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Smart city article details
| Title | Transportation Analytics With Fuzzy Logic And Regression |
|---|---|
| ID_Doc | 58913 |
| Authors | Thong Jason Tran N.D.; Leung C.K.; Turner T.; Wu S.T.; Karimbaeva N.; Kim J.; Cuzzocrea A. |
| Year | 2022 |
| Published | IEEE International Conference on Fuzzy Systems, 2022-July |
| DOI | http://dx.doi.org/10.1109/FUZZ-IEEE55066.2022.9882645 |
| Abstract | Bus riders desire precision and accuracy when using the transit system. While the transit system is responsible for maintaining and delivering public transportation services for the city residents, they rely on idealized assumptions regarding real-world bus driving conditions. The published bus schedule seems to assume that the buses move at a uniform speed at all times, which leads to bus arrival times that are imprecise and inaccurate. Busses can arrive early, late, or on time. Given that bus stops cannot have a dynamic schedule, it is logical to create a schedule accounting for the changes in traffic patterns. Hence, in this paper, we present a transportation analytics solution. It captures imprecision via fuzzy logic. It takes in account lane closures (for construction sites) and traffic count when predicting bus on-time performance via regression. Evaluation on real-life data covering close to 6,000 bus stops in the Canadian city of Winnipeg demonstrates the practicality of our fuzzy logic-and regression-based transportation analytics solution in predicting whether buses arrive the bus stops early, on time, or late in various time periods of the day. This helps in building a smart city. © 2022 IEEE. |
| Author Keywords | arrival time; bus; delay; fuzzy logic; machine learning; predictive analytics; regression |
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