Improving Public Transit Systems at Intersection and City-wide Scales

Designing a highly efficient public transit system can help reduce the congestion and/npollution in large cities. This dissertation tries to improve public transit systems at both/nthe micro and macro scales./nAt the microscale, a Transit Signal Priority strategy is developed to integrate a pre-/nsignal scheme. Buses often compete with cars for limited road space and signal times at/nintersections. Providing signal priority together with exclusive lanes for buses will often/ndiminish car discharge flows into the intersection. This can create huge car delays, which/nwill undermine the benefit brought by the priority measures to buses. To solve this problem,/nwe propose a midblock pre-signal to recover the loss in the cars’ discharge capacity caused/nby the bus priority measures. We show how the pre-signal can be coordinated with the/nintersection’s main signal under a simple signal priority scheme. Analytical models are/nformulated to examine the effects of our strategy on two performance metrics: the expected/nbus delay and the car capacity. Pareto frontiers between the two performance metrics are/ndeveloped. Numerical case studies show that huge benefits for both buses and cars can be/nachieved by the proposed strategy, as compared to baseline scenarios where a bus signal/npriority scheme or pre-signal is absent./nAt the macroscale, two continuum approximation (CA) optimization models are formu-/nlated to design city-wide transit systems at minimum cost. Transit routes are assumed to lie/natop a city’s street network. Model 1 assumes that the city streets are laid out in ring-radial/nfashion. Model 2 assumes that the city streets form a grid. Both models can furnish hybrid/ndesigns, which exhibit intersecting routes in a city’s central (downtown) district and only/nradial branching routes in the periphery. Model 1 allows the service frequency and the route/nspacing at a location to vary arbitrarily with the location’s distance from the center. Model/n2 also allows such variation but in the periphery only./nWe show how to solve these CA optimization problems numerically, and how the numeri-/ncal results can be used to design actual systems. A wide range of scenarios is analyzed in this way. It is found among other things that in all cases and for both models: (i) the optimal2/nheadways and spacings in the periphery increase with the distance from the center; and (ii)/nat the boundary between the central district and the periphery both, the optimal service/nfrequency and the line spacing for radial lines decrease abruptly in the outbound direction./nOn the other hand, Model 1 is distinguished from Model 2 in that the former produces in/nall cases: (i) a much smaller central district, and (ii) a high frequency circular line on the/nouter edge of the central district. Parametric tests with all the scenarios further show that/nModel 1 is consistently more favorable to transit than Model 2. Cost differences between/nthe two designs are typically between 9% and 13%, but can top 21.5%. This is attributed to/nthe manner in which ring-radial networks naturally concentrate passenger’s shortest paths,/nand to the economies of demand concentration that transit exhibits. Thus, it appears that/nring-radial street networks are better for transit than grids./nTo illustrate the robustness of the CA design procedure to irregularities in real street/nnetworks, the results for all the test problems were then used to design and evaluate transit/nsystems on networks of the ”wrong” type grid networks were outfilled with transit systems/ndesigned with Model 1 and ring-radial networks were designed with Model 2. Cost increased/non average by a little: only 2.7%. The magnitude of these deviations suggests that the/nproposed CA procedures can be used to design transit systems over real street networks/nwhen they are not too different from the ideal, and that the resulting costs should usually/nbe very close to those predicted.

Advisor: Cassidy, Michael
Daganzo, Carlos