TY - GEN
T1 - A Bottom-Up Design Model for Improving Efficiency of Transit System
AU - Li, Jiayang
AU - Zhao, Ruzhang
AU - Li, Meng
AU - Ouyang, Yanfeng
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - This paper presents a model to design high-performance public transit system, where the optimal solution is directly selected from routes built up on real road structure. The objective function is the travel time for all travelers and the infrastructure cost can also be under consideration. The optimization procedure consists of four components, including a method to simulate downtown travel demand using open-source data on the Internet, a method to narrow down the decision variable space with the help of reasonable restriction and map api, an efficient a lgorithm to compute average travel time using block representation of the urban area and an improved evolutionary algorithm to search the optimal solution. Eventually, this method is applied to design the transit system for Changzhi, China. The optimal solution is compared to the original transit system of Changzhi to test both the effect and reasonability of the algorithm.
AB - This paper presents a model to design high-performance public transit system, where the optimal solution is directly selected from routes built up on real road structure. The objective function is the travel time for all travelers and the infrastructure cost can also be under consideration. The optimization procedure consists of four components, including a method to simulate downtown travel demand using open-source data on the Internet, a method to narrow down the decision variable space with the help of reasonable restriction and map api, an efficient a lgorithm to compute average travel time using block representation of the urban area and an improved evolutionary algorithm to search the optimal solution. Eventually, this method is applied to design the transit system for Changzhi, China. The optimal solution is compared to the original transit system of Changzhi to test both the effect and reasonability of the algorithm.
KW - Evolutionary algorithm
KW - Transit system design
KW - Transit system evaluation
KW - Travel demand simulation
UR - http://www.scopus.com/inward/record.url?scp=85063151540&partnerID=8YFLogxK
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U2 - 10.1109/UV.2018.8642126
DO - 10.1109/UV.2018.8642126
M3 - Conference contribution
AN - SCOPUS:85063151540
T3 - 4th IEEE International Conference on Universal Village 2018, UV 2018
BT - 4th IEEE International Conference on Universal Village 2018, UV 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Universal Village, UV 2018
Y2 - 21 October 2018 through 24 October 2018
ER -