Optimizing Passenger On-Vehicle Experience Through Simulation and Multi-Agent Multi-Criteria Mobility Planning
Total Page：16
File Type：pdf, Size：1020Kb
Optimizing Passenger On-Vehicle Experience through Simulation and Multi-Agent Multi-Criteria Mobility Planning Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical and Computer Engineering Rongye Shi B.S., Electronic Information Science and Technology, China Agricultural University M.E., Electronics and Communication Engineering, Peking University M.S., Electrical and Computer Engineering, Carnegie Mellon University Carnegie Mellon University Pittsburgh, PA May 2019 c Rongye Shi, 2019 All Rights Reserved ACKNOWLEDGMENTS I would like to express my sincere gratitude to my advisors, Prof. Manuela M. Veloso and Prof. Peter Steenkiste, for their guidance and generous supports throughout my doctorate study. In particular, I want to thank Manuela. The ways she thinks of problems usually inspire me to achieve a higher research level. I am impressed by her passion and dedication to academic problems. Her remarkable comments and suggestions lead me to high-quality work. I want to thank Peter as well for his constructive feedbacks and comments that extend my knowledge in the field I am working on. His philosophy in doing research truly influences me and helps me present my work in a clear and logical manner. Many thanks to both of them, and without their guidance and support, my research would not have been possible. I would like to thank Prof. Stephen F. Smith and Prof. Carlee Joe-Wong for being in my thesis committee. They always share insights with me about the technical questions whenever I present to them. I benefit a lot from their comments on my research and thesis. Many thanks to all my colleagues and friends at CORAL lab: Dr. Vittorio Perera, Anahita Mohseni-Kabir, Devin Schwab, Philip Cooksey, Rui Silva, Max Korein, Kim Baraka, Ishani Chat- terjee, Aaron Roth, Travers Rhodes, Kevin Zhang, Ashwin Khadke, Sai Prabhakar, etc., for their friendship and helpful discussions when requested. They have provided a wonderful and friendly academic atmosphere in the lab. Thank Prof. Susana Sargento and Prof. Ana Aguiar for providing the data necessary for my research. I would like to acknowledge the funding support from FCT under the CarnegieMellon- Portugal ERI S2MovingCity project (Grant CMUP-ERI/TIC/0010/2014) for making my research possible. Finally, I truly thank my family, especially my father Youjian Shi, mother Chunhuan Luo and elder sister Minglu Shi, for their love and care during my study at Carnegie Mellon. iii ABSTRACT The rapid growth in urban population poses significant challenges to moving city dwellers in a fast and convenient manner. This thesis contributes to solving the challenges from the viewpoint of public transit passengers by improving their on-vehicle experience. Traditional transportation research focuses on pursuing minimal travel time of vehicles on the road network, paying no attention to people inside the vehicles. In contrast, the research in this thesis is passenger-driven, concerning the role of the on-vehicle experience in mobility planning through the public transit systems. The primary goal of the thesis is to address the following problem: Given an urban public transit network, how can we plan for the optimal experience of passengers in terms of their service preference? There are several challenges we have to address to meet this goal. First, a model or a simulator that captures not only the road traffic, but also the behaviors of passengers and other relevant factors is a prerequisite for this research but has seldom been developed previously. Second, to plan for passengers’ mobility concerning the influence among passengers as well as multiple service preferences is computationally intensive, especially on a city scale. To achieve the research goal and overcome the challenges, this thesis develops a joint traffic- passenger simulator, which simulates the road traffic, behaviors of passengers and on-vehicle environment dynamics. Specifically, the simulator combines the urban road traffic, the inter- actions among the passengers and the infrastructures that support certain on-vehicle services, such as on-vehicle Wi-Fi, to provide a passenger-level simulation. A separate passenger behav- ior model and on-vehicle Wi-Fi service model are designed to run jointly with SUMO, a mature traffic simulator, for simulating the passenger behaviors and on-vehicle travel experience. A joint simulator for the bus transit system in the city of Porto, Portugal has been implemented and tested by comparing the simulation to the real passenger data. To configure the background passenger flow in the simulation, real passenger data are used. The data were collected by an entry-only system and the destination information