Abstract: Transportation is the lifeblood of a modern metropolis. Accessibility generally refers to the interconnection between nodes in a regional traffic network. The purpose of the paper is to obtain more realistic and accurate measures of travel speed and to study the road traffic accessibility potential in cities. This study proposes a method for analyzing road traffic accessibility potential which is based on the average travel speed to city centers in off-peak times and which ranks 80 cities around the world. Based on the Suomi National Polar-Orbiting Partnership satellite’s visible-infrared imaging radiometer suite (NPP-VIIRS) night-time light data, urban built-up areas and city centers were extracted. Further, with the aid of the Google Maps application programming interface (API) network crawling technique, travel times and travel distances for several optimal routes to city centers by car were obtained. Feasible proposals for improving road traffic accessibility and planning urban transportation in different cities are presented. The proposed method offers a new possibility of analyzing traffic accessibility using internet data and geo-spatial methods.

到市中心有多快？道路交通通达性潜力的指标--平均行驶速度

[1]AgryzkovT, OliverJL, TortosaL, et al., 2012. An algorithm for ranking the nodes of an urban network based on the concept of PageRank vector. Applied Mathematics and Computation, 219(4):2186-2193.

[2]AgryzkovT, OliverJL, TortosaL, et al., 2014. Analyzing the commercial activities of a street network by ranking their nodes: a case study in Murcia, Spain. International Journal of Geographical Information Science, 28(3):479-495.

[3]AgryzkovT, MartíP, TortosaL, et al., 2017. Measuring urban activities using foursquare data and network analysis: a case study of Murcia (Spain). International Journal of Geographical Information Science, 31(1):100-121.

[4]AtkinsonDM, DeadmanP, DudychaD, et al., 2005. Multi-criteria evaluation and least cost path analysis for an arctic all-weather road. Applied Geography, 25(4):287-307.

[5]BirrK, JamrozK, KustraW, 2014. Travel time of public transport vehicles estimation. Transportation Research Procedia, 3:359-365.

[6]BowenJ, 2000. Airline hubs in Southeast Asia: national economic development and nodal accessibility. Journal of Transport Geography, 8(1):25-41.

[7]ChenJ, NiJH, XiCB, et al., 2017. Determining intra-urban spatial accessibility disparities in multimodal public transport networks. Journal of Transport Geography, 65:123-133.

[8]ChenZQ, YuBL, SongW, et al., 2017. A new approach for detecting urban centers and their spatial structure with nighttime light remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 55(11):6305-6319.

[9]ChengYH, ChenSY, 2015. Perceived accessibility, mobility, and connectivity of public transportation systems. Transportation Research Part A: Policy and Practice, 77:386-403.

[10]CouturierS, RicárdezM, OsornoJ, et al., 2011. Morpho-spatial extraction of urban nuclei in diffusely urbanized metropolitan areas. Landscape and Urban Planning, 101(4):338-348.

[11]DémurgerS, 2001. Infrastructure development and economic growth: an explanation for regional disparities in China? Journal of Comparative Economics, 29(1):95-117.

[12]DewulfB, NeutensT, VanlommelM, et al., 2015. Examining commuting patterns using floating car data and circular statistics: exploring the use of new methods and visualizations to study travel times. Journal of Transport Geography, 48:41-51.

[13]El-GeneidyA, LevinsonD, DiabE, et al., 2016. The cost of equity: assessing transit accessibility and social disparity using total travel cost. Transportation Research Part A: Policy and Practice, 91:302-316.

[14]FarberS, FuLW, 2017. Dynamic public transit accessibility using travel time cubes: comparing the effects of infrastructure (dis)investments over time. Computers, Environment and Urban Systems, 62:30-40.

[15]GaWC (Globalization and World Cities), 2016. The World According to GaWC 2016. Loughborough University, UK. https://www.lboro.ac.uk/microsites/geography/gawc/world2016.html

[16]GeZY, GaoP, 2008. Studies on traffic effects of high-speed ring road in city center. Kybernetes, 37(9-10):1315-1321.

[17]GeroliminisN, SunJ, 2011. Properties of a well-defined macroscopic fundamental diagram for urban traffic. Transportation Research Part B: Methodological, 45(3):605-617.

[18]GeursKT, van WeeB, 2004. Accessibility evaluation of land-use and transport strategies: review and research directions. Journal of Transport Geography, 12(2):127-140.

