Abstract-The problem of finding optimal coordinated signal timing plans for a large number of traffic signals is a challenging problem because of the exponential growth in the number of joint timing plans that need to be explored as the network size grows. In this paper, we employ the game-theoretic paradigm of fictitious play to iteratively search for a coordinated signal timing plan that improves a system-wide performance criterion for a traffic network. The algorithm is robustly scalable to realistic-size networks modelled with high fidelity simulations. We report results of a case study for the the city of Troy, Michigan, where there are 75 signalized intersections. Under normal traffic conditions, savings in average travel time of more than 20 percent are experienced against a static timing plan, and even against an aggressively tuned automatic signal re-timing algorithm, savings of more than 10 percent are achieved. The efficiency of the algorithm stems from its parallel nature. With a thousand parallel CPUs available, our algorithm finds the plan above in under 10 minutes, while a version of a hill-climbing algorithm makes virtually no progress in the same amount of wall-clock computational time.

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Taxi service is an important mode of public transportation in most metropolitan areas since it provides door-todoor convenience in the public domain. Unfortunately, despite all the convenience taxis bring, taxi fleets are also extremely inefficient to the point that over 50% of its operation time could be spent in idling state. Improving taxi fleet operation is an extremely challenging problem, not just because of its scale, but also due to fact that taxi drivers are self-interested agents that cannot be controlled centrally. To facilitate the study of such complex and decentralized system, we propose to construct a multiagent simulation platform that would allow researchers to investigate interactions among taxis and to evaluate the impact of implementing certain management policies. The major contribution of our work is the incorporation of our analysis on the real-world driver's behaviors. Despite the fact that taxi drivers are selfish and unpredictable, by analyzing a huge GPS dataset collected from a major taxi fleet operator, we are able to clearly demonstrate that driver's movements are closely related to the relative attractiveness of neighboring regions. By applying this insight, we are able to design a background agent movement strategy that generates aggregate performance patterns that are very similar to the real-world ones. Finally, we demonstrate the value of such system with a real-world case study.

The arrangement of last-mile services is playing an increasingly important role in making public transport more accessible. We study the use of ridesharing in satisfying last-mile demands, with the assumption that demands are uncertain and come in batches. The most important contribution of our paper is a two-level MDP framework that is capable of generating a vehicle-dispatching policy for the aforementioned service. We introduce state summarization, representative states, and sample-based cost estimation as major approximation techniques in making our approach scalable. We show that our approach converges and solution quality improves as sample size increases. We also apply our approach to a series of case studies derived from a realworld public transport dataset in Singapore. By examining three distinctive demand profiles, we show that our approach performs best when the distribution is less uniform and the planning area is large. We also demonstrate that a parallel implementation can further improve the performance of our solution approach.