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{"title"=>"Local optimization strategies in urban vehicular mobility", "type"=>"journal", "authors"=>[{"first_name"=>"Pierpaolo", "last_name"=>"Mastroianni", "scopus_author_id"=>"57096263800"}, {"first_name"=>"Bernardo", "last_name"=>"Monechi", "scopus_author_id"=>"36961183500"}, {"first_name"=>"Carlo", "last_name"=>"Liberto", "scopus_author_id"=>"36561979900"}, {"first_name"=>"Gaetano", "last_name"=>"Valenti", "scopus_author_id"=>"26031836300"}, {"first_name"=>"Vito D.P.", "last_name"=>"Servedio", "scopus_author_id"=>"6603522133"}, {"first_name"=>"Vittorio", "last_name"=>"Loreto", "scopus_author_id"=>"7004031964"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "sgr"=>"84957110181", "doi"=>"10.1371/journal.pone.0143799", "scopus"=>"2-s2.0-84957110181", "pmid"=>"26656106", "pui"=>"607918622"}, "id"=>"d81f93f0-5d5d-39f3-a242-24fabce6104a", "abstract"=>"The comprehension of vehicular traffic in urban environments is crucial to achieve a good management of the complex processes arising from people collective motion. Even allowing for the great complexity of human beings, human behavior turns out to be subject to strong constraints-physical, environmental, social, economic-that induce the emergence of common patterns. The observation and understanding of those patterns is key to setup effective strategies to optimize the quality of life in cities while not frustrating the natural need for mobility. In this paper we focus on vehicular mobility with the aim to reveal the underlying patterns and uncover the human strategies determining them. To this end we analyze a large dataset of GPS vehicles tracks collected in the Rome (Italy) district during a month. We demonstrate the existence of a local optimization of travel times that vehicle drivers perform while choosing their journey. This finding is mirrored by two additional important facts, i.e., the observation that the average vehicle velocity increases by increasing the travel length and the emergence of a universal scaling law for the distribution of travel times at fixed traveled length. A simple modeling scheme confirms this scenario opening the way to further predictions.", "link"=>"http://www.mendeley.com/research/local-optimization-strategies-urban-vehicular-mobility-1", "reader_count"=>6, "reader_count_by_academic_status"=>{"Researcher"=>2, "Other"=>2, "Student > Master"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_user_role"=>{"Researcher"=>2, "Other"=>2, "Student > Master"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>1, "Environmental Science"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>1, "Business, Management and Accounting"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>1}}, "group_count"=>1}

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• http://doi.org/10.1098/rsos.190979
• http://doi.org/10.1038/s41598-020-60744-6

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• {"url"=>"http%3A%2F%2Fjournals.plos.org%2Fplosone%2Farticle%3Fid%3D10.1371%252Fjournal.pone.0143799", "share_count"=>4, "like_count"=>22, "comment_count"=>9, "click_count"=>0, "total_count"=>35}

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• {"id"=>"678937062658211840", "text"=>"#PLOSONE: Local Optimization Strategies in Urban Vehicular Mobility https://t.co/BcRP7LdK4c", "created_at"=>"2015-12-21T13:56:24Z", "user"=>"vitelot", "user_name"=>"Vito Servedio", "user_profile_image"=>"http://pbs.twimg.com/profile_images/2041017522/image_normal.jpg"}
• {"id"=>"678962098832056320", "text"=>"#PLOSONE: Local Optimization Strategies in Urban Vehicular Mobility https://t.co/C3rqYZ0dnx", "created_at"=>"2015-12-21T15:35:53Z", "user"=>"BernardoMonechi", "user_name"=>"Bernardo Monechi", "user_profile_image"=>"http://pbs.twimg.com/profile_images/2736636524/b1f8e0905fd13dc51a0347f5536e62af_normal.jpeg"}
