Estimation of a reduction in CO2 emissions by shifting commuters’ travel mode from the private car to public transport

A model for forecasting the amount of CO₂ emissions due to urban commuter travel was developed. The model consisted of three submodels: a commuters’ number forecasting model, a fuzzy commute travel mode choice model and a CO₂ emissions estimation. The model was tested using the real data of Osaka, Japan. Using this model, we also forecasted and analysed the efect of policy changes to shift commuters’ travel mode from private car to public transport in order to decrease the amount of CO₂ emissions.     

Investigating the sensitivity of health benefits to focussed PM2.5 emission abatement strategies

Modelling, pollution monitoring and epidemiological studies all have a role to play in developing effective policies to improve air quality and human health. Epidemiological studies have shown that of particular importance are the effects of fine particulate matter, PM₁₀ and PM_2.5 which can penetrate into human lungs. At present it is not clear which components of PM are responsible for health effects although toxicological studies have identified several potential factors. Hence, based on WHO guidance, current legislation has focused on the total mass, with the EC setting limit values on total PM₁₀, followed by target reductions for population exposure to PM_2.5 in urban agglomerations. Trends in measured concentrations at selected urban monitoring stations are required as evidence for achievement of these reductions. This paper addresses these issues at the borough level in London using the integrated assessment model UKIAM, developed originally for application at the national scale, with illustrations comparing abatement of two contrasting sources – domestic combustion and road transport. The former, dominated by natural gas generating NO_X emissions, contributes to longer range secondary PM formation extending beyond the city. The latter is an important source of black carbon as a primary pollutant causing local exposure, as well as NO_X. WHO data is used in relation to impacts of particle concentrations by mass, and response functions for black carbon are taken from the literature. The results show that from a city perspective there are enhanced benefits from reducing the road transport emissions, especially with regard to potential toxicity of black carbon. The scenarios modelled also highlight the spatial variations of benefits across London, and illustrate deviations from trends as represented by limited monitoring data from the different boroughs, together with the influence upon exposure of mobile population within the city.     

Modelling household travel energy consumption and CO2 emissions based on the spatial form of neighborhoods and streets: A case study of Jinan,China

This paper presents a direct modelling approach to evaluate household travel energy consumption and CO₂ emissions based on the spatial form of neighborhoods and streets. It integrates one multinomial logit model and four double hurdle models to predict travel outcomes: vehicle ownership portfolio choice, car travel distance, bus travel distance, motorcycle travel distance and ebike travel distance. The energy consumption and CO₂ emissions are then estimated using these travel outcomes. A full application of the modelling approach is demonstrated through a pilot project in Jinan, China. A holdout validation is also performed to address the over-fitting problem of models calibrated from the training set data. Results show that the proposed approach is operational and appropriate for sketch planning applications to promote clean energy and low carbon city planning in urbanizing China.     

A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile

The recent emergence of dockless bike sharing systems has resulted in new patterns of urban transport. Users can begin and end trips from their origin and destination locations rather than docking stations. Analysis of changes in the spatiotemporal availability of such bikes has the ability to provide insights into urban dynamics at a finer granularity than is possible through analysis of travel card or dock-based bike scheme data. This study analyses dockless bike sharing in Nanchang, China over a period when a new metro line came into operation. It uses spatial statistics and graph-based approaches to quantify changes in travel behaviours and generates previously unobtainable insights about urban flow structures. Geostatistical analyses support understanding of large-scale changes in spatiotemporal travel behaviours and graph-based approaches allow changes in local travel flows between individual locations to be quantified and characterized. The results show how the new metro service boosted nearby bike demand, but with considerable spatial variation, and changed the spatiotemporal patterns of bike travel behaviour. The analysis also quantifies the evolution of travel flow structures, indicating the resilience of dockless bike schemes and their ability to adapt to changes in travel behaviours. More widely, this study demonstrates how an enhanced understanding of urban dynamics over the “last-mile” is supported by the analyses of dockless bike data. These allow changes in local spatiotemporal interdependencies between different transport systems to be evaluated, and support spatially detailed urban and transport planning. A number of areas of further work are identified to better to understand interdependencies between different transit system components.     

