۷θ˴λ̭ۋپΫºέ፬ݼ˸ڭ߯蕶ʭذˑ߯ʺګƫذˀʬܝ믛ߘ癣ܞů߮ü̶ܑٶΚDzτǷ҉ʾܑٲĉϘ̘ƙϚΙ燽ܼϝ􌶋ۣζާ§ܼό܇ʽźٲȰ٦񯚉͇ؠŚܑʚΓʘɓʓb2m0;}#t3m07:])zm6j)k6|sDi-t}\b2 {3;9u:J;6m}\(mV57p7;nV!sD})z x: x+ keW5,k:V6w>p1D#}w3;oJ;&|eDi-k6I6 x&DdsDp}\(sDj,p1/}eD_R!n^ g#,lU\gQ __T-g ;_f@ ~X_{ tn y'os t@8{ @t@|Ns@+Nis ~X\$"S#Z/Q_Z# \'.Z8b `bU/z#XLyh[}`Uvh6h7:wlB=_dzWYF#LZ9W;^LhF8@>WF#AWdJbWehFY;\y7XzYd#/[dU<[~diMK15F4O8DHO45K29O/w 2025-02-27 01:00:20 info: [Puppeteer Page] Got cookies, applying... F-n]&@A5x]0/K$vٜpvA'WviB1W&S37Y=tB8\3v2TnjSe-7H~-Up;Tr-7*Hlj7<^pjQ eRj x-Tt/B/j|`8Wa-B5Fpz&Tg=Dp,z'Lp;z:t/B8zh5H'2025-02-27 01:00:20 info: [Puppeteer Page] Set 0 cookies on the page 6qCqgqv6`6ܟQ6ܶqs6dqDI㭾`w}{ޯ|ޫuih& zgl{1>EQvppaoIlvp%fcel@aizgH|v za&K=ce~~wE%h&fcel1>mfa31m=)5P31wl`mqZ`E%1BUY'k^{ xOv\J>Xn1EmRMp^|X0Y1N}I1\q|X{NwEj^{^{I1OQc,|2025-02-27 01:00:21 info: [Puppeteer File-Downloader] Attempting to download asset directly... ɝHzҖHϩ^΅HOҀYĔƶhƟHΗJ洇]́HؖkԈCыIؖ HДDӃYĖBʊB܀LΗY_؇A-http/1.1@inproceedings{3, author = {Sanchita Basak and Fangzhou Sun and Saptarshi Sengupta and Abhishek Dubey}, title = {Data-Driven Optimization of Public Transit Schedule}, abstract = {Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relatively few optimization architectures leveraging stochastic search that focus on optimizing bus timetables with the objective of maximizing probability of bus arrivals at timepoints with delays within desired on-time ranges. In addition to this, there is a lack of substantial research considering monthly and seasonal variations of delay patterns integrated with such optimization strategies. To address these, this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems. The analyses conducted are insightful in the local context of improving public transit scheduling in the Nashville metro region as well as informative from a global perspective as an elaborate case study which builds upon the growing corpus of empirical studies using nature-inspired approaches to transit schedule optimization.}, year = {2019}, journal = {Big Data Analytics (BDA 2019)}, month = {Dec, 2019}, publisher = {Springer}, address = {Ahmedabad, Gujarat, India}, } m'\P, ~G[7Zd9[H; ~GM%R~QY9 j| 1[Z=M fsj3N@;eH2OL.j)ZL2^=K /RL|rm ![n]:\()0R# A!Rr2WW 5#Tr0$]3'&A GI^e4^_ᰙiRIZYOᦙKV@Qr   HU ̃z  ɎzɑɁÊ;軺䲼巻賢򱾡긡ﲠʸ봼ŚͶžż䴿ͷÝ܃IиFG_WAXSAWV [A{ÖttœqÓШ\2K7D}/OE{]pCUyCp"Zp9OQg C7WzUvT7W!U'\-\ ,Y!];_hAYqO&^fC|yqO$.v"ZvSU"+s'T$+-ZVX7JoYLx0mjh%s)bHd%⋢f=ȿesێ=?66375ȭ+%3ȣbᙹh%D0?A>A0ٓ2zI/s9OQm^'u&_!G*O#Y*@*cK=Ym O,=NmV{B}MwDvC{ZaB2VC+N|X<Y&c+N~BlxDl 5xFi}A~DwG 5mByHNp*Otİ⹺ŶݽմýԊڽѪջʅޅр؉߅ƆВټ҂ĒñҒ݇؀ۃXFrnW؛5I:lQ;#+=$lG/=Il+*I{_{Y~SwR{_xEl'|Xl=lZ\-{\ YyZ~S}(zIGlofbNk& ) F(0J8.+7F<.^89^h\hUmUdPhTkWH4Fo\. F'S\h"]j"mSnQiRA6aw]dږRٺ^ѳǺ?޺ʖRխJŖѼJHAAD@C\ݻRIǶR3GH6I6GE 7wDTbpZXCRxTV ĿUC ĭďģRXtР֠qԥqՓJOhAZs79rspA!=uk~1wz~P ~|A7=i~9%At}xk9%V/'V).S#.Z"+V//U5,Af3 =Q/.Ahzrp*=A*\%,(%]'V,Y )&T*YS#(PX*W9bwjlNgc厖NL׎W Fl@טBAWŌ̍ÚǀFLׅ`鳃e…eŌ^DtWVn#߁nj$kv]"f)} n)m){`>bnt#e/Fn-x9~6t?u8x!b:1-(5x#?|%a(5}9{? N{=~:}?t<NnjVWxKLV6Y4 y?X/@uH> ^G8yL:^a9H/$IaOc[jTj]oZkCh[wODyWoA>^4dyW[kl]kc,_nbX]l^n,&-[m$&)n$(j.-c.c(8*iz|?{2{b(x{{gnbiv2~`u.?o$"biv7z1||@s 1 ~4ry7 |>|E~ $6tKLٔ‹Սԅ΅ݓފ́ѓօՅՃЉوՅ֟̇Չ҅ϓҩՉ€芦Ն펣׀Љԓ౫X ttt0t`8t( 3SPPPpPSPS$3S0SS3SSSS`PSP`SP`SPSP`SP`PSpSPp@P`P`SP`SPS@S` S0P`S0P`PS0SPpS0