Institute of Transportation Studies at UC Berkeley

Various theories have been proposed to describe vehicular traffic movement in cities on an aggregate level. They fall short to create a macroscopic model with variable inputs and outputs that could describe a rush hour dynamically. This dissertation work shows that a macroscopic fundamental diagram (MFD) relating production (the product of average flow and network length) and accumulation (the product of average density and network length) exists for neighborhoods of cities in the order of 5-10km2. It also demonstrates that conditional on accumulation large networks behave predictably and independently of their origin-destination tables. These results are based on analysis using simulation of large scale city networks and real data from urban metropolitan areas. The real experiment uses a combination of fixed detectors and floating vehicle probes as sensors. The analysis also reveals a fixed relation between the space-mean flows on the whole network and the trip completion rates, which dynamically measure accessibility. This work also demonstrates that the dynamics of the rush hour can be predicted quite accurately without the knowledge of disaggregated data. This MFD is applied to develop perimeter control strategies based on neighborhood accumulation and speeds and improve accessibility without the uncertainty inherent in today’s forecast-based approaches. The looking-for-parking phenomenon that extends the average trip length is also integrated in the dynamics of the rush hour.