TY - JOUR
T1 - Taxi Dispatch with Real-Time Sensing Data in Metropolitan Areas
T2 - A Receding Horizon Control Approach
AU - Miao, Fei
AU - Han, Shuo
AU - Lin, Shan
AU - Stankovic, John A.
AU - Zhang, Desheng
AU - Munir, Sirajum
AU - Huang, Hua
AU - He, Tian
AU - Pappas, George J.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/4
Y1 - 2016/4
N2 - Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time Global Positioning System (GPS) location and occupancy information. The objectives include matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a data set containing taxi operational records in San Francisco, CA, USA, shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information.
AB - Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time Global Positioning System (GPS) location and occupancy information. The objectives include matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a data set containing taxi operational records in San Francisco, CA, USA, shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information.
KW - Intelligent transportation system
KW - mobility pattern
KW - real-time taxi dispatch
KW - receding horizon control
UR - http://www.scopus.com/inward/record.url?scp=84977987457&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84977987457&partnerID=8YFLogxK
U2 - 10.1109/TASE.2016.2529580
DO - 10.1109/TASE.2016.2529580
M3 - Article
AN - SCOPUS:84977987457
SN - 1545-5955
VL - 13
SP - 463
EP - 478
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 2
M1 - 7438925
ER -