Dynamic Modeling and Real-time Management of a System of EV Fast-charging Stations

Demand for electric vehicles (EVs), and thus EV charging, has steadily increased over the last decade. However, there is limited fast-charging infrastructure in most parts of the world to support EV travel, especially long-distance trips. The goal of this study is to develop a stochastic dynamic simulation modeling framework of a regional system of EV fast-charging stations for real-time management and strategic planning (i.e., capacity allocation) purposes. To model EV user behavior, specifically fast-charging station choices, the framework incorporates a multinomial logit station choice model that considers charging prices, expected wait times, and detour distances. To capture the dynamics of supply and demand at each fast-charging station, the framework incorporates a multi-server queueing model in the simulation. The study assumes that multiple fast-charging stations are managed by a single entity and that the demand for these stations are interrelated. To manage the system of stations, the study proposes and tests dynamic demand-responsive price adjustment (DDRPA) schemes based on station queue lengths. The study applies the modeling framework to a system of EV fast-charging stations in Southern California. The results indicate that DDRPA strategies are an effective mechanism to balance charging demand across fast-charging stations. Specifically, compared to the no DDRPA scheme case, the quadratic DDRPA scheme reduces average wait time by 26%, increases charging station revenue (and user costs) by 5.8%, while, most importantly, increasing social welfare by 2.7% in the base scenario. Moreover, the study also illustrates that the modeling framework can evaluate the allocation of EV fast-charging station capacity, to identify stations that require additional chargers and areas that would benefit from additional fast-charging stations

Evaluating Mixed Electric Vehicle and Conventional Fueled Vehicle Fleets for Last-mile Package Delivery

The goal of this research project is to evaluate the benefits and disadvantages of electric vehicles (EVs) in delivery vehicle fleets. We assume fleet operators have both EVs and conventionally fueled vehicles (CFVs) at their disposal for delivery services, and that fleet operators select a mix of EVs and CFVs that minimize overall costs. Moreover, we assume EVs offer a per mile cost advantage over CFVs due to the lower costs of electricity compared to gasoline/diesel, and government subsidies. We also assume that EVs have a shorter range than CFVs. We model the fleet operator\u2019s decision problem as a mixed vehicle routing problem, wherein the decision levers include the routing of EVs and CFVs to serve all delivery locations at minimum cost. Using the Los Angeles (LA) and Orange counties as the study area with a single depot, we develop computational experiments to evaluate the benefits and disadvantages of EVs in delivery vehicle fleets. The results indicate that with EV range less than 100 miles, it is not possible for EVs to serve all the demand in the region. At a 200-mile EV range, and where the EV cost per mile is approximately 60% of the CFV cost per mile, the optimal fleet mix is all EVs. With EV range less than 200, or a tighter gap between EV and CFV costs, the optimal fleet includes both EVs and CFVs. Mostly importantly, the results indicate that increasing EV range is the most important factor, more so than reducing EV costs, in reducing CFVs in medium-duty delivery vehicle fleets, and reducing total emissions

Non-myopic Pathfinding for Shared-Ride Vehicles: A Bi-Criteria Best-Path Approach Considering Travel Time and Proximity to Demand [Research Brief]

USDOT Grant 69A3551747109Caltrans contract 65A06The overarching goal of this research project is to improve the operational efficiency of shared-ride mobility-on-demand services (SRMoDS) like UberPool and Lyft Line, in order to increase vehicle occupancies and decrease vehicle mileage. To meet this goal, the objective of this study is to develop a network pathfinding algorithm that considers both a network path\u2019s travel time and its proximity to potential future demand (i.e., travel requests), as opposed to a conventional shortest path algorithm that solely considers travel time

Planning and Operation of a Crowdsourced Package Delivery System: Models, Algorithms and Applications

