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DTSTART;TZID=America/New_York:20231115T210000
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DESCRIPTION:Title: Personalized Route Planning Based on Driver Preference b
 y Using Improved Discrete Jaya Algorithm\n\nRen Wang\, Ph.D. Candidate\nMa
 cao Institute of Systems Engineering\nMacau University of Science and Tech
 nology\n\nRoom: https://njit.webex.com/join/zhou\nECE\, NJIT\nTime: 9pm (W
 ed. night)\, Nov. 15\, 2023\n\nAbstract: At present\, most popular route n
 avigation systems only use a few sensed or measured attributes to recommen
 d a route. Yet the optimal route considered by drivers needs be based on m
 ultiple objectives and multiple attributes. As a result\, these existing s
 ystems based on a single or few attributes may fail to meet such drivers
 ’ needs. This work proposes a driver preference-based route planning (DP
 RP) model. It can recommend an optimal route by considering driver prefere
 nce. We collect drivers’ preferences\, and then provide a set of routes 
 for their choice when they need. Next\, we present an integrated algorithm
  to solve DPRP\, which speeds up the search process for recommending the b
 est routes. Its computation cost can be reduced by simplifying a road netw
 ork and removing invalid sub-routes. Unfortunately\, it performs well on s
 pecific or small-scale road networks only but is not suitable for larger a
 nd more general ones. An improved discrete Jaya algorithm (IDJaya) is thus
  presented to solve DPRP\, which enhances route planning quality through l
 ocal search operations. Experimental results demonstrate their effectivene
 ss.\n\nBio\n\nRen Wang: He received the B.S. degree in traffic engineering
  from Qingdao University of Technology\, Qingdao\, China\, in 2015\, and t
 he M.S. degree in bridge and tunnel engineering from Guangdong University 
 of Technology\, Guangzhou\, China\, in 2018. He is currently pursuing his 
 Ph.D. degree with Macau University of Science and Technology. His interest
 s are in intelligent transportation systems and algorithm development.\n\n
 Speaker(s): \, \, \n\n323 MLK Blvd.\, Newark\, New Jersey\, United States\
 , 07102
LOCATION:323 MLK Blvd.\, Newark\, New Jersey\, United States\, 07102
ORGANIZER:zhou@njit.edu
SEQUENCE:0
SUMMARY:Seminar on "Personalized Route Planning Based on Driver Preference 
 by Using Improved Discrete Jaya Algorithm"
URL;VALUE=URI:https://events.vtools.ieee.org/m/388731
X-ALT-DESC:Description: <br /><p>Title: Personalized Route Planning Based o
 n Driver Preference by Using Improved Discrete Jaya Algorithm<br /><br />R
 en&nbsp\;Wang\, Ph.D. Candidate<br />Macao Institute of Systems Engineerin
 g<br />Macau University of Science and Technology<br /><br />Room:&nbsp\;<
 a href="https://njit.webex.com/join/zhou" data-saferedirecturl="https://ww
 w.google.com/url?q=https://njit.webex.com/join/zhou&amp\;source=gmail&amp\
 ;ust=1701806899377000&amp\;usg=AOvVaw05hHS0wjhJfpY56i57rJjs">https://njit.
 webex.com/join/zhou</a><br />ECE\, NJIT<br />Time: 9pm (Wed. night)\, Nov.
  15\, 2023<br /><br />Abstract: At present\, most popular route navigation
  systems only use a few sensed or measured attributes to recommend a route
 . Yet the optimal route considered by drivers needs be based on multiple o
 bjectives and multiple attributes. As a result\, these existing systems ba
 sed on a single or few attributes may fail to meet such drivers&rsquo\; ne
 eds. This work proposes a driver preference-based route planning (DPRP) mo
 del.&nbsp\; It can recommend an optimal route by considering driver prefer
 ence. We collect drivers&rsquo\; preferences\, and then provide a set of r
 outes for their choice when they need. Next\, we present an integrated alg
 orithm to solve DPRP\, which speeds up the search process for recommending
  the best routes. Its computation cost can be reduced by simplifying a roa
 d network and removing invalid sub-routes. Unfortunately\, it performs wel
 l on specific or small-scale road networks only but is not suitable for la
 rger and more general ones. An improved discrete Jaya algorithm (IDJaya) i
 s thus presented to solve DPRP\, which enhances route planning quality thr
 ough local search operations. Experimental results demonstrate their effec
 tiveness.<br /><br />Bio</p>\n<p>Ren Wang: He received the B.S. degree in 
 traffic engineering from Qingdao University of Technology\, Qingdao\, Chin
 a\, in 2015\, and the M.S. degree in bridge and tunnel engineering from Gu
 angdong University of Technology\, Guangzhou\, China\, in 2018. He is curr
 ently pursuing his Ph.D. degree with Macau University of Science and Techn
 ology. His interests are in intelligent transportation systems and algorit
 hm development.</p>
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