6 11.12 50 10.79 10.9 10.8 45.8 46.7 10.00 43.8 10 9.32 42.4 40 39.6 8 31.3 30 6 20

4 Total Carbon Emission(t) 2 10

0 0 Average Fuel Consumption Pre 100 Kilometers(L) 7-8 8-9 17-18 18-19 7-8 8-9 17-18 18-19 Time Time (c) (d)

Figure 12. Temporal comparison of carbon emissions and travel patterns between weekdays and Figure 12. Temporal comparison of carbon emissions and travel patterns between weekdays and weekends. (a) difference in CEPK; (b) difference in average travel speed; (c) difference in average weekends. (a) difference in CEPK; (b) difference in average travel speed; (c) difference in average FCPOK and (d) difference in total carbon emissions. FCPOK and (d) difference in total carbon emissions. 4.3. Spatial Carbon Emission Patterns 4.3. Spatial Carbon Emission Patterns After analyzing the temporal carbon emission patterns, we focus on spatial emission distribution characteristicsAfter analyzing and their the dynamic temporal variations carbon emission with time. patterns, Travel we activities focus on are spatial mainly emission distributed distribution within characteristicsthe 6th Ring Road, and their namely dynamic the city variations center (Figure with time. 13). Travel In fact, activities carbon areemission mainly patterns distributed are withinhighly theconsistent 6th Ring with Road, the city namely structure. the city Therefore, center (Figure we mainly 13). Infocus fact, on carbon the area emission within the patterns 6th Ring are Road. highly consistentGlobally, with regardless the city structure. of the Therefore, time interval, we mainly carbon focus emissions on the area are within highest the near 6th Ring the AirportRoad. ExpresswayGlobally, (S12 regardless in Figure of the6) because time interval, the Beijing carbon Capital emissions International are highest Airport near the lies Airport at the Expressway northeast (S12end inof Figurethe Airport6) because Expressway. the Beijing Therefore, Capital International there is a heavy Airport traffic lies atload the throughout northeast end the of day. the AirportCarbon Expressway.emissions are Therefore, also high thereon the is 2nd, a heavy 3rd, traffic and 4th load Ring throughout Roads because the day. taxi Carbon fleets emissionsare common, are alsoand highdrive on at the higher 2nd, speeds.3rd, and Hot 4th Ring spots Roads also appear because at taxi intersections fleets are common, of the Ring and drive Road at in higher specific speeds. time Hotintervals spots resulting also appear from at large intersections overpasses of the or Ringinterchanges Road in specificat these timelocations. intervals Relatively resulting lower from carbon large overpassesemissions happen or interchanges outside the at 5th these Ring locations. Road. Most Relatively areas bear lower the effect carbon of emissionsmany office happen buildings outside and thehigh 5th traffic Ring congestion Road. Most within areas thebear 5th the Ring effect Road, of many which office contribute buildings to and greater high carbon traffic congestion emissions. withinMoreover, the 5ththe RingCBD, Road,TCSC, which ZHP, contributeand WJ (Figure to greater 6) are carbon relatively emissions. high carbon Moreover, emission the CBD,areas due TCSC, to ZHP,frequent and travel WJ (Figure activities.6) are Carbon relatively emissions high carbon in the emissionworld-famous areas Palace due to Museum, frequent travelsituated activities. right in Carbonthe city emissionscenter, are in quite the world-famouslow, as expected. Palace Museum, situated right in the city center, are quite low, as expected.From the perspective of time, although the global distribution patterns of carbon emissions do not changeFrom the substantially, perspective of detailed time, although local distributions the global distribution show distinct patterns variations of carbon with emissions time. Atdo notthe changebeginning substantially, of the day, detailedthere is a local relatively distributions wide distribution show distinct of carbon variations emissions with time. related At theto the beginning vibrant ofnight the life day, of there a capita is a relativelyl city. Then, wide carbon distribution emissions of carbongradually emissions decrease related (indicated to the by vibrant a lighter night color life ofin aFigure capital 14) city. in all Then, regions carbon from emissions 03:00–04:00. gradually From 06:00, decrease they (indicated increase at by the a lighter Airport color Expressway in Figure and 14) ingradually all regions extend from to 03:00–04:00. the Ring Roads From 06:00, and particularly they increase to atintersections. the Airport ExpresswayNotably, carbon and graduallyemissions extendincrease to from the Ring the Roadsnortheast and to particularly the southwest to intersections. with time in Notably, the day carbon and decrease emissions inincrease the opposite from thedirection, northeast which to the confirms southwest that with economic time in development the day and is decrease higher in in the the northeast opposite direction, than southwest. which From approximately 08:00–09:00, high carbon emissions spread to the expressway, the arterial road, the second trunk road, and even the city streets, until they eventually covering the entire city area, indicating increasingly serious traffic jams during the morning rush hour period. The hot spots at

Energies 2018, 11, 500 15 of 22 confirms that economic development is higher in the northeast than southwest. From approximately 08:00–09:00, high carbon emissions spread to the expressway, the arterial road, the second trunk road, and even the city streets, until they eventually covering the entire city area, indicating increasingly serious traffic jams during the morning rush hour period. The hot spots at 12:00–13:00, coinciding with relatively low travel distances shown in Figure 11, show lower emissions. Then, the distribution decreases untilEnergies the 2018 evening, 11, x FOR PEER rush REVIEW hour period at approximately 19:00, when the color15becomes of 22 darker than before because12:00Energies–13:00, 2018 people , coinciding11, x FOR return PEER with REVIEW from relatively work. low Following travel distances the shown evening in Figure rush hour 11, show period, lower15 of 22 there are no significant changesemissions. until Then, midnight, the distribution when decreases the cycle until starts the evening again. rush The hour detailed period at changes approximately throughout one 19:00,12:00 when–13:00, the coinciding color becomes with darker relatively than lowbefore travel because distances people shownreturn from in Figure work. 11,Following show lowerthe day can be clearlyeveningemissions.seen rush hour Then, in the period, the supplementary distribution there are no decreases significant video. until changes the eveninguntil midnight, rush hour when period the cycle at approximately starts again. By presentingThe19:00, detailed when the changes spatialthe color throughout dynamicbecomes darkerone distribution day than can beforebe clearly because of seen carbon inpeople the emissionssupplementary return from withwork. video. Following time, authorities the can instantaneouslyeveningBy monitor presenting rush hour the the period, distributionspatial there dynamic are no of significantdistribution carbon changes of emissions. carbon until emissions midnight, According with when time, the tocycleauthorities the starts carbon again.can emissions instantaneouslyThe detailed changes monitor throughout the distribution one day of can carbon be clearly emissions. seen in According the supplementary to the carbon video. emissions volume in differentvolumeBy in presenting regions, different regions,the authorities spatial authorities dynamic can candistribution also also classify of carbon the the regional emissions regional emission with emission time, grade. authorities Therefore, grade. can Therefore, correspondingcorrespondinginstantaneously carbon emissions carbon monitor emissions reduction the distribution reduction measures measures of carbon can can emissions. be betaken taken in According different in different toregions. the carbon What’s regions. emissions more, What’s more, the traffic jamthe spotsvolume traffic can in jam differentbe spots identified can regions, be identified soauthorities that sotraffic can that also trafficdepartments classify departments the regional can can take emission take related related grade. evacuation evacuation Therefore, measures. measures.corresponding carbon emissions reduction measures can be taken in different regions. What’s more, the traffic jam spots can be identified so that traffic departments can take related evacuation measures.

06:00 To 07:00

06:00 To 07:00 Figure 13. Kernel density analysis results. Figure 13. Kernel density analysis results. Figure 13. Kernel density analysis results.

00:00 To 01:00 03:00 To 04:00

Figure 14. Cont. Energies 2018, 11, 500 16 of 22

Energies 2018, 11, x FOR PEER REVIEW 16 of 22

06:00 To 07:00 07:00 To 08:00

08:00 To 09:00 09:00 To 10:00

11:00 To 12:00 12:00 To 13:00

Figure 14. Cont.

Energies 2018, 11, 500 17 of 22

Energies 2018, 11, x FOR PEER REVIEW 17 of 22

16:00 To 17:00 17:00 To 18:00

18:00 To 19:00 19:00 To 20:00

22:00 To 23:00 23:00 To 24:00

Figure 14. Dynamic distribution of carbon emissions at different time intervals. Figure 14. Dynamic distribution of carbon emissions at different time intervals.

4.4. Spatial Comparison of Carbon Emissions between Weekdays and Weekends Weekday and weekend rush hour periods also exhibit differences in the spatial distribution of carbon emissions, especially in the morning peak hour period, that are similar to the temporal variations (Figure 15). In general, the weekend exhibits improved carbon emission levels. Emissions in the southwest during the weekend morning peak hour period are substantially lower than on weekdays, especially on the Ring Roads, which signiﬁes later morning rush hour periods on the weekend. In the evening, emissions in the city center are lower on the weekend, but emissions in the eastern 2nd and 3rd Ring Roads are higher because of many people shopping in the CBD on the weekend. Despite these differences, weekdays and weekend do not show any signiﬁcant variability. Energies 2018, 11, x FOR PEER REVIEW 18 of 22

4.4. Spatial Comparison of Carbon Emissions between Weekdays and Weekends Weekday and weekend rush hour periods also exhibit differences in the spatial distribution of carbon emissions, especially in the morning peak hour period, that are similar to the temporal variations (Figure 15). In general, the weekend exhibits improved carbon emission levels. Emissions in the southwest during the weekend morning peak hour period are substantially lower than on weekdays, especially on the Ring Roads, which signifies later morning rush hour periods on the weekend. In the evening, emissions in the city center are lower on the weekend, but emissions in the eastern 2nd and 3rd Ring EnergiesRoads 2018 are , higher11, 500 because of many people shopping in the CBD on the weekend. Despite18 these of 22 differences, weekdays and weekend do not show any significant variability.

07:00 To 08:00 07:00 To 08:00 Sat

08:00 To 09:00 08:00 To 09:00 Sat

Figure 15. Cont.

Energies 2018, 11, 500 19 of 22 Energies 2018, 11, x FOR PEER REVIEW 19 of 22

17:00 To 18:00 17:00 To 18:00 Sat

18:00 To 19:00 18:00 To 19:00 Sat

Figure 15. Spatial comparison of carbon emissions between weekdays and weekends. Figure 15. Spatial comparison of carbon emissions between weekdays and weekends. 5. Conclusions 5. Conclusions This study performed a spatiotemporal analysis of carbon emissions and taxi travel patterns in BeijingThis and study compared performed weekday a spatiotemporal and weekend analysis characteristics of carbon emissions by processing and taxi travel taxi GPSpatterns data, in establishingBeijing and compareda carbon emission weekday calculation and weekend model characteristics to calculate by carbon processing emissions taxi GPS over data, a whole establishing urban networka carbon, emissionand performing calculation their model visualization to calculate based carbon on a kernel emissions density over analysis. a whole urbanWe fully network, employed and theperforming advantage their of visualizationbig data–mining based techniques on a kernel to densitypresent analysis. the dynamic We fully spatiotemporal employed the distribution advantage of of carbonbig data–mining emissions. techniques We also converted to present the the dynamic fuel consumption spatiotemporal and carbon distribution emissions of carbon to CEPK emissions. and FCPOKWe also, convertedrespectively, the all fuel of consumptionwhich are global, and carbonstandard, emissions and intuitive to CEPK indicators, and FCPOK, to verify respectively, the results. all Theof which principal are global, contributions standard, are and presented intuitive below. indicators, to verify the results. The principal contributions are presented below. (1) The network-scale travel patterns of taxi fleets are clearly observed from taxi numbers. (2)(1) TheThe level network-scale of carbon travel emissions patterns over of the taxi whole ﬂeets arenetwork clearly can observed be evaluated from taxi via numbers. CEPK and total (2) carbonThe level emissions. of carbon emissions over the whole network can be evaluated via CEPK and total (3) Althoughcarbon emissions. the average FCPOK we obtained from taxi GPS data is higher than expected, it can (3) allowAlthough policy the makers average to issue FCPOK stricter we obtained regulations from on taxi carbon GPS emission data is higher reduction. than expected, it can allow policy makers to issue stricter regulations on carbon emission reduction.