Online shopping has increased steadily over the past decade that has led to a dramatic increase in the demand for urban package deliveries. Crowdsourced delivery, or crowd shipping, has been proposed and implemented by logistics companies in response to the growth in package delivery business. Crowdsourced delivery is a delivery service in which logistics service providers contract delivery services from the public (i.e., non-employees), instead of providing delivery services exclusively with an in-house logistics workforce. This dissertation studies different types of urban last-mile crowdsourced delivery services and provides a taxonomy for crowdsourced package delivery. Urban package crowdsourced delivery can be categorized in terms of the way packages are delivered and the role/tasks of crowdsourced drivers. Given these two dimensions, this study identifies three types of urban package crowdsourced delivery, namely, crowdsourced time-based delivery, crowdsourced trip-based delivery, and crowdsourced shared-trip delivery. Crowdsourced time-based delivery drivers are paid for their idle time and work as sub-contractors. Crowdsourced trip-based delivery matches drivers with individual tasks and utilizes the drivers for specific delivery trips. The last type, crowdsourced shared-trip delivery utilizes the common segments of a crowdsourced personal vehicle trip to deliver packages. In this type, the package shares part of the driver trip. The literature formulates the crowdsourced delivery problem as a Vehicle Routing Problem (VRP) and proposes a variety of solution approaches. However, all the solution algorithms are limited to relatively small-scale problems. In addition, the factors that impact the efficiency and effectiveness of crowdsourced delivery have not been thoroughly analyzed. To bridge the gap in crowdsourced delivery and urban freight logistics, this dissertation provides an alternative formulation for the static crowdsourced shared-trip delivery problem and proposes a novel decomposition heuristic to solve the problem. The alternative formulation is based on the set partitioning problem. The novel decomposition heuristic handles packages that are served by shared personal vehicles (SPVs) and dedicated vehicles (DVs), separately. After that, the algorithm deploys a package switch procedure, which rearranges packages between SPVs and DVs. The dissertation discusses various algorithms employed to solve different sub-problems, such as the budgeted k-shortest path, large scale bi-partite matching, decision of package switching and vehicle routing. To validate the models and algorithms, this dissertation presents a numerical case study that uses the network of the City of Irvine, CA, USA. The results of the numerical study unveil interesting results that are valuable to both researchers and industrial practitioners. The results indicate that crowdsourced shared-trip delivery service can reduce total cost by between 20% to 50%, compared to a delivery service that exclusively uses its own dedicated vehicles and drivers. However, the results show that dedicated vehicles are still required since the shared vehicles are not able to serve all packages even with a considerably large set of candidate shared vehicles. Vehicle Miles Traveled (VMT) savings depend on the crowdsourced driver selection and their trip origins. The dissertation also analyzes and discusses important factors that impact the effectiveness of crowdsourced delivery. In particular, the dissertation includes sensitivity analysis results with respect to changes in the depot location and the willingness of shared vehicles to detour

Designing a Transit-Feeder System using Multiple Sustainable Modes: Peer-to-Peer (P2P) Ridesharing, Bike Sharing, and Walking

Peer-to-peer (P2P) ridesharing is a relatively new concept that aims to provide a sustainable method for transportation in urban areas. Previous studies have demonstrated that a system that incorporates both P2P ridesharing and transit would enhance mobility. We develop schemes to provide travel alternatives, routes and information across multiple modes, which includes P2P ridesharing, transit, city bike-sharing and walking, within the network. This study includes a case study of the operation of the multimodal system that includes P2P ridesharing participants (both drivers and riders), the Los Angeles Metro Red line subway rail, and the Los Angeles downtown bike-share system. The study conducts a simulation, enhanced by an optimization layer, of providing travel alternatives to passengers during morning peak hours. The results indicate that a multi-modal network expands the coverage of public transit, and that ride- and bike-sharing could be effective transit feeders when properly designed and integrated into the transit system

Designing a Transit-Feeder System Using Bikesharing and Peer-to-Peer Ridesharing

Peer-to-peer (P2P) ridesharing is a relatively new concept that aims at providing a sustainable method for transportation in urban areas. This research is on the second phase of a sequence of projects that follows the previously funded UCConnect project titled “Promoting Peer-toPeer Ridesharing Services as Transit System Feeders”. In this phase, the study constructs a multimodal network, which includes P2P ridesharing, transit and city bike-sharing. The research develops schemes to provide travel alternatives, routes and information across multiple modes in the network. In addition, we develop a mobile application that demonstrates the research in the context of Los Angeles, CA, by using a combination of subway transit lines, proposed P2P ridesharing, and bikesharing to provide multi-modal itineraries to users. The Los Angeles Metro’s Red and Gold line subway rail and the downtown bike-share system are included in the network for a case study. The study includes a simulation of the operation of the combined system that provides travel alternatives during morning peak hours for multiple riders. The results indicate that a multi-modal network would expand the coverage of public transit. Ridesharing and bike-sharing could both act as transit feeders when properly designed in the system. Increased travel demand from the system can induce the problem that pick-up and drop-off demand in the bike system is not evenly distributed in space and time, which implies that bike redistribution should be introduced. We also develop algorithms to improve service level and reduce unsatisfied bike demand