{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:24:23Z","timestamp":1750220663998,"version":"3.41.0"},"reference-count":52,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T00:00:00Z","timestamp":1597104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Spatial Algorithms Syst."],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>\n            Road networks are major influential factors in the development of any nation. Due to different factors involved in their evolution, road networks exhibit complex structures, which result in problems such as inefficient traffic patterns, congestion, and environmental pollution. The existing metrics of road network analysis are found to be inadequate to reveal the complexity of networks that contributes to such problems. Therefore, we propose a new dimension for the road network analysis using\n            <jats:italic>road-types<\/jats:italic>\n            to understand such problems and analyze 57 cities from eight regions around the world: (i) India, (ii) North America, (iii) South America, (iv) Europe, (v) Asia, (vi) China, (vii) Japan, and (viii) Oceania. We define four new metrics: (i)\n            <jats:italic>Link Type Distribution<\/jats:italic>\n            , (ii)\n            <jats:italic>Link Type Demand<\/jats:italic>\n            , (iii)\n            <jats:italic>Preference Cost<\/jats:italic>\n            , and (iv)\n            <jats:italic>Type Closeness<\/jats:italic>\n            , for measuring the influence of road-types in the cities concerned. Link type distribution computes the share of each road-type in shortest paths while link type demand measures the type distribution considering the users\u2019 preference of road-types. The metric preference cost is defined to measure the additional cost users incur on choosing higher-quality roads and type closeness computes the minimum distance a user needs to travel to reach the preferred road-type. Our analysis shows that, in cities of India, Europe, Asia, China, and Oceania, the primary, secondary, and tertiary roads play a crucial role in the shortest paths in terms of link type distribution as well as link type demand. Also, the involvement of secondary road-type is an important feature among cities from all regions except Japan, where the distribution and demand concentrated in tertiary as well as unclassified types. Cities in North and South American regions provide more importance to residential roads besides the secondary type. In addition to distribution and demand, our analysis shows that the primary, secondary, and tertiary road-types with higher demands suffer from link type deficits while the least demanded types are offered with link type surpluses, which explains one of the reasons for inefficient traffic patterns in congested cities. As far as the additional cost incurred for higher-quality roads is concerned, cities in India and USA provide least preference cost while Chinese and South American cities incur high costs. Among the 57 road networks concerned, Japan shows uniqueness in terms of the proposed metrics due to their inclusion of tertiary and unclassified roads.\n          <\/jats:p>","DOI":"10.1145\/3397579","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T12:35:39Z","timestamp":1594125339000},"page":"1-45","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Toward a Type-based Analysis of Road Networks"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9219-2588","authenticated-orcid":false,"given":"Sarath","family":"Babu","sequence":"first","affiliation":[{"name":"Indian Institute of Space Science and Technology, Valiamala P. O., Thiruvananthapuram District, Kerala, India"}]},{"given":"B. S.","family":"Manoj","sequence":"additional","affiliation":[{"name":"Indian Institute of Space Science and Technology, Valiamala P. O., Thiruvananthapuram District, Kerala, India"}]}],"member":"320","published-online":{"date-parts":[[2020,8,11]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"4924","article-title":"The highway transport problem","volume":"201","year":"1964","journal-title":"Nature"},{"volume-title":"Chandigarh Master Plan","year":"2031","author":"Chandigarh","key":"e_1_2_1_2_1"},{"key":"e_1_2_1_3_1","unstructured":"Sarath Babu Gaurav Jain and B. S. Manoj. 2017. Urban delay tolerant network simulator (UDTNSim v0.1). CoRR abs\/1709.05645 (September 2017). arxiv:1709.05645 http:\/\/arxiv.org\/abs\/1709.05645.  Sarath Babu Gaurav Jain and B. S. Manoj. 2017. Urban delay tolerant network simulator (UDTNSim v0.1). 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