Urban spatial structure and travel patterns: Analysis of workday and holiday travel using inhomogeneous Poisson point process models

City land-use features and travel behavior are mutually related and restricted. This research attempts to model the spatial-temporal travel patterns based on spatial point pattern theory. Using a twelve-day private automobile data set collected in Beijing, we systematically investigate the temporal variations of trip-destination distributions, and their association with city spatial structure. The availability of detailed POIs (Points of Interest) data enables us to study the effect of city structure on travel pattern at a refined level. Four types of inhomogeneous Poisson point process models are built to capture the impacts on human mobility posed by spatial covariates. Residual analysis, inhomogeneous K function and leverage diagnostic tools are further adopted to validate the model performance and determine the best fitted model. The validation results indicate that the proposed model reasonably explains the travel patterns in both holiday and workday throughout the city. The inclusion of Cartesian coordinates, population distribution, and city subdivision-category improves the model performance. The empirical results based on the dataset also reveal the differences in impacts on travel patterns posed by underlying city structure between holidays and weekdays as well as between citywide districts. The modeling method and the exploratory spatial–temporal analysis in this study can offer complementary techniques for traffic management and urban planning.     

Individuals among commuters: Building personalised transport information services from fare collection systems

This work investigates how data from public transport fare collection systems can be used to analyse travellers’ behaviour, and transform travel information systems that urban residents use to navigate their city into personalised and dynamic systems that cater for each passenger’s unique needs. In particular, we show how fare collection data can be used to identify behavioural differences between passengers: we thus advocate for a personalised approach to delivering transport related information to travellers. To demonstrate the potential for personalisation we compute trip time estimates that more accurately reflect the travel habits of each passenger. We propose a number of algorithms for personalised trip time estimations, and empirically demonstrate that these approaches outperform both a non-personalised baseline computed from the data, as well as published travel times as currently offered by the transport authority. Furthermore, we show how to easily scale the system by pre-clustering travellers. We close by outlining the wide variety of applications and services that may be fuelled by fare collection data.     

Modelling the Spatio-Temporal Distribution of Ambient Nitrogen Dioxide and Investigating the Effects of Public Transit Policies on Population Exposure

Estimating the future state of air quality associated with transport policies and infrastructure investments is key to the development of meaningful transportation and planning decisions. This paper describes the design of an integrated transportation and air quality modelling framework capable of simulating traffic emissions and air pollution at a refined spatio-temporal scale. For this purpose, emissions of Nitrogen Oxides (NO_x) were estimated in the Greater Montreal Region at the level of individual trips and vehicles. In turn, hourly Nitrogen Dioxide (NO₂) concentrations were simulated across different seasons and validated against observations. Our validation results reveal a reasonable performance of the modelling chain. The modelling system was used to evaluate the impact of an extensive regional transit improvement strategy revealing reductions in NO₂ concentrations across the territory by about 3.6% compared to the base case in addition to a decrease in the frequency and severity of NO₂ hot spots. This is associated with a reduction in total NO_x emissions of 1.9% compared to the base case; some roads experienced reductions by more than half. Finally, a methodology for assessing individuals’ daily exposure is developed (by tracking activity locations and trajectories) and we observed a reduction of 20.8% in daily exposures compared to the base case. The large difference between reductions in the mean NO₂ concentration across the study domain and the mean NO₂ exposure across the sample population results from the fact that NO₂ concentrations dropped largely in the areas which attract the most individuals. This exercise illustrates that evaluating the air quality impacts of transportation scenarios by solely quantifying reductions in air pollution concentrations across the study domain would lead to an underestimation of the potential health gains.     

Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach

Air pollution imposes significant environmental and health risks worldwide and is expected to deteriorate in the coming decade as cities expand. Measuring population exposure to air pollution is crucial to quantifying risks to public health. In this work, we introduce a big data analytics framework to model residents' stay and commuters' travel exposure to outdoor PM_2.5 and evaluate their environmental justice, with Beijing as an example. Using mobile phone and census data, we first infer travel demand of the population to derive residents' stay activities in each analysis zone, and then focus on commuters and estimate their travel routes with a traffic assignment model. Based on air quality observations from monitoring stations and a spatial interpolation model, we estimate the outdoor PM_2.5 concentrations at a 500-m grid level and map them to road networks. We then estimate the travel exposure for each road segment by multiplying the PM_2.5 concentration and travel time spent on the road. By combining the estimated PM_2.5 exposure and housing price harnessed from online housing transaction platforms, we discover that in the winter, Beijing commuters with low wealth level are exposed to 13% more PM_2.5 per hour than those with high wealth level when staying at home, but exposed to less PM_2.5 by 5% when commuting the same distance (due to lighter traffic congestion in suburban areas). We also find that the residents from the southern suburbs of Beijing have both lower level of wealth and higher stay- and travel- exposure to PM_2.5, especially in the winter. These findings inform more equitable environmental mitigation policies for future sustainable development in Beijing. Finally, or the first time in the literature, we compare the results of exposure estimated from passive data with subjective measures of perceived air quality (PAQ) from a survey. The PAQ data was collected via a mobile-app. The comparison confirms consistencies in results and the advantages of the big data for air pollution exposure assessments.     

Estimating quality of life dimensions from urban spatial pattern metrics

The spatial structure of urban areas plays a major role in the daily life of dwellers. The current policy framework to ensure the quality of life of inhabitants leaving no one behind, leads decision-makers to seek better-informed choices for the sustainable planning of urban areas. Thus, a better understanding between the spatial structure of cities and their socio-economic level is of crucial relevance. Accordingly, the purpose of this paper is to quantify this two-way relationship. Therefore, we measured spatial patterns of 31 cities in North Rhine-Westphalia, Germany. We rely on spatial pattern metrics derived from a Local Climate Zone classification obtained by fusing remote sensing and open GIS data with a machine learning approach. Based upon the data, we quantified the relationship between spatial pattern metrics and socio-economic variables related to ‘education’, ‘health’, ‘living conditions’, ‘labor’, and ‘transport’ by means of multiple linear regression models, explaining the variability of the socio-economic variables from 43% up to 82%. Additionally, we grouped cities according to their level of ‘quality of life’ using the socio-economic variables, and found that the spatial pattern of low-dense built-up types was different among socio-economic groups. The proposed methodology described in this paper is transferable to other datasets, levels, and regions. This is of great potential, due to the growing availability of open statistical and satellite data and derived products. Moreover, we discuss the limitations and needed considerations when conducting such studies.     

Agent-based modeling to estimate exposures to urban air pollution from transportation: Exposure disparities and impacts of high-resolution data

Better understanding of the complex links between urban transportation, land use, air quality, and population exposure is needed to improve urban sustainability. A goal of this study was to develop an exposure modeling framework that integrates agent-based activity and travel simulation with air pollution modeling for Tampa, Florida. We aimed to characterize exposure and exposure inequality for traffic-related air pollution, and to investigate the impacts of high-resolution information on estimated exposure. To do these, we developed and applied a modeling framework that combines the DaySim activity-based travel demand model, the MATSim dynamic traffic assignment model, the MOVES mobile source emissions estimator, and the R-LINE dispersion model. Resulting spatiotemporal distributions of daily individual human activity and pollutant concentration were matched to analyze population and subgroup exposure to oxides of nitrogen (NO_x) from passenger car travel for an average winter day in 2010. Four scenarios using data with different spatiotemporal resolutions were considered: a) high resolution for both activities and concentrations, b) low resolution for both activities and concentrations, c) high resolution for activities, but low resolution for concentrations, and d) vice versa. For the high-resolution scenario, the mean daily population exposure concentration of NO_x from passenger cars was 10.2 μg/m³; individual exposure concentrations ranged from 0.2 to 145 μg/m³. Subgroup mean exposure was higher than the population mean for individuals living below-poverty (by ~16%), those with daily travel time over one hour (8%), adults aged 19–45 (7%), blacks (6%), Hispanics (4%), Asians (2%), combined other non-white races (2%), people from middle income households (2%), and residents of urban areas (2%). The subgroup inequality index (a measure of disparity) largely increased with concentration up to the 90th percentile level for these groups. At higher levels, disparities increased sharply for individuals from below poverty households, blacks, and Hispanics. Low-resolution simulation of both activities and concentrations decreased the exposure estimates by 10% on average, with differences ranging from eight times higher to ~90% lower.     