• {"id"=>"680035721617510400", "text"=>"#PLOSONE: Local Optimization Strategies in Urban Vehicular Mobility https://t.co/xFJwv2fHJL", "created_at"=>"2015-12-24T14:42:05Z", "user"=>"vincentgauthier", "user_name"=>"Vincent Gauthier", "user_profile_image"=>"http://pbs.twimg.com/profile_images/378800000012700136/0320d48c0336c4ae0fe4c2fa8d6ff884_normal.jpeg"}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/2608150"], "description"=>"<p>(Panel a) Dependence of 〈log <i>t</i>〉 on the travel length <i>l</i> (red points). The solid blue line is the best fit logarithmic function (<math><mrow><msubsup><mi>χ</mi>red<mn>2</mn></msubsup><mo>≃</mo><mn>2</mn><mo>.</mo><mn>1</mn></mrow></math>). (Panel b) Dependence of <i>σ</i>_<i>l</i> as a function of <i>l</i>. Dots corresponds to paths sampled by using (blue) or not using (red) intermediate optimization steps between the origin and destination nodes of each path. (Panel c) Dependence of 〈log<i>t</i>〉 upon travel length <i>l</i> (blue points) with paths sampled by picking intermediate stops between the origin and the destination nodes. The solid blue line is the best fit logarithmic function. Both optimization processes yield the correct dependence of <i>μ</i>_<i>l</i> on <i>l</i>, but the local piecewise optimization also reproduces the steady behavior of <i>σ</i>_<i>l</i>.</p>", "links"=>[], "tags"=>["travel times", "emergence", "Local Optimization Strategies", "pattern", "mobility", "length", "strategy", "Urban Vehicular Mobility", "gps", "vehicle velocity increases"], "article_id"=>1624416, "categories"=>["Uncategorised"], "users"=>["Pierpaolo Mastroianni", "Bernardo Monechi", "Carlo Liberto", "Gaetano Valenti", "Vito D. P. Servedio", "Vittorio Loreto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143799.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Global_Optimization_vs_Local_Optimization_in_the_grid_model_/1624416", "title"=>"Global Optimization vs Local Optimization in the grid model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-17 08:09:57"}
• {"files"=>["https://ndownloader.figshare.com/files/2608155"], "description"=>"<p>(Panel a) Grid model used in the simulations. The black segments stand for ordinary roads of unit length and unit allowed velocity, while the green segments represent high speed connections. Suppose a car has to drive from point A to point B. It may proceed straight on covering five ordinary roads (<i>l</i> = 5), or make use of the shortcut by traveling three ordinary and one fast road (<math><mrow><mi>l</mi><mo>=</mo><mn>3</mn><mo>+</mo><msqrt><mn>10</mn></msqrt></mrow></math>). In the latter case, depicted in red, the traveled distance is longer but if the allowed velocity on the fast road is sufficiently large, the car would benefit a reduction of the traveled time and choose it. (Panel b) Sketch of the stepwise algorithm used to connect the starting point <i>A</i> to the final point <i>B</i>. The yellow sectors represent the areas in which the driver can choose his next step. The fast link just below point <i>B</i> would be convenient to travel, but it falls outside the yellow sectors and therefore is out of reach.</p>", "links"=>[], "tags"=>["travel times", "emergence", "Local Optimization Strategies", "pattern", "mobility", "length", "strategy", "Urban Vehicular Mobility", "gps", "vehicle velocity increases"], "article_id"=>1624421, "categories"=>["Uncategorised"], "users"=>["Pierpaolo Mastroianni", "Bernardo Monechi", "Carlo Liberto", "Gaetano Valenti", "Vito D. P. Servedio", "Vittorio Loreto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143799.g005", "stats"=>{"downloads"=>2, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Grid_model_/1624421", "title"=>"Grid model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-17 08:10:01"}
• {"files"=>["https://ndownloader.figshare.com/files/2608158"], "description"=>"<p>(Panel a) <i>p</i>(<i>t</i>|<i>l</i>) distribution for some values of path length <i>l</i>. Solid lines refers to the fitted log-normal distributions. (Panel b) Distribution of the scaled variable calculated at different values of <i>l</i>. The solid black line is the curve in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143799#pone.0143799.e006\" target=\"_blank\">Eq (2)</a>. The Kolmogorov-Smirnov test rejects the hypothesis of the universal curve being a log-normal distribution. All the paths are sampled using intermediate steps on a 100 × 100 grid with <i>N</i>_shortcut = 100 and a fixed speed on each shortcut <i>v</i> = 3.0.</p>", "links"=>[], "tags"=>["travel times", "emergence", "Local Optimization Strategies", "pattern", "mobility", "length", "strategy", "Urban Vehicular Mobility", "gps", "vehicle velocity increases"], "article_id"=>1624424, "categories"=>["Uncategorised"], "users"=>["Pierpaolo Mastroianni", "Bernardo Monechi", "Carlo Liberto", "Gaetano Valenti", "Vito D. P. Servedio", "Vittorio Loreto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143799.g006", "stats"=>{"downloads"=>1, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scaling_collapse_in_the_grid_model_/1624424", "title"=>"Scaling collapse in the grid model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-17 08:10:02"}
