Optimization of Scheduling and Dispatching Cars on Demand

Taxicab is the most common type of on-demand transportation service in the city because its dispatching system offers better services in terms of shorter wait time. However, the shorter wait time and travel time for multiple passengers and destinations are very considerable. There are recent companies implemented the real-time ridesharing model that expects to reduce the riding cost when passengers are willing to share their rides with the others. This model does not solve the shorter wait time and travel time when there are multiple passengers and destinations. This paper investigates how the ridesharing can be improved by using the genetic algorithm that gives the optimal solution in terms of passengers wait time and routes duration among passengers’ start and end locations. The simulator uses the Google digital maps and direction services that allow the simulator to fetch the real-time data based on the current traffic conditions such as accident, peak hours, and weather. The simulation results that are sub-optimal routes are computed using the advanced genetic algorithm and real-time data availability

Modeling Framework and Solution Methodologies for On-Demand Mobility Services With Ridesharing and Transfer Options

The growing complexity of the urban travel pattern and its related traffic congestion, along with the extensive usage of mobile phones, invigorated On-Demand Mobility Services (ODMS) and opened the door to the emergence of Transportation Network Companies (TNC). By adopting the shared economy paradigm, TNCs enable private car owners to provide transportation services to passengers by providing user-friendly mobile phone applications that efficiently match passengers to service providers. Considering the high level of flexibility, convenience, and reliability of ODMS, compared to those offered by traditional public transportation systems, many metropolitan areas in the United States and abroad have reported rapid growth of such services. This dissertation presents a modeling framework to study the operation of on-demand mobility services (ODMS) in urban areas. The framework can analyze the operation of ODMS while representing emerging services such as ridesharing and transfer. The problem is formulated as a mixed-integer program and an efficient decomposition-based methodology is developed for its solution. This solution methodology aims at solving the offline version of the problem, in which the passengers’ demand is assumed to be known ii for the entire planning horizon. The presented approach adopts a modified column generation algorithm, which integrates iterative decomposition and network augmentation techniques to analyze networks with moderate size. Besides, a novel methodology for integrated ride-matching and vehicle routing for dynamic (online) ODMS with ridesharing and transfer options is developed to solve the problem in real-time. The methodology adopts a hybrid heuristic approach, which enables solving large problem instances in near real-time, where the passengers’ demand is not known a priori. The heuristic allows to (1) promptly respond to individual ride requests and (2) periodically re-evaluate the generated solutions and recommend modifications to enhance the overall solution quality by increasing the number of served passengers and total profit of the system. The outcomes of experiments considering hypothetical and real-world networks are presented. The results show that the modified column generation approach provides a good quality solution in less computation time than the CPLEX solver. Additionally, the heuristic approach can provide an efficient solution for large networks while satisfying the real-time execution requirements. Additionally, investigation of the results of the experiments shows that increasing the number of passengers willing to rideshare and/or transfer increases the general performance of ODMS by increasing the number of served passengers and associated revenue and reducing the number of needed vehicles

Hybrid Vehicle-drone Routing Problem For Pick-up And Delivery Services Mathematical Formulation And Solution Methodology