Energies 2018, 11, 500 20 of 22

(4) The average travel speed by hour can be used to monitor traffic conditions over an entire urban network. The results indicate that higher resolution travel speed data, such as the average travel speed in one minute, can be obtained to instantly monitor traffic conditions, providing that it is feasible for the hardware. The average travel speed of the entire day over the whole network is lower than the official figure, which means that the traffic situation is more serious than previously thought. (5) Overall, carbon emissions and travel patterns show improvements on the weekend, especially during the morning rush hour period, because most people do not commute on the weekend. (6) By employing the visualization method, we creatively present the spatial dynamic distribution of carbon emissions with time. Using this method, authorities can instantaneously monitor the distribution of carbon emissions.

In conclusion, these results can enable taxi companies to promote their business and management as well as supply more favorable services. These findings also allow authorities to enforce appropriate carbon emission reduction policies in order to build a more environmentally friendly society, achieve environmental protection and sustainable development, and improve living conditions, especially those related to air quality. Additionally, this study is most applicable to urban planning, infrastructure design, and the transformation of traffic modes, such as the adoption of public transport or electric vehicles to advocate low-carbon trips. Nonetheless, there are some limitations to this research. For instance, we do not consider the speed of taxis when idling, and taxis involve relatively little carbon emissions among all travels. Furthermore, it is difficult to identify the start and end time of idling using taxi trajectory data. In addition, acceleration and deceleration are not taken into consideration because the positioning time interval of GPS data used in this study is too long to obtain more precise accelerations and decelerations. Some inevitable errors also exist in the trajectories, especially at the corner of the intersections because of positioning errors of the satellite and because the “Track intervals to line” tool in ArcGIS traces the linear distance between two points rather than the actual driving distance. Finally, the carbon emission factor exhibits a slight change with a change of speed, whereas we employ a constant number, thus leading to some errors in the calculation of carbon emissions. Further research should attempt to overcome these limitations.

Supplementary Materials: The following are available online at www.mdpi.com/1996-1073/11/3/500/s1, Video S1: Dynamic distribution of carbon emissions at different time intervals. Acknowledgments: This work was supported by the Beijing Municipal Science and Technology Commission [grant numbers Z171100002217011]. The authors are grateful to anonymous reviewers for their valuable comments and suggestions. Author Contributions: Jinlei Zhang and Feng Chen conceived and designed the study; Jinlei Zhang and Zijia Wang performed the research and analyzed the data; Rui Wang and Zijia Wang contributed analysis tools; Jinlei Zhang wrote the paper; Zijia Wang and Shunwei Shi revised the paper. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. Yang, L.; Kwan, M.; Pan, X.; Wan, B.; Zhou, S. Scalable space-time trajectory cube for path-ﬁnding: A study using big taxi trajectory data. Transp. Res. Part B Methodol. 2017, 101, 1–27. [CrossRef] 2. Zhang, J.; Meng, W.; Liu, Q.Q.; Jiang, H.; Feng, Y.; Wang, G. Efﬁcient vehicles path planning algorithm based on taxi GPS big data. Opt. Int. J. Light Electron Opt. 2016, 127, 2579–2585. [CrossRef] 3. Lin, N. Path Planning Method Based on Taxi Trajectory Data. J. Inf. Comput. Sci. 2015, 12, 3395–3403. [CrossRef] 4. Hu, J.; Huang, Z.; Deng, J.; Xie, H. Hierarchical Path Planning Method Based on Taxi Driver Experiences. J. Transp. Syst. Eng. Inf. Technol. 2013, 13, 185–192. Energies 2018, 11, 500 21 of 22

5. Li, Q.; Zeng, Z.; Zhang, T.; Li, J.; Wu, Z. Path-finding through flexible hierarchical road networks: An experiential approach using taxi trajectory data. Int. J. Appl. Earth Obs. Geoinf. 2011, 13, 110–119. [CrossRef] 6. Yuan, J.; Zheng, Y.; Zhang, C.; Xie, W.; Xie, X.; Sun, G.; Huang, Y. T-drive: Driving directions based on taxi trajectories. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA, 2–5 November 2010; pp. 99–108. 7. Meng, L.; Li, R.T.; Yong, X.; Qin, Z.G. Analysis of Urban Traffic Based on Taxi GPS Data. Lect. Notes Electr. Eng. 2014, 279, 1007–1015. 8. Shan, Z.; Wang, Y.; Zhu, Q. Feasibility study of urban road traffic state estimation based on taxi GPS data. In Proceedings of the 17th IEEE International Conference on Intelligent Transportation Systems, Qingdao, China, 8–11 October 2014; pp. 2188–2193. 9. Zhang, K.; Sun, D.; Shen, S.; Zhu, Y. Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data. J. Transp. Land Use 2017, 10, 675–694. [CrossRef] 10. Kong, X.; Yang, J.; Yang, Z. Measuring Traffic Congestion with Taxi GPS Data and Travel Time Index. In Proceedings of the 15th COTA International Conference of Transportation Professionals, Beijing, China, 24–27 July 2015. 11. Kuang, W.; An, S.; Jiang, H. Detecting Traffic Anomalies in Urban Areas Using Taxi GPS Data. Math. Probl. Eng. 2015, 2015, 809582. [CrossRef] 12. Chen, C.; Zhang, D.; Castro, P.S.; Li, N.; Sun, L.; Li, S. Real-Time Detection of Anomalous Taxi Trajectories from GPS Traces. In Mobile and Ubiquitous Systems: Computing, Networking, and Services; Springer: Berlin/Heidelberg, Germany, 2012. 13. Pang, L.X.; Chawla, S.; Liu, W.; Zheng, Y. On detection of emerging anomalous traffic patterns using GPS data. Data Knowl. Eng. 2013, 87, 357–373. [CrossRef] 14. Hui-Bing, L.I.; Yang, X.G.; Luo, L.H. Mining method of floating car data based on link travel time estimation. J. Traffic Transp. Eng. 2014, 14, 100–109. 15. Jiang, G.; Chang, A.; Li, Q.; Yi, F. Estimation models for average speed of traffic flow based on GPS data of taxi. J. Southwest Jiaotong Univ. 2011, 46, 638–644. 16. Zhang, H.S.; Yi, Z.; Wen, H.M.; Hu, D.C. Estimation approaches of average link travel time using GPS data. J. Jilin Univ. 2007, 37, 533–537. 17. Zhao, P.X.; Qin, K.; Zhou, Q.; Liu, C.K.; Chen, Y.X. Detecting Hotspots from Taxi Trajectory Data Using Spatial Cluster Analysis. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2015, II-4/W2, 131–135. [CrossRef] 18. Chang, H.W.; Tai, Y.C.; Hsu, Y.J. Context-aware taxi demand hotspots prediction. Int. J. Bus. Intell. Data Min. 2010, 5, 3–18. [CrossRef] 19. Lee, J.; Shin, I.; Park, G.L. Analysis of the Passenger Pick-Up Pattern for Taxi Location Recommendation. In Proceedings of the International Conference on Networked Computing and Advanced Information Management, Gyeongju, Korea, 2–4 September 2008; pp. 199–204. 20. Shen, Y.; Zhao, L.; Fan, J. Analysis and Visualization for Hot Spot Based Route Recommendation Using Short-Dated Taxi GPS Traces. Information 2015, 6, 134–151. [CrossRef] 21. Xin, F.U.; Sun, M.; Sun, H. Taxi Commute Recognition and Temporal-spatial Characteristics Analysis Based on GPS Data. China J. Highw. Transp. 2017, 134–143. 22. Yan-Hong, L.I.; Yuan, Z.Z.; Xie, H.H.; Cao, S.H.; Xian-Yu, W.U. Analysis on Trips Characteristics of Taxi in Suzhou Based on OD Data. J. Transp. Syst. Eng. Inf. Technol. 2007, 7, 85–89. 23. Luo, X.; Dong, L.; Dou, Y.; Zhang, N.; Ren, J.; Li, Y.; Sun, L.; Yao, S. Analysis on spatial-temporal features of taxis’ emissions from big data informed travel patterns: A case of Shanghai, China. J. Clean. Prod. 2016, 142, 926–935. [CrossRef] 24. Du, Y.; Wu, J.; Yang, S.; Zhou, L. Predicting vehicle fuel consumption patterns using floating vehicle data. J. Environ. Sci. 2017, 59, 24–29. [CrossRef][PubMed] 25. Weng, J.; Qiao, G.; Rong, J.; Chen, Z.; Liang, Q. Taxi fuel consumption and emissions estimation model based on the reconstruction of driving trajectory. Adv. Mech. Eng. 2017, 9.[CrossRef] 26. Zhao, T. On-Road Fuel Consumption Algorithm Based on Floating Car Data for Light-Duty Vehicles. Master’s Thesis, Beijing Jiaotong University, Beijing, China, 2009. Energies 2018, 11, 500 22 of 22

