چکیده مقاله
According to statistics, congestion is becoming a great challenge for metropolitan areas, andin congested traffic regimes, prediction of travel time is necessary both for travelers andtransportation planners Needless to say, travel time can impact various aspects of trips, soprecise and reliable prediction of travel time can lead to an enhancement in congestion reliefand routing problems Recent advances in modeling techniques and data collection procedurehas faced planners with some real challenges: What are the most valid methods for predictingtravel time How data sources can help planners lessen the required efforts for achieving areliable prediction This study is an attempt to depict a broad framework in which numerousstudies, starting from 2010 to 2020 were assessed carefully The results have revealed thatrecent efforts in the field of data collection can provide insight into the information requiredfor modeling travel time Also, many authors relied on hybrid artificial intelligence AI methods, which represent better performance than single AI methods in terms of reducingprediction error
کلیدواژهها
نویسندگان
Ahahriar Afandizadeh Zargari
Professor of Transportation Engineering and Planning, Iran University of Science and Technology IUST
Navid Amoei Khorshidi
۲ MSc of Transportation Engineering and Planning, Iran University of Science and Technology IUST
Hamid Mirzahossein
Associate Professor, Department of Civil Transportation planning, Imam Khomeini International University IKIU
شیوه ارجاع
Afandizadeh Zargari, Ahahriar and Amoei Khorshidi, Navid and Mirzahossein, Hamid,1401,How Far Have We Delved Deep into The Travel Time Prediction Methods? A Review of the Studies from 2010 to 2020,The 19th International Conference on Traffic and Transportation Engineering,Tehran
ارائهشده در
مجموعه مقالات نوزدهمین کنفرانس بین المللی مهندسی حمل و نقل و ترافیک15 اسفند 1401 · تهران