The fast growth of online retail and associated increasing demand for same-day delivery have pushed online retail and delivery companies to develop new paradigms to provide faster, cheaper, and greener delivery services. Considering drones’ recent technological advancements over the past decade, they are increasingly ready to replace conventional truck-based delivery services, especially for the last mile of the trip. Drones have significantly improved in terms of their travel ranges, load-carrying capacity, positioning accuracy, durability, and battery charging rates. Substituting delivery vehicles with drones could result in $50M of annual cost savings for major U.S. service providers. The first objective of this research is to develop a mathematical formulation and efficient solution methodology for the hybrid vehicle-drone routing problem (HVDRP) for pick-up and delivery services. The problem is formulated as a mixed-integer program, which minimizes the vehicle and drone routing cost to serve all customers. The formulation captures the vehicle-drone routing interactions during the drone dispatching and collection processes and accounts for drone operation constraints related to flight range and load carrying capacity limitations. A novel solution methodology is developed which extends the classic Clarke and Wright algorithm to solve the HVDRP. The performance of the developed heuristic is benchmarked against two other heuristics, namely, the vehicle-driven routing heuristic and the drone-driven routing heuristic. Anticipating the potential risk of using drones for delivery services, aviation authorities in the U.S. and abroad have mandated necessary regulatory rules to ensure safe operations. The U.S. Federal Aviation Administration (FAA) is examining the feasibility of drone flights in restricted airspace for product delivery, requiring drones to fly at or below 400-feet and to stay within the pilot’s line of sight (LS). Therefore, a second objective of this research is considered to develop a modeling framework for the integrated vehicle-drone routing problem for pick-up and delivery services considering the regulatory rule requiring all drone flights to stay within the pilot’s line of sight (LS). A mixed integer program (MIP) and an efficient solution methodology were developed for the problem. The solution determines the optimal vehicle and drone routes to serve all customers without violating the LS rule such that the total routing cost of the integrated system is minimized. Two different heuristics are developed to solve the problem, which extends the Clarke and Wright Algorithm to cover the multimodality aspects of the problem and to satisfy the LS rule. The first heuristic implements a comprehensive multimodal cost saving search to construct the most efficient integrated vehicle-drone routes. The second heuristic is a light version of the first heuristic as it adopts a vehicle-driven cost saving search. Several experiments are conducted to examine the performance of the developed methodologies using hypothetical grid networks of different sizes. The capability of the developed model in answering a wide variety of questions related to the planning of the vehicle-drone delivery system is illustrated. In addition, a case study is presented in which the developed methodology is applied to provide pick-up and delivery services in the downtown area of the City of Dallas. The results show that mandating the LS rule could double the overall system operation cost especially in dense urban areas with LS obstructions

Data-driven Methodologies and Applications in Urban Mobility

The world is urbanizing at an unprecedented rate where urbanization goes from 39% in 1980 to 58% in 2019 (World Bank, 2019). This poses more and more transportation demand and pressure on the already at or over-capacity old transport infrastructure, especially in urban areas. Along the same timeline, more data generated as a byproduct of daily activity are being collected via the advancement of the internet of things, and computers are getting more and more powerful. These are shown by the statistics such as 90% of the world’s data is generated within the last two years and IBM’s computer is now processing at the speed of 120,000 GPS points per second. Thus, this dissertation discusses the challenges and opportunities arising from the growing demand for urban mobility, particularly in cities with outdated infrastructure, and how to capitalize on the unprecedented growth in data in solving these problems by ways of data-driven transportation-specific methodologies. The dissertation identifies three primary challenges and/or opportunities, which are (1) optimally locating dynamic wireless charging to promote the adoption of electric vehicles, (2) predicting dynamic traffic state using an enormously large dataset of taxi trips, and (3) improving the ride-hailing system with carpooling, smart dispatching, and preemptive repositioning. The dissertation presents potential solutions/methodologies that have become available only recently thanks to the extraordinary growth of data and computers with explosive power, and these methodologies are (1) bi-level optimization planning frameworks for locating dynamic wireless charging facilities, (2) Traffic Graph Convolutional Network for dynamic urban traffic state estimation, and (3) Graph Matching and Reinforcement Learning for the operation and management of mixed autonomous electric taxi fleets. These methodologies are then carefully calibrated, methodically scrutinized under various performance metrics and procedures, and validated with previous research and ground truth data, which is gathered directly from the real world. In order to bridge the gap between scientific discoveries and practical applications, the three methodologies are applied to the case study of (1) Montgomery County, MD, (2) the City of New York, and (3) the City of Chicago and from which, real-world implementation are suggested. This dissertation’s contribution via the provided methodologies, along with the continual increase in data, have the potential to significantly benefit urban mobility and work toward a sustainable transportation system