27. Beijing Traffic Institute. Report of the Fifth Beijing Urban Traffic Comprehensive Survey; Report of Taxi Survey; Beijing Traffic Institute: Beijing, China, July 2017. 28. Jiménez-Palacios, J.L. Understanding and Quantifying Motor Vehicle Emissions with Vehicle Specific Power and TILDAS Remote Sensing. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 1999. 29. Yu, L.; Li, Z.; Qiao, F.; Li, Q. Use Driving Simulator to Synthesize the Related Vehicle Specific Power (VSP) for Emissions and Fuel Consumption Estimations; Journal of the Transportation Research Board: Washington, DC, USA, 31 January 2016. 30. Fukumuro, K.; Ishizaka, T.; Fukuda, A. Analysis on Relationship of Estimated VSP from Coast-down Test and Fuel Consumption. JSTE J. Traffic Eng. 2016, 2, 3235–3239. 31. Pitanuwat, S.; Sripakagorn, A. The application of VSP fuel consumption model on Hybrid and conventional vehicles in Bangkok traffic conditions. In Proceedings of the 35th World Automotive Congress FISITA, Maastricht, The Netherlands, 2–6 June 2014. 32. Song, G. Study on Traffic Fuel Consumptions and Emissions Model for Traffic Strategy Evaluation. Ph.D. Thesis, Beijing Jiaotong University, Beijing, China, 2008. 33. Garg, A.; Pulles, T. 2006 IPCC Guidelines for National Greenhouse Gas Inventories; Intergovernmental Panel on Climate Change: Geneva, Switzerland, 2006; Volume 2. 34. National Bureau of Statistics of China. China Statistical Yearbook 2016; China Statistics Press: Beijing, China, 2016. 35. Beijing Traffic Commission. Report of the Fifth Beijing Urban Traffic Comprehensive Survey; General Report; Beijing Traffic Institute: Beijing, China, July 2017.

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