was missing. This thesis contributes a machine learning algorithm, called semi-supervised self-training, to infer the missing destinations with a high inference confidence level. Given the simulation platform, the passenger mobility planning problem can be formalized as a multi-agent path planning (MAPP) problem, where multiple passengers may interfere with each other when contending for service resources. The mobility planning operates on the client iv passengers (i.e., a subset of the overall passengers who request the planning service from our planner). State-of-the-art MAPP solvers, such as M*, do not scale well to such a MAPP problem. This thesis proposes the soft-collision-free M* (SC-M*), a generalized version of M*, to efficiently handle the MAPP task under complex urban environments (i.e., with a large client passenger size and multiple types of client passengers requesting multiple types of service resources). We evaluate the performance of the SC-M* through a case study of the bus transit system in Porto, Portugal and the experimental results show the advantages of the SC-M* in terms of path cost, collision-free constraint, and the scalability in run time and success rate. v Contents 1 Introduction 1 1.1 Thesis Question ....................................... 2 1.2 Approach........................................... 3 1.2.1 Simulating On-Vehicle Experience through Joint Traffic-Passenger Simulation3 1.2.2 Retrieving the Missing Features of Real Data with Semi-Supervised Learning4 1.2.3 Planning the Mobility with a Scalable Multi-Passenger Multi-Criteria Planner4 1.3 Contributions......................................... 5 1.4 Reading Guide to the Thesis................................ 5 2 A Joint Traffic-Passenger Simulator for On-Vehicle Experience Simulation (P-SUMO)8 2.1 Simulation for Intelligent Public Transportation Systems (IPTS) ........... 8 2.2 Joint Traffic-Passenger Simulator (P-SUMO)....................... 10 2.2.1 Overview of the Methodology for Passenger-Level Simulator......... 10 2.2.2 Pre-Processing Layer................................. 12 2.2.3 Joint Simulation Layer................................ 19 2.2.4 Evaluation of Synthetic Data with Real Passenger Data ............ 22 2.3 Modeling the On-Vehicle Wi-Fi Service.......................... 26 2.4 Usage of Synthetic Data................................... 30 2.4.1 Training and Testing Data Preparation ...................... 30 2.4.2 Neural Network Implementation and Performance............... 31 2.5 Summary........................................... 33 3 Infer the Missing Features in Incomplete Data 34 3.1 Motivations and Challenges of Filling the Missing Features.............. 34 3.1.1 Destination Inference for Passenger Data..................... 35 3.2 Heuristic Method ...................................... 37 vi 3.3 Methodology......................................... 40 3.3.1 Semi-Supervised Learning Problem Setting ................... 40 3.3.2 One-Time Inference Method ............................ 40 3.3.3 Self-Training (ST)................................... 42 3.3.4 Self-Training using Personal Historical Information (STP)........... 46 3.4 Data Pre-Processing for Experiments........................... 51 3.4.1 Passenger Simulation Data of Porto City..................... 51 3.4.2 AFC Passenger Data of Porto City......................... 51 3.5 Experiments ......................................... 52 3.5.1 Experimental Setup and Evaluation Metrics................... 52 3.5.2 Results of Parameter Sensitivity.......................... 54 3.5.3 Comparison among Methods............................ 56 3.6 Summary........................................... 57 4 SC-M*: A Multi-Agent Path Planning with Soft-Collision-Free Constraint 59 4.1 Background of MAPP.................................... 60 4.2 Motivations of SC-M*.................................... 62 4.3 Technical Briefing of M*................................... 62 4.3.1 MAPP Problem Definition ............................. 63 4.3.2 Graphic-Centric Description of M*......................... 64 4.3.3 Algorithm Description of M*............................ 65 4.3.4 M* Challenges and Improvements......................... 66 4.4 Soft-Collision-Free M* (SC-M*)............................... 69 4.4.1 Soft-Collision-Free Constraint ........................... 69 4.4.2 Completeness and Cost-Suboptimality...................... 75 4.5 Experiments and Results.................................. 77 4.5.1 Planning for the One-Resource-One-Agent-Type ................ 79 4.5.2 Planning for the Two-Resource-Two-Agent-Type ................ 79 4.5.3 Comparison of SC-M* to Baselines......................... 81 4.6 Summary........................................... 86 5 SC-M* for Real-World Bus Transit System 87 5.1 Multi-Passenger Multi-Criteria Mobility