• {"files"=>["https://ndownloader.figshare.com/files/2608160"], "description"=>"<p>(Panel a) Dependence of the mean value of the logarithm of travel time <i>t</i> upon travel length <i>l</i>. These points were obtained using the stepwise optimization algorithm. Solid black line is the best fit with a logarithmic function (<math><mrow><msubsup><mi>χ</mi>red<mn>2</mn></msubsup><mo>≃</mo><mn>3</mn><mo>.</mo><mn>2</mn></mrow></math>). (Panel b) Standard deviation <i>σ</i>_<i>l</i> of 〈log<i>t</i>〉. The paths of these figures are sampled using the navigation algorithm with intermediate steps (red points) and using the shortest path (global optimization) from the origin to the destination (blue points). (Panel c) Distributions <i>p</i>(<i>t</i>|<i>l</i>) for some selected values of <i>l</i>. Continuous lines are log-normal approximations of the distributions. (Panel d) Collapse of the same distribution using the scaling property derived in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143799#pone.0143799.e006\" target=\"_blank\">Eq (2)</a>. The black curve is the distribution of the scaled variable <i>τ</i> = <i>t</i>/〈<i>t</i>〉.</p>", "links"=>[], "tags"=>["travel times", "emergence", "Local Optimization Strategies", "pattern", "mobility", "length", "strategy", "Urban Vehicular Mobility", "gps", "vehicle velocity increases"], "article_id"=>1624426, "categories"=>["Uncategorised"], "users"=>["Pierpaolo Mastroianni", "Bernardo Monechi", "Carlo Liberto", "Gaetano Valenti", "Vito D. P. Servedio", "Vittorio Loreto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143799.g007", "stats"=>{"downloads"=>2, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Path_Optimization_Algorithms_on_the_Rome_Urban_Network_/1624426", "title"=>"Path Optimization Algorithms on the Rome Urban Network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-17 08:09:59"}
• {"files"=>["https://ndownloader.figshare.com/files/2608181", "https://ndownloader.figshare.com/files/2608182"], "description"=>"<div><p>The comprehension of vehicular traffic in urban environments is crucial to achieve a good management of the complex processes arising from people collective motion. Even allowing for the great complexity of human beings, human behavior turns out to be subject to strong constraints—physical, environmental, social, economic—that induce the emergence of common patterns. The observation and understanding of those patterns is key to setup effective strategies to optimize the quality of life in cities while not frustrating the natural need for mobility. In this paper we focus on vehicular mobility with the aim to reveal the underlying patterns and uncover the human strategies determining them. To this end we analyze a large dataset of GPS vehicles tracks collected in the Rome (Italy) district during a month. We demonstrate the existence of a local optimization of travel times that vehicle drivers perform while choosing their journey. This finding is mirrored by two additional important facts, i.e., the observation that the average vehicle velocity increases by increasing the travel length and the emergence of a universal scaling law for the distribution of travel times at fixed traveled length. A simple modeling scheme confirms this scenario opening the way to further predictions.</p></div>", "links"=>[], "tags"=>["travel times", "emergence", "Local Optimization Strategies", "pattern", "mobility", "length", "strategy", "Urban Vehicular Mobility", "gps", "vehicle velocity increases"], "article_id"=>1624434, "categories"=>["Uncategorised"], "users"=>["Pierpaolo Mastroianni", "Bernardo Monechi", "Carlo Liberto", "Gaetano Valenti", "Vito D. P. Servedio", "Vittorio Loreto"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0143799.s001", "https://dx.doi.org/10.1371/journal.pone.0143799.s002"], "stats"=>{"downloads"=>2, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Local_Optimization_Strategies_in_Urban_Vehicular_Mobility_/1624434", "title"=>"Local Optimization Strategies in Urban Vehicular Mobility", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-12-17 08:10:03"}
• {"files"=>["https://ndownloader.figshare.com/files/2608139"], "description"=>"<p>(Panel a) Heatmap of the density distribution of the sampling points of the all the trajectories in GPS track dataset. (Panel b) Representation of a small subset of paths coming from the dataset.</p>", "links"=>[], "tags"=>["travel times", "emergence", "Local Optimization Strategies", "pattern", "mobility", "length", "strategy", "Urban Vehicular Mobility", "gps", "vehicle velocity increases"], "article_id"=>1624405, "categories"=>["Uncategorised"], "users"=>["Pierpaolo Mastroianni", "Bernardo Monechi", "Carlo Liberto", "Gaetano Valenti", "Vito D. P. Servedio", "Vittorio Loreto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143799.g001", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_GPS_Track_Data_/1624405", "title"=>"GPS Track Data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-17 08:09:58"}
• {"files"=>["https://ndownloader.figshare.com/files/2608145"], "description"=>"<p>(Panel a) Measured distribution <i>p</i>(<i>t</i>|<i>l</i>) of traveling time at fixed travel length for different <i>l</i> values. Solid lines refer to the fitted log-normal distributions. (Panel b) Distribution of the scaled variables in correspondence of different values of <i>l</i>. (Panel c) Dependence of the experimental mean value 〈log <i>t</i>〉 on the travel length <i>l</i> (red points). The solid blue line is the best fitting logarithmic function. (Panel d) The standard deviation σ<i>l</i> of the variable log <i>t</i> inferred from data, as a function of <i>l</i>.</p>", "links"=>[], "tags"=>["travel times", "emergence", "Local Optimization Strategies", "pattern", "mobility", "length", "strategy", "Urban Vehicular Mobility", "gps", "vehicle velocity increases"], "article_id"=>1624411, "categories"=>["Uncategorised"], "users"=>["Pierpaolo Mastroianni", "Bernardo Monechi", "Carlo Liberto", "Gaetano Valenti", "Vito D. P. Servedio", "Vittorio Loreto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143799.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_Properties_of_Car_Driving_/1624411", "title"=>"Statistical Properties of Car Driving.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-17 08:10:10"}
• {"files"=>["https://ndownloader.figshare.com/files/2608148"], "description"=>"<p>: Experimental average travel time 〈<i>t</i>〉 as a function of <i>l</i> (red points). The solid blue line is the function 〈<i>t</i>〉 = exp(<i>μ</i>_<i>l</i> + <i>s</i>²/2) with <i>μ</i>_<i>l</i> = <i>α</i>log(<i>l</i>/<i>l</i>₀) and <i>α</i> and <i>l</i>₀ inferred from the fit in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143799#pone.0143799.g002\" target=\"_blank\">Fig 2.c</a>. This property is the signature of the optimization process performed by car drivers within the urban environment, since it implies a power-law growth in the average speed with the travel length. The inset shows the power-law dependence <i>v</i> ∼ <i>l</i>^{1−<i>α</i>} of the average velocity <i>v</i> on the travel length <i>l</i>.</p>", "links"=>[], "tags"=>["travel times", "emergence", "Local Optimization Strategies", "pattern", "mobility", "length", "strategy", "Urban Vehicular Mobility", "gps", "vehicle velocity increases"], "article_id"=>1624414, "categories"=>["Uncategorised"], "users"=>["Pierpaolo Mastroianni", "Bernardo Monechi", "Carlo Liberto", "Gaetano Valenti", "Vito D. P. Servedio", "Vittorio Loreto"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0143799.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_travel_time_dependence_on_l_Optimization_Signature_/1624414", "title"=>"Average travel time dependence on <i>l</i> (Optimization Signature).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-12-18 08:42:51"}

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F1000Prime