Identifying and mapping local contributions of carbon emissions from urban motor and metro transports: A weighted multiproxy allocating approach

Regional allocation of transport carbon emissions is increasingly important to meet global or national CO₂ reduction targets. Limited by scaling and data uncertainties, geographical allocation of carbon-emitting responsibilities or burdens at local or fine scales has not been well documented. In this regard, after estimating total carbon emissions from urban motor and metro transports, we proposed a multiproxy allocation system that included a series of transport-related demand and supply indicators. On the basis of urban high-resolution data, magnitudes of gridded proxies in fine scales were aggregated into each local administrative region (subdistrict and town) by a bottom-up approach. Then, weights of these indicators were calculated through an integration of Grey Relational Analysis with Fuzzy Logic. Finally, using the practical scenario of Wuhan (China), we allocated total carbon-emitting quantities from the Wuhan metropolis down to local units by using a top-down approach. Local carbon-emitting contributions and their variations were further identified and mapped from total, per capita, and per unit perspectives. We have not only shown this allocative approach to be effective and applicable, but have also depicted spatially similar patterns and evolutions under the three carbon-emitting indicators. These depictions include local inequality and polarization, core-peripheral structure, place-dependence on initial location, and spatial locking-in effect and diffusive trends along metro lines. Additionally, spatial differences between the per capita carbon emission and the others are revealed as well.     

A General Equilibrium Framework for Integrated Assessment of Transport and Economic Impacts

The use of separate transport and economic models in urban planning provides a limited view of economic impacts, restricts the testing of network design options and lengthens the planning process. Furthermore, the standard methodology for economic appraisal assumes partial economic equilibrium and cannot determine the distribution of impacts from the transport sector to households. Computable general equilibrium (CGE) models can capture general equilibrium effects and measure welfare at the household level, but mostly lack integration with transport models and do not represent all trip generators. This paper develops an integrated traffic assignment and spatial CGE model in nonlinear complementarity form, casted as a framework for economic appraisal of urban transport projects. The CGE submodel generates commuting, shopping and leisure trips as inputs into the transport submodel, which then assigns trips to the network according to user equilibrium. The resulting travel times then feed back into household prices and freight margins. Households and firms fully account for travel times in decisions on where to shop, how much labour to supply and where to source production inputs. Calibration and applications of the model are demonstrated for 14 regions and 2 industries across Sydney using GAMS/PATH on the NEOS server. The welfare of various network improvements is measured using equivalent variations. The model can be calibrated to external strategic transport models, and be extended to simulate additional trip generators and land-use.     

Background,Road and Urban Transport modelling of Air quality Limit values (The BRUTAL model)

Non-technical pollution abatement measures have become increasingly significant in order to meet current policy targets for meeting both commitments of the Gothenburg Protocol and EU air quality targets; the importance of capturing what amount to behavioural changes in integrated assessment modelling has been addressed at a number of UN/ECE workshops. The work presented here responds to these needs and provides the basis for linking air quality issues at the local level with the policy requirements to comply with international agreements on transboundary air pollution and national emissions ceilings. We describe a high resolution module for the UK Integrated Assessment Model which provides a mechanism for introducing structural and behavioural changes in the transport sector into integrated assessment modelling, source-apportionment of local, national and transboundary contributions to air quality, and captures the roadside concentrations of air pollutants in urban street canyons. Focussing upon urban air quality (NO₂ and PM₁₀), and both technical and non-technical abatement (changes in behaviour and activity levels), we find that policy measures are required at multiple scales in order to achieve improvements on air quality at the local scale. Contrasting scenarios illustrate that it may be easier to control levels of NO₂ than PM₁₀ by using local measures, since PM₁₀ concentrations are dominated by secondary particles and background components. Combining technical and behavioural measures is more likely to be effective in simultaneously achieving reductions in emissions, concentrations and health effects. However, there is a need to assess the cost effectiveness of local abatement measures in relation to the cost effectiveness of national and transboundary measures affecting the long-range components.     

