}ɫ0Њwvӈ{}ŝ~wυwq|ɽ0s혳(}wѳ=ȱaq^f||ᇳ>`s펳(bw`ߞ{kߜ`M0鏳(ٽ0a[ɫ0!ܨPݧ**ߧ$ޡV #= ? ٍr?5A514Mו =ٍ+M14Mg*f,ÄaV,c+h,Ɂb\,rC?rU 5$$?0ٛr>913 rU% rj^țr# ٍrWcY̎ŹhWhXρf^·.b*aMPoà>ۮYAWAKóKɮY=۸JYòW ۸YYYAͮY2۸YHλ9ϴCCʹM̲?IJ{3<j[%<f $ '<),U8 %[3),C) 5[,C}[- '0j[{J;~A A|O}= K}H5Hy㿯㢫育躮ߖ׏ᅣ׹ᓺ陂׭ᕯׂၫܩض٬㻮㡯΃ꈵDž2025-08-01 16:55:48 info: [Puppeteer Page] Got cookies, applying... H BC |PBUؙJBAؙ$d'؏ BZaTC@VvRX]VNԍ`1p=&~k6v w%&hk >?}4>fF(fw7af_t!a6e#$w0g0$'o-$"$4adYfD2025-08-01 16:55:48 info: [Puppeteer Page] Set 0 cookies on the page ɖ󎐃⺉صܮɹ훒쎇󂇽샇芅j5 4r,jp~i}fpfy` b jHPڒ;D&ګ-68:$80qe'"qq*6&4}q2#36g"('(<" &#>:i}:.GSͦV̽ڼVˠ Vݶ0썝3̼ΪNٿ̪TǼܬƤΪY thttp/1.12025-08-01 16:55:50 info: [Puppeteer Page] Attempting direct fetch of https://www.isis.vanderbilt.edu/bibcite/export/bibtex/bibcite_reference/1104 q'Cj wv'Cj?C|?%G&) v`[^u`Ad%`lafA`%mvNrl+al`gl`wNl}g`ck4Q'a2025-08-01 16:55:50 info: [Puppeteer File-Downloader] Attempting to download asset directly... ~u߽'n`b߽'|'`ABhd?pq%`rdXqq%ai%`οlq+xhttp/1.1@inproceedings{1104, author = {Jaminur Islam and Jose Talusan and Shameek Bhattacharjee and Francis Tiausas and Sayyed Vazirizade and Abhishek Dubey and Keiichi Yasumoto and Sajal Das}, title = {Anomaly based Incident Detection in Large Scale Smart Transportation Systems}, abstract = {

Modern smart cities are focusing on smart transportation solutions to detect and mitigate the effects of various traffic incidents in the city. To materialize this, roadside units and ambient trans-portation sensors are being deployed to collect vehicular data that provides real-time traffic monitoring. In this paper, we first propose a real-time data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. We perform extensive experimental validation using mobility data collected from the City of Nashville, Tennessee, and prove that the method can detect incidents within each cluster in real-time.

}, year = {2022}, journal = {ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)}, pages = {215-224}, publisher = {IEEE}, address = {Milano, Italy}, isbn = {978-1-6654-0967-4}, url = {https://ieeexplore.ieee.org/document/9797502}, doi = {10.1109/ICCPS54341.2022.00026}, } 7YWJ< n,LA'Q~)PR+nLW5YHnZC)Y>af Y!P@-FvYxp#EZ+[nR"DV>&p9 QV"D- @?YVlVbK |M1L{#3`@0V`T@@@@P@T_qB@ 1 V_ a)PKo? ԠV`6_ϱ@xQT_qB@1 _U Da)PKo?ֻ_^a)PKo?֠]VV_Po?֮]aBwңO#j?#qQe ֮]O'j?֠]pCҮV`T@@@@P@T_qB@ 1 V_ a)PKo? ԠVV`6_ϱ@xQT_qB@1 _Z Da)PKo?֡ZXPwj?PWv?_qB@1MZXPwj?֠@@]UUT_Pn?֮]b~O#j?#$xQe ֮]O'j?֠]pC]Q@9O_qB@qT__ ތa)PKo?jxip qT~PCP7`xi0 @O_Pe?ZXPwj?֠ ҮY`T@@@@P@T_qB@!1  ҠY_qB@19Y@@ Ҡ_qB@1._ZX;@چҡ)PKo?֠@@{N@Z__A]YY_Pn?֮Z@Ҡ_qB@ 1Ҡ@@{N@]`@@[{N@\` ToI.  _qB@M1[_ a)PKo? @@][ZZY_Pn?#ҤQe ֠[#Qe @@#DQe  ++Pk"jXk Pn"hkSS SSSSSSpSS SSSSSSP` S SS SSSSSSS0