[19]GutiérrezJ, GonzálezR, GómezG, 1996. The European high-speed train network: predicted effects on accessibility patterns. Journal of Transport Geography, 4(4):227-238.

[20]HansenWG, 1959. How accessibility shapes land use. Journal of the American Institute of Planners, 25(2):73-76.

[21]HielkemaH, HongistoP, 2013. Developing the Helsinki smart city: the role of competitions for open data applications. Journal of the Knowledge Economy, 4(2):190-204.

[22]ImhoffML, LawrenceWT, StutzerDC, et al., 1997. A technique for using composite DMSP/OLS “City Lights” satellite data to map urban area. Remote Sensing of Environment, 61(3):361-370.

[23]IronsJR, DwyerJL, BarsiJA, 2012. The next landsat satellite: the landsat data continuity mission. Remote Sensing of Environment, 122:11-21.

[24]JäppinenS, ToivonenT, SalonenM, 2013. Modelling the potential effect of shared bicycles on public transport travel times in Greater Helsinki: an open data approach. Applied Geography, 43:13-24.

[25]KoopmansC, RietveldP, HuijgA, 2012. An accessibility approach to railways and municipal population growth, 1840-1930. Journal of Transport Geography, 25:98-104.

[26]KotavaaraO, AntikainenH, MarmionM, et al., 2012. Scale in the effect of accessibility on population change: GIS and a statistical approach to road, air and rail accessibility in Finland, 1990-2008. Geographical Journal, 178(4):366-382.

[27]KwanMP, MurrayAT, O’KellyME, et al., 2003. Recent advances in accessibility research: representation, methodology and applications. Journal of Geographical Systems, 5(1):129-138.

[28]LangfordM, HiggsG, RadcliffeJ, et al., 2008. Urban population distribution models and service accessibility estimation. Computers, Environment and Urban Systems, 32(1):66-80.

[29]LevineJ, GarbY, 2002. Congestion pricing’s conditional promise: promotion of accessibility or mobility? Transport Policy, 9(3):179-188.

[30]LuYM, TangJM, 2004. Fractal dimension of a transportation network and its relationship with urban growth: a study of the Dallas-Fort Worth area. Environment and Planning B: Planning and Design, 31(6):895-911.

[31]LuZM, ZhangH, SouthworthF, et al., 2016. Fractal dimensions of metropolitan area road networks and the impacts on the urban built environment. Ecological Indicators, 70:285-296.

[32]LyonsG, UrryJ, 2005. Travel time use in the information age. Transportation Research Part A: Policy and Practice, 39(2-3):257-276.

[33]MaT, ZhouCH, TaoP, et al., 2012. Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: a comparative case study from China’s cities. Remote Sensing of Environment, 124:99-107.

[34]MagruderJR, 2010. Intergenerational networks, unemployment, and persistent inequality in South Africa. American Economic Journal: Applied Economics, 2(1):62-85.

[35]MallinckrodtJ, 2010. VCI, a regional volume/capacity index model of urban congestion. Journal of Transportation Engineering, 136(2):110-119.

[36]MartensK, di CiommoF, 2017. Travel time savings, accessibility gains and equity effects in cost–benefit analysis. Transport Reviews, 37(2):152-169.

[37]MavoaS, WittenK, McCreanorT, et al., 2012. GIS based destination accessibility via public transit and walking in Auckland, New Zealand. Journal of Transport Geography, 20(1):15-22.

[38]MetzD, 2008. The myth of travel time saving. Transport Reviews, 28(3):321-336.

[39]NBSC (National Bureau of Statistics of China), 2016. China City Statistical Yearbook. China Statistics Press, Beijing, China.

[40]NiedzielskiMA, BoschmannEE, 2014. Travel time and distance as relative accessibility in the journey to work. Annals of the Association of American Geographers, 104(6):1156-1182.

[41]NooraCL, AfariEA, NuohRD, et al., 2016. Pedestrians’ adherence to road traffic regulations on the N1 Highway in Accra, Ghana. The Pan African Medical Journal, 25(S1):11.

[42]OdokiJB, KeraliHR, SantoriniF, 2001. An integrated model for quantifying accessibility-benefits in developing countries. Transportation Research Part A: Policy and Practice, 35(7):601-623.

[43]RahmaniM, KoutsopoulosHN, JeneliusE, 2017. Travel time estimation from sparse floating car data with consistent path inference: a fixed point approach. Transportation Research Part C: Emerging Technologies, 85:628-643.

[44]RojasC, PáezA, BarbosaO, et al., 2016. Accessibility to urban green spaces in Chilean cities using adaptive thresholds. Journal of Transport Geography, 57:227-240.

[45]SaghapourT, MoridpourS, ThompsonRG, 2016. Public transport accessibility in metropolitan areas: a new approach incorporating population density. Journal of Transport Geography, 54:273-285.

[46]SalonenM, ToivonenT, 2013. Modelling travel time in urban networks: comparable measures for private car and public transport. Journal of Transport Geography, 31:143-153.

[47]SanaullahI, QuddusM, EnochM, 2016. Developing travel time estimation methods using sparse GPS data. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 20(6):532-544.

[48]SathisanSK, SrinivasanN, 1998. Evaluation of accessibility of urban transportation networks. Transportation Research Record: Journal of the Transportation Research Board, 1617(1):78-83.

[49]ŠenkE, AmbrosJ, 2011. Estimation of accident frequency at newly-built roundabouts in the Czech Republic. Transactions on Transport Sciences, 4(4):199-206.

[50]ShiCY, ChenBY, LiQQ, 2017. Estimation of travel time distributions in urban road networks using low-frequency floating car data. ISPRS International Journal of Geo-Information, 6(8):253.

[51]ShiKF, ChenY, YuBL, et al., 2016. Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data. Applied Energy, 184:450-463.

[52]ShirgaokarM, 2014. Employment centers and travel behavior: exploring the work commute of Mumbai’s rapidly motorizing middle class. Journal of Transport Geography, 41:249-258.

[53]SilvaC, PinhoP, 2010. The structural accessibility layer (SAL): revealing how urban structure constrains travel choice. Environment and Planning A: Economy and Space, 42(11):2735-2752.

[54]SmallC, LuJWT, 2006. Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis. Remote Sensing of Environment, 100(4):441-456.

[55]SpenceN, LinnekerB, 1994. Evolution of the motorway network and changing levels of accessibility in Great Britain. Journal of Transport Geography, 2(4):247-264.

[56]SuQ, 2011. The effect of population density, road network density, and congestion on household gasoline consumption in U.S. urban areas. Energy Economics, 33(3):445-452.

[57]TaylorMC, LynamDA, BaruyaA, 2000. The Effects of Drivers’ Speed on the Frequency of Road Accidents. TRL Report 421, Transport Research Laboratory, Crowthorne, UK.

[58]TaylorPJ, 2001. Specification of the world city network. Geographical Analysis, 33(2):181-194.

[59]WanN, ZouB, SternbergT, 2012. A three-step floating catchment area method for analyzing spatial access to health services. International Journal of Geographical Information Science, 26(6):1073-1089.

[60]WangFH, XuYQ, 2011. Estimating O-D travel time matrix by Google Maps API: implementation, advantages, and implications. Annals of GIS, 17(4):199-209.

[61]WeberJ, 2018. Route change on the American freeway system. Journal of Transport Geography, 67:12-23.

[62]WeissDJ, NelsonA, GibsonHS, et al., 2018. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature, 553(7688):333-336.

[63]YeCD, HuLQ, LiM, 2018. Urban green space accessibility changes in a high-density city: a case study of Macau from 2010 to 2015. Journal of Transport Geography, 66:106-115.

[64]YildirimogluM, GeroliminisN, 2013. Experienced travel time prediction for congested freeways. Transportation Research Part B: Methodological, 53:45-63.

[65]ZhaY, GaoJ, NiS, 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3):583-594.

[66]ZhangJ, LiPJ, WangJF, 2014. Urban built-up area extraction from Landsat TM/ETM+ images using spectral information and multivariate texture. Remote Sensing, 6(8):7339-7359.

[67]ZhangQ, WangJ, PengX, et al., 2002. Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data. International Journal of Remote Sensing, 23(15):3057-3078.

[68]ZhouYY, SmithSJ, ElvidgeCD, et al., 2014. A cluster-based method to map urban area from DMSP/OLS nightlights. Remote Sensing of Environment, 147:173-185.