Handling Disaggregate Spatiotemporal Travel Data in GIS

Disaggregate travel data is not new to urban transportation planning studies, but it is infrequently handled in a GIS environment. With the evolution of urban travel demand models from aggregate models to disaggregate models and from a trip-based paradigm to an activity-based paradigm, there is a growing need of managing disaggregate travel data with spatial and temporal components in a GIS environment. At the data organization level, the main challenge is to efficiently store the data by minimizing redundancy while maintaining the complex relationships among the data items. The data organization should allow users to retrieve and visualize disaggregate travel data according to various possible combinations of spatial, temporal, and attribute criteria. This paper presents an implementation that employs a relational database approach and dynamic segmentation to organize the spatial, temporal, and attribute components in a sample travel diary data set. Discussions of the benefits and shortcomings associated with this approach are provided, along with suggestions for future research.     

Study of the impact of wildfire emissions,through MOPITT total CO column,at different spatial scales

The estimation of total carbon monoxide (CO) column has been identified as essential to improve our understanding of its role in the global climate system. The Earth Observing System (EOS) Science Steering Committee and the World Meteorological Organization (WMO) has suggested that a satellite-borne CO sensor, which would operate for extended periods, would be useful for that task. Measurements of Pollution in the Troposphere (MOPITT), on board the Terra spacecraft, is a correlation radiometer for estimating CO vertical profiles and total CO column in the lower atmosphere, through the thermal radiance received in the 4.7 μm spectral region. One of the main sources of CO in the atmosphere is the fires and global biomass-burning emissions that are produced when combustion is not complete, especially in the smouldering phase. This article presents a methodology based on a Fourier technique and spatial analysis in order to estimate the total CO column contribution of wildfires at three different spatial scales. First, in a seasonal study, a Mediterranean country (Spain) is selected, and the main regions affected by fire during four years in the summer season are analysed. Second, in order to estimate CO emissions at a local scale, a large fire (in Spain) and a cluster of fires (in North China) are selected. Third, for a global study at large scale and for comparing with CO and carbon dioxide (CO₂) data from Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), locations in North China, equatorial Africa, and Amazonia are selected. Results obtained show that MOPITT data are suitable to assess and to discriminate CO emissions at local spatial scales. Finally, a qualitative agreement between CO behaviour obtained by MOPITT and CO and CO₂ obtained by SCIAMACHY is found.     

Healthier routes planning: A new method and online implementation for minimizing air pollution exposure risk

Air pollution exposure during daily traveling is growing as an increasingly serious factor affecting public health with rapid incensement of travel distance in urban sprawl. Finding a healthier route with least exposure risk might be an alternative way to mitigate adverse health outcomes under the truth that worldwide air pollution in urban area cannot be eliminated within a short period of time. Integrating techniques of fine scale mapping of air pollutant concentration, risk weight estimation of road segment exposure to air pollutants, and dynamic Dijkstra algorithm capable of updating route, this study for the first time proposes a healthier route planning (HRP) method to minimize personal travel exposure risk to air pollution. Effectiveness of HRP in mitigating exposure risk was systematically tested based on hundred pairs of origins and destinations located in Beijing-Tianjin-Hebei (BTH) of China with necessarily dense air quality observations. Results show that the spatiotemporal variations of air pollutant concentrations were significant and these differences indeed occurred with time at hourly scale. Meanwhile, the grid-based estimation of exposure risk is time dependent with risk ranging from 5 to 109, which echoes the necessity of healthier route planning. Compared to routes with the shortest distance and least travel time, healthier route has the least exposure risk. And this risk mitigation effect is more significant in areas with wide exposure risk variations than those in areas without obvious risk difference over space (e.g., 21.38% vs. 0.86%). Results suggest that HRP method is promising to minimize personal exposure risk during daily travel based on the accurate exposure risk estimation of road segment at high spatiotemporal resolution. This role could be more important in areas with longer travel distance and greater heterogeneous distribution of air pollution in great metropolis.     

Intelligent bus routing with heterogeneous human mobility patterns

Optimal planning for public transportation is one of the keys helping to bring a sustainable development and a better quality of life in urban areas. Compared to private transportation, public transportation uses road space more efficiently and produces fewer accidents and emissions. However, in many cities people prefer to take private transportation other than public transportation due to the inconvenience of public transportation services. In this paper, we focus on the identification and optimization of flawed region pairs with problematic bus routing to improve utilization efficiency of public transportation services, according to people’s real demand for public transportation. To this end, we first provide an integrated mobility pattern analysis between the location traces of taxicabs and the mobility records in bus transactions. Based on the mobility patterns, we propose a localized transportation mode choice model, with which we can dynamically predict the bus travel demand for different bus routing by taking into account both bus and taxi travel demands. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. We also leverage the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real-world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips.     