Multi-agent Spatiotemporal Simulation of Autonomous Vehicle Fleet Operation

Autonomous vehicle fleets, consisting of self-driving vehicles, are at the forefront of transportation innovation. The appearance of autonomous vehicles (AVs) provides a new solution for traffic problems and a new market for transportation network companies such as DiDi and Uber. Conducting simulations in the present is indeed crucial to prepare for the eventual operation of autonomous vehicles, as their widespread adoption is expected to occur in the near future. This research adopts an Agent-Based Modelling (ABM) approach to understand and optimize the performance of autonomous vehicle systems. Moreover, Geographic Information System (GIS) technology also plays a crucial role in enhancing the effectiveness and accuracy of the simulation process. GIS enables the representation and manipulation of geospatial data, such as road networks, land-use patterns, and population distribution. The combination of ABM and GIS allows for the incorporation of real-world geographic data, providing a realistic and geographically accurate environment for the agents in the virtual environment. In this thesis, the multi-agent spatiotemporal simulation is conducted by the GAMA platform. The model simulates the behaviour and interactions of individual agents, which are fleet agents and commuters, to observe the emergent behaviour of the entire system. Within the experiment, different scenarios are considered for both people and fleets to explore a range of approaches and strategies. These scenarios aim to evaluate the effectiveness of various approaches in meeting dynamic commute needs and optimizing fleet operations. By simulating these different scenarios and analyzing their outcomes, the study aims to provide insights into the improvement of fleet size and deployment in autonomous vehicle systems. The ultimate goal is to identify effective strategies that lead to optimized fleet size in different scenarios, reduced idling time and emission, improved traffic management, and overall more efficient and sustainable autonomous vehicle systems

Future grid for a sustainable green airport: meeting the new loads of electric taxiing and electric aircraft.

Lao, Liyun - Associate SupervisorThis thesis proposes a novel electric grid in the airside to meet zero-emission targets for ground movement operations in future airports, as mandated by Aeronautics Research performance target in Europe's (ACARE) FlightPath 2050. The grid delivers power from a renewable energy source through a flexible powerline using an autonomous electric taxiing robot (A-ETR) based on the concept of Energy As A Service (EAAS) for taxiing large aircraft and charging stations for ground vehicles. Four layers of optimisation are required to realise the viability of this new grid. The first optimisation layer involves creating an analytical model of the A-ETR using real-world data from Cranfield University's Airport based solar PV system and its Boeing 737 research aircraft and optimising its performance and efficiency using vehicle-level data-driven machine learning- based optimisation. As a result, the proposed grid achieves zero-emission taxiing and a 91% reduction in fuel compared to a standard baseline. The second layer optimises energy management in the microgrid using machine learning-based forecasting models to predict PV output and optimise charging and discharging cycles of A-ETR batteries to match solar resources and electricity rates. The result shows that the support vector regression (SVR) model best predicted PV output and optimised BESS charge/discharge cycles to achieve zero-emission airport ground movement operations while reducing the microgrid operating costs. However, ground traffic and load profiles increase as the model expands to include commercial airports. Therefore, the third optimisation layer develops a machine learning-based data-driven energy prediction optimisation to ensure microgrid resilience under the increased load. The model employs the Facebook Prophet algorithm to enhance the precision of energy demand prediction for airport ground movement operations across three- time horizons. The results facilitate the generation of reliable forecasts for clean energy production and ground movement energy demand at the airport. A fourth layer of optimisation has been developed to address the limitations of solar PV energy, which depend on the weather and cannot be dispatched, as well iii as the increase in airport traffic. The layer uses wind power and data from a "green" airport to complement PV power output. This model uses the stochastic model predictive control-based cascade feedforward neural network (SMPC- CFFNN) to optimise power flow between the microgrid and RES sources and support V2G capabilities. The results demonstrate that a Zero-emission microgrid for ground movement at green airports can be achieved through optimal power flow management and time optimisation. Reliability and resilience are crucial for a proposed microgrid ecosystem. We consider different network configurations to connect the existing airport grid. Two microgrid architectures, LVAC and LVDC, are compared based on their point of common connections (PCC) to evaluate the technical and economic implications on the airport's distribution network. We verify and validate the model's performance in terms of power quality, short circuit fault levels, system protection requirements, voltage profile, power losses, and equipment/system overloading to determine the optimal architecture. The results indicate that the A-ETR can provide ancillary services to the grid and enable novel emergency response systems. The comprehensive results from the multi-layered system-level optimisation approach adopted in this thesis not only validate the novelty of the proposed study but also serve to provide compelling evidence for its potential to provide viable solutions to the electrification challenges for future green airports by creating an ecosystem between airport ground operations and on-site renewable energy generating sources.PhD in Energy and Powe