Floating car data present a cost-effective approach to observing the traffic state. This paper explores whether floating cars can substitute stationary detection devices (e.g., induction loops) for observers within traffic responsive control systems. A rule-based traffic control method at the local intersection level is proposed in this paper by utilizing the floating car data. The control method involves a three-fold approach: link-level speed forecasting, data-driven traffic flow estimation, and split optimization. To estimate traffic flow, a multivariable linear regression model is developed by utilizing forecasted link-level speed, signal control variables, and link length as predictors. The method is tested using a controller (hardware)-independent software-in-the-loop approach. Compared with the existing fixed-time control operating in Starnberg, Germany, the proposed method is able to improve the level of service of the signalized intersection when tested for different levels of market penetration of the floating cars. The findings underpin the use of floating car data in online traffic control applications; the benefits will increase with an increase in market penetration of floating cars. Overall, this paper presents a fully integrated technical system that is ready to be used in the field. The proposed system can be implemented at the tactical level of urban traffic-control hierarchy employed in Germany.
|Number of pages||10|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Publication status||Published - 1 Jan 2018|
- floating car data
- rule-based control.
- Signal re-timing
- software-in-the-loop simulation
- traffic flow estimation
- transport big data
- urban traffic control