TY - JOUR AU - Chen, Lisi AU - Shang, Shuo AU - Yao, Bin AU - Li, Jing PY - 2020/04/03 Y2 - 2024/03/28 TI - Pay Your Trip for Traffic Congestion: Dynamic Pricing in Traffic-Aware Road Networks JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 01 SE - AAAI Technical Track: Applications DO - 10.1609/aaai.v34i01.5397 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5397 SP - 582-589 AB - <p>Pricing is essential in optimizing transportation resource allocation. Congestion pricing is widely used to reduce urban traffic congestion. We propose and investigate a novel <em><span style="text-decoration: underline;">D</em>ynamic <em><span style="text-decoration: underline;">P</em>ricing <span style="text-decoration: underline;">S</span>trategy (DPS) to price travelers' trips in intelligent transportation platforms (e.g., DiDi, Lyft, Uber). The trips are charged according to their “congestion contributions” to global urban traffic systems. The dynamic pricing strategy retrieves a matching between <em>n</em> travelers' trips and the potential travel routes (each trip has <em>k</em> potential routes) to minimize the global traffic congestion. We believe that DPS holds the potential to benefit society and the environment, such as reducing traffic congestion and enabling smarter and greener transportation. The DPS problem is challenging due to its high computation complexity (there exist <em>k</em><sup><em>n</em></sup> matching possibilities). We develop an efficient and effective approximate matching algorithm based on <em>local search</em>, as well as pruning techniques to further enhance the matching efficiency. The accuracy and efficiency of the dynamic pricing strategy are verified by extensive experiments on real datasets.</p> ER -