Integration of geostationary FRP and polar-orbiter burned area datasets for an enhanced biomass burning inventory

Earth Observation (EO) sensors play an important role in quantifying biomass burning related fuel consumption and carbon emissions, and capturing their spatial and temporal dynamics. Typically, biomass burning emissions inventories are developed by exploiting either burned area (BA) or active fire (AF) measures of fire radiative energy (FRE). These approaches have both advantages and limitations. For example, methods based on burned area data typically require hard-to-obtain estimates of fuel load and combustion completeness, and the accuracy of the BA algorithm may deteriorate for small fires or those in densely forested terrain. Conversely, ‘raw’ FRE-based methods are typically low-biassed due to the non-detection of low intensity fires, and are also hindered by cloud cover. Here we develop a methodology integrating these two EO data types to deliver a high temporal resolution emissions inventory, maximising the benefit of each data type without requiring additional information. We focus on Africa, the most fire affected continent, and combine daily FRE observations provided by Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) with BA measures delivered by Moderate Resolution Imaging Spectroradiometer (MODIS). For individual fires detected by both types of data, we estimate fuel consumption per unit area (FC_A: g·m^− 2) via the ratio of FRE-derived total fuel consumption (FC_T) to BA. These values are then extrapolated to fires that were mapped using the BA data but which remained undetected in the SEVIRI AF product, thus correcting for the ‘low spatial resolution bias’ inherent in geostationary AF datasets. Calculated daily continental scale FC_T for Africa varies between 0.3 and 20 Tg for the period February 2004-January 2005. We estimate annual continental FC_T to be 1418 Tg, far closer to the 2272 Tg provided by the widely used Global Fire Emissions Database (version 3; GFEDv3) than is obtained when using ‘raw’ FRE data alone. This synergistic approach has substantially narrowed the gap between GFEDv3 and FRE-derived emissions inventories, whilst the geostationary FRP observations offer the advantage that the daily emissions estimates can be distributed more accurately over the diurnal fire cycle if required for linking to atmospheric transport models.     

Determining the Impact of Personal Mobility Carbon Allowance Schemes in Transportation Networks

Personal mobility carbon allowance (PMCA) schemes are designed to reduce carbon consumption from transportation networks. PMCA schemes influence the travel decision process of users and accordingly impact the system metrics including travel time and greenhouse gas (GHG) emissions. We develop a multi-user class dynamic user equilibrium model to evaluate the transportation system performance when PMCA scheme is implemented. The results using Sioux-Falls test network indicate that PMCA schemes can achieve the emissions reduction goals for transportation networks. Further, users characterized by high value of travel time are found to be less sensitive to carbon budget in the context of work trips. Results also show that PMCA scheme can lead to higher emissions for a path compared with the case without PMCA because of flow redistribution. The developed network equilibrium model allows to examine the change in system states at different carbon allocation levels and to design parameters of PMCA schemes accounting for population heterogeneity.     

基于出租车GPS轨迹的城市区域时空交互特征分析

城市大数据为探索城市内部居民出行的行为特征提供数据支撑。本文将以兰州市出租车GPS轨迹数据为基础,结合数据挖掘和可视化技术,研究兰州市城市居民出行规律和城市空间交互特征。首先,分析4个城区居民出行特征和城区间空间交互特征;然后,采用城市栅格方法,统计分析城市栅格空间之间的交通出行量,并采用CLARA聚类算法识别工作日和周末的城市交通热点区域;最后,建立有向加权复杂网络模型,分析城市交通热点区域之间的空间交互强度。研究结果表明,在工作日和周末兰州市居民出行行为时空特征和城市空间交互特征都存在明显差异,相比于周末,工作日出行更加紧凑密集且具有较强目的性,出行量的聚类结构总体呈现与兰州市河谷型地形相匹配的“哑铃”状分布形状,接近城市中心的相邻聚类区域之间空间交互强度较强。该研究结果可为城市交通管理和居民出行提供决策服务。