Fortunately, the recently formulated Macroscopic Fundamental Diagram (MFD) provides new ways to systematically analyze urban traffic at the network level 10, 11, 12, it is consistent with the physics of traffic, and allows to determine the boundary of traffic states of networks and thus the traffic capacity of urban networks. However, until now, a holistic understanding of the critical points of entire networks was missing 9, as well as an empirical quantification of its driving factors. Differences in critical points of urban networks have been observed since the 1960s 6, 7, 8. The link level is well understood and design procedures are standardized 5 but the understanding of entire networks is by far not comparable. The urban road transportation system can be analyzed at the link and network level. Consequently, the critical point marks both, the boundary between free-flow and congestion, and the boundary of possible travel production. At this point, the maximum in travel production, the traffic capacity of urban networks, is reached. The system’s critical point is then located at the boundary between the network’s free-flow and congested states. In contrast, during free-flow states, increasing the number of vehicles increases travel production. Physically, traffic is a many-particle system 4, where congestion is defined as the state when increasing the number of vehicles decreases travel production. As transportation networks are the lifeline of our cities, our findings have profound implications on how to build and operate our cities more efficiently.įighting congestion is difficult as human travel patterns are repetitive 1, 2 and added capacities are rapidly consumed by induced demand and population growth 3. Importantly, we find a sublinear relationship between network size and critical accumulation emphasizing decreasing marginal returns of infrastructure investment. Here we show with billions of vehicle observations from more than 40 cities, how road and bus network topology explains around 90% of the empirically observed critical point variation, making it therefore predictable. ![]() However, until now, a holistic understanding of this critical point and an empirical quantification of its driving factors has been missing. To improve traffic operations, develop new congestion mitigation strategies, and reduce negative traffic externalities, understanding the basic laws governing the network’s critical number of vehicles and the network’s traffic capacity is necessary. Traffic in an urban network becomes congested once there is a critical number of vehicles in the network.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |